Q2 2025 MongoDB Inc Earnings Call

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Operator: Good day, and thank you for standing by.

Operator: Welcome to the MongoDB's 2nd quarter fiscal year 2025 conference call. At this time, all participants are going to listen to the only mode. After the speaker's presentation, there will be a question-and-answer session. To ask a question during the session, you will need to press star 1-1 on your telephone. You will then hear an automated message advising your hand is raised. To destroy your question, please press star 1-1 again. Please be advised that today's conference is being recorded.

Speaker Change: Good day and thank you for standing by. Welcome to the MongoDB's.

Speaker Change: Second Quarter, fiscal year 2025, Conference Call. At this time, all participants are in the Sonone Mode. After this speaker's presentation, there will be a question and answer session. To ask a question during this session, you will need to press star 1 on your telephone.

Speaker Change: He will then hear an automated message advising your hand is raised. To destroy your question, please press star one one again. Please be advised six days' conferences today recorded.

Operator: I would now like to turn the call over to your speaker for today.

Brian Denyeau: Brian Denyeau, please go ahead. Thank you, Lisa. Good afternoon and thank you for joining us today to review MongoDB's 2nd quarter fiscal 2025 for natural results, which we announced at our press release issued after the closing market today. Joining me in the call today are Dave Ittycheria, President and CEO of MongoDB, and Michael Gordon. MongoDB, CEO, CEO and CFO.

Speaker Change: I would now like to turn the call over to your speaker for today.

Speaker Change: Please go ahead.

MongoDB: Thank you, Lisa. Good afternoon, and thank you for joining us today to review MongoDB 2nd quarter fiscal 2020-25 for natural results. Which we announced our press list issued after the closing of the market today.

Speaker Change: Jordan and the call today are Dave Ittycheria, President C. of MongoDB and Michael Gordon, MongoDB, C.O.O. and CFO.

Brian Denyeau: During this call, we will make four looking statements, including statements related to our market and future growth opportunities. Our expectations for the macroeconomic environment in fiscal 2025 and the impact of AI, the benefits of our product platform, our competitive landscape, customer behaviors, our financial guidance, and our planned investments and growth opportunities in AI. These statements are still to a variety of risks and uncertainties, including the results of operations and financial conditions that cause actual results to differ materially from our expectations. For discussion of the material risks and uncertainties that can affect our actual results, please refer to the risk described in our quarterly report on Form 10-Q for the quarter ended April 30th, 2024, filed with the SEC at May 31st, 2024.

Speaker Change: During this call, we will make four looking statements, including statements related to our market and future growth opportunities

Speaker Change: Our expectations for the macroeconomic environment in fiscal 2025 and the impact of AI, the benefits of our product platform, our competitive landscape, customer behaviors, our financial guidance, and our planned investments in growth opportunities in AI.

Speaker Change: These statements are similar to a variety of risks and uncertainties, including the results of operations and financial conditions, because that could cause actual results to differ materially from our expectations.

Speaker Change: For discussion of the material risks in uncertainty is going to affect our actual results. Please refer to the risk to the scrapper in our corollary report on Form 10Q. For the quarter-ended April 30th, 2024, followed the SEC at May 31st, 2024.

Brian Denyeau: Any four of these statements made this call reflect our views only as of today, and we undertake no obligations to update them except as required by law.

Speaker Change: Any 40 statements made in this car reflect our views only as up-to-date, and we undertake no obligations to update them, except to be required by law.

Brian Denyeau: Additionally, we will discuss non-GAAP financial measures in this conference call. Please refer to the tables in our earnings release on the investor relations portion of our website for a reconciliation of these measures for their most directly comparable GAAP for the actual measure.

Speaker Change: Initially we were to discuss non-gap financial measure in a scarf with a call.

Speaker Change: Please refer to the tables in our earned release on the Investor Relations portion of our website for a reconciliation of these measures for their most directly comparable gap for the answer measure.

Brian Denyeau: With that, I'd like to throw the call over today.

Dev Ittycheria: Thanks, Brian, and thank you to everyone for joining us today. I am pleased to report that we had a good quarter and executed well against our large market opportunity. Let's begin by reviewing our second quarter results before giving you a broader company update. We generated revenue of $478 million, a 13% year of year increase against a very difficult year of year compare and above the high end of our guidance. Atlas revenue grew 27% year of year, representing 71% of revenue. We generated non-GAAP operating income of $52.5 million for 11% non-GAAP operating margin, and we ended the quarter with over 50,700 customers.

Speaker Change: With that, I'd like to throw a call over to Dave.

Dave Ittycheria: Thanks Brian, and thank you to everyone for joining us today. I'm pleased to report that we had a good quarter and executed well against our large market opportunity. Let's begin by reviewing our second quarter results before giving you a broader company update.

Speaker Change: We generate revenue of 478 million, a 13% year of year increase against a very difficult year of year compare and above the high end of our guidance.

Speaker Change: At this revenue grew 27% year of year representing 71% of revenue. We generate a non-gap operating income of 52.5 million for 11% non-gap operating margin, and we end the quarter with over 50,700 customers.

Dev Ittycheria: Overall, we were pleased with the performance in the second quarter. We had a strong new business quarter, and we saw improving sales productivity year over year. We saw strength across the board with both Atlas and enterprise advance exceeding our expectations, demonstrating the enduring appeal of our run anywhere strategy. Our Q2 performance reinforced our belief the slow start to the new business Q1 was purely operational, and we feel good about our new business outlook in the second half of the year. Moving on to Atlas consumption, the quarter played out modestly better than our expectations. Michael will discuss consumption trends in more detail.

Speaker Change: Overall, we were pleased with the performance in the second quarter.

Speaker Change: We had a strong new business quarter and we saw improving sales productivity year over year. We saw strength across the board with both outlets and enterprise advance exceeding our expectations.

Speaker Change: Demonstrating the Enduring Appeal of a Run Anywhere Strategy. Our Q2 performance reinforced our belief the slow start to the new business Q1 was purely operational and we feel good about our new business outlook in the second half of the year.

Speaker Change: Moving on to Atlas consumption, the quarter plate out modestly better than our expectations. Michael will discuss consumption trends in more detail. Finally, retention rates remain strong in Q2 demonstrating the quality of our product and the mission criticality of our platform.

Dev Ittycheria: Finally, retention rates remain strong in Q2, demonstrating the quality of our product and the mission criticality of our platform. Our performance is quarter reinforced our confidence and our ability to execute on a long-term opportunity. As we said before, companies today rely on software to express their business strategy. This trend has driven our success for the past decade, and we anticipate it will continue to do so for the foreseeable future. Even with our success to date, we only have a low single-digit share in one of the largest and fastest-growing markets in all of software. When you combine this foundational tailwind with the opportunities for our customers to incorporate generated AI into their businesses and modernize their legacy application state, it is clear that MongoDB has multiple long-term growth opportunities.

Speaker Change: Rob. Performance this quarter reinforced a confidence in our ability to execute on a long-term opportunity.

Rob: And as you said before, companies today rely on software to express their business strategy.

Rob: This trend has driven our success for the past decade and we anticipate it will continue to do so for the foreseeable future.

Speaker Change: Even with our success today, we only have a little single dish shared in one of the largest and fastest grain markets in all of software.

Speaker Change: When you combine this foundational tailwind with the opportunities for customers to incorporate generative AI into their businesses and modernize the legacy application state, it is clear that MongoDB has multiple long-term growth opportunities.

Dev Ittycheria: Turning to AI, AI continues to be an additional long-term opportunity for our business. At the start of the fiscal year, we told you that we didn't expect AI to be a meaningful tailwind for our business in fiscal year 2025, which has proven accurate. Based on recent peer commentary, it seems that the industry now mostly agrees with this view. Companies are currently focusing their spending on the infrastructure layer AI, and are still largely experimenting with AI applications. Inference workloads will come and should benefit MongoDB greatly in the long run, but we are still very early when the monetization of AI apps will take time.

Speaker Change: Turning to the AI AI continues to be an additional launcher map to need for our business.

Speaker Change: At the start of the fiscal year, we told you that we didn't expect AI to be a meaningful tailwind for our business in fiscal year 2025, which has proven accurate. Based on recent pure commentary, it seems that the industry now mostly agrees with this view.

Speaker Change: Company is currently focusing their spending on the infrastructure layer AI and are still largely experimenting with AI applications. Infference workloads will come and should benefit MongoDB greatly in the long run, but we are still very early when the monetization of AI apps will take time.

Dev Ittycheria: AI demand is the question of when, not if, and our discussions with customers and partners give us increasing conviction that we are the ideal data layer for AI apps for a number of key reasons. First, more than any other type of modern workload, AI-driven workloads require the underlying database to be capable of processing queries against rich and complex data structures quickly and efficiently. Our flexible document model is uniquely positioned to help customers build sophisticated AI applications because it is designed to handle different data types: your source data, vector data, metadata, and generated data right alongside your live operational data, updating the need for multiple database systems and complex batches.

Speaker Change: A.I. demand is the question of when not if and our discussions with customers and partners give us increasing conviction that we are the ideal data layer for AI apps for a number of key reasons.

Speaker Change: First, more than any other type of modern workload, AI-driven workloads required the underlying database to be capable of processing queries against rich and complex data structures quickly and efficiently.

Speaker Change: Our Flexible Document model is uniquely positioned to help customers build sophisticated AI applications because it is designed to handle different data types.

Speaker Change: Your source data, vector data, metadata, and generate data right alongside your live operational data, updating the need for multiple database systems and complex back end architectures.

Dev Ittycheria: Second, MongoDB offers a high performance and scalable architecture. As the latency of LLM's improve, the value of using real-time operational data for AI apps will become even more important. Third, we are seamlessly integrated with leading app development frameworks and AI platforms, enabling developers to incorporate MongoDB into the existing workflows while having the flexibility to choose the LLM and other specific tools that best suit their needs. Fourth, we meet or exceed the security and compliance requirements expected from an enterprise database, including enterprise-grade encryption, authorization, and auditability. Lastly, customers can run MongoDB anywhere on-premise or as a fully managed service in one of the 118 global cloud regions across three hyper-scalers, giving them the flexibility to run workloads to best meet their application use cases and business needs.

Speaker Change: Second, MongoDB offers a high performance in scalable architecture as the latency of LM, LM's improve. The value of using real-time operational data for AI apps will become even more important.

Speaker Change: Third, we are seamlessly integrated with leading app development frameworks and AI platforms, enabling developers to incorporate MongoDB into the existing workflows while having the flexibility to choose the LLM and other specific tools that best suit their needs.

Speaker Change: Fourth, we meet or see the security and compliance requirements expected from an enterprise database, including enterprise-grade encryption, authorization and auditability.

Speaker Change: Lastly, customers can run MongoDB anywhere, on-premise, or as a fully managed service in one of the 118 global cloud regions across three hyper-scalors, giving them the flexibility to run workloads to best meet their application use cases and business needs.

Dev Ittycheria: We see three main opportunities where we believe AI will accelerate our business over time. The first is that the cost of building applications in the world of AI will come down, as we've seen with every previous platform shift, creating more applications and more data requiring more databases. The second opportunity is for us to be the database of choice for customers building greenfield AI applications. While we see that there's tremendous amount of interest in and planning for new AI-powered applications, the complexity and fast-moving nature of the AI ecosystem slows customers down. That's why we launched the MongoDB AI Applications program, or MAP, which became generally available to customers last month.

Speaker Change: We see three main opportunities where we believe AI will accelerate our business over time. The first is that the cost of building applications in the world of AI will come down, as we've seen with every previous platform shift, creating more applications and more data, requiring more databases.

Speaker Change: The second option is for us to be the database of choice for customers, building green field AI applications.

Speaker Change: While we see that this tremendous amount of interest in and planning for new AI powered applications, the complexity and fast moving nature of the AI ecosystem slows customers down. That's why we launched the MongoDB AI applications program or map which became generally available to customers last month.

Dev Ittycheria: MAP brings together a unique ecosystem, including the three major cloud providers, AWS, Azure, and GCP, as well as Accenture and AI pioneers like Anthropic and CoHIR. MAP offers customers reference architectures and end-to-end technology stack that includes pre-built integrations, professional services, and a unified support system to help customers quickly build and deploy AI applications. The third opportunity is to help customers modernize the legacy application state. As you know, this segment of the market is a massive opportunity for us as most of the existing 80 billion-plus database industry is built on dated relational architecture. Modernizing legacy applications has always been part of our business, and we have taken steps of the years to simplify and demystify this complex process through partnerships, education, and most recently our relational migrator product.

Speaker Change: Matt brings together a unique ecosystem, including the three major cloud providers, A. Love US, Azure and GCP.

Speaker Change: as well as Accenture and AI pioneers like Anthropicon cohere. Map offers customers' reference architectures and end-to-end technology stack that includes pre-built integrations, professional services, and a unified support system to help customers quickly build and deploy AI applications.

Speaker Change: The third option is to help customers modernize the legacy application state. As you know, this segment of the market is a massive opportunity for us, as most of the existing 80 billion plus database industry is built on data's relational architecture.

Speaker Change: Modernizing Legacy applications has always been part of our business and we have taken steps of the years to simplify and demystify this complex process through partnerships, education, and most recently our relational migrator product.

Dev Ittycheria: AI offers a potential step function improvement, lowering the cost and reducing their time and risk to modernize legacy applications. For that reason, earlier this year we launched several pilots with our customers where we worked with them to modernize mission critical applications, leveraging both AI tooling and services. The early results from these pilots are very exciting, as our customers are experiencing significant reductions in time and cost of modernization. In particular, we have seen dramatic improvements in time and cost to rewrite application code and generate test suite. We see increasing interest from customers that want to modernize the legacy application state, including large enterprise customers.

Speaker Change: AI offers a potential step-function improvement lowering the cost and reducing their time and risk to modernize legacy applications. For that reason, earlier this year we launched several pilots with a customer's where we worked with them to modernize mission-proofable applications, leveraging both AI tooling and services.

Speaker Change: The early results from these pilots are very exciting, as our customers are experiencing significant reductions in time and cost of modernization. In particular, we have seen dramatic improvements in time and cost to rewrite application code and generate test suites.

Speaker Change: We see increasing interest from customers that want to modernize the legacy application state, including large enterprise customers.

Dev Ittycheria: As a CIO, one of the world's largest insurance companies said about our pilot, this is the first tangible return he's seen on his AI investments. While still early days and generating meaningful revenue from this program will take time, we're excited about the results of our pilots and the growing pipeline of customers eager to modernize the legacy of state.

Speaker Change: As a CIO, one of the world's largest insurance companies said about a pilot, this is the first tangible return you've seen on his AI investments.

Speaker Change: Well, it's still early days and generating meaningful revenue from this program will take time. We're excited about the results of our pilots and the growing pipeline of customers eager to modernize the Lexi Estate.

Dev Ittycheria: Finally, I understand that there are a lot of questions about the current business conditions and the macro environment more broadly. So let me give you a sense of what we're seeing across the business. As a reminder, when thinking of the macro influence on our business, it's important to distinguish between consumption of existing workloads and new business. Starting with consumption of existing applications on our platform, this is where we have historically seen a macro impact, as usage of applications impacted by the underlying business conditions of our customers. As we discussed in our last earnings call, in Q1, we did see broad-based consumption growth slowdown, suggesting some macro softening.

Speaker Change: Finally, I understand that there are a lot of questions about the current business conditions and the macro environment more broadly. So let me give you a sense of what we're seeing across the business. As a reminder, when thinking of the macro influence on our business, it's important to distinguish between consumption of existing workloads and new business.

Speaker Change: Starting with consumption of existing applications on a platform, this is where we have historically seen a macro impact. As usage of applications impacted by the underlying business conditions of our customers.

Speaker Change: As we discussed in our last earnings call, in Q1 we did see broad-based consumption growth slow down suggesting some macrosoffing. Our usage trends suggest a similar macro environment in Q2 as in Q1, even though Q2 Atlas consumption growth was modestly ahead of our expectations.

Dev Ittycheria: Our usage trend suggests a similar macro environment in Q2 as in Q1, even though Q2 Atlas consumption growth was modestly ahead of our expectations. Moving on to new business, we generally have not seen the macro environment impact our ability to win new business, and that was true in Q2 as well. We realized that this is different from what you hear from some other software vendors. Ultimately, software application development continues even in uncertain environments, as customers know they need to continue investing in internally developed software to run the business as well as to drive competitive differentiation.

Speaker Change: Moving on to new business, we generally have not seen the macro environment impact or ability when new business, and that was true in Q2 as well.

Speaker Change: We realize that this is different from what you hear from some other software vendors. Ultimately, software application development continues even in uncertain environments as customers know they need to continue investing in internally developed software to run the business as well to drive competitive differentiation.

Dev Ittycheria: In addition, we still have relatively low market share in a large market, which means we have an opportunity to gain share in any environment.

Speaker Change: In addition, we still have relatively low market share in a large market, which means we have an company to gain share in any environment.

Dev Ittycheria: Now, I'd like to spend a few minutes reviewing the adoption trends of MongoDB across our customer base. Customers across industries and around the world are running mission critical projects on MongoDB Atlas, leveraging the full power of our developer data platform, including Fanatics, Occidental Petroleum, and Indeed. Fanatics Betting and Gambling, a division of the sports ecosystem company Fanatics, leverages MongoDB to significantly enhance their user experience of their mobile app. Initially, the team launched a platform on Postgres, but faced challenges with scalability, flexibility, and excessive complexity. After migrating to MongoDB Atlas, the team also integrates Atlas Search to provide users with a better experience to find all available betting options.

Speaker Change: [inaudible]

Speaker Change: Now, I'd like to spend a few minutes reviewing the adoption trends of MongoDB across our customer base.

Speaker Change: Customers across industries and around the world are running mission-critical projects on MongoDB Atlas, leveraging the full power of our developer data platform, including fanatics, accidental petroleum and indeed.

