Q2 2026 MongoDB Inc Earnings Call
After the presentation, there will be a question and answer session.
Participate you would need to press star one one on your telephone you will then hear a message advising your hand is raised.
To withdraw your question simply press Star one again please.
Please note. This conference is being recorded now it's my pleasure to turn the call over to Brian Daniels from ICR. Please go ahead.
Thank you Carmen.
Good afternoon, and thank you for joining us today to review <unk> second quarter fiscal 2026 financial results, which we announced in our press release issued after the close of market today.
Joining me on the call today are David <unk>, President and CEO of <unk>, Mike Berry CFO of <unk>.
Just liberate Margaret <unk>, as new Vice President of Investor Relations.
During this call we will make forward looking statements, including statements related to our market and our future growth opportunities our opportunity to win new business, our expectations regarding Atlas consumption growth the impact of non Atlas business and multi year license revenue the long term opportunity of AI, our financial guidance and underlying assumptions and our investments in growth opportunities.
Carmen: Good day, everyone, and welcome to Mongodb Inc's second quarter fiscal year 2026 earnings call. At this time, all participants are in a listen-only mode. After the presentation, there will be a question and answer session. To participate, you will need to press star 11 on your telephone. You will then hear a message advising your hand is raised. To withdraw your question, simply press star 11 again. Please note this conference is being recorded. Now, it's my pleasure to turn the call over to Brian Denyeau from ICR. Please go ahead.
Speaker #2: Good day, everyone, and welcome to Mongodb Inc's second-quarter fiscal year 2026 earnings call. At this time, all participants are in a listen-only mode. After the presentation, there will be a question and answer session.
Got it.
These statements are subject to a variety of risks and uncertainties, including the results of operations and financial conditions.
<unk> actual results to differ materially from our expectations.
For a discussion of the material risks and uncertainty is going to affect our actual results. Please refer to the risks described in our quarterly report on Form 10-Q for the quarter ended April 32025 filed with the SEC on June 4th 2025.
Speaker #2: To participate, you will need to press star 11 on your telephone. You will then hear a message advising your hand is raised. To withdraw your question, simply press star 11 again.
Any forward looking statements made on this call reflect our views only as of today and we undertake no obligation to update them, except as required by law.
Speaker #2: Please note this conference is being recorded. Now, it is my pleasure to turn the call over to Brian Denyeau from ICR. Please go ahead.
Additionally, we will discuss non-GAAP financial measures on this conference call.
Please refer to the tables in the earnings release on the Investor Relations portion of our website for a reconciliation of these measures the most directly comparable GAAP financial measure.
Brian Denyeau: Thank you, Carmen. Good afternoon, and thank you for joining us today to review MongoDB's second quarter fiscal 2026 financial results, which we announced in our press release issued after the close of the market today. Joining me on the call today are Dev Ittycheria, President and CEO of MongoDB, Mike Berry, CFO of MongoDB, and Jess Lubert, MongoDB's new Vice President of Investor Relations. During this call, we will make forward-looking statements, including statements related to our market and future growth opportunities, our opportunity to win new business, our expectations regarding Atlas consumption growth, the impact of non-Atlas business and multi-year license revenue, the long-term opportunity of AI, our financial guidance and underlying assumptions, and our investments and growth opportunities in AI.
Speaker #3: Thank you, Carmen. Good afternoon, and thank you for joining us today to review Mongodb's second-quarter fiscal 2026 financial results. Which we announced in our press release issued after the close of the market today.
I'd like to turn the call over to Dave.
Thank you, Brian and thank you to everyone for joining us today.
Speaker #3: Joining me in the call today are David Ittycheria, President and CEO of Mongodb; Mike Berry, CFO of Mongodb; and Jess Lubert, Mongodb's new Vice President of Investor Relations.
For discussing our strong quarter I wanted to remind everyone about our upcoming Investor day, which will take place on September 17th at the Javits Center in New York City during adopt local conference we will spend the day discussing the investments, we're making to drive durable growth and margin expansion and our view of the future of <unk>.
Speaker #3: During this call, we will make forward-looking statements, including statements related to our market and future growth opportunities. Our opportunity to win new business, our expectations regarding at-risk consumption growth, the impact of non-at-risk business and multi-year license revenue, the long-term opportunity of AI, our financial guidance and underlying assumptions, and our investments in growth opportunities in AI.
Forward to seeing you there.
Now onto Q2 I'm pleased to report another strong quarter as we continued to execute against our large market opportunity. Let me start with our results before giving you a broader company update.
We generated revenue of $591 million up 24% year over year and above the high end of our guidance Atlas revenue grew 29% year over year, representing 74% of total revenue.
Brian Denyeau: These statements are subject to a variety of risks and uncertainties, including the results of operations and financial conditions that can cause actual results to differ materially from our expectations. For discussion of the material risks and uncertainties that could affect our actual results, please refer to the risks described in our quarterly report on Form 10-Q for the quarter ended April 30th, 2025, filed with the SEC on June 4th, 2025. Any forward-looking statements made in this call reflect our views only as of today, and we undertake no obligation to update them except as required by law. Additionally, we will discuss non-GAAP financial measures on this conference call. Please refer to the tables in the earnings release on the Investor Relations portion of our website for a reconciliation of these measures to the most directly comparable GAAP financial measure.
Speaker #3: These statements are subject to a variety of risks and uncertainties, including the results of operations and financial conditions, that could cause actual results to differ materially from our expectations.
We delivered non-GAAP operating income of $87 million or 15% non-GAAP operating margin and we ended the quarter with over 59 nine.
Speaker #3: For discussion of the material risks and uncertainties that could affect our actual results, please refer to the risks described in our quarterly report on Form 10-Q for the quarter ended April 30th, 2025, filed with the SEC on June 4th, 2025.
900 customers.
<unk> performance was strong accelerating to 29% year over year growth up from 26% in Q1, our customer additions were also robust we have added over 5000 customers over the last two quarters. These results reflect the strength among ub's platform, our flexible flexible document model expanded capabilities like search and vector search.
Speaker #3: Any forward-looking statements made in this call reflect our views only as of today, and we undertake no obligation to update them except as required by law.
Speaker #3: Additionally, we will discuss non-GAAP financial measures on 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 to the most directly comparable GAAP financial measure.
Enterprise readiness and the ability to run anywhere many of our recently added customers are building AI applications underscoring how our value proposition is resonating for AI and why <unk> is emerging as a key component of the AI infrastructure stack at the same time, we significantly outperformed on operating margin demonstrating that we can drive durable revenue.
Brian Denyeau: With that, I'd like to turn the call over to Dave.
Speaker #3: With that, I'd like to turn the call over to Dave.
Dev Ittycheria: Thank you, Brian, and thank you to everyone for joining us today. Before discussing our strong quarter, I want to remind everyone about our upcoming Investor Day, which will take place on September 17th at the Javits Center in New York City during our Dot Local conference. We'll spend the day discussing the investments we're making to drive durable growth and margin expansion and our view of the future. I look forward to seeing you there. Now, on to Q2. I'm pleased to report another strong quarter as we continue to execute against a large market opportunity. Let me start with our results before giving you a broader company update. We generated a revenue of $591 million, up 24% year-over-year and above the high end of our guidance. Atlas revenue grew 29% year-over-year, representing 74% of total revenue.
Speaker #4: Thank you, Brian, and thank you to everyone for joining us today. Before discussing our strong quarter, I want to remind everyone about our upcoming investor date, which will take place on September 17th at the Javits Center in New York City during our .local conference.
Speaker #4: We'll spend the day discussing the investments we're making to drive durable growth and margin expansion and our view of the future. I look forward to seeing you there.
Growth, while expanding profitably in short our results show that customers are choosing among the DB, Let me tell you why.
First <unk>, an enterprise ready database capable of meeting the most stringent enterprise requirements over 70% of the Fortune 500, as well as seven of the 10 largest banks 14 of the largest 15 health care companies nine of the 10 largest manufacturers globally. Among the customers <unk> is a battle tested enterprise platform.
Speaker #4: Now, onto Q2, and please report another strong quarter as we continue to execute against our large market opportunity. Let me start with our results before giving you a broader company update.
Speaker #4: We generated a revenue of $591 million up 24% year over year and above the high end of our guidance. At-risk revenue grew 29% year over year, representing 74% of total revenue.
We relied on by some of the most sophisticated and demanding organizations of the world in part because of our strong enterprise posture across security durability availability and performance.
Dev Ittycheria: We delivered non-GAAP operating income of $87 million for a 15% non-GAAP operating margin, and we ended the quarter with over 59,900 customers. Atlas performance was strong, accelerating to 29% year-over-year growth, up from 26% in Q1. Our customer additions were also robust. We have added over 5,000 customers over the last two quarters. These results reflect the strength of MongoDB's platform, our flexible document model, expanded capabilities like search and vector search, enterprise readiness, and the ability to run anywhere. Many of our recently added customers are building AI applications, underscoring how our value proposition is resonating for AI and why MongoDB is emerging as a key component of the AI infrastructure stack. At the same time, we significantly outperformed on operating margin, demonstrating that we can drive durable revenue growth while expanding profitably. In short, our results show that customers are choosing MongoDB. Let me tell you why.
Speaker #4: We delivered non-GAAP operating income of $87 million for a 15% non-GAAP operating margin. And we ended the quarter with over $59,900 customers. At-risk performance was strong, accelerating to 29% year over year growth, up from 26% in Q1, our customer additions were also robust.
Atlas enabled one of the world's largest automakers to overcome postgresql scalability and flexibility limits, while reducing complexity. The companys management console attracts over $8 5 million vehicles, requiring a modern scheme or to handle both structured and unstructured data something post risks could not handle ultimately atlas consolidated infrastructure accelerated innovation.
Speaker #4: We have added over 5,000 customers over the last two quarters. These results reflect the strength of MongoDB's platform, our flexible document model, expanded capabilities like search and vector search, enterprise readiness, and the ability to run anywhere.
In support of the scale of millions of connected vehicles.
Speaker #4: Many of our recently added customers are building AI applications, underscoring how our value proposition is resonating for AI and why MongoDB is emerging as a key component of the AI infrastructure stack.
Second marketing be suitable for a broad range of use cases, including the most mission critical and transaction intensive applications. Among DBS also supported full asset transactions for more than six years, ensuring strong consistency and data integrity at scale. This is why some of the world's most demanding transactional workloads run on lung would be today.
Speaker #4: At the same time, we significantly outperformed on operating margin, demonstrating that we can drive durable revenue growth while expanding profitably. In short, our results show that customers are choosing MongoDB. Let me tell you why.
For example, Deutsche Telekom selected <unk> Atlas as the foundation for its internal developer platform, which includes mission critical workloads by contract management device purchases and billing for 30 million customers with 90 Atlas clusters, managing over 60 million customer records Deutsche Telekom's customer data platform now handles 15 times the concur.
Dev Ittycheria: First, MongoDB is an enterprise-ready database capable of meeting the most stringent enterprise requirements. Over 70% of the Fortune 500, as well as 7 of the 10 largest banks, 14 of the largest 15 healthcare companies, 9 of the 10 largest manufacturers globally are MongoDB customers. MongoDB is a battle-tested enterprise platform relied on by some of the most sophisticated and demanding organizations in the world, in part because of our strong enterprise posture across security, durability, availability, and performance. Atlas enabled one of the world's largest automakers to overcome postgressive scalability and flexibility limits while reducing complexity. The company's management console tracks over 8.5 million vehicles, requiring a modern schema to handle both structured and unstructured data, something Postgres could not handle. Ultimately, Atlas consolidated infrastructure, accelerated innovation, and supported the scale of millions of connected vehicles.