Speaker Change: Phenetics, betting and gambling, a division of the sports ecosystem, company, phenetics, leverages MongoDB to significantly enhance or use experience of their mobile app. Initially, the team launched a platform on Postgres but faced challenges with scalability, flexibility and excessive complexity.

Speaker Change: from MyGray into MongoDB Atlas, the team also integrates Atlas search to provide users with a better experience to find all available betting options.

Dev Ittycheria: With Atlas handing scaling, partitioning, and operations, developers can focus on writing code and improving the user experience. Looking ahead, Fanatics plans to continue to expand on MongoDB Atlas as they ensure they can operate at scale as they prepare for the start of the NFL season. Laureal, McKesson, and Nationwide Building Society are turning to MongoDB to modernize applications. Laureal's tech accelerator, a department dedicated to catalyzing digital innovation at Laureal, is utilizing MongoDB for an application designed to bring products and solutions to market while quickly improving employee efficiency. The team's previous database solution had limited out-of-the-box functionality and was unable to handle the complex calculations needed to retrieve and restructure large amounts of data from their data warehouse.

Speaker Change: with Atlas Hanting Scaling, Partitioning and Operations, developers can focus on writing code and reprusing the user experience. Looking ahead, fanatic plans to continue to expand on MongoDB Atlas as they ensure they can operate at scale as they prepare for the start of the NFL season.

Speaker Change: L'Oreal, McCeston and nationwide building society are turning to MongoDB to modernize applications.

Speaker Change: L'Oreal's Tech Accelerator, a department dedicated to catalyzing digital innovation at L'Oreal, is utilizing MongoDB for an application designed to bring products and solutions to market, while quickly improving employee efficiency.

Speaker Change: The team's previous database solution had limited out of the box functionality and was unable to handle the complex calculations needed to retrieve and restructure large amounts of data from their data warehouse.

Dev Ittycheria: Laureal migrated to MongoDB Atlas to streamline the application architecture and simplify a previously highly complex and time-consuming data access layer. With this migration, Laureal achieved a 40-fold performance improvement. On Atlas, existing code is easier to maintain, more scalable, and more efficient, making life easier for developers. Mature companies and startups alike are using MongoDB to help deliver the next wave of AI-powered applications to their customers, including Delivery Hero, General Alley, and Quest Flow. Delivery Hero, a longtime MongoDB Atlas customer, is the world's leading local delivery platform, operating in 70-plus countries across four continents. Their quick commerce service enables customers to select fresh produce for delivery from local grocery stores.

Speaker Change: L'Oreal migrated to MongoDB Atlas to streamline the application architecture and simplify a previously highly complex and time consuming data access layer.

Speaker Change: With this migration, Laureale achieved a 40-fold performance improvement. On Atlas existing code is easier to maintain more scalable and more efficient, making like easier for developers.

Speaker Change: But your company is in star up to like a using MongoDB to help deliver the next wave of AI powered applications to the customers, including delivery, hero, general, and quest flow.

Speaker Change: Delivery Hero, a long time MongoDB Alice customers, the world's leading local delivery platform, operating in 70-plus countries across four continents.

Speaker Change: There are quick commerce service enables customers to select fresh produce for delivery from local grocery stores.

Dev Ittycheria: Approximately 10% of the inventory is fast-moving, perishable produce that can go quickly out of stock. The company risked losing revenue and increasing customer churn if the customer didn't have viable alternatives to their first choice. To address these risks, they are now using state-of-the-art AI models in MongoDB Atlas Vector Search to give hyper-personalized alternatives to customers in real time if items they want to order are out of stock. With the introduction of MongoDB Atlas Vector Search, the data science team recognized that they could build a highly performant real-time solution more quickly and for less cost than alternative technologies.

Speaker Change: A proximate 10% of the inventory is fast moving perishable produce that can go quickly out of stock.

Speaker Change: The company risk losing revenue and increasing customer churn if the customer didn't have viable alternatives to their first choice. To address these risks, they are now using state-of-the-art AI models and MongoDB Atlas vector search to give hyper-personalized alternatives to customers. In real time, if items they want to order are out of stock.

Speaker Change: With the introduction of MongoDB Alice Spectre Search, the data science team recognized that they could build a highly-performance real-time solution more quickly and for less costs than alternative technologies.

Dev Ittycheria: In summary, we had a healthy Q2 with both Atlas and EA exceeding expectations. We saw a strong new business quarter and improved sales productivity and were confident in our ability to keep winning new business in the second half of the year. Looking forward, we see great opportunity to help our customers modernize legacy applications and build the next generation of AI-powered applications.

Speaker Change: And summary, we had a healthy Q2 with both Atlas and EA exceeding expectations.

Speaker Change: We saw a strong U-business quarter and improved sales productivity and we are confident in our ability to keep winning new business in the second half of the year

Speaker Change: Looking forward, we see great opportunity to help our customers modernize legacy applications and build the next generation of AI powered applications. With that, here's Michael. Thank you. I'll begin with the detailed review of our second quarter results and then finish with our outlook for the third quarter and full fiscal year 2025.

Michael Gordon: With that, here's Michael. Thanks, Dave. I'll begin with a detailed review of our second quarter results and then finish with our outlook for the third quarter and full fiscal year 2025. First, I'll start with our second quarter results. Total revenue in the quarter was $478.1 million. Up 13% year-over-year and above the high end of our guides. As a reminder, while we have difficult compares throughout fiscal 25, we were facing a particularly difficult year-of-year comparison in Q2, given we had a number of large multi-year partnership licensing deals in Q2 last year. Under 66 accounting rules, we recognized the license component upfront, which creates a difficult compare a year later.

Michael Gordon: First I'll start with our second quarter results.

Michael Gordon: Colour revenue in the quarter was $478.1 million. Up 13% year of a year and above the high end of our guidance. As a reminder, we have difficult comparison out fiscal 25. We're facing a particularly difficult year of a year comparison in Q2. Given we had a number of large multi-year partnership licensing deals in Q2 last year.

Michael Gordon: Under six or six accounting rules, we recognize the license component upfront, which creates a difficult compare at your later.

Michael Gordon: Shifting to our product mix, but sort of that less. Atlas grew 27% in the quarter compared to the previous year and now represents 71% of the total revenue compared to 63% in the second quarter of fiscal 2024 and 70% last quarter. We recognized Atlas revenue primarily based on customer consumption of our platform, and that consumption is closely related to end user activity of their applications. Let me provide some context on Atlas consumption in the quarter. First, as a reminder, in Q1, Atlas consumption growth was below our expectations, and we updated our growth assumptions for the remainder of the year.

Speaker Change: Schifting Door product mix, let's start with Alice. Atlas grew 27% in the quarter, compared to the previous year, and now represents 71% of the total revenue, compared to 63% in the second quarter of fiscal 2024, and 70% last quarter.

Speaker Change: We recognize that what's revenue primarily based on customer consumption of our platform, and that consumption is closely related to end-use reactivity of their applications.

Speaker Change: Let me provide some context on Atlas consumption in the quarter. First, as a reminder, in Q1, Atlas consumption growth was below our expectations, but we updated our growth assumptions for the remainder of the year.

Michael Gordon: Chair. In Q2, Atlas consumption growth was modestly ahead of those updated expectations across the board. While this is encouraging, consumption growth is still below our original forecast from the beginning of the year. Turning to non-Atlas reference, non-Atlas came in modestly ahead of our expectations in the quarter, as we continue to have success selling incremental workloads into our existing customer base. On a year-over-year basis, non-Atlas revenue was down 13% due to the especially difficult compare, at reference to earlier.

Speaker Change: In Q2, Atlas consumption growth was modestly ahead of those updated expectations across the board.

Speaker Change: Well, this is encouraging, consumption growth is still below our regional forecast from the beginning of the year.

Speaker Change: John. Turning to non-Atlas reference, non-Atlas came in modestly ahead of our expectations in the quarter, as we continue to have success selling incremental workloads into our existing customer base. On a year-over-year basis, non-Atlas revenue is down 13% due to the especially difficult compare our reference earlier.

Michael Gordon: Turning to customer growth. During the second quarter, we grew our customer base by approximately 1,500 customers sequentially, bringing our total customer count to over 50,700, which is up from over 45,000 in the year-go period. Of our total customer count, over 7,300 are direct sales customers, which compares to over 6,800 in the year-go period. The growth in our total customer count is being driven primarily by Atlas, which had over 49,200 customers at the end of the quarter, compared to over 43,500 in the year-go period. It is important to keep in mind that growth in our Atlas customer count reflects new customers among the DB in addition to existing EA customers adding incremental Atlas workloads.

John: Turning to customer growth.

John: During the second quarter, we grew our customer base by approximately 1,500 customers sequentially, bringing our total customer count to over 50,700, which is up from over 45,000 in the year of the year, go period.

John: Of our total customer count, over 7,300 are direct sales customers.

John: West, which compares to over 6,800 in the year Go Period.

John: The growth in our total customer count is being driven primarily by Atlas.

John: which had over 49,200 customers at the end of the quarter compared to over 43,500 in the year of the period. It is important to keep in mind the growth that our Atlas customer count reflects new customers among a DB in addition to existing EA customers adding incremental Atlas workloads.

Michael Gordon: Continuing on, in Q2, our net AR expansion rate was approximately 119%. The decline versus historical periods is attributable to a smaller contribution from expanding customers. We ended the quarter with 2,189 customers, with at least $100,000 in ARR and annualized MRR, up from 1,855 in the year-go period.

John: Continuing on, in Q2, on a net-air expansion rate was approximately 119% that a decline versus historical periods is attributable to a smaller contributions from expanding customers.

John: We ended the quarter with 2,189 customers, with at least $100,000 in ARR and annualized MRR up from 1,855 in the year of Go Period.

Michael Gordon: Moving down the income statement, I'll be discussing our results on a non-GAAP basis unless otherwise noted. Of course, profit in the second quarter was 360.8 million dollars, representing a gross margin of 75%, which is down from 78% in the year-go period. Our year-to-year margin decline is primarily driven by a lower mix of high-margin upfront license revenue compared to last Q2, as well as Atlas growing as the percentage of the overall business. Our income from operations was $52.5 million, or an 11% operating margin for the second quarter, compared to a 19% margin in the year-go period.

Speaker Change: Moving down the income statement, I'll be discussing our results on a non-gap basis unless otherwise noted.

Speaker Change: Gross profit in the second quarter was $360.8 million, representing a gross margin of 75% which is down from 78% in the year of Go Period.

Speaker Change: Our year-year margin decline is primarily driven by a lower mix of high margin upfront licensed revenue compared to last Q2, as well as Atlas growing as a percentage of the overall business.

Speaker Change: Our income from operations was $52.5 million or an 11% operating margin for the second quarter. Compared to a 19% margin in the year, go period.

Michael Gordon: The primary reason for a more favorable operating income results versus guidance is our revenue outperformance. In addition, Q2 operating income benefited from the timing of certain marketing and other spend, which we now expect to incur in the second half of the year. Net income in the second quarter was $59 million, or 70 cents per share, based on 83.8 million diluted weighted average shares outstanding. This compares to a net income of $76.7 million or 93 cents per share, on 82.5 million diluted weighted average shares outstanding in the year-go period.

Speaker Change: The primary reason for a more favorable operating income results versus guidance is our revenue outperforming.

Speaker Change: In addition, Q2 operating and competitively from a timing of certain marketing and other spend, which we now expect to incur in the second half of the year.

Speaker Change: Net income in the second quarter was $59 million or 70 cents per share based on 83.8 million diluted weighted average shares outstanding.

Speaker Change: This compares to a net income of $76.7 million for 93 cents per share, on 82.5 million diluted weighted average shares outstanding in the year of the period.

Michael Gordon: Turning to the balance sheet and cash flow, we ended the second quarter with $2.3 billion in cash, cash equivalents, short-term investments, and restricted cash. Operating cash flow in the second quarter was negative $1.4 million. After taking into consideration approximately $2.6 million in capital expenditures and principal repayments of finance lease liabilities, free cash flow was negative $4 million in the quarter. This compares to free cash flow of negative $27.3 million in the year-go period.

Speaker Change: Turning to the balance sheet and cash flow. We ended the second quarter with $2.3 billion in cash, cash equivalent, short-term investments and restricted cash.

Speaker Change: Operating Cash Flow in the second quarter was negative $1.4 million.

Speaker Change: After taking into consideration approximately $2.6 million in capital expenditures and principal repayments of finance lease liabilities, free cash flow was negative $4 million in the quarter.

Speaker Change: This compares the free cash flow of negative $27.3 million in the year of Go Period.

Michael Gordon: In addition, this quarter, we also received $176 million in cash from the settlement of the cap calls associated with our 2020-4 convertible. I now like to turn to our outlook for the third quarter and full fiscal year 2025. For the third quarter, we expect revenue to be in the range of $493 million to $497 million. We expect non-GAAP income from operations to be in the range of $57 million to $60 million and non-GAAP net income per share to be in the range of $0.65 to $0.68 based on 84.6 million estimated diluted weighted average shares. For the full fiscal year 2025, we expect revenue to be in the range of $1.92 million to $1.93 million, non-GAAP income from operations to be in the range of $187 million to $195 million, and non-GAAP net income per share to be in the range of $2.33 to $2.47 based on $84.3 million estimated diluted weighted average shares outstanding.

Speaker Change: In addition to this quarter, we also received 176 million dollars in cash from the settlement of the cap calls associated with our 2024 convertible notes.

Speaker Change: I'd now like to turn to our outlook for the third quarter and full fiscal year 2025.

Speaker Change: For the third quarter, we expect revenue to be in the range of $493 million to $497 million.

Speaker Change: We expect non-gap income from operations to be in the range of $57 million to $60 million and non-gap net income per share to be in the range of $65 to $68 based on $84.6 million estimated the most diluted weighted average shares outstanding.

Speaker Change: For the full fiscal year 2025, we expect revenue to be in the range of $1.92 billion to $1.93 billion. Non-gap income from operations, we in the range of $187 million to $195 million. And non-gap income per share, we in the range of $2.33.

Speaker Change: $2.47, based on 84.3 million estimated delivery-weighted affords share without standing.

Michael Gordon: Note that the non-gap net income per share guidance for the third quarter and full fiscal year 2025 include the non-gap tax provisions of approximately 20%, and now provide some context around our updated guidance. First, because of the stronger than expected Atlas consumption in Q2, our starting Atlas ARR for the back half of the year is higher than we anticipated it may. As a result, we're raising our Atlas revenue forecast to reflect that higher starting point. We are not changing our underlying Atlas Q3 and Q4 consumption growth rate assumptions. Second, we are slightly increasing our EA assumptions for the rest of the year to reflect the strength of the second half EA pipeline.

Speaker Change: Note that the non-gap net income per share guidance for the third quarter and full fiscal year 2025 includes a non-gap tax provision of approximately 20%.

Speaker Change: I'll provide some context around our updated guidance.

Speaker Change: First, because of the stronger than expected Atlas consumption in Q2, our starting Atlas ARA for the back half of the year is higher than we anticipated today.

Speaker Change: As a result, we're raising our Atlas Revenue forecasts to reflect that higher starting point. We are not changing our underlying Atlas Q3 and Q4 consumption growth rate assumptions.

Speaker Change: Second, we're slightly increasing our EA assumptions for the rest of the year to reflect the strength of the second half EA pipeline.

Michael Gordon: Finally, thanks to the performance in Q2 and the increased revenue outlook, we now expect a 10% operating margin at the midpoint of our fiscal 25 guidance. We will continue investing to capture our long term opportunity with a focus on our strategic priorities.

Speaker Change: Finally, thanks to the performance in Q2 and the increased revenue outlook, we now expect a 10% operating margin at the midpoint of our fiscal 25 guidance.

Speaker Change: who will continue investing to capture our long-term opportunity for the focus on our strategic priorities.

Michael Gordon: Separately, although we don't provide guidance on cash flow, we wanted to call out two items that will impact fiscal 25 cash flow but benefit our long-term financial profile. In Q2, we started paying some of our cloud provider commitments up front in exchange for better economics. We see this as a very low risk, high ROI use of our cash and one that will benefit our gross margin going forward. These prepayments will represent a negative impact to our operating cash flow in the back half of the year of roughly $20 million per quarter. In addition, in Q3, we will incur approximately $20 to $25 million of catbacks to acquire IPv4 addresses, which will allow us to further reduce our cloud infrastructure costs in the future.

Speaker Change: We don't provide guidance on cash flow. We wanted to call out two items that will impact fiscal 25 cash flow, but benefit our long-term financial profile.

Speaker Change: In Q2, we started paying some of our cloud provider commitments upfront in exchange for better economics. We see this as a very low risk high ROI use of our cash, and one that will benefit our gross margin going forward.

Speaker Change: These prepayments will represent a negative impact or operating cash flow in the back half of the year, a roughly $20 million per quarter.

Speaker Change: In addition, in Q3, we will incur approximately $20 to $25 million of CapX to acquire IPB4 addresses which will allow us to further reduce our cloud infrastructure costs in the future.

Michael Gordon: To summarize, we're pleased with our second quarter results and especially our ability to win new business. We have a small share, one of the largest and fastest growing markets in all of software. The secular tailwinds that are back are getting stronger in the age of AI, and we're excited about the future. We'll continue investing judiciously and focusing on our execution to capture this long-term opportunity.

Speaker Change: To summarize, we're pleased with our second quarter results, and especially our ability to win new business. We have a small share when the largest and fastest growing markets in all its software. The secular tailwinds that are back are getting stronger in the age of AI, and we're excited about the future.

Speaker Change: We'll continue investing deliciously and focusing on our execution to capture this long-term opportunity.

Operator: With that, we'd like to open it up to questions.

Operator: Operator? Thank you. As a reminder, if you would like to ask a question, please press Star 11 on your telephone. We also ask that you limit yourself to one question and one follow-up. As well, please wait for your name and company to be announced before you proceed with your question. One moment while we take the first question.

Speaker Change: with that. We'd like to open up two questions. Operator.

Speaker Change: Thank you, as a reminder, if you would like to ask a question, please press star 1-1 on your telephone. We also ask that you limit yourself to one question and one follow-up.

Speaker Change: As well, please wait for your name and company to be announced before you proceed with your question.

Sanjit Singh: And the first question today is going to come from Sanjit, scene of Morgan Stanley; your line. Hi, thank you for taking a question, and we're going to grab on to you two nice and nice to see Mongo meeting and raising again. Dave, I wanted to follow up on some of the changes that the operational changes you're making to the business that you announced last quarter, just in terms of the update in terms of realigning some of the sales incentives to drive higher workload quality and while maintaining some of that record workload growth that you saw last year.

Speaker Change: One moment while we take the first question.

Speaker Change: And the first question today is going to come from Sanjit Singh of Morgan Stanley, your line is open.

Sanjit Singh: Hi, thank you for taking the question to ingratz on on YouTube, Michael. Nice to see Mongo beating and raising again.

Sanjit Singh: Dave, I wanted to follow up on some of the changes that the operational changes you're making to the business that you announced last quarter. Just in terms of the update in terms of re-aligning some of the sales incentives to drive higher workload quality and while maintaining some of that.

Dev Ittycheria: Could you sort of walk us through what you what what what the changes were you you put in place and how much confidence you have that that you will drive on better workload growth as we get into early next year. Yeah, sure, so first of all, thanks, Sanjit. Just to remind everyone, we made some slight incentive comp changes to really get our reps to focus to have a little bit more balance on size versus volume of workloads acquired. These changes were well received by the field. We had a good workload quarter. As you just described, workload starts small, so it's too early. While we're pleased with our results, obviously it's too early to declare victory and to really see if this is going to have a material impact and changes. But clearly, we're happy with what we've seen so far.

Speaker Change: Replicator Workbook that you saw last year, because you sort of walk us through what the change of where you put in place and how much confidence you have that you will drive better workbook gross, especially getting into early next year.

Dave Ittycheria: Yeah, sure. So first of all, thanks, budget. Just to remind of everyone, we made some slight incentive of comp changes to really get our reps to focus, to have a little bit more balance on size.

Dave Ittycheria: versus Volume of Workloads acquired.

Speaker Change: These changes were well received by the field. We had a good workload quarter, as you just described, workload starts small. So it's too early, while we're pleased with our results, obviously, it's too early to declare a victory. And to really see if this is going to have a material impact and changes, but clearly we're happy with what we've seen so far.

Michael Gordon: Right, then as a follow up for Michael, you know remember if the you know Q4 last year you gave us the update on sort of the unused credit dynamics as we start to think about the back of this year is there anything else that we should think about that I mean you just mentioned. And some of the castle impacts just just now, does there anything on sort of like the revenue side of the equation that we should keep in mind? I kind of noticed at the end of this year there's this more holiday days in the quarter and Q4. Does that have an impact anything that we should be then you would like to call out in terms of our modeling when we think about expectations for at less more broadly going into the end of the year.

Speaker Change: Right. And then a follow-up for Michael. Yeah, I remember at the, you know, Q4 last year, you gave us the update on sort of the unused credit dynamics.

Speaker Change: As we start to think about the back half of this year, is there anything else that we should think about that? I mean, you just mentioned some of the castle in past just now. Is there anything on sort of like the revenue side of the equation that we should keep in mind? I kind of know that at the end of this year there's it's more.

Speaker Change: Hall of A days and a quarter in Q4, does that have an impact? Anything that we should be, that you would like to call out in terms of our modeling when we think about expectations for at least more broadly going into the end of the year.

Michael Gordon: Yeah, I think there are a few things to take into account that have been throughout the year, but of course are reflected in the updated guide specifically on at last. Throughout the year, we have, you know, tough compares given the headwinds from the back of the unused commitments. That's obviously hardest, you know, as the year goes on; that sort of number, you know, builds, so that makes for a tough year of your comparison. And the other thing to think about in the context of at less is, you know, the ending year dynamic is really a compounding over the course of the year. As we talked about at the beginning of the year, we got off to a slower start, and some of the prior year workloads, those cohorts were growing at slow rates, and so that does affect and kind of compound over the course of the year. And so I think that's important to keep in mind, is factored and is factored into our guidance.

Speaker Change: Yeah, I think there are a few things to take into account that have been throughout the year, but of course, are reflected in the updated guide.

Speaker Change: Specifically on Atlas throughout the year we have tough compares given the headwinds from the lack of the unused commitments. That's obviously hardest, as the year goes on that sort of number builds. So that makes for a tough year of a year of comparison.

Speaker Change: And the other thing to think about in the context of Atlas is, you know, the ending year dynamic is really a compounding over the course of the year, as we talked about it at the beginning of the year. We got off to a slower start and some of the prior...

Speaker Change: I think that's important to keep in mind as factored and as factored into our guidance.

Michael Gordon: And then looking at the revenue picture more broadly, you know, moving away from Atlas and thinking about, you know, EA, we've had throughout the year a headwind on EA, and we'll have that as a multi-year headwind in the back half as well. The only other things I think I would call out is you know we saw a slower seasonal rebound in Q1, and so we're expecting that slower seasonal rebound to, you know, occur in Q3 as well. And then, as you mentioned, Q4 is a seasonally weaker quarter, and so all those are some of the considerations to think about when you're thinking about the rest.

Speaker Change: and then looking at the revenue picture more broadly moving away from Atlas and thinking about, you know, EA, we've had throughout the year headwind on EA and we'll have that as a multi-year headwind in the back half as well.

Speaker Change: The only other things I think I would call out.

Speaker Change: is, you know, we saw a slower seasonal rebound in Q1 and so we're expecting that slower seasonal rebound to, you know, occur in Q3 as well and then as you mentioned Q4 is a season only weaker quarter and so all those are some of the considerations to think about when you're thinking about the rest of the year.

Operator: Thank you. One moment for the next question.

Speaker Change: Good evening, sir.

Speaker Change: Thank you, one moment for the next question.

Raimo Lenschow: And our next question today will be coming from the line of Raimo, Lynn Chow, of Barclays. Your line is open. Hey, thank you. Two questions from me. Michael, on the EE, you kind of talked about like a slightly better pipeline second half. And I'm going to can you talk a little about is that kind of still the continuation of what we saw in Q2 Q3 last year where people wanted to modernize their kind of self service footprint ahead of the cloud for them and move to the cloud or what's the dynamic there. And then one for these like there's a big debate about like reference architecture on AI.

Rainbow: and our next question today. We'll be coming from the line of rainbow.

Glen's Chow: Glen's Chow.

Speaker Change: of Barclays Your Line is Open.

Glen Chow: Hey, thank you two questions from me. Michael, on the EE, you kind of talked about like it's like you'd better pipeline second half. Can you talk a little about it? Is that kind of still the continuation of what we saw in Q2Q3 last year where people wanted to modernize?

DEER: Deer, kind of self-service footprint.

DEER: Ahead of the cloud to the cloud, or what's the dynamic there. And then one for the, like...

Michael Gordon: And I think it's a little bit too early, but it comes up with investors a lot. Like what are you seeing there. I know AI infrastructure. We're still in the early earnings, but what are you seeing there in terms of people engaging with you. Thank you, and good to see you back on track. Thank you. Thanks, Raimo. On the EA question, I would say there are lots of reasons why we're seeing strengths of EA. I wouldn't uniquely or exclusively tie it to, you know, AI. I think it's broadly, you know, in support and shows the strength of the run anywhere strategy.

Speaker Change: There's a big debate about like reference architecture on AI and I think it's a little bit too early but it comes up with investors and all like what are you seeing there? I know AI infrastructure we're still in the early endings but what are you seeing there in terms of people engaging with you? Thank you and good to see you back on track. Thank you.

Speaker Change: Thanks, Brian. On the EA question, I would say there are lots of reasons why we're seeing strengths of EA. I wouldn't uniquely or exclusively tie it to, you know, AI. I think it's broadly, you know, in support and shows the strength of the run anywhere strategy. The other thing that I'd call out is, you know, given the six or six dynamics as a relate to the EA, we're always sensitive to sort of the multi year aspect and that sort of what's

Michael Gordon: The other thing that I'd call out is given the six to six dynamics as it relates to EA, we're always sensitive to sort of the multi-year aspect and that's sort of what's, you know, provided, you'll give the strength of multi-year in fiscal 24, some of their tough compares here in fiscal 25. I think what we've seen in terms of the pipeline is strength in EA broadly. So not just a multi-year phenomenon, but really sort of a volume phenomenon as well. That we're seeing when we kind of look out on the horizon, and that is reflected in the updated guide.

Speaker Change: You know, provided, you know, give them a strength and multi-year in fiscal 24, so the tough compares here in fiscal 25.

Speaker Change: I think what we've seen in terms of the pipeline is strength in EA broadly so not just a multi-year phenomenon but really sort of a volume phenomenon as well that we're seeing when we kind of look out on the horizon and that is reflected in the updated guide.

Michael Gordon: Thanks, Raimo. On the question about AI, I think in terms of reference architectures. I think it's important to understand that I said this in the prepared remarks: is that, unlike most of the workloads, AI-driven workloads really require the underlying database to be capable of processing queries against very rich and complex data structures, both quickly and efficiently. And MongoDB is well positioned to do that. You know, we can, we can basically unify and handle source data, vector data, metadata, generated data from your LLM right alongside your live operational data. And then, as the performance of these LLMs and latency of these LLMs increase, accessing real-time data becomes really important.

Rhino: Thanks, and Rhino on the question about AI, I think in terms of reference architectures, I think it's important to understand and I said this in the prepared remarks is that

Speaker Change: Um, unlike most of the workloads AI driven workloads really require the underlying database to be capable of processing queries against very rich and complex data structures.

Speaker Change: both quickly and efficiently.

Speaker Change: and MongoDB's well positioned to do that, you know, we can...

Speaker Change: We can basically unify and handle source data, vector data, metadata.

Speaker Change: and I'm going to talk to you about the data from your LLM, right alongside your live operational data, and then as the performance of these LLMs and latency of these LLMs increase, accessing real-time data becomes really important, like say you're calling and talking to a customer support chatbot.

Michael Gordon: Like say you're calling and talking to a customer support chatbot that you want that chatbot to have update information about that customer so that they can provide the most relevant and accurate information.

Speaker Change: That you want that chat about to have up to date information about that customer so that they can provide the most relevant and accurate information possible.

Michael Gordon: And possible. You know, there are some questions about LLMs, whether a general purpose LLM or fine-tune LLM, what the trade-offs are. Our belief is that, given the performance of LLMs, you're going to see the general purpose LLMs probably win and will use RAG as the predominant approach to marry generally available data with proprietary data. And then you are starting to see things like advanced RAG use cases where you get much more sophisticated ways to ask complex questions, provide more accurate and detailed answers, and better adapt to different types of information and queries. And so that's what we're seeing.

Speaker Change: You know, there are some questions about LLMs, whether a general purpose LLM or a fine-tune LLM, what the trade-offs are.

Speaker Change: Art belief is that given the performance of LLM, you're going to see...

Speaker Change: The General Purpose LLams probably win and will use Rag as the predominant approach to marry, you know, generally available data with proprietary data.

Speaker Change: and then your certainty things like advanced rags, use cases where you get much more sophisticated.

Speaker Change: Waste to you.

Speaker Change: asked complex questions, provide more accurate and detailed answers, and better adapt to different types of information and queries, and so that's what we're seeing. I think it's a quickly evolving space, but we feel very good about our positioning for a, even though it's still very early days.

Michael Gordon: I think it's a quickly evolving space, but we feel very good about our positioning for AI even though it's still very early days.

Operator: Thank you.

Operator: One moment for the next question.

Speaker Change: Okay, that was thank you.

Speaker Change: Thank you, one moment for the next question.

Kasthuri Rangan: And our next question will be coming from Kasthuri Rangan of Goldman Sachs; your line is open. It's perfect.

Speaker Change: and our next question will be coming from Cash Reagan.

Kasthuri Rangan: I was about to call Raimo and say, "Can you please explain reference architectures?" I'll buy you a nice glass of wine. So hopefully he'll take me up on that offer because that was a very technical question. Thank you.

Cash Reagan: of Goldman Sachs, your line is open.

Cash Reagan: It's perfect. I was about to call Rhymo and say can you please explain reference architectures I'll buy you a nice glass of wine so hopefully he'll take me up on that offer because that was a very technical question. Thank you. But anyway, coming back to the call here, Dave a question for you. Why is EA doing so well? So you talked about it pipeline in the second half of the year.

Dev Ittycheria: But anyway, going back to the call here, Dave, a question for you. Why is EA doing so well? We talked about a pipeline in the second half of the year. Last year, we had some significant wins for EA. We were supposed to be on this cloud journey. So definitely, Atlas has reached parity in many senses. It can support big scale applications, what not. Secures to get your thought on why is EA still an important piece of the business. And I suppose when you look at AI, could something surprise us? I know that you've had this view that we're building infrastructure first and then the platform and applications.

Dave Ittycheria: Last year we had some significant wins for EA.

Speaker Change: We are supposed to be on this cloud journey, so definitely Atlas is reached parity in many senses.

Speaker Change: It can support big scale applications, whatnot. Secures to get your thought on why is EA still an important piece of the business.

Speaker Change: and of course, when you look at AI, could something surprise us, I know, that you've had this view that we're building infrastructure first and then the platform and applications.

Dev Ittycheria: Could it be by any chance a different way to approach AI in this cycle and that we don't really need applications. But somehow, these elements are going to be a perfect replacement for the way we think about all of our applications. I'm just curious to see what the devil's advocate you might be if someone wears skeptical off the whole AI applications build out on top of the infrastructure and platform. Thank you so much. Yeah, so thanks, Cass. What I would just say is, we did have a better than expected both Atlas and EA quarters. There's no question about that.

Unknown Executive: Good Day, and thank you for standing by.

Unknown Executive: Welcome to the MongoDB's second quarter fiscal year 2025 conference call. At this time, all participants are going to listen to only mode. After the speaker's presentation, there'll be a question and answer session. To ask a question during the session, you will need to press star 1-1 on your telephone. You will then hear an automated message advising your hand is raised. To destroy your question, please press star 1-1 again. Please be advised that today's conference is being recorded.

Speaker Change: Good, could it be by any chance a different way to approach AI in this cycle and that we don't really need applications, but somehow these elements are going to be a perfect replacement for the way we think about old word applications. I'm just curious to see what the devil's advocate, you might be someone wear a skeptical of the whole AI applications build out on top of the infrastructure and platform. Thank you so much.

Speaker Change: Yeah, so thanks, Cass. What I would just say is, you know, we did have a better than expected, both Atlas and EA quarter. There's no question about that. I don't want to, you know, suddenly say that some, you know, inflection point on EA and our business, I think.

Dev Ittycheria: I don't want to suddenly say that some inflection point on EA and our business; I think I would really, you know, attributed to one better execution. And two, customers do really appreciate our run anywhere strategy. There's still lots of customers either for regulatory reasons or other reasons who want to run workloads on-premise. And they're not going away. I mean, we've been in this cloud journey for 10 years. Some workloads are just very hard to move to the cloud, or some workloads for many customers don't make sense to work, you know, move to the cloud at least not anytime soon.

Unknown Executive: I would now like to turn the call over to your speaker for today.

Brian Denyeau: Brian Denyeau, please go ahead. Thank you, Lisa. Good afternoon and thank you for joining us today to review MongoDB's second quarter fiscal 2025 financial results, which we announced our press release issued after the closing market today. Joining me in the call today are Dave Iducharia, President and CEO of MongoDB and Michael Gordon, MongoDB, COO and CFO. During this call, we will make four looking statements, including statements related to our market and future growth opportunities.

Speaker Change: I would really, you know, attributed to one better execution and to customers do really appreciate or run anywhere strategy. There's still lots of customers either for regulatory reasons or other reasons who want to run workloads on premise.

Speaker Change: and they're not going away. We've been in this cloud journey for 10 years and if...

Speaker Change: Some workloads are just very hard to move to the cloud or some workloads for many customers don't make sense to move to the cloud, at least not anytime soon, so the fact that they can build it on MongoDB and have the optionality to move it to the cloud later very easily is something that's very compelling for customers.

Brian Denyeau: Our expectations for the macroeconomic environment in fiscal 2025 and the impact of AI, the benefits of our product platform, our competitive landscape, customer behaviors, our financial guidance, and our planned investments in growth opportunities in AI. These statements are subject to a variety of risks and uncertainties, including the results of operations and financial conditions, because that could cause actual results to differ materially from our expectations. For discussion of the material risks and uncertainties that can affect our actual results, please refer to the risk described in our quarterly report on form 10Q.

Dev Ittycheria: So the fact that they can, you know, build it on MongoDB and have the optionality to move it to the cloud later very easily is something that's very compelling for customers. With regards to AI, I mean, we probably see most of the AI workloads in the cloud. But there are definitely lots of customers looking at using open-source LLMs, in particular, things like Lama and running those workloads locally. Obviously, it means that they have also to have access to, you know, Nvidia hardware like GPUs. But we do see some customers do that. Again, I wouldn't suggest that that's also an inflection point or cause for the EA performance.

Speaker Change: With regards to AI, I mean we've predominantly seen most of the AI workloads in the cloud.

Speaker Change: But there are definitely lots of customers looking at using open source LLMs.

Speaker Change: and particular things like Lama and running those workloads locally. Obviously it means that they have also to have access to.

Speaker Change: and Video Hardware like GPUs, but we do see some customers do that again. I wouldn't suggest that that's also...

Brian Denyeau: For the quarter ended April 30th, 2024 followed the SEC at May 31st, 2024. Any four of these statements made in this call reflect our views only as up today, and we undertake no obligations to update them, except as required by law. Additionally, we will discuss non-gap financial measures in this conference call.