Speaker #4: First, MongoDB is an enterprise-ready database capable of meeting the most stringent enterprise requirements. Over 70% of the Fortune 500, as well as 7 of the 10 largest banks, 14 of the largest 15 healthcare companies, and 9 of the 10 largest manufacturers globally, are MongoDB customers.
Logins on legacy systems by consolidating these high volume transaction intensive applications among DB Deutsche Telekom has improved resiliency accelerated innovation and delivered a step change in customer engagement.
Speaker #4: MongoDB is a battle-tested enterprise platform relied on by some of the most sophisticated and demanding organizations in the world, in part because of our strong enterprise posture across security, durability, availability, and performance.
Third <unk> redefined what's core for the database by natively, including capabilities like search vector search embedding and stream processing.
Speaker #4: At-risk enabled one of the world's largest automakers to overcome post-gressive scalability and flexibility limits while reducing complexity. The company's management console tracks over 8.5 million vehicles, requiring a modern schema to handle both structured and unstructured data, something Postgres could not handle.
<unk> another database like post growth is not an apples to apples comparison take a global E. Commerce application that manages inventory and order data, while enabling product discovery through sophisticated search across millions of skus. The.
Speaker #4: Ultimately, at-risk consolidated infrastructure, accelerated innovation, and support of scale of millions of connected vehicles. Second, Mongodb is suitable for a broad range of use cases, including the most mission-critical and transaction-intensive applications.
The choice for this application operating margin to be a post crisis between montney to be or.
Dev Ittycheria: Second, MongoDB is suitable for a broad range of use cases, including the most mission-critical and transaction-intensive applications. MongoDB has also supported full asset transactions for more than six years, ensuring strong consistency and data integrity at scale. This is why some of the world's most demanding transactional workloads run on MongoDB today. For example, Deutsche Telekom selected MongoDB Atlas as the foundation for its internal developer platform, which includes mission-critical workloads like contract management, device purchases, and billing for 30 million customers. With 90 Atlas clusters managing over 60 million customer records, Deutsche Telekom's customer data platform now handles 15 times the concurrent logins of legacy systems. By consolidating these high-volume, transaction-intensive applications on MongoDB, Deutsche Telekom has improved resiliency, accelerated innovation, and delivered a step change in customer engagement.
Or <unk> plus other offerings like pine cone elastic until here for embedding <unk> complete solution allows developers to spend less time stitching together and maintaining a patchwork of disparate systems and more time building differentiated functionality that drives the business forward.
Speaker #4: Mongodb has also supported full asset transactions for more than six years, ensuring strong consistency and data integrity at scale. This is why some of the world's most demanding transactional workloads run on Mongodb today.
For example, Archie back of Brazilian Bank with $2 7 million active customers migrated our content management systems during customer records from post Crestwood to Atlas.
Speaker #4: For example, Deutsche Telekom selected Mongodb Atlas as the foundation for its internal developer platform, which includes mission-critical workloads like contract management, device purchases, and billing for 30 million customers.
As data volumes group post presses and flexibility in tax.
Task execution latency drove performance issues and the database like sophisticated secondary indexes and full text search hurting sales of core offerings, such as loans insurance and card approvals Archie Bank was constantly updating the database and manual scaling infrastructure, which is both time consuming and error prone with Atlas Archie bond gains are resilient.
Speaker #4: With 90 at-risk clusters managing over 60 million customer records, Deutsche Telekom's customer data platform now handles 15 times the concurrent logins of legacy systems.
Speaker #4: By consolidating these high-volume transaction-intensive applications on Mongodb, Deutsche Telekom has improved resiliency, accelerated innovation, and delivered a step change in customer engagement. Third, Mongodb has redefined what's core for the database by natively including capabilities like search, vector search, embeddings, and stream processing.
<unk> system that handle rising demand and support new services, delivering nearly five times better performance and 90% low cost all with no outages.
Dev Ittycheria: Third, MongoDB has redefined what's core for the database by natively including capabilities like search, vector search, embeddings, and stream processing. Comparing MongoDB to another database like Postgres is not an apples-to-apples comparison. Take a global e-commerce application that manages inventory and order data while enabling product discovery through sophisticated search across millions of SKUs. The choice for this application is not between MongoDB or Postgres, it's between MongoDB or Postgres plus other offerings like Pinecone, Elastic, and Cohere for embeddings. MongoDB's complete solution allows developers to spend less time stitching together and maintaining a patchwork of disparate systems and more time building differentiated functionality that drives the business forward. For example, Algebank, a Brazilian neobank with 2.7 million active customers, migrated their content management system storing customer records from Postgres to Atlas.
Fourth <unk> is emerging as the standard for AI applications over the last few quarters, we've seen a strengthen our self serve channel driven in part by AI Native startups, choosing Atlas as a foundation for their applications in the enterprise segment adoptions real but early much of the activity today centers on employee productivity tools and packaged IC solutions enterprise.
Speaker #4: Comparing Mongodb to another database like Postgres is not an apples-to-apples comparison. Take a global e-commerce application that manages inventory and order data while enabling product discovery through sophisticated search across millions of SKUs.
Speaker #4: The choice for this application is not between Mongodb or Postgres; it's between Mongodb or Postgres plus other offerings like Pinecone, Elastic, and Cohere, for embeddings.
Prices are still in the very early stages of building their own custom applications that will transform their business.
Speaker #4: Mongodb's complete solution allows developers to spend less time stitching together and maintaining a patchwork of disparate systems, and more time building differentiated functionality that drives the business forward.
We consistently hear from customers that when teams drive scale from vibe coated prototypes built on relational back ends to enterprise grade deployments. These platforms quickly hit limits and flexibility scalability and performance across Sharps and increasingly enterprises are unified platform is resonating strongly in.
Speaker #4: For example, Algibank, a Brazilian neobank with 2.7 million active customers, migrated their content management systems storing customer records from Postgres to Atlas. As data volumes grew, Postgres' inflexibility and tax execution latency drove performance issues, and the database lacked sophisticated secondary indexes and full-text text search, hurting sales of core offerings such as loans, insurance, and card approvals.
In the enterprise segment, a leading electric vehicle company chose Atlas and vector serves to Power's autonomous driving platform after testing vector search against post <unk> for their in vehicle voice assistant.
Dev Ittycheria: As data volumes grew, Postgres's inflexibility and task execution latency drove performance issues, and the database lacked sophisticated secondary indexes and full-text search, hurting sales of core offerings such as loans, insurance, and card approvals. Algebank was constantly updating the database and manually scaling infrastructure, which was both time-consuming and error-prone. With Atlas, Algebank gained a resilient, flexible system that handled rising demand and supported new services, delivering nearly five times better performance and 90% lower costs, all with no outages. Fourth, MongoDB is emerging as a standard for AI applications. Over the last few quarters, we've seen a strength in our self-serve channel, driven in part by AI-native startups choosing Atlas as the foundation for their applications. In the enterprise segment, adoption is real but early. Much of the activity today centers on employee productivity tools and packaged ISV solutions.
Selected <unk> to be for superior performance at scale and stronger ROI. They now rely on Atlas to handle over 1 billion vectors and expect 10 times growth in data usage by next year.
Speaker #4: Algibank was constantly updating the database and manually scaling infrastructure, which was both time-consuming and error-prone. With Atlas, Algibank gained a resilient, flexible system that handled rising demand and supported new services, delivering nearly five times better performance and 90% lower costs, all with no outages.
That red.
Deborah a well funded AI native platform with proven founders disrupting to help desk market built agent OLS. Its complete identity platform that autonomous handles billions of monthly requests on Atlas that Rev accelerated development velocity lower costs and scaled globally with low latency by using Atlas <unk>.
Speaker #4: Fourth, Mongodb is emerging as a standard for AI applications. Over the last few quarters, we've seen a strength in our self-service channel, driven in part by AI-native startups choosing Atlas as the foundation for their applications.
<unk> also leverages Atlas retro search for semantic search enriching its knowledge graph and <unk> with domain specific content.
Speaker #4: In the enterprise segment, adoption is real but early. Much of the activity today centers on employee productivity tools and package ISV solutions. Enterprises are still in the very early stages of building their own custom AI applications that will transform their business.
Companies in nearly every industry and across every geography are choosing not going to be because we deliver the features performance cost effectiveness and AI readiness. They need all in one platform. As we look ahead, we remain confident among the best position to lead both the current wave of digital transformation and the next wave powered by AI with that here's Mike.
Dev Ittycheria: Enterprises are still in the very early stages of building their own custom AI applications that will transform their business. We consistently hear from customers that when teams try to scale from buying coded prototypes built on relational backends to enterprise-grade deployments, these platforms quickly hit limits in flexibility, scalability, and performance. Across startups and increasingly enterprises, our unified platform is resonating strongly. In the enterprise segment, a leading electric vehicle company chose Atlas and VectorSearch to power its autonomous driving platform. After testing VectorSearch against Postgres PGVector for their in-vehicle voice assistant, they selected MongoDB for superior performance at scale and stronger ROI. They now rely on Atlas to handle over 1 billion vectors and expect 10 times growth in data usage by next year.
Speaker #4: We consistently hear from customers that when teams try to scale from vibe-coded prototypes built on relational backends, to enterprise-grade deployments, these platforms quickly hit limits in flexibility, scalability, and performance.
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 fiscal year 2006.
Speaker #4: Across startups and increasingly enterprises, our unified platform is resonating strongly. In the enterprise segment, a leading electric vehicle company chose Atlas and vector search to power its autonomous driving platform.
Discussing our results on a non-GAAP basis, unless otherwise noted.
Speaker #4: After testing vector search against Postgres PG vector for their in-vehicle voice assistant, they selected Mongodb for superior performance at scale and stronger ROI. They now rely on Atlas to handle over 1 billion vectors and expect 10 times growth in data usage by next year.
As Dave mentioned, we had a great quarter as we exceeded all of our guidance ranges and are increasing our full year guidance across the board.
Now onto the results.
In the second quarter total revenue was $591 million up 24% year over year and above the high end of our guidance.
Dev Ittycheria: DevRev, a well-funded AI-native platform with proven founders disrupting the helpdesk market, built AgentOS, its complete agentic platform that autonomously handles billions of monthly requests on Atlas. DevRev accelerated development velocity, lowered costs, and scaled globally with low latency by using Atlas. AgentOS also leverages Atlas VectorSearch for semantic search, enriching its knowledge graph and LLMs with domain-specific content. Companies in nearly every industry and across every geography are choosing MongoDB because we deliver the features, performance, cost-effectiveness, and AI readiness they need, all in one platform. As we look ahead, we remain confident MongoDB's position to lead both the current wave of digital transformation and the next wave powered by AI. With that, here's Mike.
Speaker #4: DevRed, DevRev, a well-funded AI-native platform with proven founders disrupting the helpdesk market, built AgentOS, its complete agentic platform with autonomously handles billions of monthly requests on Atlas.
Shifting to our product mix Atlas revenue outperformed our expectations in year over year growth accelerated to 29% in the quarter and now represents 74% of total revenue.
Speaker #4: DevRev, the accelerated development velocity, lowered costs, and scaled globally with low latency by using Atlas. AgentOS also leverages Atlas vector search for semantic search, enriching its knowledge graph and LLMs with domain-specific content.
This compares to 71% in the second quarter of fiscal 'twenty, five and 72% last quarter.
Had an impressive atlas growth quarter, which benefited in part from the strong start to consumption in may that we referenced on our last call as well as broad based strength, especially in larger customers in the U S.
Speaker #4: Companies in nearly every industry and across every geography are choosing Mongodb because we deliver the features, performance, cost-effectiveness, and AI readiness they need, all in one platform.
Speaker #4: As we look ahead, we remain confident Mongodb's position to lead both the current wave of digital transformation and the next wave powered by AI.
Let me provide some context on atlas consumption in the quarter and.
In Q2 Atlas consumption growth was strong and relatively consistent with last year's growth rates. This.
Speaker #4: With that, here's Mike.
Michael Berry: 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 fiscal year 26. I will be discussing our results on a non-GAAP basis unless otherwise noted. As Dave mentioned, we had a great quarter as we exceeded all of our guidance ranges and are increasing our full-year guidance across the board. Now, on to the results. In the second quarter, total revenue was $591 million, up 24% year-over-year and above the high end of our guidance. Shifting to our product mix, Atlas revenue outperformed our expectations, and year-over-year growth accelerated to 29% in the quarter and now represents 74% of total revenue. This compares to 71% in the second quarter of fiscal 25 and 72% last quarter.
Speaker #5: 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 fiscal year 26.
This drove the acceleration in revenue as well as the growth in absolute revenue dollars year to date for the first half of fiscal 'twenty six.
Speaker #5: I will be discussing our results on a non-GAAP basis and less otherwise noted. As Dave mentioned, we had a great quarter, as we exceeded all of our guidance ranges and are increasing our full-year guidance across the board.
Turning to non Atlas revenue came in ahead of our expectations in the quarter as we continue to have success selling incremental workloads into our existing customer base non.
Non outlets IRR, which reflects the underlying revenue growth of this product line without the impact of changes in duration grew 7% year over year.
Speaker #5: Now, onto the results. In the second quarter, total revenue was $591 million, up 24% year over year, and above the high end of our guidance.
In addition to the good underlying trends in non Atlas in Q2, we also benefited from more multiyear deals unexpected.
Speaker #5: Shifting to our product mix, Atlas revenue outperformed our expectations in year over year growth, accelerated to 29% in the quarter, and now represents 74% of total revenue.
Factoring our customers' desire to commit to building with Mongo DB long term.
Similarly half of their non Atlas revenue outperformance versus guidance was attributable to more multi year outperformance.
Speaker #5: This compares to 71% in the second quarter of fiscal 25 and 72% last quarter. We had an impressive Atlas growth quarter, which benefited in part from the strong start-to-consumption in May that we referenced on our last call, as well as broad-based strength, especially in larger customers in the US.
Michael Berry: We had an impressive Atlas growth quarter, which benefited in part from the strong start to consumption in May that we referenced on our last call, as well as broad-based strength, especially in larger customers in the US. Let me provide some context on Atlas consumption in the quarter. In Q2, Atlas consumption growth was strong and relatively consistent with last year's growth rates. This drove the acceleration in revenue, as well as the growth in absolute revenue dollars year to date for the first half of fiscal 26. Turning to non-Atlas, revenue came in ahead of our expectations in the quarter as we continue to have success selling incremental workloads into our existing EA customer base. Non-Atlas ARR, which reflects the underlying revenue growth of this product line without the impact of changes in duration, grew 7% year over year.
We had another strong quarter for customer adds in the second quarter as we grew our customer base by approximately 2800 sequentially.
And the total customer count to 59900, which is up from over 5700 in the year ago period.
Speaker #5: Let me provide some context on Atlas consumption in the quarter. In Q2, Atlas consumption growth was strong and relatively consistent with last year's growth rates.
This quarter, we incorporated new customers added from the voyage AIA acquisition to our customer count representing 300 of the 2800 added.
Speaker #5: This drove the acceleration in revenue as well as the growth in absolute revenue dollars year to date for the first half of fiscal 26.
The growth in our total customer count is being driven primarily by Atlas, which had over 58300 customers at the end of the quarter compared to over 49200, and the year ago period and.
Speaker #5: Turning to non-Atlas, revenue came in ahead of our expectations in the quarter as we continued to have success selling incremental workloads into our existing EA customer base.
It is important to keep in mind the growth in our Atlas customer count reflects new customers demand go DB. In addition to existing EAA customers deploying workloads on Atlas for the first time.
Speaker #5: Non-Atlas ARR, which reflects the underlying revenue growth of this product line without the impact of changes in duration, grew 7% year over year. In addition to the good underlying trends in Non-Atlas, in Q2 we also benefited from more multi-year deals than expected, reflecting our customers' desire to commit to building with MongoDB long-term.
Of our total customer count over 7300 are direct sales customers a decline of 200 customers sequentially and flat year over year. These.
Michael Berry: In addition to the good underlying trends in non-Atlas, in Q2, we also benefited from more multi-year deals than expected, reflecting our customers' desire to commit to building with MongoDB long term. Approximately half of the non-Atlas revenue outperformance versus guidance was attributable to multi-year outperformance. We had another strong quarter for customer ads in the second quarter as we grew our customer base by approximately 2,800 sequentially, bringing the total customer count to 59,900, which is up from over 50,700 in the year-ago period. This quarter, we incorporated new customers added from the Voyage AI acquisition to our customer count, representing 300 of the 2,800 added. The growth in our total customer count is being driven primarily by Atlas, which had over 58,300 customers at the end of the quarter, compared to over 49,200 in the year-ago period.
These metrics are largely due to our decision to reallocate a portion of our go to market resources from the mid market to the enterprise channel starting in the second half of last year.
Speaker #5: Approximately half of the non-Atlas revenue outperformance versus guidance was attributable to multi-year outperformance. We had another strong quarter for customer ads in the second quarter as we grew our customer base by approximately 2,800 sequentially, bringing the total customer count to 59,900, which is up from over 50,700 in the year ago period.
This does not impact our total customer count, but as an output of fewer self serve originated customers being elevated to our direct sales team as we move upmarket.
In Q2, our total company net expansion rate was approximately 119%, which is consistent with recent quarters.
We ended the quarter with 2564 customers with at least 100000.
Speaker #5: This quarter we incorporated new customers added from the Voyage AI acquisition to our customer count, representing 300 of the 2,800 added. The growth in our total customer count is being driven primarily by Atlas, which had over 58,300 customers at the end of the quarter, compared to over 49,200 in the year ago period.
<unk>, representing 17% growth versus the year ago period.
Moving down the income statement gross profit in the second quarter was $436 million, representing a gross margin of 74%, which is down from 75% in the year ago period, our year over year gross margin decline is primarily driven by Atlas growing as a percent of the overall business.
Michael Berry: It is important to keep in mind the growth in our Atlas customer count reflects new customers to MongoDB, in addition to existing EA customers deploying workloads on Atlas for the first time. Of our total customer count, over 7,300 are direct sales customers, a decline of 200 customers sequentially and flat year over year. These metrics are largely due to our decision to reallocate a portion of our go-to-market resources from the mid-market to the enterprise channel, starting in the second half of last year. This does not impact our total customer count, but is an output of fewer self-serve originated customers being elevated to our direct sales team as we move up market. In Q2, our total company net ARR expansion rate was approximately 119%, which is consistent with recent quarters.
Speaker #5: It is important to keep in mind the growth in our Atlas customer count reflects new customers to Mongodb in addition to existing EA customers deploying workloads on Atlas for the first time.
Our income from operations was $87 million for a 15% operating margin compared to 11% in the year ago period. We are very pleased with our stronger than expected margin result, operating margin results, which benefited mainly from our revenue outperformance. Additionally.
Speaker #5: Of our total customer count, over 7,300 are direct sales customers, a decline of 200 customers sequentially and flat year over year. These metrics are largely due to our decision to reallocate a portion of our go-to-market resources from the mid-market to the enterprise channel, starting in the second half of last year.
Additionally, I would like to provide a little context on the modest restructuring we undertook in the quarter.
It impacted less than 2% of employees and resulted in approximately $5 million of one time charges, which we have excluded from our non-GAAP financials.
Speaker #5: This does not impact our total customer count, but is an output of fewer self-serve originated customers being elevated to our direct sales team, as we move up market.
This action is consistent with the key priorities I outlined for you last quarter to identify ways to both reallocate existing spend to higher ROI opportunities and be more disciplined about incremental spending we are focused on running an efficient scalable business that supports growth in revenue.
Speaker #5: In Q2, our total company net AR expansion rate was approximately 119%, which is consistent with recent quarters. We ended the quarter with 2,564 customers with at least $100,000 in ARR, representing 17% growth versus the year ago period.
Michael Berry: We ended the quarter with 2,564 customers with at least $100,000 in ARR, representing 17% growth versus the year-ago period. Moving down the income statement, gross profit in the second quarter was $436 million, representing a gross margin of 74%, which is down from 75% in the year-ago period. Our year-over-year gross margin decline is primarily driven by Atlas growing as a percent of the overall business. Our income from operations was $87 million for a 15% operating margin, compared to 11% in the year-ago period. We are very pleased with our stronger-than-expected margin result, operating margin results, which benefited mainly from our revenue outperformance. Additionally, I'd like to provide a little context on the modest restructuring we undertook in the quarter. It impacted less than 2% of employees and resulted in approximately $5 million of one-time charges, which we have excluded from our non-GAAP financials.
<unk> profitability to drive long term shareholder value.
Speaker #5: Moving down the income statement, gross profit in the second quarter was $436,000,000, representing a gross margin of 74%, which is down from 75% in the year ago period.
Net income in the second quarter was $87 million or $1 per share based on 87 million diluted shares outstanding. This compares to a net income of $59 million or <unk> 70 per share on 84 million diluted shares outstanding in the year ago period.
Speaker #5: Our year over year gross margin decline has primarily driven by Atlas growing, as a percent of the overall business. Our income from operations was $87,000,000 for a 15% operating margin, compared to 11% in the year ago period.
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.
Speaker #5: We are very pleased with our stronger-than-expected margin result, operating margin results, which benefited mainly from our revenue outperformance. Additionally, I'd like to provide a little context on the modest restructuring we undertook in the quarter.
During the quarter, we spent $200 million to repurchase approximately 930000 shares which was under our previously announced $1 billion total share repurchase authorization.
Operating cash flow was well above our expectations at $72 million and free cash flow was $70 million, which compares to negative $1 million and negative $4 million, respectively in the year ago period.
Speaker #5: It impacted less than 2% of employees, and resulted in approximately $5 million of one-time charges, which we have excluded from our non-GAAP financials. This action is consistent with the key priorities I outlined for you last quarter to identify ways to both reallocate existing spend to higher ROI opportunities and be more disciplined about incremental spending.
Michael Berry: This action is consistent with the key priorities I outlined for you last quarter to identify ways to both reallocate existing spend to higher ROI opportunities and be more disciplined about incremental spending. We are focused on running an efficient, scalable business that supports growth in revenue and profitability to drive long-term shareholder value. Net income in the second quarter was $87 million, or $1 per share, based on 87 million diluted shares outstanding. This compares to a net income of $59 million, or $0.70 per share, on 84 million diluted shares outstanding in the year-ago period. 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. During the quarter, we spent $200 million to repurchase approximately 930,000 shares, which was under our previously announced $1 billion total share repurchase authorization.
Our strong cash flow results were driven primarily by strong operating profit and higher cash collections.
Before turning to our outlook in greater detail I'd like to share the key points driving how we're looking at the rest of fiscal year 'twenty six.
Speaker #5: We are focused on running an efficient, scalable business that supports growth in revenue and profitability to drive long-term shareholder value. Net income in the second quarter was $87 million, or $1 per share, based on 87 million diluted shares outstanding.
Number one we are raising our expectations for our revenue based on our confidence in Atlas as well as our strong performance in the first half of the year, providing a higher starting point for Atlas heading into the second half.
Speaker #5: This compares to a net income of $59,000,000, or 70 cents per share, on $84,000,000 diluted shares outstanding in the year ago period. 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.
Number two we are increasing our operating margin guidance by 150 basis points at the high end, reflecting our strong Q2 performance and continued focus on margin improvement.
And number three we are raising our operating margin guidance, while still continuing to make incremental investments for growth with a focus on R&D and developer awareness.