Dev Ittycheria: I think it's very, very early days, and most of those are experiments that customers are running. But clearly, we feel really good about running anybody's strategy. And as I've said, we've said in the past, we are investing in basically introducing search and vector search to our community product, and that will then show up in EA. So EA is definitely an area where we're also investing from a product point of view.

Speaker Change: and inflection point or cause for the EA, performance I think is very, very early days and most of those are experiments that customers are running. But clearly we feel really good about running any very strategy and as I said, we set it in the past.

Unknown Executive: Please refer to the tables in our earnings release on the investor relations portion of our website, for a reconciliation of these measures for their most directly comparable gap financial measure.

Speaker Change: We are investing in basically introducing a search and vector search to our community product and that will then show up in EA so EA is definitely an area where we're also investing from a product point of view.

Dev Ittycheria: With that, I'd like to throw the call over to Dave. Thanks, Brian, and thank you to everyone for joining us today. I am pleased to report that we had a good quarter in executed well against our large market opportunity. Let's begin by reviewing our second quarter results before giving you a broader company update. We generated revenue of $478 million, a 13% year of year increase against a very difficult year of year compare, and above the high end of our guidance.

Tyler Radke: Thank you. One moment for the next question. And our next question will be coming from the line of Tyler Radke of City. Your line is open. Hey, this is Tyler Radke. I think the question was for me. It can hear the name. But thanks for taking the question. Dave, you talked about a Postgres displacement in the quarter. And I think that's the first time you've talked about that, at least in recent quarters. So I was wondering if you could just sort of frame for us. What use cases do you come across them? I know it is popular in terms of a SQL displacement for migrating legacy applications.

Speaker Change: Episode 2

Speaker Change: Thank you one moment for the next question

Speaker Change: Episode 2

Speaker Change: and our next question will be coming from the line of Tyler Rettking of City, your line is open.

Dev Ittycheria: Atlas revenue grew 27% year of year representing 71% of revenue. We generated non-gap operating income of $52.5 million for 11% non-gap operating margin, and we ended the quarter with over 50,700 customers. Overall, we were pleased with the performance in the second quarter. We had a strong new business quarter and we saw improving sales productivity year of year. We saw strength across the board with both Atlas and enterprise advance exceeding our expectations, demonstrating the enduring appeal of our run anywhere strategy.

Speaker Change: Hi, this is Tyler Radke, I think the question is for me, can you hear the name? Thanks for taking the question.

Speaker Change: David talked about a postgres displacement.

Speaker Change: in the quarter and uh...

Speaker Change: I think that's the first time you've talked about that, at least in...

Speaker Change: and recent quarters, so it's wondering if you could just sort of...

Speaker Change: Frame for us, what use cases do you come across them? I know it is popular in terms of a sequel displacement.

Dev Ittycheria: If you could just frame for us sort of the competitive environment in Postgres, both the open source stuff and some of the new venture-backed startups in the space. Thank you. Yeah, sure. So thanks for the question. Yeah, it's important to understand that Postgres has been around for almost 40 years. I mean, it's Postgres. The name is turned, turned from post ingress. So that technology has been around a long time. As you said, they're really the beneficiary of lift and shift from Oracle SQL Server and MySQL. So they're kind of consolidating the relational market. In terms of why do we compete or why do we win?

Dev Ittycheria: Our Q2 performance reinforced our belief the slow start to the new business Q1 was purely operational, and we feel good about our new business outlook in the second half of the year. Moving on to Atlas consumption, the quarter played out modestly better than our expectations.

Speaker Change: for migrating on legacy applications. If you could just frame for a sort of the competitive environment in Postgres, both the open source stuff and some of the new venturebacks startups in this day. Thank you.

Speaker Change: Yeah, sure, so thanks for the question. Yeah, it's important to understand that Postgres has been around for almost 40 years. I mean, it's Postgres, the name is turned from, you know, Post-Engress.

Dev Ittycheria: Michael will discuss consumption trends in more detail. Finally, retention rates remain strong in Q2, demonstrating the quality of our product and the mission criticality of our platform. Our performance is quarter reinforced our confidence and our ability to execute on a long-term opportunity.

Speaker Change: So that technology has been around long time. As you said, they're really the beneficiary of lift and shift from Oracle, SQL Server and my SQL, so they kind of consolidating the relational market.

Dev Ittycheria: As we said before, companies today rely on software to express their business strategy. This trend has driven our success for the past decade, and we anticipate it will continue to do so for the foreseeable future. Even with our success to date, we only have a low single digit share in one of the largest and fastest-growing markets in all of software. When you combine this foundational tailwind with the opportunities for customers to incorporate generative AI into their businesses and modernize their legacy application state, it is clear that MongoDB has multiple long-term growth opportunities.

Dev Ittycheria: I would say it's a few things. One, our scheme of flexibility, you know, it's a very, you know, MongoDB has a very flexible scheme, allowing you to store documents in a JSON-like format. So this is beneficial for application structures that evolve over time. We can horizontally scale. So we're making it very easy to distribute data across multiple servers or virtual servers. It's for applications that require massive amounts of data, performance of large data sets. Again, we can handle that better than Postgres. The built-in charting allows for automatic data distribution. And then we also, I think, developer productivity, the JSON-like format and flexible schema can lead to faster development cycles, especially for customers who really work in agile environments.

Speaker Change: In terms of why do we compete or why do we win? I would say it's a few things, one is scheme of flexibility.

Speaker Change: It's a very flexible scheme allowing you to store documents in a JSON format so this is beneficial.

Speaker Change: for applications, structures that evolve over time. We can horizontally scale, so we're making it very easy to distribute data across multiple servers or virtual servers for applications that require mass amount of data. Performance of large data sets. Again, we can handle that better than Postgres.

Dev Ittycheria: Turning to AI, AI continues to be an additional long-term opportunity for our business. At the start of the fiscal year, we told you that we didn't expect AI to be a meaningful tailwind for our business in fiscal year 2025, which has proven accurate. Based on recent peer commentary, it seems that the industry now mostly agrees with this view. Companies are currently focusing their spending on the infrastructure layer, AI, and are still largely experimenting with AI applications.

Speaker Change: The built in shardings allows for automatic data distribution.

Speaker Change: and then we also, I think, develop a productivity that Jason liked format and flexible scheme. I can lead to faster development cycles, especially for customers who really work in agile environments.

Dev Ittycheria: So we feel like we compete or win rates against Postgres are high. But again, there's lots of decisions being made where we're not party to, where people are just doing a lift and shift off a legacy platform or they just want to stay on relational because that's what they know. And obviously, it's our job to educate them on the benefits of MongoDB, but we feel good about our competitor position against Postgres.

Speaker Change: So we feel like we compete, our win rates against Postgres are high, but again, there's lots of decisions being made where we're not party to where people are just doing a lift and shift off.

Dev Ittycheria: Inference workloads will come and should benefit MongoDB greatly in the long run, but we are still very early when the monetization of AI apps will take time. AI demand is the question of when, not if, and our discussions with customers and partners give us increasing conviction that we are the ideal data layer for AI apps for a number of key reasons. First, more than any other type of modern workload, AI driven workloads require the underlying database to be capable of processing queries against rich and complex data structures quickly and efficiently.

Speaker Change: Likesy platform or they just want to stay on relation, because that's what they know. And obviously it's our job to educate them on the benefits of MongoDB, but we feel good about our competitive position against Postgres.

Michael Gordon: Thank you.

Michael Gordon: And follow-up questions for Michael. You talked about how consumption and atlas in the quarter tracked a bit better than planned. Sounds like it was not a macro-related improvement. What do you think the driver that was? And are you seeing any improvement in some of the recently acquired workloads, those starting to ramp up better? Or maybe they have different seasonal patterns than you thought? Any color on sort of that driver of performance would be helpful. Thank you. Yeah, sure. I would say that, yes, Q2 consumption growth was better than our expectations. That was great to see.

Speaker Change: The End.

Speaker Change: Thank you and...

Michael Gordon: Follow-up questions for Michael. You talked about how consumption and atlas in the quarter track the bit better than planned sounds like it was not a...

Dev Ittycheria: Our flexible document model is uniquely positioned to help customers build sophisticated AI applications because it is designed to handle different data types, your source data, vector data, metadata, and generated data, right alongside your live operational data, updating the need for multiple database systems and complex backend architectures. Second, MongoDB offers a high performance and scalable architecture, as the latency of LLM improves, the value of using real-time operational data for AI apps will become even more important.

Speaker Change: McRow related improvement.

Speaker Change: Well, what do you think the driver that was and are you seeing any improvement in some of the recently acquired workloads, those starting to ramp up better, or maybe they have different seasonal patterns than you thought. Any color on sort of that driver of performance would be helpful. Thank you.

Dev Ittycheria: Third, we are seamlessly integrated with leading app development frameworks and AI platforms, enabling developers to incorporate MongoDB into the existing workflows while having the flexibility to choose the LLM and other specific tools that best suit their needs. Fourth, we meet or exceed the security compliance requirements expected from an enterprise database, including enterprise-grade encryption, authorization, and auditability. Lastly, customers can run MongoDB anywhere on-premise or as a fully managed service in one of the 118 global cloud regions across three hyper-skillers, giving them the flexibility to run workloads to best meet their application use cases and business needs.

Speaker Change: Yeah, sure. I would say that, yes, Q2 consumption growth was better than our expectations that was great to see.

Michael Gordon: I would describe it as within a reasonable or typical range of outcomes. And so there are no signs that we've seen that would specifically point to anything, any material changes in the underlying macroeconomic environment, better or worse. We've certainly seen to your question on sort of the workloads and sort of some of the cohorting. We did see a little better performance there as well. And in line with what we saw elsewhere, but there's still below our original expectations. So I think that's probably the key couple things there. Thank you.

Speaker Change: I would describe it as within a reasonable or typical, you know, range of outcomes and so there are no signs that we've seen that would specifically point to anything, you know, material changes in the underlying macroeconomic environment better or worse.

Speaker Change: We've certainly seen, you know, to your question on sort of the workload sort of some of the cohorting.

Speaker Change: We did see a little better performance there as well and you know in line with we'll see you know elsewhere but there's still below our regional expectations. So I think that's probably the key couple things there.

Operator: Thank you, one more.

Brad Reback: question. And our next question for today would be coming from Brad Reback of Cecil. Your line is open. Great, thanks very much. Dev, on your commentary around the modernization pilots, how should we think about timing as that being a bit of a tailwind to the overall growth rate? Yeah, so again, just to make sure everyone understands, you know, the legacy relational application market or database market is quite large. It's over 80 billion, and it's a massive option for us. And since day one, since our IPO, we've been getting customers to migrate off relational to MongoDB.

Speaker Change: Thank you.

Dev Ittycheria: We see three main opportunities where we believe AI will accelerate our business over time. The first is that the cost of building applications in the world of AI will come down as we've seen with every previous platform shift, creating more applications and more data, requiring more databases. The second opportunities for us to be the database of choice for customers, building greenfield AI applications. While we see that this tremendous amount of interest in and planning for new AI-powered applications, the complexity and fast-moving nature of the AI ecosystem slows customers down.

Speaker Change: And our next question for today, we'll be coming from Brad Reback of Steephil, your line is open.

Brad Reback: Good morning. Thank you very much. Dave, on your commentary around the modernization pilots, how should we think about timing as that being a bit of a tailwind to the overall growth rate?

Speaker Change: Yeah, so again, just to make sure everyone understands, you know, the legacy relational application market or database market is quite large. It's over 80 billion and it's a massive opportunity for us and since day one, since our IPO we've been getting customers to migrate off relational to MongoDB.

Dev Ittycheria: That's why we launched the MongoDB AI Applications Program or MAP, which became generally available to customers last month. MAP brings together a unique ecosystem, including the three major cloud providers, AWS, Azure and GCP, as well as Accenture and AI pioneers like Anthropic and CoHIR. MAP offers customers reference architectures and end-to-end technology stack that includes pre-built integrations, professional services, and a unified support system to help customers quickly build and deploy AI.

Dev Ittycheria: But one of the biggest friction points has been that while it's easy to move the data, you can map the schema from a relational schema to a document schema. And you can automate that. The biggest stumbling block is that the customer has to, or some third party has to, rewrite the application, which by definition creates more costs, more time, and in some cases more risk, especially for older apps where the development teams who built those apps no longer exist. So what's been compelling about AI is that AI has finally created a shortcut to overcome that big hurdle.

Speaker Change: But one of the biggest friction points has been that while it's easy to move the data, you can map the schema from a relational schema to a...

Speaker Change: To a document scheme and you can automate that. The biggest stumbling block is that the customer has to or or some third party has to rewrite the application, which by definition creates more costs, more time and in some cases more risk.

Dev Ittycheria: This is a very application. The third opportunity is to help customers modernize a legacy application state. As you know, this segment of the market is a massive opportunity for us as most of the existing 80 billion plus database industry is built on dated relational architecture. Modernizing legacy applications has always been part of our business and we have taken steps of the years to simplify and demystify this complex process through partnerships, education, and most recently our relational migrator product.

Speaker Change: Especially for older apps where the development teams who built those apps no longer exist.

Speaker Change: So what's been compelling about AI is that AI is finally created a shortcut to overcome that big hurdle.

Dev Ittycheria: And so essentially you can start basically diagnosing the code, understand the code, you know, recreate a modern version of that code, and generate test sweeps to make sure the new code performs like the old code. So that definitely gets people's interest because now all of a sudden what may take, you know, years or multi-years, you can do in a lot less time. And the pilots that we have done, the time and cost savings have been very, very compelling.

Speaker Change: And so essentially you can start basically diagnosing the code, understand the code, you know, recreate a modern version of that code and generate tests. We just make sure the new code performs like the old code. So that definitely gets people's interest because now will sudden what may take.

Dev Ittycheria: AI offers a potential step function improvement lowering the cost and reducing their time and risk to modernize legacy applications. For that reason earlier this year we launched several pilots with our customers where we work with them to modernize mission critical applications, applications, leveraging both AI tooling and services. The early results from these pilots are very exciting as our customers are experiencing significant reductions in time and cost of modernization. In particular, we have seen dramatic improvements in time and cost to rewrite application code and generate test suite.

Speaker Change: You know, years or multi years, you can do in a lot less time and the pilots that we have done, the time and cost savings have been very, very compelling.

Dev Ittycheria: That being said, we're in the very early days. There's a lot of interest. We have a growing pipeline of customers across frankly all parts of the world from North America to AMIA and even the pack room. And so we're quite excited about the opportunity.

Speaker Change: That being said, we're in the very early days. There's a lot of interest. We have a growing pipeline of customers across.

Dev Ittycheria: We see increasing interest from customers that want to modernize the legacy application state including large enterprise customers. As a CIO, one of the world's largest insurance companies said about our pilot, this is the first tangible return he's seen on his AI investments. While still early days and generating meaningful revenue from this program will take time, we're excited about the results of our pilots and the growing pipeline of customers eager to modernize the legacy of state.

Speaker Change: Frankley all parts of the world from North America to Mia and even the Pack Rim. And so we're quite excited about the opportunity, but again, I would say it's very early days, but there's a number of reasons why I would say that customers are...

Dev Ittycheria: But again, I would say it's very early days. But there's a number of reasons why I would say that customers are very focused on this. What the cost of licensing and maintaining legacy apps is becoming too high to bear. In many cases, the regulatory compliance requirements are forcing customers to upgrade. There's a whole end of life of critical technologies, notably side base, that's forcing customers to act. There's a ton of technical depth on these legacy platforms that limits the organization's flexibility to do things with AI. And canally customers have also soured on the traditional approach of using large systems integration projects that are very costly and take a long time.

Speaker Change: Very focused on this, what the cost of licensing and maintaining legacy apps is becoming too high to bear. In many cases, the regulatory and compliance requirements are forcing customers to upgrade this whole end of life.

Dev Ittycheria: Finally, I understand that there are a lot of questions about the current business conditions and the macro environment more broadly. So let me give you a sense of what we're seeing across the business. As a reminder, when thinking of the macro influence on our business, it's important to distinguish between consumption of existing workloads and new business. Starting with consumption of existing application or platform, this is where we have historically seen a macro impact as usage of applications impacted by the underlying business conditions of our customers.

Speaker Change: of critical technologies, notably side-based as forcing customers to act.

Speaker Change: This is a ton of technical depth on these legacy platforms that limits the organization selects ability to do things with AI and cannelly customers have also sowered on the traditional approach of using large.

Dev Ittycheria: So this whole approach is definitely getting their attention.

Speaker Change: You know, systems and integration projects that are very costly and take a long time so this whole approach is definitely going their attention

Dev Ittycheria: As we discussed in our last earnings call, in Q1, we did see brought based consumption growth slow down, suggesting some macro softening. Our usage trends suggest a similar macro environment in Q2 as in Q1, even though Q2 Atlas consumption growth was modestly ahead of our expectations. Moving on to new business, we generally have not seen the macro environment impact our ability to win new business, and that was true in Q2 as well.

Dev Ittycheria: That's great. And then one fast follow-up, it feels like we've spoken about EA more than typical on this call, which is great. As we think about the back half pipeline, is the composition similar in so much as it's predominantly existing customers? Are you beginning to see an uptake in met new customers there as well. Thanks. Yeah, I would say that it's predominantly existing customers that are doing this. You know, we're maybe doubling down on EA and expanded the footprint of EA, and that's typically the driver for EA business. Thanks very much. Thank you.

Speaker Change: That's great and then one fast follow-up, it feels like we're spoken about E.A. more than typical on this call, which is great. As we think about the back half-type line, is the composition similar in so much as it's predominantly existing customers, or are you beginning to see an uptick and met new customers there as well? Thanks.

Dev Ittycheria: We realized that this is different from what you hear from some other software vendors. Ultimately, software application development continues even in uncertain environments as customers know they need to continue investing in internally developed software to run the business as well as to drive competitive differentiation. In addition, we still have relatively low market share in a large market, which means we have an opportunity to gain share in any environment.

Speaker Change: Yeah, I would say that it's predominantly existing customers that are doing this, you know, who are maybe doubling down on EA and expanding the footprint of EA. And that's typically the driver for EA business.

Operator: Thank you, and one moment for the next question, please.

Speaker Change: Thanks very much.

Speaker Change: Thank you.

Speaker Change: Thank you and one moment for the next question, please.

Dev Ittycheria: Now, I'd like to spend a few minutes reviewing the adoption trends of MongoDB across our customer base. Customers across industries and around the world are running mission critical projects on MongoDB Atlas, leveraging the full power of our developer data platform, including fanatics, occidental petroleum, and indeed. Fanatics betting and gambling, a division of the sports ecosystem company fanatics leverages MongoDB to significantly enhance the use experience of their mobile app.

Karl Keirstead: And our next question will be coming from Karl Keirstead of UBS. Your line is open. Okay great. Hey David, the beginning of the call you mentioned that Mongo's slow start to the year was quote purely operational. But on the Q1 call, when you were describing what happened, you described it as very much a broad-based macro issue, so I'm curious. Should I interpret that comment as you, mic, in the team have done a bit of a rethink and might be changing, modestly at least, your explanation about the Q1 results. Yeah, actually just to be clear, I think just to make sure everyone understands what we called out was that the consumption of existing workloads was broad based. The slow down was broad based across both Geos and channels.

Speaker Change: and our next question, we'll be coming from call.

Speaker Change: Care Spend.

Care Spend: Of UBS, your line is open.

Care Spend: Okay great hey Dave at the beginning of the call you mentioned that

Dave Ittycheria: Mongo's low start to the year was quote purely operational.

Care Spend: But on the Q1 call when you're describing what happened, you described it as very much a broad-based macro issue. So I'm curious, should I interpret that common as you, Mike and the team have done a bit of a rethink and might be changing modestly at least your explanation about the Q1 results?

Dev Ittycheria: Initially, the team launched a platform on Postgres, but faced challenges with scalability, flexibility, and excessive complexity. After migrating to MongoDB Atlas, the team also integrates Atlas Search to provide users with a better experience to find all available betting options. With Atlas handing scaling, partitioning, and operations, developers can focus on writing code and improving the user experience. Looking ahead, Fanatics plans to continue to expand on MongoDB Atlas as they ensure they can operate at scale as they prepare for the start of the NFL season.

Mike: Yeah, actually, just to be clear, I think just to make sure everyone understands what we call that was that the consumption of existing workloads was broad-based

Dev Ittycheria: It was a new business just got off to a slower start, so that was what we called out in our Q1 call, and with our Q2 call and results, as you can see, you know new business in a performed better as expected. Then expected on both Atlas and EA, which is why we believe the Q1 issue was an operational issue, not a Q1 new business. Sorry, was an operational issue, not a macro issue. The macro issue was all about the slow down in consumption because Q1 typically tends to be a seasonally strong quarter for us, and that didn't happen.

Speaker Change #102: The slowdown was broad-based across both geos and channels.

Speaker Change #103: It was a new business.

Speaker Change #103: It just got up to slower stars so that was what we called out in our Q1 call.

Dev Ittycheria: Laureal, McKesson, and Nationwide Building Society are turning to MongoDB to modernize applications. Laureal's tech accelerator, a department dedicated to catalyzing digital innovation at Laureal, is utilizing MongoDB for an application designed to bring products and solutions to market while quickly improving employee efficiency. The team's previous database solution had limited out-of-the-box functionality and was unable to handle the complex calculations needed to retreat and restructure large amounts of data from their data warehouse. Laureal migrated to MongoDB Atlas to streamline the application architecture and simplify a previously highly complex and time-consuming data access layer. With this migration, Laureal achieved a 40-fold performance improvement. On Atlas, existing code is easier to maintain, more scalable, and more efficient, making life easier for developers.

Speaker Change #103: and with our Q2 call and results, as you can see, you know, new business in a performed better, as expected, on both Atlas and EA, which is why we believe the Q1 issue.

Speaker Change #103: was an operational issue, not a Q1 new business, sorry, was an operational issue, not a macro issue. The macro issue was all about the slowdown and consumption, because Q1 typically tends to be a seasonally strong quarter for us, and that didn't happen.

Michael Gordon: Yeah, I think that's really important, Carl, just to keep in mind when we talk about macro could have those two different effects, right? The new business piece and then the expansion of existing workloads. And so what we were talking about, as they've said in Q1, was the existing workload expansion; the operational piece was on the new business side. And I think the key thing that we've observed, really, in a wide range of, you know, macroeconomic conditions in our almost seven years above a company is, with exception at one quarter. We've been able to execute quite well in a new business opportunity, really in most environments, given that we have, you know, a huge market, you know, relatively low share, strong product, and a talented team that execute well. And so I think that's probably helpful just to understand as we're so using the terms, just to make sure we don't trip ourselves up.

Speaker Change #103: Yeah, I think that's really important, Carl, just to keep in mind when we talk about Macro, it had those two different effects, right, the new business fees

Speaker Change #103: and then the expansion of existing workloads and so what we're talking about as Dave said in Q1 was the existing workload expansion, the operational piece.

Speaker Change #104: was on the new business side, and I think the key thing that we've observed.

Dev Ittycheria: Mature companies and startups alike are using MongoDB to help deliver the next wave of AI-powered applications to their customers, including delivery hero, generalie, and quest flow. Delivery hero, a long-time MongoDB Atlas customer, is the world's leading local delivery platform, operating in 70-plus countries across four continents. Their quick commerce service enables customers to select fresh produce for delivery from local grocery stores. Approximately 10% of the inventory is fast-moving, perishable produce that can go quickly out of stock.

Speaker Change #105: really in a wide range of, you know, macroeconomic conditions in our almost seven years of of a company is with the exception of that one quarter, we've been able to execute quite well as a new business.

Speaker Change #105: Opportunity, really in most environments.

Speaker Change #105: I'm giving that we have a huge market, relatively low-share, strong product and a talented team that executes well and so I think that's probably helpful just to understand as we're still using the terms just to make sure we don't trip ourselves up. Yep, okay, that's great. And then my follow up for you, Mike.

Michael Gordon: Yep, okay, that's great. And then my follow-up to you, Mike, is when you were pressed in prior questions about the second half, most of the variables that you brought up were, frankly, you know, headwinds. Things like tough compares. But if I look objectively, your three Q total revenue got of 497, and this is my assumption, but I assume kind of a normal beat that's going to result in sequential total revenue growth that's actually the highest I think that MongoDB has ever put up. So clearly there's something good that you're embedding in that three-Q guide. What is that specifically? Yeah, so I'm not sure I follow all the math on the fly, and I'll happy to follow up obviously with any of that. But if I just think about the Q3 guide, I won't belabor the headwinds, but they exist, and which sort of, you know, been through those as you've called out. But maybe to talk about the couple of positive things that we see that lead to sort of, you know, our improved outlook or increased revenue forecast for the balance of the year, I'd say it's really two things. In Atlas, it's reflecting the stronger than expected, you know, Q2, and therefore the starting AR at the beginning of Q3 is higher because we haven't seen any change in the underlying, you know, macro. The actual growth assumptions for Q3 and Q4 we have remain unchanged, so you're supplying, you know, growth to a higher base. But that, you know, as the base keeps getting bigger and bigger, that does matter, and that flows to, you know, and then secondly was the positive impact of the increased strength in the EA pipeline for the second half of the year and the impact on that revenue. So those are probably the two things that I call out on the positive side of the revenue, in addition to, of course, having everyone keep in mind the tough compare and some of the headwinds we talked about.

Dev Ittycheria: The company risk-loosing revenue and increasing customer churn, if the customer didn't have viable alternatives to their first choice. To address these risks, they are now using state-of-the-art AI models and MongoDB Atlas Vector Search to give hyper-personalized alternatives to customers in real time if items they want to order are out of stock. With the introduction of MongoDB Atlas Vector Search, the data science team recognized that they could build a highly-performing real-time solution more quickly and for less cost than alternative technologies.

Speaker Change #106: When you were pressed in fire questions about the second half, most of the variables that you brought up were, were frankly, you know, headwinds, things like tough compares.

Speaker Change #107: If I look objectively, you're three cute total revenue god of 497, and this is my assumption, but I assume kind of a normal beat. That's going to result in sequential total revenue growth that's actually the highest, I think.

Speaker Change #108: That MongoDB has ever put up So clearly there's something good that you're embedding in that 3Q guide What is that specifically?

Dev Ittycheria: In summary, we had a healthy Q2 with both Atlas and EA exceeding expectations. We saw a strong new business quarter and improved sales productivity and were confident in our ability to keep winning new business in the second half of the year. Looking forward, we see great opportunity to help our customers modernize legacy applications and build the next generation of AI-powered applications.

Speaker Change #109: Yeah, so I'm not sure I follow all the math on the fly and I'm happy to follow up obviously with any of that But if I just think about the Q3 guide, I won't belabor the the headwinds, but they exist and which should have been through those as you'd called out But maybe to talk about the couple of Positive things that we see that lead just sort of you know are improved outlook or increased Revenue forecast for the balance of the year. I take really two things

Michael Gordon: With that, here's Michael. Thanks, Dave. I'll begin with a detailed review of our second quarter results and then finish with our outlook for the third quarter and full fiscal year 2025. First, I'll start with our second quarter results. Total revenue in the quarter was $478.1 million. Up 13% year-of-year and above the high end of our guides. As a reminder, while we have difficult compares throughout fiscal 25, we were facing a particularly difficult year-of-year comparison in Q2, given we had a number of large multi-year partnership licensing deals in Q2 last year.

Speaker Change #110: In Atlas, it's reflecting the stronger than expected Q2, and therefore the starting ARR at the beginning of Q3 is higher, because we haven't seen any change in the underlying macro, the actual...

Speaker Change #111: Growth Assumptions for Q3 and Q4 we have remain unchanged so you're supplying growth to a higher base but that is the base keeps getting bigger and bigger that does matter and that flows through and then secondly was the positive impact.

Michael Gordon: Under 66 accounting rules, we recognized the license component upfront which creates a difficult compare a year later. Shifting to our product mix, but sort of that less. Atlas grew 27% in the quarter compared to the previous year and now represents 71% of the total revenue compared to 63% in the second quarter of fiscal 2024 and 70% last quarter. We recognized Atlas revenue primarily based on customer consumption of our platform and that consumption is closely related to end user activity of their applications.

Speaker Change #112: of the increased strength in the EA pipeline for the second half of the year and the impact on that revenue. So those would probably be the two things that I'd call out on the positive side of the revenue. In addition to, of course, having everyone keep in mind the tough compared and some of the headwinds we talked about. Okay, that's helpful, Mike. Thanks a lot.

Michael Gordon: Okay, that's helpful, Mike. Thanks a lot.

Operator: Thank you. Thank you, and one moment for the next questions.

Speaker Change #113: Thank you. Thank you and one moment for the next questions.

Michael Gordon: Let me provide some context on Atlas consumption in the quarter. First, as a reminder, in Q1, Atlas consumption growth was below our expectations and we updated our growth assumptions for the remainder of the year. In Q2, Atlas Consumption Growth was modestly ahead of those updated expectations across the board. While this is encouraging, Consumption Growth is still below our original forecast from the beginning of the year. Turning to non-Atlas Rep. Non-Atlas came in modestly ahead of our expectations in the quarter, as we continue to have success selling incremental workloads into our existing customer base.

Jason Ader: Our next question will be coming from Jason Ader, William Blair. Your line is open. Yeah, thank you. I'm trying to understand the Q4 implied revenue guide, Michael. You know, just the implied sequential growth there. There's only one and a half percent; if I look at your full year relative to what you said specifically on Q3. That would be well below the historical seasonal pattern in Q4. So just want to understand there's something going on in Q4 that's different, just sequential. I understand the year over year is tough, but the sequential seems well below where you've normally been.

Speaker Change #114: Our next question will be coming from Jason Aver of William Blair, your line is open.

Jason Aver: Yeah, thank you. I'm trying to understand the Q4 implied revenue guide, Michael.

Jason Aver: You know, just the implied sequential growth there is only one and a half percent of it. Look at your full year, it's up to what you said specifically on Q3.

Michael Gordon: On a year-over-year basis, non-Atlas Revenue was down 13% due to the especially difficult compare I referenced earlier. Turning to customer growth during the second quarter, we grew our customer base by approximately 1,500 customers sequentially, bringing our total customer count to over 50,700, which is up from over 45,000 in the year-go period. Of our total customer count, over 7,300 are direct sales customers, which compares to over 6,800 in the year-go period. The growth in our total customer count is being driven primarily by Atlas, which had over 49,200 customers at the time.

Michael Gordon: That would be well below the historical seasonal pattern in 2004, so just want to understand is there something going on in 2004 that's different just to questions with I understand the year over year.

Speaker Change #116: is tough, but the sequential seam, well below where you've normally been and, you know, there's something specific that we should think about there.

Michael Gordon: And you know, there's something specific that we should think about there.

Michael Gordon: Yeah, I think they're two things. So obviously it sounds like you get the year-over-year, but just to make sure everyone's sort of on the same page is making sure that people understand, you know, the tough compare for Atlas specifically around the unused commitments, and then EA, the multi-year, and then more broadly. You know, we had one; we had left growth in Q1, that compounds, and then also we talked about how we have a, we're assuming just in the same way that we saw in Q1, we're assuming less of a seasonal rebound in Q3; that has implications for Q4.

Speaker Change #117: Yeah, I think they're two things, so obviously it's time to get the year every year, but just to make sure everyone's on the same page is making sure that people understand.

Speaker Change #118: You know the tough compare for Atlas specifically around the unused commitments and then EA the multi year and then more broadly, you know we had one we had left.

Michael Gordon: The end of the quarter compared to over 43,500 in the year-go period. It is important to keep in mind the growth in our Atlas customer count reflects new customers among the DB in addition to existing EA customers adding incremental Atlas workloads. Continuing on, in Q2, our net AR expansion rate was approximately 119%, the decline versus historical periods is attributable to a smaller contribution from expanding customers. We ended the quarter with 2,189 customers with at least $100,000 in ARR and annualized MRR up from 1,855 in the year-go period.

Speaker Change #118: Growth in Q1, that compounds, and then also we talked about how we have, we're assuming just in the same way that we saw in Q1, we're assuming less of a seasonal rebound in Q3, that has implications for Q4.

Michael Gordon: Gotcha. Okay.

Michael Gordon: And then just one quick follow-up on gross margins. Did you talk about Atlas gross margin in the quarter, and then how material will the impact be from the prepayments and the IPV for purchases on Atlas gross margins over time? Yeah, so we didn't specifically break out Atlas gross margin in the quarter, but you know, they continue to be lower, but obviously we've pretty significantly shrunk the delta that does explain some of the, you know, comparison on the year-over-year basis. We didn't give specific guidance going forward. If you think about the two changes, you know, that we called out on the cashless side, those will benefit us in terms of gross margin. There won't be a significant benefit in fiscal 25, so we'll obviously talk about that more when we get to the fiscal 26 guide, but it's a very excellent use of our cash and, you know, good ROI in terms of improving those economics further.

Speaker Change #119: Gotcha. Okay, and then just one quick follow-up on Gross margins. Did you talk about Atlas Gross Margin in the quarter and then how material would the impact be from the pre-payments and the IPv4?

Michael Gordon: Moving down the income statement, I'll be discussing our results on a non-GAP basis unless otherwise noted. Gross profit in the second quarter was $360.8 million, representing a gross margin of 75%, which is down from 78% in the year-go period. Our year-to-year margin decline is primarily driven by a lower mix of high margin upfront license revenue compared to last Q2, as well as Atlas growing as the percentage of the overall business. Our income from operations was $52.5 million or an 11% operating margin for the second quarter compared to a 19% margin in the year-go period.

Speaker Change #120: Purchases on Atlas Gross margins over to you.

Speaker Change #120: Time.

Speaker Change #121: Yeah, so we didn't specifically break out Atlas Gross Morgan in the quarter, but they continue to be lower, but obviously we've pretty significantly shrunk the delta that does explain.

Sam: Sam of the…

Speaker Change #123: You know, comparison on the year of your basis, we didn't give specific guidance going forward.

Speaker Change #124: Um, if you think about...

Speaker Change #124: The two changes that we called out on the cashless side, those will benefit.

Ross: Ross, in terms of gross margin, there won't be a significant benefit in fiscal 25s, so we'll obviously talk about that more when we get to the fiscal 26s guide, but it's a very excellent use of our cash and good ROI in terms of improving those economics further.

Michael Gordon: The primary reason for a more favorable operating income results versus guidance is our revenue outperformance. In addition, Q2 operating income benefited from the timing of certain marketing and other spend, which we now expect to incur in the second half of the year. Net income in the second quarter was $59 million or 70 cents per share based on 83.8 million diluted weighted average shares outstanding. This compares to a net income of $76.7 million or 93 cents per share on 82.5 million diluted weighted average shares outstanding in the year-go period.

Michael Gordon: Thank you.

Bradley Sills: One moment for the next question. And our next question will be coming from Brad Fills of Bank of America. Your line is open. Oh, great. Thank you so much. Great to hear the continued strength and new workloads here. That's been a very consistent theme here throughout all this.

Speaker Change #126: Thank you.

Speaker Change #127: Thank you, one moment for the next question.

Speaker Change #128: Episode 2

Speaker Change #129: And our next question will be coming from Brad Sills of Bank of America, your line is open.

Michael Gordon: Turning to the balance sheet and cash flow, we ended the second quarter with $2.3 billion in cash, cash equivalence, short-term investments, and restricted cash. Operating cash flow in the second quarter was negative $1.4 million. After taking into consideration approximately $2.6 million in capital expenditures and principal repayments of finance lease liabilities, free cash flow was negative $4 million in the quarter. This compares the free cash flow of negative $27.3 million in the year-go period. In addition, this quarter, we also received $170.6 million in cash from the settlement of the CAP calls associated with our 2024 convertible note.