Speaker #5: During the quarter, we spent $200 million to repurchase approximately 930,000 shares, which was under our previously announced $1 billion total share repurchase authorization. Operating cash flow was well above our expectations at 72 million dollars, and free cash flow was 70 million dollars, which compares to negative 1 million and negative 4 million dollars, respectively, in the year ago period.
Now moving onto our full year guidance I'd like to provide some incremental comments on our expectations first as we discussed we had a strong start to the year and are confident on our ability to drive continued revenue and profitability growth.
Michael Berry: Operating cash flow was well above our expectations at $72 million, and free cash flow was $70 million, which compares to negative $1 million and negative $4 million, respectively, in the year-ago period. Our strong cash flow results were driven primarily by strong operating profit and higher cash collections. Before turning to our outlook in greater detail, I'd like to share the key points driving how we are looking at the rest of fiscal year 26. Number one, we are raising our expectations for revenue based on our confidence in Atlas, as well as a strong performance in the first half of the year, providing a higher starting point for Atlas heading into the second half. Number two, we are increasing our operating margin guidance by 150 basis points at the high end, reflecting our strong Q2 performance and continued focus on margin improvement.
We are raising our full year revenue guidance by $70 million, including the $38 million outperformance in Q2.
Speaker #5: Our strong cash flow results were driven primarily by strong operating profit and higher cash collections. Before turning to our outlook in greater detail, I'd like to share the key points driving how we are looking at the risk of fiscal year 2026.
This reflects excuse me. This reflects the strong Q2 consumption benefiting revenue in the second half and our continued confidence in Atlas growth.
All in this implies mid twenties percentage growth for Atlas in the second half of the year.
Speaker #5: Number one, we are raising our expectations for revenue based on our confidence in Atlas, as well as a strong performance in the first half of the year providing a higher starting point for Atlas, heading into the second half.
Second incorporating our strong performance in the first half we expect non outlet subscription revenue will now be down in the mid single digits for the year compared to our prior expectation of high single digit decline.
Speaker #5: Number two, we are increasing our operating margin guidance by 150 basis points at the high end, reflecting our strong Q2 performance and continued focus on margin improvement.
We also expect a headwind from multi year license revenue for fiscal 2006 to now be $40 million due to the Q2 outperformance compared to our prior expectation of approximately $50 million.
Michael Berry: And number three, we are raising our operating margin guidance while still continuing to make incremental investments for growth with a focus on R&D and developer awareness. Now, moving on to our full-year guidance, I'd like to provide some incremental comments on our expectations. First, as we discussed, we had a strong start to the year and are confident in our ability to drive continued revenue and profitability growth. We are raising our full-year revenue guidance by $70 million, including the $38 million outperformance in Q2. This reflects the strong Q2 consumption benefiting revenue in the second half and our continued confidence in Atlas growth. All in, this implies mid-20s percentage growth for Atlas in the second half of the year.
Speaker #5: And number three, we are raising our operating margin guidance while still continuing to make incremental investments for growth with a focus on R&D and developer awareness.
Please note, we expect non Atlas <unk> will continue to grow year over year.
Finally, we are raising our expectations for operating margin of 14% at the high end up from 12, 5% in our prior quarter guidance.
Speaker #5: Now moving on to our full-year guidance, I'd like to provide some incremental comments on our expectations. First, as we discussed, we had a strong start to the year, and our confidence in our ability to drive continued revenue and profitability growth.
This reflects the better than expected revenue performance the impact of our more disciplined approach to investing for growth.
And our increased focus on efficiency.
Speaker #5: We are raising our full-year revenue guidance by $70 million dollars, including the $38 million outperformance in Q2. This reflects--excuse me--this reflects the strong Q2 consumption benefiting revenue in the second half and our continued confidence in Atlas growth.
For fiscal year 'twenty six we now expect revenue to be in the range of 2.34 to three 6 billion, an increase of $70 million from our prior guide.
We are raising our non-GAAP income from operations expectations by $44 million and are now targeting a range of $321 million to $331 million and non-GAAP net income per share to be in the range of $3 64 to $3 73.
Speaker #5: All in, this implies mid-20s percentage growth for Atlas in the second half of the year. Second, incorporating our strong performance in the first half, we expect non-Atlas subscription revenue will now be down in the mid-single digits for the year, compared to our prior expectation of high single-digit decline.
Michael Berry: Second, incorporating our strong performance in the first half, we expect non-Atlas subscription revenue will now be down in the mid-single digits for the year, compared to our prior expectation of high single-digit decline. We also expect the headwind from multi-year license revenue for fiscal 26 to now be $40 million due to the Q2 outperformance, compared to our prior expectation of approximately $50 million. Please note, we expect non-Atlas ARR will continue to grow year over year. Finally, we are raising our expectations for operating margin to 14% at the high end, up from 12.5% in our prior quarter guidance. This reflects the better-than-expected revenue performance, the impact of our more disciplined approach to investing for growth, and our increased focus on efficiency.
Based on 87 4 million diluted shares outstanding.
Note that the non-GAAP net income per share guidance for the third quarter and fiscal year 2006 assumes a non-GAAP tax provision of 20%.
Speaker #5: We also expect to headwind for multi-year license revenue for fiscal 26 to now be $40 million dollars, due to the Q2 outperformance, compared to our prior expectation of approximately $50 million.
Moving on to our Q3 guidance a few things to keep in mind.
First we expect to see a low 20% year over year percentage decline in the non Atlas business. After the strong multiyear outperformance we experienced in Q3 of fiscal year 'twenty five as.
Speaker #5: Please note, we expect non-Atlas ARR will continue to grow year over year. Finally, we are raising our expectations for operating margin to 14% at the high end, up from 12.5% in our prior quarter guidance.
As a reminder, Q3 of last year was our strongest multiyear revenue quarter and is the largest portion of the multiyear headwind.
Speaker #5: This reflects the better-than-expected revenue performance, the impact of our more disciplined approach to investing for growth, and our increased focus on efficiency. For fiscal year 26, we now expect revenue to be in the range of $2.34 to $2.36 billion, an increase of 70 million dollars from our prior guide.
Second we expect operating margin will be lower than in Q2, primarily due to the expected sequential decline in non Atlas revenue, which is very high margin revenue in.
Michael Berry: For fiscal year 26, we now expect revenue to be in the range of $2.34 to $2.36 billion, an increase of $70 million from our prior guide. We are raising our non-GAAP income from operations expectations by $44 million and are now targeting a range of $321 to $331 million, and non-GAAP net income per share to be in the range of $3.64 to $3.73, based on 87.4 million diluted shares outstanding. Note that the non-GAAP net income per share guidance for the third quarter and fiscal year 26 assumes a non-GAAP tax provision of 20%. Moving on to our Q3 guidance, a few things to keep in mind. First, we expect to see a low 20% year-over-year percentage decline in the non-Atlas business after the strong multi-year outperformance we experienced in Q3 of fiscal year 25.
In addition, it is also impacted by the timing of operating expenses, specifically, R&D hiring and seasonality of our marketing investments.
Speaker #5: We are raising our non-GAAP income from operations expectations by 44 million dollars, and are now targeting a range of $321 to $331 million dollars, and non-GAAP net income per share to be in the range of $3.64 cents, to $3.73 cents, based on $87.4 million diluted shares outstanding.
With that context, I will now turn to our outlook for the third quarter.
For the third quarter, we expect revenue to be in the range of $587 million to $592 million. We expect non-GAAP income from operations to be in the range of $66 million to $70 million and non-GAAP net income per share to be in the range of 76 to 79.
Speaker #5: Note that the non-GAAP net income per share guidance for the third quarter and fiscal year 26 assumes a non-GAAP tax provision of 20%. Moving on to our Q3 guidance, a few things to keep in mind.
Based on 87 7 million diluted shares outstanding.
To summarize we had a very strong quarter. We are pleased with our ability to drive revenue growth across our business and increase our operating profit expectations.
Speaker #5: First, we expect to see a low 20% year-over-year percentage decline in the non-Atlas business after the strong multi-year outperformance we experienced in Q3 of fiscal year 2025.
We remain incredibly excited about the opportunity ahead, and we will continue to invest responsibly to drive long term shareholder value.
Michael Berry: As a reminder, Q3 of last year was our strongest multi-year revenue quarter and is the largest portion of the multi-year headwind. Second, we expect operating margin will be lower than in Q2, primarily due to the expected sequential decline in non-Atlas revenue, which is very high margin revenue. In addition, it is also impacted by the timing of operating expenses, specifically R&D hiring and seasonality of our marketing investments. With that context, I will now turn to our outlook for the third quarter. For the third quarter, we expect revenue to be in the range of $587 to $592 million. We expect non-GAAP income from operations to be in the range of $66 to $70 million, and non-GAAP net income per share to be in the range of $76 to $79, based on 87.7 million diluted shares outstanding. To summarize, we had a very strong quarter.
Speaker #5: As a reminder, Q3 of last year was our strongest multi-year revenue quarter and is the largest portion of the multi-year headwind. Second, we expect operating margin will be lower than in Q2, primarily due to the expected sequential decline in non-Atlas revenue, which is very high-margin revenue.
I would also like to take a moment to extend a warm welcome to just Leubert, our new Vice President of Investor Relations, who started with us yesterday.
<unk> joins us from Juniper networks, where he led their investor relations effort, including the most recent including most recently, helping the company navigate the acquisition by Hewlett Packard Enterprise.
Speaker #5: In addition, it is also impacted by the timing of operating expenses, specifically R&D hiring and the seasonality of our marketing investments. With that context, I will now turn to our outlook for the third quarter.
We're excited to have him onboard and eager to see the impact of his work.
Last but not least we look forward to seeing many of you on a few weeks at our Investor day. Please reach out to our Investor relations team at IR, App Mongo DB dot com with any questions.
Speaker #5: For the third quarter, we expect revenue to be in the range of $587 to $592 million, we expect non-GAAP income from operations to be in the range of $66 to $70 million, and non-GAAP net income per share to be in the range of $76 to $79 cents, based on 87.7 million diluted shares outstanding.
With that we'd like to open it up for questions Carmen take it away.
Montana is a reminder, that is star one one to get in the queue and wait for your name to be announced to withdraw that question simply press star one again.
Our first question is from Sanjay <unk> with Morgan Stanley. Please proceed.
Speaker #5: To summarize, we had a very strong quarter. We are pleased with our ability to drive revenue growth across the business and increase our operating profit expectations.
Hi, Thank you for taking my question and congrats on a heck of a quarter.
Michael Berry: We are pleased with our ability to drive revenue growth across the business and increase our operating profit expectations. We remain incredibly excited about the opportunity ahead and will continue to invest responsibly to drive long-term shareholder value. I would also like to take a moment to extend a warm welcome to Jess Lubert, our new Vice President of Investor Relations, who started with us yesterday. Jess joins us from Juniper Networks, where he led their investor relations effort, including most recently helping the company navigate the acquisition by Hewlett Packard Enterprise. We're excited to have him on board and eager to see the impact of his work. Last but not least, we look forward to seeing many of you in a few weeks at our Investor Day. Please reach out to our Investor Relations team at ir@mongodb.com with any questions.
In Q2, I wanted to dive into some of the drivers into Q2, when I look at the acceleration Atlas, which has now accelerated for two quarters in rail and I kind of just look at the sequential dollar adds I had that up.
Speaker #5: We remain incredibly excited about the opportunity ahead and will continue to invest responsibly to drive long-term shareholder value. I would also like to take a moment to extend a warm welcome to Jess Lubert, our new Vice President of Investor Relations, who started with us yesterday.
More than $41 million to $40 million in Q2, which is kind of the strongest sequential dollar that we've seen in quite some time in what's been a pretty sober sort of cloud spending environment.