Brad Sills: Oh, great. Thank you so much. Great to hear the continued strength and new workloads here that's been very consistent. Team here throughout all this.

Dev Ittycheria: I did want to ask about some of the newer services like vector and stream processing. You know, how are those contributing to the strength you're seeing or sustained strength you're seeing in new workloads? Yeah, Brad, thanks for the question. So, in terms of search, we're seeing solid momentum in search. We're having success with that business is starting to grow and we just introduced a new capability called search nodes, which allows customers to optimize their search appointments by asymmetrically scaling, you know, specifically those dedicated for search versus the rest of the nodes on the cluster. It also helps dealing with use cases that are very search intensive.

Brad Sills: I did want to ask about some of the newer services like Vector and Stream Process.

Singh: Singh, you know, how are those contributing to the strength you're seeing and or sustained strength?

Singh: Thanks for seeing and the new work.

Singh: Gloves.

Speaker Change #132: Yeah, Brian, thanks for the question. So in terms of search, we're seeing solid momentum in search, we're having success with that business. It's starting to grow and we just introduced a new capability called search nodes, which allows customers to optimize their search deployments by asymmetrically scaling, you know, specific nodes dedicated for search versus the rest of the nodes on their cluster. It also helps.

Michael Gordon: And now I'd like to turn to our outlook for the third quarter and full fiscal year 2025. For the third quarter, we expect revenue to be in the range of $493 million to $497 million. We expect non-gap income from operations to be in the range of $57 million to $60 million. And non-gap net income per share to be in the range of $0.65 to $0.68 based on 84.6 million estimated deluded weighted average shares.

Dev Ittycheria: One of the largest gaming companies in the world, we platform the content moderation platform from a doctor B elastic and dynamo to Atlas and Atlas search and using Atlas search nodes for workload isolation, high performance. On vector, we're continuing growth and adoption, and we see a vector as effective in attracting new customers to the MongoDB platform. A world-renowned financial news organization, which was already running on Atlas, migrated from Elastic Search to Atlas Search using search nodes to take advantage of our vector search capabilities to build a site search that combined electrical search with semantic search.

Speaker Change #133: Dealing with use cases that are very search intensive. One of the largest gaming companies in the world, replad from the content moderation platform, from a docDB elastic and dynamo to Atlas and Atlas search and using Atlas search nodes for workload isolation and high performance.

Speaker Change #133: On Vector, we're conducting growth in adoption, and we see a vector as effective in attracting new customers to the MongoDB platform, a world-renowned financial news organization which was already running an Atlas migrated from Elastic Search to Atlas Search using search nodes.

Michael Gordon: For the full fiscal year 2025, we expect revenue to be in the range of $1.92 million to $1.93 million, non-gap income from operations to be in the range of $187 million to $195 million, and non-gap net income per share to be in the range of $2.33 to $2.47 based on $84.3 million estimated deluded weighted average shares. Note that the non-gap net income per share guidance for the third quarter and full fiscal year 2025 include the non-gap tax provisions of approximately 20%.

Speaker Change #133: to take advantage of our vector search capabilities to build a site search that can talk, that combines electrical search with some antics search.

Dev Ittycheria: To find the most relevant articles for user query, a European energy company built a geospatial search application using Atlas and search and vector search, and the apples are built on prem and two clouds to vectorize geospatial data and facilitate research and discovery.

Speaker Change #133: Defined the most relevant articles for user queries.

Speaker Change #133: and a European Energy Company built a geospatial search application using Atlas and search and vector search.

Speaker Change #134: and the apples have built on-prem, but end two clouds to that surprise geospatial data and facilitate research and discovery.

Dev Ittycheria: And then we recently, you know, announced streaming of stream processing, I should say, you know, GA in and local New York in May, and we've seen strong interest. It's still early days, but we're seeing interest from a variety of industries ranging from automotive to retail to transportation, who all want to work with streaming data and want it to be able to take actions on that data to, you know, drive their business. And so, you know, customers are very pleased with the performance of the product and how easy it is to use. But again, it's just early days since we just launched it only in May.

Michael Gordon: And now I'll provide some context around our updated guidance. First, because of the stronger than expected Atlas consumption in Q2, our starting Atlas ARR for the back half of the year is higher than we anticipated it may. As a result, we're raising our Atlas revenue forecast to reflect that higher starting point. We are not changing our underlying Atlas Q3 and Q4 consumption growth rate assumptions. Second, we are slightly increasing our EA assumptions for the rest of the year to reflect the strength of the second half EA pipeline.

Speaker Change #135: And then we recently announced streaming, a stream processing, I should say, you know, GA in the...

Speaker Change #136: and Dot Local New York and May, and we've seen strong interest. It's still early days, but we're seeing interest from a variety of industries ranging from automotive to retail to transportation.

Speaker Change #137: who are all who work with.

Speaker Change #137: Streaming data, I wanted to be able to take actions on that data to drive their business. And so customers are very pleased with the performance of the product and how easy it is to use. But again, it's just early days since we just launched it on only in May.

Michael Gordon: Finally, thanks to the performance in Q2 and the increased revenue outlook, we now expect a 10% operating margin at the midpoint of our fiscal 25 guidance. We will continue investing to capture our long term opportunity with a focus on our strategic priorities.

Dev Ittycheria: Wonderful, great to hear.

Michael Gordon: One more if I may please. On the last earnings call, you mentioned how the slowdown and consumption was broad based across industries and workload types, which led you to believe that it was a macro related impact. Thank you. I would describe it as broad based, but I would describe it within sort of a reasonable range of outcomes and so no clear indication that macro is improving or deteriorating, and just a good quarter. Wonderful. Thank you.

Speaker Change #138: Well, wonderful. Great to hear. One more, if I may please, I'm a last earnings call. You mentioned how the slow down and consumption was brought based across industries and workload types, which led you to believe that it was a macro related impact.

Michael Gordon: Separately, although we don't provide guidance on cash flow, we wanted to call out two items that will impact fiscal 25 cash flow, but benefit our long term financial profile. In Q2, we started paying some of our cloud provider commitments upfront in exchange for better economics. We see this as a very low risk high ROI use of our cash and one that will benefit our gross margin going forward. These prepayments will represent a negative impact to our operating cash flow in the back half of the year of roughly $20 million per quarter. In addition, in Q3, we will incur approximately $20 to $25 million of catbacks to acquire IPv4 addresses, which will allow us to further reduce our cloud infrastructure costs in the future.

Speaker Change #139: Yeah, with some of the improvement you've seen this quarter I know it's early, but was that also broad-based and could you?

Speaker Change #140: and I want you to do a set of movies that I'm making.

Speaker Change #141: Bean, some improvement in the underlying macro, or was it more outside to certain types of industries?

Speaker Change #141: Services.

Lewis: Lewis, thank you.

Lewis: I would describe it as broad bass, but I would describe it within a reasonable range of outcomes and so no clear indication that macros improving or deteriorating and just a good quarter.

Rishi Jaluria: One more for the next question. And our next question will be coming from Rishi Jaluria of RBC Capital Markets. Your line is open. Wonderful. Hey, this is Rishi Jaluria from RBC. Two questions.

Speaker Change #143: Wonderful. Thank you.

Speaker Change #144: Thank you. One moment for the next question.

Russia: And our next question will be coming from Russia, the area of RBC Capital Markets. Your line is open.

Michael Gordon: To summarize, we're pleased with our second quarter results and especially our ability to win new business. We have a small share, one of the largest and fastest growing markets in all of software, the secular tailwinds that are back are getting stronger in the age of AI and we're excited about the future. We'll continue investing judiciously and focusing out our execution to capture this long-term opportunity.

Dev Ittycheria: Maybe Dave, I wanted to first start out going back to some of what we talked about, which is the MongoDB versus Postgres debate. One of the kind of popular theories out there is what debates out there is which architecture is better suited to new generative AI applications, especially to truly do believe AI will lead to a re-platforming of software similar to what we saw with the cloud. I know you talked a little bit about reference architecture earlier in Q&A, but maybe could you walk us through why you believe MongoDB is better suited to new generative AI native applications versus Postgres, and then I've got a quick follow.

Grishigilaria: I wonder if I'll say, hey, this is for Grishigilaria from OBC two questions, maybe Dave I wanted to first start out going back to some of what you talked about, which is good.

Grishigilaria: MongoDB versus Postgres debate, you know, one of the kind of popular theories out there is work with the ways out there is which architecture is better suited to new gender to AI applications, especially if we truly do believe AI will lead to a repladforming of softness in what we saw with the cloud. I know you talked a little bit about reference architecture earlier in Q&A, but maybe could you walk us through why you believe MongoDB is better suited.

Unknown Executive: With that, we'd like to open it up to questions. Operator? Thank you, as a reminder, if you would like to ask a question, please press star 1-1 on your telephone.

Unknown Executive: We also ask that you limit yourself to one question and one follow-up. As well, please wait for your name and company to be announced before you proceed with your question. One moment while we take the first question.

Dev Ittycheria: Yeah, sure. So very quickly, the reason we believe that we're well positioned to win these new workloads is that AI-driven workloads require the underlying database to be capable of processing queries against rich and complex data structures. As you know, with AI, the data structures can be very, very complex. So that means that the data structure, the data can be large and obviously not in any consistent size. MongoDB is designed to handle these different data structures, and I talked about, you know, we can help unify meta data, operational data, vector data, and generated all in one platform.

Speaker Change #147: to new gender-to-day-eye-made applications versus post-grace, and then I've got to click follow.

Sanjit Singh: And the first question today is going to come from Sanjit, scene of Morgan's family, your line. Hi, thank you for taking a question to direct on on Q2, nice to see Mongo meeting and raising again. Dave, I wanted to follow up on some of the changes that the operational changes you're making to the business that you announced last quarter.

Speaker Change #148: Yeah sure, so, so, so, so, so, very quickly, um...

Speaker Change #149: The reason we believe that we're well positioned to win these new workloads is that AI-driven workloads require the underlying database to be capable of processing.

Speaker Change #149: A queries against rich and complex data structures, as you know with...

Dev Ittycheria: Just in terms of the update in terms of realigning some of the sales incentives to drive higher workload quality and while maintaining some of that record workload growth that you saw last year, could you sort of walk us through what the changes were you put in place, and how much confidence you have that you will drive on better workload growth as we get into early next year. Yeah, sure. So first of all, thanks, Anjit.

Speaker Change #149: A.I. The data structures can be very, very complex.

Speaker Change #149: So, that means that the data can be large and not in any consistent size. I'm going to be designed to handle these different data structures and I talked about, you know, we can help identify.

Speaker Change #149: Metadata, Operational Data, Vector Data, and Generated All in one platform. Relational databases and Postgres is one of them, have limitations in terms of what they can handle different types of data. In fact,

Dev Ittycheria: Relational databases and Postgres is one of them have limitations in terms of what they can handle different types of data. In fact, when the data gets too large, these relational databases have to do what's called off-road storage, and it creates a performance overhead on these relational platforms. Postgres has this thing called TOAST, which stands for the oversized storage attribute storage technique, and it's basically a way to handle these different data types, but it creates a massive performance overhead. So we believe that we are architecturally far better for these more complex AI workloads than relational databases, and we believe that ultimately we are the ideal data layer for AI applications.

Dev Ittycheria: Just a reminder for everyone, we made some slight incentive comp changes to really get our reps to focus, to have a little bit more balance on size versus volume of workloads acquired. These changes were well received by the field. We had a good workload quarter as we just described. Workload starts small, so it's too early, while we're pleased with our results, obviously, it's too early to declare a victory and to really see if this is going to have a material impact and changes, but clearly we're happy with what we've seen so far.

Speaker Change #149: The data gets too large, these relational databases have to do what's called off-row storage. And it becomes, it creates a performance overhead on these relational platforms.

Speaker Change #149: Postgres has this thing called Toast, which is stands for the oversized storage attribute storage technique.

Speaker Change #149: And it's basically a way to handle these different data types, but it creates a massive performance overhead. So we believe that we are architecturally far better for these more complex work flows than relational databases.

Michael Gordon: Right, and then as a follow up for Michael, I remember at the Q4 last year, you gave us the update on sort of the unused credit dynamics. As we start to think about the back half of this year, is there anything else that we should think about that? I mean, you just mentioned some of the pastoral impacts just now. Is there anything on sort of like the revenue side of the equation that we should keep in mind?

Speaker Change #149: and we believe that, ultimately, we are the ideal data layer for AI applications. Obviously, it's early days, as I said, you know, we haven't seen a lot of inference workloads in production.

Dev Ittycheria: Obviously, it's early days as I said. You know, we haven't seen a lot of inference workloads in production, but architecturally we feel very good about our ability to compete against relational databases and Postgres in particular. That being said, Postgres is popular because they are the beneficiary of people who want to stay on relational and who have kind of lived in relational for the last 30, 40 years but had to have. We feel like we can win our share of business. Thank you.

Speaker Change #149: But architecturally we feel very good about our ability to compete against relational databases and postgres in particular. That being said, postgres is popular because they're the beneficiary of people who want to stay on relational and who've kind of lived in relation to the last 30, 40 years.

Michael Gordon: I kind of noticed at the end of this year, there's this more holiday days and a quarter in Q4. Does that have an impact? Anything that we should be that you would like to call out in terms of our modeling, when we think about expectations for at less more broadly going into the end of the year? Yeah, I think there are a few things to take into account that have been throughout the year, but of course, are reflected in the updated guide.

Speaker Change #149: But had to had, we feel like we can, you know, win our share business.

Dev Ittycheria: That's really helpful. And then maybe just sticking on the theme of AI, we've talked before in the past that AI is just driving a lot of new code, making developers significantly more productive. Have you seen that behavior in any of your existing customers on Atlas, where maybe their utilization rate goes up or the number of applications, the customer goes up, anything like that, that understanding it super early might at least give you some early education, that the increased developer productivity could turn into a real tailwind in terms of consumption of the underlying Atlas tree. Thank you.

Speaker Change #150: Thank you, that's really helpful. And then maybe just sticking on the payment AI, we've talked before in the past that AI is just driving a lot of new code, you can develop a significant name out for Dr. Dave.

Speaker Change #151: Have you seen that behavior in any existing customers on Atlas, where maybe their utilization rate goes up with a number of applications, build customer goes up. Anything like that, understanding it super early might at least give you some early education that the increased developer productivity could turn into a real tailwind, a terms of consumption of the underlying Atlas for you. Thank you.

Michael Gordon: Specifically, on Atlas throughout the year, we have tough compares given the headwinds from the lack of the unused commitments. That's obviously hardest, you know, as the year goes on, that sort of number builds, so that makes for a tough year of year comparison. The other thing to think about in the context of Atlas is, you know, the ending year dynamic is really a compounding over the course of the year, as we talked about at the beginning of the year, we got off to a slower start and some of the prior year workloads, those cohorts were growing at slower rates and so that does affect and kind of compound over the course of the year.

Dev Ittycheria: Yes, so this is a common question I ask our customers when I meet with them in terms of what code generation tools they are using and what benefits they're gaining. The answers tend to be a little bit all over the map. Some people see 10, 15% productive improvement. Some people say 20, 25% productivity improvement. Some people say it helps my senior developers be more productive. Some people say it helps my junior developers become more like senior developers. So the answers tend to be all over the map. There's no question in my mind that, with this platform shift, like previous platform shifts, the cost of building apps will come down.

Speaker Change #152: Yes, so this is a common question I ask our customers when I meet with them in terms of what co-generation tools are they using and what benefits they're gaining. The answer is tend to be a little bit all over the map, some people see it.

Speaker Change #153: 10, 15% proactive improvement. Some people say 20, 25% productivity improvement.

Speaker Change #153: and some people say it helps my senior developers be more productive.

Michael Gordon: And so I think that's important to keep in mind as factored and as factored into our guidance. And then looking at the revenue picture more broadly, you know, moving away from Atlas and thinking about, you know, EA, we've had throughout the year a headwind on EA and we'll have that as a multi-year headwind in the back half as well. The only other things I think I would call out is, you know, we saw a slower seasonal rebound in Q1 and so we're expecting that slower seasonal rebound to, you know, occur in Q3 as well. And then as you mentioned, Q4 is a seasonally weaker quarter. And so all those are some of the considerations to think about when you're thinking about the risks.

Speaker Change #153: Some people say it helps my junior developers become more like senior developers so the answers tend to be all of the map. There's no question in my mind.

Unknown Executive: Thank you, one moment for the next question.

Speaker Change #153: that with this platform shift, like previous platform shifts, the cost of building apps will come down.

Dev Ittycheria: And so, by definition, more apps will be produced, which will generate more data, which requires more databases. Now, obviously we're in the early days, and much like I've lived through other platform shifts, like going to the internet, a lot of people built out the, there's all this dark fiber that was built out to service all this potential demand. It didn't happen. People thought it was maybe over and flight it. And then, before we knew, the internet transformed the way we work, the way we live, the way we socialize. And so I think that's the same thing happening here.

Speaker Change #153: and so by definition, more absolutely produced, which will generate more data, which requires more databases. Now, obviously, we're in the early days and much like, you know, I've lived through other platforms shifts, like going to the internet.

Speaker Change #153: There's all this dark fiber that was built out to service all this potential demand, it didn't happen, people thought

Speaker Change #153: It was maybe over-inflated and then before we knew the internet transformed the way we work, the way we live, the way we socialize.

Dev Ittycheria: I think it's all on the come, but I think we're well positioned for that opportunity. Wonderful.

Speaker Change #153: I think that's the same thing happening here, I think it's all on the come but I think you know we're well positioned for that opportunity.