Speaker #5: Jess joins us from Juniper Networks, where he led their investor relations effort, including the most recent--including most recently helping the company navigate the acquisition by Hewlett Packard Enterprise.
I was wonder if you can.
Give us some sense of the drivers of.
The strong sequential adds this quarter I know you pointed to me but.
If anything you can give us from Bob Michael workload perspective, or any other new factors, maybe the workloads from last year, starting to ramp I'd, just like to understand that trajectory a little bit better.
Speaker #5: We're excited to have him on board and eager to see the impact of his work. Last but not least, we look forward to seeing many of you in a few weeks at our investor day.
Speaker #5: Please reach out to our investor relations team at ir@mongodb.com with any questions. With that, we'd like to open it up for questions. Carmen, take it away.
Yes. Thank you. Thanks for the question. So clearly we're really pleased by the quarter and really pleased by the accelerating growth in Atlas I would say.
Michael Berry: With that, we'd like to open it up for questions. Carmen, take it away.
A lot of it was due to the workloads that we acquired over the past year, especially with our move up market that are growing faster and becoming bigger than previous workloads. We've seen so I think the move up market is really paying off.
Carmen: Thank you so much. And as a reminder, that is star 11 to get in the queue and wait for your name to be announced. To withdraw the question, simply press star 11 again. Our first question is from Sanjit Singh with Morgan Stanley. Please proceed.
Speaker #2: Thank you so much, and as a reminder that it's star 11 to get in the queue, and wait for your name to be announced.
Speaker #2: To withdraw the question, simply press star 11 again. Our first question is from Sanjit Singh, with Morgan Stanley, please proceed.
And what we're also seeing is that there's great uptake of some of the other capabilities, we offer like search and vector search that are also adding to that growth of those workloads.
Analyst: Hi, thank you for taking that question, and congrats on a heck of a quarter in Q2. I wanted to dive into some of the drivers into Q2. When I look at the acceleration Atlas, which is now accelerated for two quarters in a row, and I kind of just look at the sequential dollar ads, I had that up, you know, more than 40, more than 40 million in Q2, which is kind of the strongest sequential dollar ads we've seen in quite some time in what's been a pretty sober sort of cloud spending environment. So I was wondering if you could, you know, give us some sense of the drivers of, you know, of the strong sequential ads of this quarter.
Speaker #6: Hi, thank you for taking the question, and congrats on a heck of a quarter in Q2. I wanted to dive into some of the drivers into Q2.
And then as we mentioned, we also acquired a ton of new customers.
The self serve customers tend to spend less on a per customer basis, but we also have added lots of customers over the last six months and I think Thats also helping drive some of the growth.
Speaker #6: When I look at the acceleration Atlas, which is now accelerated for two quarters in a row, and I kind of just look at the sequential dollar adds, I had that up, you know, more than 40, more than 40 million in Q2, which is kind of the strongest sequential dollar adds we've seen in quite some time, and what's been a pretty sober sort of cloud spending environment.
Yes.
Color I wanted to follow up on the go to market side over the last couple of years, we've been sort of tinkering and optimizing the go to market organization across sort of.
Speaker #6: So I was wondering if you could, you know, give us some sense of the drivers of, you know, of of the strong sequential adds of this quarter.
Territory investment, but also sort of quotas and moving to incremental consumption.
Analyst: I know you pointed to May, but if anything you can give us from like a workload perspective or any other new factors, maybe the workloads from last year are starting to ramp. I'd just love to understand that trajectory a little bit better.
Speaker #6: I know you pointed to May, but if anything, you can give us from a workload perspective or any other new factors. Maybe the workloads from last year are starting to ramp.
Could you give us an update on.
The state of operations for the sales force today and.
And in some sense, if I look at the customer adds it seems like things are humming quite well, but just want you to understand.
Speaker #6: I just love to understand that trajectory a little bit better.
Dev Ittycheria: Yeah, Sanjit, thank you. Thanks for the question. So clearly, we're really pleased by the quarter and really pleased by the accelerating growth in Atlas. I would say a lot of it was due to the workloads that we acquired over the past year, especially with our move-up market that are growing faster and becoming bigger than previous workloads we've seen. So I think the move-up market is really paying off. And what we're also seeing is that there's a great uptick of some of the other capabilities we offer, like search and vector search, that are also adding to that growth of those workloads. And then, as we mentioned, we've also acquired a ton of new customers. Obviously, the self-serve customers tend to spend less on a per-customer basis, but we obviously have added lots of customers over the last six months.
How like what's the seat of the organization that that'd be very helpful.
Speaker #4: Yeah, so I just thank you, thanks for the question. So clearly, we're really pleased by the quarter and really pleased by the accelerating growth in Atlas.
Yes sure so.
Nothing really has changed we're just doubling down on what we said previously we are moving upmarket. So we're focusing our high end sale.
Speaker #4: I would say, a lot of it was due to the workloads that we acquired over the past year, especially with our move up market, that are growing faster and and becoming bigger than previous workloads we've seen.
Sales force focused on the most sophisticated and demanding customers. These are typically enterprise customers all around the world.
And then we are using our self serve channel to better serve the SMB market. I know there are a lot of questions about where we kind of abandoning the self serve at the early stage market by this move and I think the results over the last couple of quarters have shown that we are not I think we're just becoming much more effective in serving that market, while also being very effective in growing.
Speaker #4: So I think the move up market is really paying off. and, what we're also seeing is that there's a great uptick of some of the other capabilities we offer like search and vector search that are also adding to that growth of those workloads.
Speaker #4: and then as we mentioned, we also acquired a ton of new customers, obviously the self-serve customers tend to spend less on a per customer basis, but we obviously have added lots of customers over the last six months.
<unk>.
Sure.
Our wallet share in these larger accounts. So so were really just continuing with the strategy that we articulated before and obviously, we're pleased with the results.
Dev Ittycheria: And I think that's also helping drive some of the growth.
Speaker #4: And I think that's also helping drive, some of the growth.
Analyst: Yeah, that's great color. I wanted to follow up on the go-to-market side. You know, over the last couple of years, we've been sort of tinkering and optimizing the go-to-market organization across, you know, sort of, you know, territory investment, but also sort of quotas and moving to incremental consumption. Could you give us an update on the state of operations for the sales force today? And in some sense, you know, if I look at the customer ads, it seems like things are humming quite well. But just to get to understand, you know, how, like, what's the state of the organization today, that'd be really helpful.
Speaker #6: Yeah, that's a that's a that's great color. I wanted to, follow up on the go-to-market side, you know, over the last couple of years we've been sort of tinkering and optimizing the go-to-market organization across, you know, sort of, you know, territory investment, but also sort of quotas and moving to incremental consumption.
Appreciate the thoughts guys. Thank you.
Thank you Sanjay.
Thank you. Our next question is from Raimo <unk> with Barclays. Please proceed.
Perfect. Thank you first of all congrats to Jeff all the best.
Two quick questions from me and staying on that theme of self service.
Speaker #6: Could you give us an update on the state of operations for Salesforce today? In some sense, you know, if I look at the customer ads, it seems like things are humming quite well. But just to get an understanding, you know, what's the state of the organization today? That'd be really helpful.
That acceleration in deep.
Obviously, you changed things around but it kind of.
Southern region, despite kind of you actually moving up market.
Dev Ittycheria: Yeah, sure. So nothing really has changed. We're just doubling down on what we've said previously. We are moving up markets. We're focusing our high-end, you know, sales force focus on the most sophisticated and demanding customers. You know, these are typically enterprise customers all around the world. And then we're using a self-serve channel to better serve the SMB market. I know there are a lot of questions about where we're kind of abandoning the self-serve at the early-stage market by this move. And I think the results over the last couple of quarters have shown that we are not. I think we're just becoming much more effective in serving that market while also being very effective in growing, you know, our wallet share in these larger accounts. So we're really just continuing with the strategy that we articulated before. And obviously, we're pleased with the results.
You help us understand what's driving that a little bit and then I had one follow up for Mike.
Speaker #4: Yeah, sure. So, nothing really has changed, we're just doubling down on what we said previously, we are moving up markets, so we're focusing, our high end, you know, Salesforce, focus on the most sophisticated and demanding customers, you know, these are typically enterprise customers all around the world.
Yes, I mean, clearly the output metrics look really good but I would say the work around self serve began.
<unk> has been going on for a while the team is really good at running experiments using a data driven approach to figure out what's working to figure out what's not working a new motion that we're also doing that showing good results as going after sequel developers, who don't really know mongo, DB and attracting them to our platform really.
Speaker #4: And then, we're using our self-serve channel to better serve the SMB market. I know there are a lot of questions about where we kind of abandoning the self-serve, the early stage market by this move, and I think the results over the last couple of quarters have shown that we are not.
Speaker #4: I think we're just becoming much more effective in serving that market while also being very effective in growing, you know, our wallet share in these larger accounts.
Helping them understand the value proposition among DB, even running like things like office hours, where we spent time with sequel developers to explain the benefits of of modeling data in a document database and all of these experiments and tactics that we're doing which are very data driven are really paying off and may Petra used to run that.
Speaker #4: So, so we're really just continuing with the strategy that we articulated before, and obviously we're pleased with the results.
Analyst: Appreciate the thoughts, Dave. Thank you.
Speaker #6: Appreciate the thoughts, Dave. Thank you.
Dev Ittycheria: Thank you, Sanjit.
Speaker #4: Thank you, Sanjit.
Carmen: Thank you. Our next question is from Raymond Lynch with Barclays. Please proceed.
Group is now our CMO and she has a strong team under her and we feel really good about what that self serve team thats been doing but again, we don't want to declare victory too early but obviously, we're very pleased with the results.
Speaker #2: Thank you. Our next question is from Ray Mullenchild with Barclays. Please proceed.
Analyst: Perfect. Thank you. First of all, congrats to Jess. All the best. Two quick questions from me. Staying on that theme of self-service, that acceleration, Dave, obviously, you know, you changed things around, but it kind of accelerated despite kind of you actually moving up market. Like, can you help us understand that and what's driving that a little bit? And then have one follow-up for Mike.
Speaker #6: Perfect, thank you. first of all, congrats to Jess, all the best. two quick questions from me. staying on that theme of of self-service, that acceleration, Dave, obviously, you know, you changed things around, but it it kind of, it's accelerated despite kind of you actually moving up market.
Yes, that's really nice to see.
It might be.
First of all for all accident, that's closer to the AAR for further non Atlas alone E. Part of it is kind of really helpful. If you think about the.
I get the logic around the renewal cohorts, especially Q3 and.
Speaker #6: Like, can you help us understand then what's driving that a little bit? And then I had one follow-up for Mike.
Am I doing the vast correctly that exiting next year that part of the business looks more interesting because the cohort looks better I'd like just trying to get your idea.
Dev Ittycheria: Yeah, I mean, I mean, clearly, the output metrics look really good, but I would say the work around self-serve began, you know, has been going on for a while. The team is really good at running experiments, using a data-driven approach to figure out what's working, to figure out what's not working. A new motion that we're also doing that's showing good results is going after SQL developers who don't really know MongoDB and attracting them to our platform, really, you know, helping them understand the value proposition of MongoDB, even running like things like office hours where we spend time with SQL developers to explain the benefits of modeling data on a document database. And all of these experiments and tactics that we're doing, which are very data-driven, are really paying off.
Speaker #4: Yeah, I mean, I mean clearly the output metrics look really good, but I would say the workaround self-serve began, you know, has been going on for a while.
And maybe you Michael you can give it to us because you're just too. Thank you.