Raimo Lenschow: And our next question today, we'll be coming from the line of Raimo Lenschow of Barclays. Your line is open. Hey, thank you. Two quick questions from me. Michael, on the EE, you kind of talked about like a slightly better pipeline in the second half. And we'll be coming from the line of Raimo Lenschow of Barclays. Your line is open. Hey, thank you. Two quick questions from me. Can you talk a little about is that kind of still the continuation of what we saw in Q2Q3 last year where people wanted to modernize their kind of self service footprint ahead of the cloud for them and move to the cloud or what's the dynamic there.

Operator: Thank you.

Operator: Thank you. One moment for the next question. And our next question will be coming from Brent Braseland of Piper Sandler. Your line is open. Thank you. This is Brent Braseland, Piper Sandler. Dave, I wanted to go back to Atlas. And the question here is, given all the moving parts here around counting, tough compares, unused commitments, Atlas growth looked really strong. I think 30% plus on a normalized basis after adjusting for unused commitments. That's before any benefit from AI. Can you just go back to the growth algorithm for Atlas here? I asked because 30% normalized growth would be 3x faster than industry growth, employing a meaningful share capture of new software builds.

Speaker Change #154: Wannable thank you.

Speaker Change #154: And our next question will be coming from Brent Braceline of Piper Sandler. Your line is open.

Brent Braceline: Thank you, this is Bud Braceland, Piper Sandler. Dave, I wanted to go back to Atlas.

Speaker Change #156: and the question here is,

Bud Braceland: Given all the moving parts here around accounting, tough compares

Raimo Lenschow: And then one for these like there's a big debate about like reference architecture on AI. And I think it's a little bit too early, but it comes up with investors a lot like what are you seeing there? I know AI infrastructure. We're still in the early innings, but what are you seeing there in terms of people engaging with you? Thank you and good to see you back on track. Thank you. Thanks, Raimo.

Speaker Change #158: Unused Committments. Adam's growth looked really strong. I think 30% plus on an organized basis after adjusting for unused commitments. That's before any benefit from AI. Can you just go back?

Speaker Change #159: To the growth algorithm for Atlas here, I asked because 30% normalized growth would be 3x faster than industry growth in applying a meaningful share capture of new software build. So maybe we worthwhile if you just go walk through what what is driving.

Raimo Lenschow: On the EA question, I would say there are lots of reasons why we're seeing strengths of EA. I wouldn't uniquely or exclusively tie it to, you know, AI. I think it's broadly, you know, in support and shows the strength of the run anywhere strategy. The other thing that I'd call out is given the six to six dynamics as it relates to EA, we're always sensitive to sort of the multiyear aspect and that's sort of what's, you know, provided, you'll give the strength of multiyear in fiscal 24, some of the tough compares here in fiscal 25.

Brent Bracelin: So maybe it's worthwhile if you just go walk through what is driving the momentum on the Atlas side that is again 2, 3x faster than the industry.

Speaker Change #160: The momentum on the Atlas side that is again two, three, expansion in the injury.

Dev Ittycheria: Well, what I would say is obviously we're pleased with our results. What I would say is one, Atlas is the most widely available cloud service by definition because it runs across the largest hyper scalers, you know, 118 regions around the world. So no matter who you are, you can run on Atlas almost anywhere in the world. Second, because MongoDB is a general purpose database, you can use us for almost any conceivable use case. So we're not limited to some narrow set of use cases or a narrow set of users who have a very particular set of need.

Speaker Change #160: Um...

Speaker Change #161: Well, what I would say is obviously we're pleased with our results. What I would say is one atlas is the most widely available cloud service by definition because it runs across the largest through hyperscalers, you know, 118 regions around the world.

Raimo Lenschow: I think what we've seen in terms of the pipeline is strength in EA broadly, so not just a multiyear phenomenon, but really sort of a volume phenomenon as well. That we're seeing when we kind of look out on the horizon, and that is reflected in the updated guide. Thanks, Raimo. On the question about AI, I think in terms of reference architectures, I think it's important to understand that I said this in the prepared remarks is that unlike most of the workloads, AI driven workloads really require the underlying database to be capable of processing queries against very rich and complex data structures, both quickly and efficiently.

Speaker Change #161: So no matter who you are, you can run on Atlas almost anywhere in the world.

Speaker Change #161: Second, because MongoDB is a general purpose database, you can use us for almost any conceivable use case, so we're not limited to some narrow set of use cases or narrow set of users who have a very particular set of need that helps.

Dev Ittycheria: That helps. Three. We have a large community of developers. We have millions of developers all around the world from Asia to North America to Amia who are using MongoDB to address some problem. And so that also obviously, because of the popularity of MongoDB, we are the world's most popular modern database. I think that also speaks to, you know, the strength of the product. And then I would say, you know, we try and execute well. Obviously, you know, we got up to a slower start than we liked in Q1, but we are very, very focused on execution.

Speaker Change #161: Three, we have a large community of developers, we have millions of developers all around the world from Asia to North America to America, who are using MongoDB.

Speaker Change #161: to address some problem and so that also obviously because of the popularity among the B. We are the world's most popular modern database. I think that also speaks to you know the strength of the product.

Raimo Lenschow: And MongoDB is well positioned to do that. You know, we can, we can basically unify and handle source data, vector data, metadata, generated data from your LLM right alongside your live operational data, and then as the performance of these LLMs and latency of these LLMs increase, accessing real time data becomes really important. Like say you're calling and talking to a customer support chatbot that you want that chatbot to have update information about that customer so that they can provide the most relevant and accurate information possible.

Speaker Change #162: and then I would say, we try and execute well. Obviously, we got up to a slower start than we liked in Q1.

Dev Ittycheria: We have a really good team both in terms of our go-to-market team, as well as our product and the supporting ecosystem teams, our partner team, engineering team.

Speaker Change #162: But we are very, very focused on execution.

Speaker Change #162: We have a really good team, both in terms of our go-to-market team, as well as our product and the supporting ecosystem teams, our partner team, engineering team. And so we all, you know, trying to put our heads down and we were obviously disappointed by Q1 and we're really focused on.

Michael Gordon: And so we all, you know, try and put our heads down and we were obviously disappointed by Q1, and we're really focused on growing the business as fast as we. Perfect, and then Michael, just quick follow up at ART margins. Guidance implies 10% plus ART margins in the second half, particularly as the revenue run right here gets to 2 billion X in the year. Can you continue to invest in sales capacity while balancing double-digit ART margins at this scale? Yeah, so specifically around the second half, we've obviously increased the full year guide, and that flows through.

Raimo Lenschow: You know, there are some questions about LLMs, whether a general purpose LLM or a fine tune LLM, what the trade-offs are. Our belief is that given the performance of LLMs, you're going to see the general purpose LLMs probably win and will use RAG as the predominant approach to marry generally available data with proprietary data. And then you are starting to see things like advanced RAG use cases where you get much more sophisticated ways to ask complex questions, provide more accurate and detailed answers, and better adapt to different types of information and queries. And so that's what we're seeing.

Speaker Change #162: Grant the business as fast as we can.

Speaker Change #163: was quick follow up at Alt Margins, guidance implies 10% plus Alt Margins in the second half. Particularly as the revenue run right here gets to 2 billion eggs in the year. Can you continue to invest in sales capacity while balancing double digits at this scale?

Speaker Change #164: Yes, specifically around the second half, we've obviously increased the full-year guide.

Michael Gordon: We did talk about some of the timing issues between halves. We are incrementally on a marginal basis, investing into back half of the year in our AI initiatives, but I think if you look at the two year view, we're still making 500 basis points, you know, of margin progress over the last two years. So, yeah, I think it's a balancing act, Brent. I think the key thing is we have such a large, you know, opportunity and such attractive investment areas that we'll try and continue to do both. Hopefully you.

Speaker Change #165: and that was the rule. We did talk about some of the timing issues between us. We are incrementally.

Raimo Lenschow: I think it's a quickly evolving space, but we feel very good about our positioning for AI even though it's still very early days. Thank you.

Speaker Change #166: On a marginal basis, it's testing in the back half of the year in our AI initiative, but I think if you look at the two-year view.

Speaker Change #166: We're still making 500 basis points, you know, a margin progress over the last two years. So yeah, I think it's a balancing act, Brent, I think the key thing is we have such a large, you know, opportunity and such attractive investment areas that we'll try and continue to do both.

Unknown Executive: One moment for the next question.

Kasthuri Rangan: And our next question will be coming from Kasthuri Rangan of Goldman Sachs, your line is open. It's perfect.

Eric Heath: Thank you. One moment for the next question. And our next question will be coming from Eric Heat of KeyBank Capital Markets. Your line is open. Hey, good. Thanks for taking the question. Just a couple of quick ones for me.

Brent Braceline: Couple, thank you.

Dev Ittycheria: I was about to call Raimo and say, can you please explain reference architectures? I'll buy you a nice glass of wine, so hopefully he'll take me up on that offer because that was a very technical question. Thank you. But anyway, going back to the call here, Dave, a question for you. Why is EA doing so well? We talked about a pipeline of the second half of the year. Last year, we had some significant wins for EA.

Brent Braceline: Thank you. One moment for the next question.

Speaker Change #167: And our next question will be coming from Eric Heat, of keeping capital markets. Your line is open.

Michael Gordon: Mike was curious to get a better understanding and just maybe some of your assumptions on why you're expecting a slower than typical rebound coming out of 3Q. And then Dave, thinking about the third quarter here in the fiscal year end, just what are some of your expectations for that article, which I assume is usually a big EA opportunity. And thanks. Yeah, so quickly just on Q3, I think we're informed by our recent experience, including the fact that the seasonal rebound in Q1 of this year was slower than what we've typically seen. And so that's what we're taking into account when we think about Q3.

Eric Heat: Hey, good afternoon. Thanks for taking the question. Just a couple of quick ones for me. Mike was curious to get a better understanding and maybe some of your assumptions on why you're expecting a slower than typical rebound coming out of 3-Q. And then Dave, thinking about the third quarter here in the bed fiscal year and just...

Dev Ittycheria: We were supposed to be on this cloud journey, so definitely Atlas has reached parity in many senses. It can support big scale applications, whatnot. Secures to get your thought on why is EA still an important piece of the business. And I suppose when you look at AI, could something surprise us? I know that you've had this view that we're building infrastructure first and then the platform and applications. Could it be by any chance a different way to approach AI in this cycle and that we don't really need applications, but somehow these elements are going to be a perfect replacement for the way we think about all of our applications?

Dave Ittycheria: I'm your expectations for that article which I assume is usually a big EA opportunity, and thanks.

Speaker Change #169: Yeah, so quickly just on Q3, I think we're informed by our recent experience, including the fact that the season will rebound and Q1 of this year was slower than what we've typically seen and so that's what we're taking into account when we think about Q3 and that's really what's driving it.

Michael Gordon: And that's really what's driving it. Yeah.

Dev Ittycheria: And on the, I think you said, if I heard you collected, you're talking about the Fed, not this, your public sector and the Fed in particular. Yes. I mean, obviously, their fiscal year ends in September. We have a good team there. We have Fed ramp on red. So we are well suited for workloads that have that requirement. We are not yet Fed ramp high certified. So that is something that we're working on as well to try and expand the envelope of opportunities we can pursue. And then, as you implied, there's also an opportunity to sell EA to these customers.

Speaker Change #170: Yeah, and on the, I think you said, if I heard you collect you, you talked about the feds, you're public sector and the feds, I mean, obviously they're fiscal year ends in September

Dev Ittycheria: I'm just curious to see what the devil's advocate you might be if someone wears skeptical of the whole AI applications built out on top of the infrastructure and platform. Thank you so much. Yeah, so thanks, Cass. What I would just say is we did have a better than expected, both Atlas and EA quarters. There's no question about that. I don't want to suddenly say that some inflection point on EA in our business.

Speaker Change #171: We uh...

Fedram Audred: We have a good team there, we have Fedram Audred, so we are well suited for workloads that have that requirement. We are not yet Fedram High certified, so that is something that we're working on as well.

Fedram Audred: to try and expand the envelope of openings we can pursue. And then, as you implied, there's also an opportunity to sell EA to these customers and we're doing that as well. Obviously, all that is baked into our guide.

Dev Ittycheria: And we're doing that as well. Obviously, all that is baked into our guide. For how we think about Q3 and the second half of the year. But we feel we feel good about the opportunities in that segment. Thank you.

Dev Ittycheria: I think I would really, you know, attributed to one better execution. And two, customers do really appreciate or run anywhere strategy. There's still lots of customers either for regulatory reasons or other reasons who want to run workloads on premise and they're not going away. I mean, we've been in this cloud journey for 10 years and some workloads are just very hard to move to the cloud or some workloads for many customers don't make sense to work, you know, move to the cloud at least not anytime soon.

Dev Ittycheria: So the fact that they can, you know, build it on MongoDB and have the optionality to move it to the cloud later very easily is something that's very compelling for customers. With regards to AI, I mean, we'd probably see most of the AI workloads in the cloud, but there are definitely lots of customers looking at using open source LLMs in particular things like Lama and running those workloads locally. Obviously it means that they have also to have access to, you know, Nvidia hardware like GPUs, but we do see some customers do that.

Fedram Audred: for how we think about Q3 in the second half of the year, but we feel good about the opportunities in that segment.

Operator: And this does conclude today's Q&A session.

Dev Ittycheria: I would now like to turn the call back to Dave. It is chariot for closing remarks. Please go ahead. Thank you. And again, I want to thank everyone for joining us today. We're pleased with our Q2 results and our continued execution against our large market opportunity. While the macro environment remains mixed, our ability to win new business remains strong across both Atlas and EA. And as we look ahead, we believe we are the ideal data layer for our applications. And we continue to see increased demand from customers to modernize their legacy applications. And last but not least, we will continue investing judiciously and focusing on our execution to capture this long-term opportunity.

Dave Ittycheria: Thank you, and this does conclude today's Q&A session. I would now like to turn to call back to Dave. Ittycheria, 4 closing remarks, please go ahead.

Dave Ittycheria: Thank you, and I want to thank everyone for joining us today. We're pleased with our Q2 results and our continued execution against our large market opportunity.

Dave Ittycheria: Well, the macro environment remains mixed our ability to win new business remains strong across both Atlas and EA and as we look ahead we believe we are the ideal data layer for applications and we continue to see increased demand from customers to modernize their legacy applications.

Dave Ittycheria: and last but not least, we will continue investing judiciously and focusing on our execution to capture this long-term opportunity. Thank you for joining us today and we'll talk to you soon. Take care.

Dev Ittycheria: Thank you for joining us today.

Operator: And we'll talk to you soon. Take care.

Operator: This thus concludes today's conference call; you may all disconnect.

Dev Ittycheria: Again, I wouldn't suggest that that's also an inflection point or cause for the EA performance. I think it's very, very early days and most of those are experiments that customers are running, but clearly we feel really good about running anybody's strategy. And as I've said in the past, we are investing in basically introducing search and vector search to our community product and that will then show up in EA. So EA is definitely an area where we're also investing from a product point of view.

Speaker Change #173: This best conclude today's conference call, you may all disconnect.

Unknown Executive: Thank you. One moment for the next question.

Tyler Radke: And our next question will be coming from the line of Tyler Radke. Of City, your line is open. Hi, this is Tyler Radke. I think the question was from me can hear the name, but thanks for taking the question. Dave, you talked about a post-gress displacement in the quarter. And I think that's the first time you've talked about that at least in recent quarters. So I was wondering if you could just sort of frame for us.

Tyler Radke: What use cases do you come across them? I know it is popular in terms of a sequel displacement for migrating up legacy applications. If you could just frame for us sort of the competitive environment and impact post-gress, both the open source stuff and some of the new venture back startups in the space. Thank you. Yeah, sure. So thanks for the question. Yeah, it's important to understand that post-gress has been around for almost 40 years.

Tyler Radke: I mean, it's post-gress. The name is turned, turned from post ingress. So that technology has been around a long time. As you said, they're really the beneficiary of lift and shift from Oracle SQL Server and my SQL. So they're kind of consolidating the relational market. In terms of why do we compete or why do we win? I would say it's a few things. One, our scheme of flexibility, you know, it's a very, you know, MongoDB has a very flexible scheme allowing you to store documents in a JSON-like format.

Tyler Radke: So this is beneficial for application structures that evolve over time. We can horizontally scale. So we're making it very easy to distribute data across multiple servers or virtual servers for applications that require mass amounts of data, performance of large data sets. You know, again, we can handle that better than post-gress. You know, the built-in charting allows for automatic data distribution. You know, and then we also, I think developer productivity, the JSON-like format and flexible schema can lead to faster development cycles, especially for customers who really work in agile environments.

Tyler Radke: So we feel like we compete or win rates against post-gress or high. But again, there's lots of decisions being made where we're not party to where people are just doing a lift and shift off. You know, a legacy platform or they're just, they're just want to stay on relational because that's what they know. And obviously it's our job to educate them on the benefits of MongoDB, but we feel good about our competitor position against post-gress.

Michael Gordon: Thank you. And follow-up question for Michael, you talked about how consumption and atlas in the quarter tracked a bit better than planned. Sounds like it was not a macro-related improvement. What do you think the driver that was? And are you seeing any improvement in some of the recently acquired workloads, those starting to ramp up better, or maybe they have different seasonal patterns than you thought. Any color on sort of that driver of outperformance would be helpful.

Michael Gordon: Thank you. Yeah, sure. I would say that yes, Q2 consumption growth was better than our expectations. That was great to see. I would describe it as within a reasonable or typical range of outcomes. And so there are no signs that we've seen that would specifically point to anything. Any material changes in the underlying macroeconomic environment better or worse. We've certainly seen, you know, your question on sort of the workloads and sort of some of the cohorting.

Michael Gordon: We did see a little better performance there as well. And, you know, in line with what we saw elsewhere, but they're still below our original expectations. So I think that's probably the key couple things there. Thank you.

Unknown Executive: Thank you one more, question.

Brad Reback: And our next question for today would be coming from Brad Reback of Cecil. Your line is open. Great. Thanks very much. Dev, on your commentary around the modernization pilots, how should we think about timing as that being a bit of a tailwind to the overall growth rate? Yeah, so again, just to make sure everyone understands, you know, the legacy relational application market or database market is quite large. It's over 80 billion. And it's a massive opportunity for us. And since day one, since our IPO, we've been getting customers to migrate off relational to MongoDB.