Speaker #4: The team is really good at running experiments, using a data-driven approach, to figure out what's working, to figure out what's not working. A new motion that we're also doing that's showing, good results is going after SQL developers who don't really know Mongodb, and attracting them to our platform, really, you know, helping them understand the value proposition of Mongodb, even running like things like office hours where we spend time with, you know, SQL developers to explain the benefits of of modeling data in a on a document database.
Sure. So thanks for the question, so I'm going to hold that answer until we get to Q3 of next year, because it kind of depends on what happens in Q3 of this year. So the one thing is as we've talked about the big impact in Q3 of this year as the multi year, we'll see how it all it comes back next year, but it really depends very much on how we do in Q3 this year.
Yeah, Okay perfect. Thank you, but thanks for the disclosure.
Paul.
Youre welcome Thanks Raimo.
Thank you so much and our next question comes from Tyler Radke with Citi. Please proceed.
Speaker #4: And all of these experiments and tactics that we're doing, which are very data-driven, have are really paying off. And, May Petra used to run that group, is now our CMO.
Hey, Thanks for taking the question.
Dev Ittycheria: And May Petre, who used to run that group, is now our CMO, and she has a strong team under her, and we feel really good about what that self-serve team has been doing. But again, we don't want to declare a victory too early, but obviously, we're very pleased with the results.
Nice job on the Atlas growth wanted to dig into the AI commentary that you had Dave obviously last quarter you talked about cursor.
Speaker #4: And she has a strong team under her, and we feel really good about what that self-serve team has been doing. But again, we don't want to declare victory too early; but obviously, we're very pleased with the results.
Which obviously is ramping up significantly in terms of their IRR and I think you called out many examples this quarter, including.
Analyst: Yeah, no, that's really nice to see. And Mike, thank you, first of all, for all the extra disclosure. The ARR for the non-Atlas or EA part is kind of really helpful. If you think about the, I get the logic around the renewal cohorts, especially Q3, but am I doing the math correctly that actually next year that part of the business looks more interesting because the cohort looks better? Like just trying to get your idea, or maybe you might not even give it to us because you just do ARR. Thank you.
Speaker #6: Yeah, no, that's really nice to see. And then, Mike, thanks first of all for all the excellent disclosure, the ARR for for the non-Atlas or EA part is kind of really helpful.
<unk> vehicle company, it sounded like expecting pretty significant growth there, but how much of that is playing into the atlas strength that youre seeing here in the quarter any way to quantify.
Speaker #6: If you think about the, I get the logic around the renewal cohorts, especially Q3, but in, am I doing the rough correctly that actually next year that part of the business looks more interesting because the cohort looks better?
So quarter or use cases, whether it's vector search or maybe even if you. If you throw in voyage just help us understand if thats starting to move the needle.
Speaker #6: Like just trying to get your idea or, and maybe you might not even give it to us because you just do ARR. Thank you.
Because it sounds like there's some pretty high profile wins in there.
Michael Berry: Sure. So thanks for the question. So I'm going to hold that answer until we get to Q3 of next year because it kind of depends on what happens in Q3 of this year. So the one thing is, as we've talked about, the big impact in Q3 of this year is the multi-year. We'll see how it comes back next year, but it really depends, Raymond, on how we do in Q3 this year.
Speaker #4: Sure, so thanks for the question. So, I'm going to hold that answer till we get to Q3 of next year because it kind of depends on what happens in Q3 of this year.
Yes, thanks for the question Tyler.
While we're adding thousands of AI native customers I would tell you that the growth that we delivered this quarter was was not.
Speaker #4: So the one thing is, as we've talked about, the big impact in Q3 of this year is the multi-year. We'll see how it how it comes back next year, but it really depends way more on how we do in Q3 this year.
Not material to that.
To that growth growth is really driven by our core business and our core customer base and so.
Analyst: Yeah. OK, perfect. Thank you, but thanks for the disclosure. Really helpful.
Speaker #6: Yeah, okay, perfect. Thank you, but thanks for the disclosure. Really helpful.
And while we're very happy with the the EA customers increasingly choosing <unk>. It was not a material mover of the needle for our growth.
Michael Berry: You're welcome.
Dev Ittycheria: Thanks, Raymond.
Speaker #4: You're welcome. Thanks, Raymond.
Carmen: Thank you so much. And our next question comes from Tyler Radtke with CITI. Please proceed.
Speaker #2: Thank you so much, and our next question comes from Tyler Radke with Citi. Please proceed.
Analyst: Hey, thanks for taking the question and nice job on the Atlas growth. I wanted to dig into the AI commentary that you had, Dave. Obviously, last quarter, you talked about Cursor, which obviously is ramping up significantly in terms of their ARR. And I think you called out many examples this quarter, including an autonomous vehicle company. It sounds like, you know, expecting pretty significant growth there. But how much of that is playing into the Atlas strength that you're seeing here in the quarter? Any way to quantify, you know, that cohort or use cases, whether it's, you know, vector search or maybe even if you throw in Voyage, just help us understand if that's starting to move the needle because it sounds like there's some pretty high-profile wins in there.
Great and then follow up on the migration opportunity.
Speaker #7: Hey, thanks for taking the question. and nice job on the the Atlas growth. what is it dig into the AI commentary that you had, Dave, obviously last quarter you talked about cursor, which which obviously is is ramping up significantly in terms of their ARR, and I think you called out, many examples this quarter including, autonomous vehicle, company sounds like, you know, expecting pretty significant growth there.
You have been investing.
In relational migrate or youre working with companies like cognition to accelerate.
<unk>.
Migration opportunity.
And you've seen professional services ramp up a little bit but where.
Have you started to see sort of the time too.
Migration or re platform improve a bit just anything you could share in terms of that migration opportunity. If thats started to improve in terms of velocity or size of the workload migration.
Speaker #7: But how much of that is is playing into the Atlas strength that you're seeing here in the quarter? You know, any way to quantify, you know, that cohort or use cases, whether it's, you know, vector search or maybe even if you if you throw in Voyage, just help us understand if that's starting to move the needle, because it sounds like there's there's some pretty high-profile wins in there.
Migration will be helpful. Thank you.
Yes, sure. So yes, we're super excited about what we call App monetization of legacy App monetization Youll hear a lot more about this at Investor day in September Tyler, but what I will say is that the value proposition is very clear.
Dev Ittycheria: Yeah, so thanks for the question, Tyler. While we're adding thousands of AI-native customers, I will tell you that the growth that we delivered this quarter was not material to that growth. The growth was really driven by our core business and our core customer base. And so, you know, while we're very happy with the AI customers increasingly choosing MongoDB, it was not a material mover of the needle for our growth.
Speaker #4: Yeah, so thanks for the question, Tyler. while we're adding thousands of AI-native customers, I will tell you that the growth that we delivered this quarter was was, you know, not material to that, to that growth.
Customers are very very motivated to try and modernize these legacy systems for a wide variety of reasons.
Speaker #4: The growth is really, driven by our core business and our core customer base, and so, and you know, while we're very happy with the, you know, the AI customers, increasingly choosing Mongodb, it was not a material, a mover of the needle for our growth.
We are seeing a lot of progress we've actually brought in a new leader a new product leader, who brings a lot of depth and scale, especially around AI to help us build the tooling to leverage AI to really.
Drive more automation in terms of how we analyze and re factor. The code. We brought in a new leader last quarter to help really helped drive the delivery and the go to market efforts around op Mod, So we're definitely beating up resources and.
Analyst: Great. And then follow up on the migration opportunity. I know you've been investing in Relational Migrator. You know, you're working with companies like Cognition to accelerate the code migration opportunity. And you've seen professional services ramp up a little bit. But where have you started to see sort of the time to migration or replatform improve a bit? Just anything you could share in terms of that migration opportunity, if that's started to improve in terms of velocity or size of workload, migration would be helpful. Thank you.
Speaker #7: Great, and then follow-up on the the migration opportunity, I know you know you've been investing in relational migrator, you know, you're working with with companies like Cognition to to accelerate the the code, migration opportunity.
I would say that we are investing a lot in product and there's a lot more to do and I would say.
Speaker #7: and and you've seen professional services ramp up a little bit, but where have you started to see sort of the the time to, migration or or re-platform improve a bit?
This is something that we're very excited about but it will drive more of a longer term growth less.
It won't be as pronounced in terms of this year, but we're very very excited about the opportunity and we're definitely we'll spend more time discussing this and what were actually doing on the product side in September.
Speaker #7: Just anything you could share, in terms of that, migration opportunity, if that's, started to to improve in terms of velocity or or size of workload, migration would be helpful.
Thank you.
Thank you one moment for our next question comes from Jason Ader with William Blair. Please proceed.
Speaker #7: Thank you.
Dev Ittycheria: Yeah, sure. So yes, we're super excited about what we call app modernization or legacy app modernization. You'll hear a lot more about this at Investor Day in September, Tyler. But what I will say to you is that the value proposition is very clear. Customers are very, very motivated to try and modernize these legacy systems for a wide variety of reasons. We are seeing a lot of progress. We've actually brought in a new leader, new product leader, who brings a lot of depth and scale, especially around AI, to help us build the tooling to leverage AI to really, you know, drive more automation in terms of how we analyze and refactor the code. We brought in a new leader last quarter to help really drive the delivery and the go-to-market efforts around AppMod. So we're definitely beefing up resources.
Speaker #4: Yeah, sure. so yes, we're super excited about, what we call app modernization or legacy app modernization. You'll hear a lot more about this at investor day in September, Tyler.
Yes. Thank you.
Dave I was hoping you could talk about some of the kind of latest industry developments just on the technology side.
Speaker #4: But what I will say to you is that the value proposition is very clear. customers are very, very motivated to try and modernize these legacy systems for a wide variety of reasons.
In particular, I'm thinking about lake base from data bricks and document DP document DB in the Linux Foundation can you just comment on both those things.
Speaker #4: we, we are seeing a lot of progress. We've actually brought in a new leader, new product leader, who brings a lot of depth and scale, especially around AI, to help us build the tooling to leverage AI to really drive more automation in terms of how we analyze and refactor the code.
How they might impact Mongo DB and how you differentiate.
Yes so.
So let me tackle them one by one.
Clearly what we're seeing is that the strategic high ground for AI, especially when it comes to in for instance, <unk> TP. So we've talked about this on the last call where some companies that acquired early stage.
Speaker #4: we brought in a new leader. Last quarter, to help really help drive the delivery and the go-to-market efforts around, AppMod. So we're definitely beefing up resources, and, I would say that, we're investing a lot in product and, there's a lot more to do.
LTP.
Startups.
Dev Ittycheria: And I would say that we're investing a lot in product, and there's a lot more to do. And I would say this is something that we're very excited about, but it'll drive more of our longer-term growth. It won't be as pronounced in terms of this year, but we're very, very excited about the opportunity, and we definitely will spend more time discussing this and what we're actually doing on the product side in September.
And what it really spoke to and those companies have spoken about their organic efforts to build a new <unk> platform and I think what I spoke to was the fact that the building loyalty platform, that's ready and mission critical and enterprise can serve the most demanding requirements of enterprises is not trivial and I think they basically thrown the towel and decided to do these acquisitions.
Speaker #4: And I would say, this is something that we're very excited about, but it'll drive more of our longer-term growth, less it won't be as pronounced in terms of this year, but we're very, very excited about the opportunity and we're definitely will spend more time discussing this, and what we're actually doing on the product side, in September.
And what it just reinforces that all TPS strategic high ground for AI and we believe that if now customers are going to be choosing what OTT platform that they want for AI just given our architecture just given the fact that we have a durable architectural advantage in terms of Jason on support which addresses messy.
Analyst: Thank you.
Speaker #7: Thank you.
Carmen: Thank you. One moment for our next question. It comes from Jason Ader with William Blair. Please proceed.
Speaker #2: Thank you. One moment for our next question. It comes from Jason Ader with William Blair. Please proceed.