Dev Ittycheria: But one of the biggest friction points has been that, while it's easy to move the data, you can map the schema from a relational schema to a document schema and you can automate that, the biggest stumbling block is that the customer has to, or some third party, has to rewrite the application, which by definition creates more costs, more time, and in some cases more risk, especially for older apps where the development teams who build those apps no longer exist. So what's been compelling about AI is that AI is finally created a shortcut to overcome that big hurdle.

Dev Ittycheria: And so essentially you can start basically diagnosing the code, understand the code, you know, recreate a modern version of that code, and generate test sweeps, make sure the new code performs like the old code. So that definitely gets people's interest because now all of a sudden what may take, you know, years or multi years, you can do in a lot less time. And the pilots that we have done, the time and cost savings have been very, very compelling.

Dev Ittycheria: That being said, we're in the very early days. There's a lot of interest. We have a growing pipeline of customers across, frankly, all parts of the world, from North America to Amia and even the Packrim. And so we're quite excited about the opportunity.

Dev Ittycheria: But again, I would say it's very early days. But there's a number of reasons why I would say that customers are very focused on this. What the cost of licensing and maintaining legacy apps is becoming too high to bear. In many cases, the regulatory and compliance requirements are forcing customers to upgrade. There's a whole end of life of critical technologies, notably side base that's forcing customers to act. There's a ton of technical depth on these legacy platforms that limits the organization's flexibility to do things with AI. And cannelly customers have also soured on the traditional approach of using large systems and integration projects that are very costly and take a long time. So this whole approach is definitely getting their attention.

Dev Ittycheria: That's great. And then one fast follow up. It feels like we've spoken about EA more than typical on this call, which is great. As we think about the back half pipeline, is the composition similar in so much as it's predominantly existing customers? Are you beginning to see an uptake and met new customers there as well? Thanks. Yeah, I would say that it's predominantly existing customers that are doing this, you know, we're, we're maybe doubling down on EA and expanded the footprint of EA. And that's typically the driver for EA business. Thanks very much. Thank you.

Unknown Executive: One moment for the next question, please.

Karl Keirstead: And our next question will be coming from Karl Keirstead of UBS. Your line is open. Okay, great. Hey, David, at the beginning of the call, you mentioned that Mongo's slow start to the year was quote, purely operational. But on the Q1 call when you were describing what happened, you described it as very much a broad based macro issue. So I'm curious, should I interpret that comment as you, Mike and the team have done a bit of a rethink and might be changing modestly.

Karl Keirstead: At least your explanation about the Q1 results. Yeah, actually just to be clear, I think just to make sure everyone understands what we called out was that the consumption of existing workloads was broad based, the slow down was broad based across both Geos and channels. It was a new business, it just got up to slower start. So that was what we called out in our Q1 call and with our Q2 call and results.

Karl Keirstead: As you can see, you know, new business in a performed better as expected. Then expected on both Atlas and EA, which is why we believe the Q1 issue was an operational issue, not a not a Q1 new business, sorry, was an operational issue, not a macro issue. The macro issue was all about the slow down and consumption because Q1 typically tends to be a seasonally strong quarter for us and that didn't happen.

Karl Keirstead: Yeah, I think that's really important, Carl, just to keep in mind, when we talk about macro could have those two different effects, right, the new business piece and then the expansion of existing workloads. And so what we were talking about as Dave said in Q1 was the existing workload expansion, the operational piece was on the new business side. And I think the key thing that we've observed really in a wide range of, you know, macro economic conditions in our almost seven years as a public company is with exception at one quarter.

Karl Keirstead: We've been able to execute quite well in a new business opportunity really in most environments, given that we have, you know, a huge market, you know, relatively low share strong product and a talented team that executes well. And so I think that's probably helpful just to understand as we're using the terms just to make sure we don't trip ourselves up. Yep. Okay, that's great. And then my follow up to you, Mike, is when you were pressed in prior questions about the second half, most of the variables that you brought up were frankly, you know, headwinds, things like tough compares.

Karl Keirstead: But if I look objectively, your three Q total revenue got of 497, and this is my assumption, but I assume kind of a normal beat, that's going to result in sequential total revenue growth. That's actually the highest, I think, that MongoDB has ever put up. So clearly there's something good that you're embedding in that three Q guide. What is that specifically? Yeah, so I'm not sure I'd follow all the math on the fly, and I'll happy to follow up obviously with any of that.

Karl Keirstead: But if I just think about the Q3 guide, I won't belabor the headwinds, but they exist. And we should have been through those as you've called out. But maybe to talk about the couple of positive things that we see that lead to sort of, you know, our improved outlook or increased revenue forecast for the balance of the year. It's really two things. In Atlas, it's reflecting the stronger than expected, you know, Q2.

Karl Keirstead: And therefore, the starting AR, the beginning of Q3 is higher because we haven't seen any change in the underlying macro. The actual growth assumptions for Q3 and Q4 we have remain unchanged, so you're just applying growth to a higher base, but that, you know, as the base keeps getting bigger and bigger, that does matter. And that flows through. And then secondly was the positive impact of the increased strength in the EA pipeline for the second half of the year and the impact on that revenue.

Karl Keirstead: So those are probably the two things that I call out on the positive side of the revenue in addition to, of course, having everyone keep in mind the tough compare and some of the headwinds. We talked about. Okay, that's helpful, Mike. Thanks a lot. Thank you. Thank you, and one moment for the next questions.

Jason Ader: Our next question will be coming from Jason Ader, William Blair. Your line is open. Yeah, thank you.

Michael Gordon: I'm trying to understand the Q4 implied revenue guide, Michael. You know, just the implied sequential growth there. There's only one and a half percent of I look at your full year relative to what you said specifically on Q3. That would be well below the historical seasonal pattern in Q4. So just want to understand is there is something going on in Q4 that's different, just sequential. I understand the year over year is tough, but the sequential seem well below where you've normally been and you know, there's something specific that we should think about there.

Michael Gordon: Yeah, I think they're two things. So obviously, it sounds like you get the year of a year, but just to make sure everyone's sort of on the same page is making sure that people understand, you know, the tough compare for Atlas specifically around the unused commitments and then EA, the multi year, and then more broadly, you know, we had one. We had left growth in Q1, that compounds. And then also we talked about how we have a we're assuming just in the same way that we saw in Q4. Q1, we're assuming less of a seasonal rebounding Q3, that has implications for Q4. Gotcha. Okay.

Michael Gordon: And then just one quick follow up on gross margins. Did you talk about Atlas gross margin in the quarter and then how material will the impact be from the prepayments and the IPV for purchases on Atlas gross margins over time? Yeah, so we didn't specifically break out Atlas gross margin in the quarter, but you know, they continue to be lower, but obviously we've pretty significantly shrunk the delta that does explain some of the, you know, comparison on the year of a year basis, we didn't give specific guidance going forward.

Michael Gordon: If you think about the two changes, you know, that we called out on the cashless side, those will benefit us in terms of gross margin, there won't be a significant benefit in fiscal 25. So we'll obviously talk about that more when we get to the fiscal 26 guide, but it's a very excellent use of our cash and, you know, good ROI in terms of improving those economics further. Thank you. One moment for the next question.

Brad Reback: And our next question will be coming from Brad Phil's of Bank of America. Your line is open. Oh, great. Thank you so much. Great to hear the continued strength and new workloads here. That's been very consistent. Theme here throughout all this.

Dev Ittycheria: I did want to ask about some of the newer services like vector and stream processing. You know, how are those contributing to the strength you're seeing or sustained strength you're seeing in new workloads? Yeah, Brad. Thanks for the question. So in terms of search, we're seeing solid momentum and search. We're having success with that business is starting to grow. And we just introduced a new capability called search nodes, which allows customers to optimize their search appointments by asymmetrically scaling, you know, specific nodes dedicated for search versus the rest of the notes on their cluster.

Dev Ittycheria: It also helps dealing with use cases that are very search intensive. One of the largest gaming companies in the world re-platform the content moderation platform from a doctor B elastic and dynamo to Atlas and Atlas search and using Atlas search nodes for workload isolation high performance. On vector, we're continuing growth and adoption. And we see a vector as effective in attracting new customers to the MongoDB platform. A world-renowned financial news organization, which was already running on Atlas migrated from elastic search to Atlas search using search nodes to take advantage of our vector search capabilities to build a site search that can.

Dev Ittycheria: I combined lexical search with semantic search to find the most relevant are, you know, articles for for user query. And a European energy company built a geospatial search application using Atlas and search and vector search. And the apples are built on prem, but in and to clouds to vectorize geospatial data and facilitate research and discovery.

Dev Ittycheria: And then we recently, you know, announced streaming of stream processing, I should say. You know, GA in and local New York in May and we've seen strong interest. It's still early days, but we're seeing interest from a variety of industries ranging from automotive to retail to transportation. Who are all want to work with streaming data and want it to be able to take actions on that data to drive their business. And so, you know, customers are very pleased with the performance of the product and how easy it is to use, but again, it's just early days since we just launched it only in May. Wonderful, great to hear.

Michael Gordon: One more if I may please. On the last earnings call, you mentioned how the slowdown in consumption was broad based across industries and workload types, which led you to believe that it was a macro related impact. You know, with some of the improvement you've seen this quarter, I know it's early, but was that also broad based and could you deduce that maybe that might mean some improvement in the underlying macro or was it more outside to certain types of industries and services.

Michael Gordon: Thank you, I would describe it as broad based, but I would describe it within sort of a reasonable range of outcomes and so no clear indication that macros, you know, improving or deteriorating and just just a good quarter. Wonderful, thank you.

Unknown Executive: Thank you, one more for the next question.

Rashi Gineria: And our next question will be coming from Rashi, Gineria of RBC capital markets, your line is open. Wonderful. Hey, this is a reschedulary from RBC, two questions. Maybe Dave, I wanted to first start out going back to some of what you talked about, which is the MongoDB versus Postgres debate. One of the kind of popular theories out there is what debates out there is which architecture is better suited to new generative AI applications, especially if you truly do believe AI will lead to a re-platforming of software similar to what we saw with the cloud.

Rashi Gineria: I know you talked a little bit about reference architecture earlier in Q&A, but maybe could you walk us through why you believe MongoDB is better suited to new generative AI native applications versus Postgres and then I've got to quick follow. Yeah, sure. So very quickly, the reason we believe that we're well positioned to win these new workloads is that AI-driven workloads require the underlying database to be capable of processing queries against rich and complex data structures.

Rashi Gineria: As you know, with AI, the data structures can be very, very complex, so that means that the data can be large and obviously not in any consistent size. MongoDB is designed to handle these different data structures, and I talked about, you know, we can help unify meta data, operational data, vector data, and generated all in one platform. Relational databases, and Postgres is one of them, have limitations in terms of what they can handle different types of data.

Rashi Gineria: In fact, when the data gets too large, these relational databases have to do what's called off-road storage, and it creates a performance overhead on these relational platforms. Postgres has this thing called toast, which stands for the oversized storage, attribute storage technique, and it's basically a way to handle these different data types, but it creates a massive performance overhead. So we believe that we are architecturally far better for these more complex AI workloads than relational databases, and we believe that ultimately we are the ideal data layer for AI applications.

Rashi Gineria: Obviously, it's early days, as I said, you know, we haven't seen a lot of inference workloads in production, but architecturally we feel very good about our ability to compete against relational databases and Postgres in particular. That being said, Postgres is popular because they have the beneficiary of people who want to stay on relational, and who've kind of lived in relational for the last 30, 40 years, but had to had, we feel like we can, you know, win our share business.

Dev Ittycheria: Thank you, that's really helpful. And then maybe just sticking on the theme of AI, we've talked before in the past that AI is just driving a lot of new code, making developer significant and more productive. Have you seen that behavior in any of your existing customers on Atlas where maybe their utilization rate goes up or the number of applications built for customer goes up? Anything like that that understanding it super early might at least give you some early education that the increased developer productivity could turn into a real tailwind in terms of consumption of the underlying Atlas for you.

Dev Ittycheria: Thank you. Yeah, so this is a common question I ask our customers when I meet with them in terms of what code generation tools are they using and what benefits they're gaining. The answers tend to be a little bit all over the map. Some people see 10, 15% productive improvement. Some people say 20, 25% productivity improvement. Some people say it helps my senior developers be more productive. Some people say it helps my junior developers become more like senior developers.

Dev Ittycheria: So the answers tend to be all of the map. There's no question in my mind that with this platform shift like previous platform shifts, the cost of building apps will come down. And so by definition more apps will be produced, which will generate more data, which requires more databases. Now, obviously we're in the early days and much like I've lived through other platform shifts like going to the internet. A lot of people built out the, you know, there's all this dark fiber that was built out to service all this potential demand.

Dev Ittycheria: It didn't happen. People thought it was maybe over inflated. And then before we knew the internet transformed the way we work, the way we live, the way we, you know, socialize. And so I think that's the same thing's happening here. I think it's all on the come. But, but I think, you know, we're well positioned for that opportunity.

Unknown Executive: Wonderful. Thank you.

Unknown Executive: One moment for the next question.

Brent Bracelin: And our next question will be coming from Brent Braseland of Piper Sandler. Your line is open. Thank you. This is but Braseland Piper Sandler. Dave, I wanted to go back to Atlas. And, and the question here is given all the moving parts here around accounting, tough compare. Here's unused commitments. Atlas growth looked really strong. I think 30% plus on a normalized basis after adjusting for unused commitments. That's before any benefit from AI.

Dev Ittycheria: Can you just go back to the growth algorithm for Atlas here? I asked because 30% normalized growth would be 3X faster than industry growth, implying a meaningful share capture of new software builds. So maybe we worthwhile if you just go walk through what what is driving the momentum on on the Atlas side that is again, 2, 3X faster than the industry?

Dev Ittycheria: Well, what I would say is obviously we're pleased with our results. What I would say is one Atlas is the most widely available cloud service by definition because it runs across the largest hyper scalers, you know, 118 regions around the world. So no matter who you are, you know, you can run on Atlas almost anywhere in the world. Second, because MongoDB is a general purpose database, you can use us for almost any conceivable use case.

Dev Ittycheria: So we're not limited to some narrow set of use cases or narrow set of users who who who have a very particular set of needs. That helps. Three, we have a large community of developers. We have millions of developers all around the world from Asia to North America to Amia, who are using MongoDB to address some problem. And so that also obviously because of the popularity of MongoDB, we are the world's most popular modern database. I think that also speaks to, you know, the strength of the product.

Michael Gordon: And then I would say, you know, we try and execute well. Obviously, you know, we got up to a slower start than we liked in Q1, but we are very, very focused on execution. We have a really good team both in terms of our go-to-market team as well as our product and the supporting ecosystem teams, our partner team, engineering team. And so we all, you know, try and put our heads down and we were obviously disappointed by Q1 and we're really focused on growing the business as fast as we can.

Michael Gordon: Perfect, and then Michael, just quick follow up at ART margins. Guidance implies 10% plus ART margins in the second half, particularly as the revenue run right here gets to 2 billion X in the year. Can you continue to invest in sales capacity while balancing double digit ART margins at this scale? Yes, specifically around the second half. We've obviously increased the full year guide. And that flows through. We did talk about some of the timing issues between heves.

Michael Gordon: We are incrementally on a marginal basis investing into back half of the year in our AI initiatives, but I think if you look at the two year view, we're still making 500 basis points, you know, a margin progress over the last two years. So yeah, I think it's a balancing act, Brent, I think the key thing is we have such a large, you know, opportunity and such attractive investment areas that we'll try and continue to do both. Couple for you. Thank you.

Unknown Executive: One moment for the next question.

Eric Heath: And our next question will be coming from Eric Heat of Keybank Capital Markets. Your line is open. Hey, good. Thanks for taking the question. Just a couple of quick ones for me.

Dev Ittycheria: Mike was curious to get a better understanding and just maybe some of your assumptions on why you're expecting a slower than typical rebound coming out of 3Q. And then Dave, thinking about the third quarter here in the fiscal year and just what are some of your expectations for that article, which I assume is usually a big EA opportunity and thanks. Yeah, so quickly just on Q3, I think we're informed by a recent experience, including the fact that the seasonal rebound in Q1 of this year was slower than what we've typically seen.

Dev Ittycheria: And so that's what we're taking into account when we think about Q3 and that's really what's driving it. Yeah, and on the, I think you said, if I heard you collected, you're talking about the Fed that this your public sector and the Fed in particular. Yes, I mean, obviously their fiscal year ends in September. We have a good team there. We have Fed ramp on red. So we are well suited for workloads that have that requirement.

Dev Ittycheria: We are not yet Fed ramp high certified. So that is something that we're working on as well. To try and expand the envelope of opportunities we can pursue. And then on and as you as you implied, there's also an opportunity to sell EA to these customers. And we're doing that as well. Obviously all that is baked into our guide for how we think about Q3 and the second half of the year. But we feel we feel good about the, you know, about the opportunities in that segment. Thank you.

Dev Ittycheria: And this does conclude today's Q&A session.

Dev Ittycheria: I would now like to turn the call back to Dave.

Dev Ittycheria: It is area for closing remarks. Please go ahead. Thank you. And again, I want to thank everyone for joining us today. We're pleased with our Q2 results and our continued execution against our large market opportunity. While the macro environment remains mixed, our ability to win new business remains strong across both Atlas and EA. And as we look ahead, we believe we are the ideal data layer for applications and we continue to see increased demand from customers to modernize their legacy applications. And last but not least, we will continue investing judiciously and focusing on our execution to capture this long term opportunity.

Unknown Executive: Thank you for joining us today and we'll talk to you soon. Take care.

Unknown Executive: This thus concludes today's conference call. You may all disconnect. Thank you for your time, and I'll see you next time.