Dev Ittycheria: Yeah, thank you. Dave, I was hoping you could talk about some of the kind of latest industry developments just on the technology side. In particular, I'm thinking about LakeBase from Databricks and then DocumentDB in the Linux Foundation. Can you just comment on both those things and how they might impact MongoDB and how you differentiate? Yeah, so let me tackle them one by one. Clearly, what we are seeing is that the strategic high ground for AI, especially when it comes to inference, is OLTP. So we talked about this on the last call where some companies had acquired early-stage OLTP startups.
Speaker #8: Yeah, thank you. Dave, I was hoping you could talk about some of the latest industry developments just on the technology side. In particular, I'm thinking about Lakehouse from Databricks and then DocumentDB in the Linux Foundation.
Complicated and highly interdependent and constant changing data structures. The fact that we integrated search and Dr. Serge I think really helps us position going after AI with regards to your second question around the Linux Foundation I think what this really also suggests shows us that.
Speaker #8: Can you just comment on both those things and, you know, how they might impact MongoDB and how you differentiate?
Real Jason is much more important now with AI than ever before and the clones and bolt ons.
Speaker #4: Yeah, so a couple, so let me tackle them one by one. clearly what we are seeing is that the strategic high ground for AI, especially when it comes to inference, is OLTP.
That have traded off features and performance and develop experience have just not met customer expectations and candidly what I see this is that the hyperscale or <unk>.
Speaker #4: So we talked about this on on the last call where some companies that acquired early stage, OLTP, you know, startups, and what it really spoke to, and those companies had spoken about their organic efforts to build an OLTP platform, and I think what it spoke to was the fact that they building an OLTP platform that's ready and mission-critical and enterprise can serve the most demanding requirements of enterprises is not trivial.
Investing less in really handing off to the open source community too.
To kind of really take on the bulk of the work in terms of product development or hyperscale or partnerships remains strong.
Dev Ittycheria: And what it really spoke to, and those companies had spoken about their organic efforts to build an OLTP platform, and I think what it spoke to was the fact that building an OLTP platform that's ready and mission-critical and enterprise can serve the most demanding requirements of enterprises is not trivial. And I think they basically threw in the towel and decided to do these acquisitions. And what it just reinforces is that OLTP is the strategic high ground for AI.
And I think we have the right open source model, where we can balance the access to free software, while preserving the ability to both generate and capture value.
Great. Thank you and then just one quick follow up.
Speaker #4: And, I think they basically threw in the towel and decided to do these acquisitions. And, what it just reinforces is that, OLTP is the strategic high ground for AI, and we believe that if if now customers are going to be choosing what OLTP platform to that they want for AI, just given our architecture, just given the fact that we have a durable architectural advantage in terms of JSON support, which addresses messy complicated and highly interdependent and constantly changing data structures, the fact that we've integrated search and vector search, I think really helps us position going after AI.
Why don't we hear so much about post kras adoption for AI startups, you talked about the success you guys are having.
Dev Ittycheria: And we believe that if now customers are going to be choosing what OLTP platform they want for AI, just given our architecture, just given the fact that we have a durable architectural advantage in terms of JSON support, which addresses messy, complicated, and highly interdependent and constantly changing data structures, the fact that we integrated search and vector search, I think really helps us position going after AI. With regards to your second question around the Linux Foundation, I think what this really also shows is that, you know, real JSON is much more important now with AI than ever before. And the clones and bolt-ons that have traded off features and performance and developer experience have just not met customer expectations.
But post <unk> has the disadvantages that you've talked about.
Multiple times scalability, Jason on sport, how come we hear so much about that.
Kind of at least that the stages of AI.
Yes.
That's a good that's a really good question and I think it's important to understand and we spend a lot of time, we have now invested in the team in the Bay area that spends a lot of time with the star committed what's become clear as Landstar founders don't think that hard about their database choice. They kind of go with what they know and what we are seeing is that as some of these startups are scaling their run into <unk>.
Speaker #4: With regards to your second question around the Linux Foundation, I think what this really also suggests shows is that, you know, real JSON is much more important now with AI than ever before.
Speaker #4: And the clones and bolt-ons and, you know, that have traded off features and performance and developer experience have just not met customer expectations. And candidly, what I see this is that the hyperscalers are investing less and really handing off to the open-source community to, to kind of really take on the bulk of the, work in terms of product development, our hyperscaler partnerships remain strong, and, I think we have the right open-source model where we can balance the access to free software while preserving the ability to build, generate, and capture value.
Scaling challenges challenges with post close.
And what we've talked about in the past like when you add adjacent will use Jason B on post <unk> two kilobyte document.
Dev Ittycheria: And candidly, what I see this is that the hyperscalers are investing less and really handing off to the open-source community to kind of really take on the bulk of the work in terms of product development. Our hyperscaler partnerships remain strong. And I think we have the right open-source model where we can balance the access to free software while preserving the ability to both generate and capture value.
Bigger starts really creating performance problems because post growth has to do something called off ROE storage, which creates enormous performance overheads and so.
Developers need a platform that can handle structured semi structured unstructured data.
They need a platform that performs well.
And they need a platform that can scale as they grow and what we are hearing clearly from the startup communities that post stress in many cases is not scaling for them and they are now coming to us and so we feel really good about our position, but the reality is that a lot of these AD founders kind of start with what they know what they are used in the past and only.
Analyst: Great, thank you. And then just one quick follow-up. Why do we hear so much about Postgres adoption for AI startups? You talked about the success you guys are having. But if Postgres has the disadvantages that you've talked about, you know, multiple times, scalability, JSON support, how come we hear so much about that, you know, kind of at least in the early stages of AI?
Speaker #8: Great, thank you. And then just one quick follow-up, why do we hear so much about Postgres adoption for AI startups? You talked about the success you guys are having.
Speaker #8: but if Postgres has the disadvantages that you've talked about, you know, multiple times, scalability, JSON support, how come we hear so much about that?
When the business are scaling to this start recognizing the challenges and we realized we need to do more developer education and do more work and so we're investing a lot in the startup community, but running a big event in October in San Francisco with a big Hackathon, and we are inviting a lots of customers to participate but that's just the start of a meaningful investment we're making in the bay area and the.
Speaker #8: you know, kind of at least in the early stages of AI.
Dev Ittycheria: Yeah, that's a really good question. And I think it's important to understand. And we spend a lot of time, we have now invested in a team in the Bay Area that spends a lot of time with the startup community. What's become clear is a lot of these startup founders don't think that hard about their database choice. They kind of go with what they know. And what we are seeing is that as some of these startups are scaling, they're running into real scaling challenges with Postgres. And we've talked about this in the past. Like when you add a JSON, when you use JSON-B on Postgres, a two-kilobyte document or bigger starts really creating performance problems because Postgres has to do something called off-row storage, which creates enormous performance overheads. And so the developers need a platform that can handle structured, semi-structured, unstructured data.
Speaker #4: Yeah, that's a that's a that's a good, that's a really good question. And I think it's important to understand, and we spend a lot of time, we have now invested in a team in the Bay Area that spends a lot of time with the startup community.
Speaker #4: What's become clear is that a lot of these startup founders don't think that hard about their database choice. They kind of go with what they know.
Startup community too.
We think their decisions around just going with what they know.
Speaker #4: And what we are seeing is that as some of these startups are scaling, they're running to real scaling challenges with Postgres. And what, you know, and we've talked about this in the past, like when you add a JSON, when you use JSON B on Postgres, 2 kilobyte document or or or bigger starts really creating performance problems because Postgres has to do something called off-row storage, which creates enormous performance overheads.
Thank you.
Thank you.
One moment for our next question that comes from Mike <unk> with Needham. Please proceed.
Hey, Thanks for taking the questions guys I just wanted to come back to Atlas, specifically and Mike Appreciate last quarter, you gave us some very granular color around the Atlas trends was hoping to get an update on how Atlas trends played out this quarter.
Speaker #4: And so the, you know, developers need a platform that can handle, structured, semi-structured, unstructured data, and they need obviously a platform that performs well.
Or just at the very least why why we didn't see such a broad based strength from large customers. This quarter. Thank you.
Dev Ittycheria: They need obviously a platform that performs well. And they need a platform that can scale as they grow. And what we're hearing clearly from the startup community is that Postgres, in many cases, is not scaling for them, and they're now coming to us. And so we feel really good about our position, but the reality is that a lot of these AI founders kind of starve with what they know or what they've used in the past. And only when the business starts scaling do they start recognizing the challenges. And we realize we need to do more developer education and do more work. And so we're investing a lot in the startup community. We're running a big event in October in San Francisco with a big hackathon, and we're inviting lots of customers to participate.
Speaker #4: and they need a platform that can, scale as they grow. And what we're hearing clearly from the startup community is that Postgres in many cases is not scaling for them, and they're now coming to us.
Sure. Thanks for the question, Mike So when we talk about consumption in the second quarter for Atlas as we've talked about it perform well grew 29% year over year as we talked about might the consumption growth were relatively consistent with last year and as we talked about on the last call. We started out with a strong may and we saw broad based strength across.
Speaker #4: And so we feel really good about our position, but the reality is that a lot of, you know, these AI founders kind of start with what they know, or what they've used in the past, and only when the business starts scaling do they start recognizing the challenges.
Most of the Geos and segments, so nothing to call out there, but we did see notable strength in our larger customers in the U S and if we dive deeper on that one as Dave talked about we are seeing similar some workloads from our larger customers grow for longer and expand more than we havent seen in the past so that's good.
Speaker #4: And we realize we need to do more developer education and do more work, and so we're investing a lot in the startup community. We're running a big event in October in San Francisco with a big hackathon, and we're inviting lots of customers to participate.
Dev Ittycheria: But that's just the start of a meaningful investment we're making in the Bay Area and the AI startup community to rethink their decisions around just going with what they know.
Speaker #4: But that's just to start of a meaningful investment we're making in the Bay Area and the AI startup community to, rethink their decisions around just going with what they know.
While there is many moving parts in the consumption of business. We also expect that there is benefit from our go to market changes and given the preponderance of our strategic accounts being in the U S. No no surprise that we saw that gross growth mostly in the U S. And then lastly, Mike there is some benefit from comparing it to a little slower growth in Q.
Analyst: Thank you.
Speaker #8: Thank you.
Carmen: Thank you. One moment for our next question. That comes from Mike Sykos with Needham. Please proceed.
Speaker #2: Thank you. One moment for our next question. That comes from Mike Cycles, with Needham. Please proceed.
Analyst: Hey, thanks for taking the questions, guys. I just wanted to come back to Atlas specifically. And Mike, I appreciate last quarter you gave us some very granular color around Atlas trends. I was hoping we could get an update on how Atlas trends played out this quarter, or just at the very least, why we did see such broad-based strength from large customers this quarter. Thank you.
Speaker #7: Hey, thanks for taking the questions, guys. I just wanted to come back to Atlas specifically. And Mike, I appreciate last quarter you gave us some some very granular color around Atlas trends.
One so that would be the detail on Q2 as it relates to consumption growth.
Thank you for that and if I could just squeeze maybe one more in on the the outperformance that we saw this quarter from the multi year deals maybe I'm just misunderstanding here, but.
Speaker #7: I was hoping we could get an update on how Atlas trends played out this quarter. Or, just at the very least, why why we did see such broad-based strength from large customers this quarter.
My assumption was the reason we were facing this outperformance was really tied to the fact that in prior years, we've had some pretty big deals on the multiyear upfront.
Speaker #7: Thank you.
Michael Berry: Sure. Thanks for the question, Mike. So when we talk about consumption in the second quarter for Atlas, as we talked about, it performed well, grew 29% year over year. As we talked about, Mike, the consumption growth were relatively consistent with last year. And as we talked about on the last call, we started out with a strong May, and we saw broad-based strength across most of the geos and segments. So nothing to call out there. But we did see notable strength in the larger customers in the US. And if we dive deeper on that one, as Dave talked about, we are seeing some workloads from our larger customers grow for longer and expand more than we have seen in the past. So that's good. While there's many moving parts in the consumption business, we also expect that there is benefit from our go-to-market changes.
Speaker #5: Sure, thanks for the question, Mike. So when we talk about consumption in the second quarter for Atlas, as we talked about, it performed well grew 29% year over year.
And so to see some of these deals coming this year is that a function of customers renewing earlier, which is helping you fill that at larger David that we previously expected to demonstrate our assumption or can you can you help me think through that a little bit more.
Speaker #5: As we talked about, Mike, the consumption growth were relatively consistent with last year. And as we talked about on the last call, we started out with a strong May, and we saw a broad-based strength across most of the geos and segments, so nothing to call out there.
So thanks for the golf analogy no. It did not fill the debit. So in Q2. It was really it was good underlying strength in AOR growth and then greater than expected multiyear there were really no pull forwards Mike and.
Speaker #5: But we did see notable strength in the larger customers in the US. And if we dive deeper on that one, as Dave talked about, we are seeing some workloads from our larger customers grow for longer, and expand more than we haven't seen in the past, so that's good.
This is a hard business to forecast because sometimes even customers don't know whether theyre going up for an annual renewal of our multi year. So it was there was no pull forwards and there was nothing out of the ordinary very importantly, we left the net the non Atlas assumptions consistent with our last guidance hence.
Speaker #5: While there's many moving parts in the consumption business, we also expect that there is benefit from our go-to-market changes, and given the preponderance of our strategic accounts being in the US, no no surprise that we saw that growth mostly in the US.
Michael Berry: And given the preponderance of our strategic accounts being in the US, no surprise that we saw that growth mostly in the US. And then lastly, Mike, there is some benefit from comparing it to a little slower growth in Q1. So that would be the detail on Q2 as it relates to consumption growth.
Speaker #5: And then lastly, Mike, there is some benefit from, comparing it to a little slower growth in Q1. So that would be the detail on Q2.
Pulling down the multiyear headwind from 50 to 40 and again nothing to call out on Q2, no no pull forwards and there were really no large multi years in there. It was just crossed a good subset of customers.
Speaker #5: as it relates to consumption growth.
Analyst: Thank you for that. And if I could just squeeze maybe one more in on the outperformance that we saw this quarter from the multi-year deals. And maybe I'm just misunderstanding here, but my assumption was the reason we were facing this outperformance was really tied to the fact that in prior years, we've had some pretty big deals on the multi-year front. And so to see some of these deals come in this year, is that a function of customers renewing earlier, which is helping fill that larger divot that we previously expected? Is that a fair assumption? Or can you help me think through that a little bit more? Thank you.
Speaker #4: Thank you for that. And if I could just squeeze maybe one more in on the the outperformance that we saw this quarter from the multi-year deals, and maybe I'm just misunderstanding here, but I I my assumption was the reason we were facing this outperformance was really tied to the fact that in prior years we've had some some pretty big deals on the multi-year front.
Thank you again.
Yes.
Thank you. Our next question comes from the line of Alex Zukin with Wolfe Research. Please proceed.
Hey, guys. Thanks for squeezing me in and I'll Echo the congrats on.
Speaker #4: And so, to see some of these deals come in this year, is that a function of customers renewing earlier, which is helping fill that larger divot that we previously expected?
True.
Truly amazing quarter.
I guess, David when you think about the AI comments that you've talked about both in the press release and in the call maybe just a little bit more nuance and the use cases not necessarily that you are seeing kind of contribute materially today, but the differentiation of the platform.
Speaker #4: Is that a fair assumption, or can you help me think through that a little bit more? Thank you.
Michael Berry: So thanks for the golf analogy. No, it did not fill in the divot. So in Q2, it was really, it was good underlying strength in ARR growth and then greater than expected multi-year. There were really no pull forwards, Mike. And this is a hard business to forecast because sometimes even customers don't know whether they're going to opt for an annual renewal or a multi-year. So there were no pull forwards, and there was nothing out of the ordinary. Very importantly, we left the non-Atlas assumptions consistent with our last guidance, hence pulling down the multi-year headwind from 50 to 40. And again, nothing to call out on Q2. No pull forwards. And there were really no large multi-years in there. It was just across a good subset of customers.
Speaker #5: So thanks for the golf analogy. No, it did not fill the divot. So, in Q2, it was really good underlying strength in ARR growth, and then greater than expected multi-year.
We're able to incrementally take market share as it becomes available both in net new kind of AI native companies, but also in some of your larger existing companies or customers that are starting to modernize for this kind of conversational AI native era, where are you.
Speaker #5: There were really no pull-forwards, Mike. And this is a hard business to forecast because sometimes even customers don't know whether they're going to opt for an annual renewal or a multi-year.
Speaker #5: So, there were no pull-forwards, and there was nothing out of the ordinary. Very importantly, we left the net non-Atlas assumptions consistent with our last guidance.
Seeing the most momentum in terms of workload construction and scale and when do you. When do you think we should expect to.
Speaker #5: Hence, pulling down the multi-year headwind from 50 to 40. And again, nothing to call out on Q2, no pull-forwards, and there were really no large multi-years in there.
Actually start seeing that contribute more materially to the growth and consumption.
Yes. So thanks for the question Alex a couple of points again, we're very pleased with the results of this quarter, but I would say the AI cohort was not a material driver of the growth that being said what we are seeing is a lot of customers very very interest in architecture. Let me again walk through why one where adjacent on database Jay.
Speaker #5: It was just across a good subset of customers.
Analyst: Thank you again.
Speaker #4: Thank you again.
Michael Berry: Yep.
Speaker #5: Yep.
Carmen: Thank you. Our next question comes from the line of Alex Zukin with Wolf Research. Please proceed.
Speaker #2: Thank you. Our next question comes from the line of Alex Sukin with Wolf Research. Please proceed.
<unk> is the best way to express and model, the complicated and messy and highly interdependent and costly evolving data structures that you have to deal with in the real world. So that's point number one so it's much easier to do that in <unk> and to do that on some <unk>.
Analyst: Hey, guys, thanks for squeezing me in. And I'll echo the congrats on a truly, truly amazing quarter. I guess, Dave, when you think about the AI comments that you've talked about, both in the press release and in the call, maybe just a little bit more nuance on the use cases, not necessarily that you're seeing kind of contribute materially today, but the differentiation in the platform that you're able to.Incrementally
Speaker #7: Yes, thanks for squeezing me in and, and I'll echo the congrats, on truly, truly amazing quarter. I I guess, Dave, when you think about the AI comments that you've talked about, both in the press release and in the call, maybe just a little bit more nuance in the use cases, not necessarily that you're seeing kind of contribute materially today, but the differentiation in the platform, that you're able to incrementally take market share as it becomes available both in net new kind of AI native companies, but also in some of your larger existing companies, or customers that are starting to modernize for this kind of conversational or AI native era.
Kind of setup on top of a relational database.
<unk> is that we integrate search and vector search. So you can do very sophisticated things to what people call hybrid search and retrieval you can do very sophisticated things and finding information quickly, which is a very unique differentiator for us. So what this means that rather than stitching together multiple systems. You can do this all among <unk> so it becomes less complexity.
Speaker 1: take market share, as it becomes available both in net new kind of AI-native companies, but also in some of your larger existing companies, or customers that are starting to modernize for this kind of conversational or AI-native era. Where are you seeing the most momentum in terms of workload, construction and scale? And when do you, when do you think we should expect to kind of actually start seeing that contribute more materially to the growth, in consumption?
<unk> and lower costs. The third thing is that we've now embedded voyage models on our platform right. So.
Speaker #7: Where are you seeing the most momentum in terms of workload, construction, and scale? And when do you think we should expect to actually start seeing that contribute more materially to the growth in consumption?
If you control the embedding layer you sit at the gateway of meeting of AI right. What the embedding miles do is really our bridge between a companys private data and the LLM, so that becomes really important because the better.
Carmen: Yeah, so, so thanks for the question, Alex. a couple of points. Again, you know, we're really pleased with the results of this quarter, but I would say the AI cohort, you know, was not a material driver of the growth. That being said, what we are seeing is a lot of customers very, very interested in our architecture. And let me again walk through why. You know, one, we're a JSON database. JSON is the best way to express and model the complicated and messy and highly interdependent and constantly evolving data structures that you have to deal with in the real world. So that's point number one. So it's much easier to do that on MongoDB than to do that on some kludgy, you know, kind of setup on top of a relational database.
Speaker #4: Yeah, so thanks for the question, Alex. A couple of points. Again, you know, we're really pleased with the results of this quarter, but I would say the AI cohort, you know, was not a material driver of the growth.
Quality of embedding model the better the quality of the signal of your own data. So that reduces things like hallucinations are just bad outputs and so customers are now as people start carrying more and more about like high higher stake use cases, they really want to ensure this outputs are high and the fact that as part of our platform we can enable.
Speaker #4: That being said, what we are seeing is a lot of customers very, very interested in our architecture. And let me again walk through why.
For you to do auto embedding it becomes an incredibly <unk>.
Selling feature in terms of the market, but I would say is that the.
The enterprise uptake of AI is still early.
I've said this for a couple of years now and I think a lot of people were a little skeptical about a service proving to be true as we predicted.
Carmen: Second is that we integrate search and vector search, so you can do very sophisticated things to what people call hybrid search and retrieval. You can do very sophisticated things in finding information quickly, which is a very unique differentiator for us. So what this means is that rather than stitching together multiple systems, you can do this all on MongoDB, so it becomes less complexity and lower cost. The third thing is that we've now embedded voice models on our platform, right? So the, you know, if you control the embedding layer, you sit at the gateway of meaning, of AI, right? What the embedding models do is really a bridge between a company's private data and the LLM. So that becomes really important because the better the quality of the embedding model, the better the quality of the signal of your own data.
The lack of skills and the lack of trust with AI systems is kind of slowing.
<unk> been very cautious about deploying out where it is being deployed is really on end user productivity, whether its developers with co gen tools, our business users using tools to summarize documents.
Extract data or things like deflecting tickets from people to two systems with like conversational AI.
I think you are starting to see the first steps in people deploying agent based systems.
And I can talk a little bit about that but that's that's still very very early.
Small <unk> some of them are taking off we're really driving most of the impact, but the real enduring value will come when you talk to a customer today most of them. We ask them is really transforming a business. They will say no. Yes, we're seeing some productivity gains here and there, but it's not really transforming that business I think the real enduring value will come when they build customer.
Carmen: So that reduces things like hallucinations or just bad outputs. And so customers are now, as people start caring more and more about like, you know, higher stakes use cases, they really want to ensure those outputs are high. And the fact that it's part of our platform, we can enable you to do auto-embeddings. It becomes an incredibly, you know, compelling feature. In terms of the market, what I would say is that, you know, the enterprise uptake of AI is still early. I've said this for a couple of years now, and I think a lot of people were a little skeptical of what I said, but it's proving to be true. As we predicted, like, you know, the lack of skills and the lack of trust with AI systems is kind of slowing, you know, people are being very cautious about deploying AI.
Solutions that.
Truly transform their business, whether it's a drive.
New revenue opportunities or dramatically reduce their existing cost structure, but we're really pleased I mentioned this electric car company Thats very tech savvy, that's using modern DB I should.
And one of the fastest growing startups in the Bay area has has that big Unmonitored Dev.
Dev Rev. The company going after that helped US space has built their own agenda platform. Among TB. So we feel really good about.
What this all portends for the future, but as I said it was a small part of our growth this quarter.
Carmen: Where it is being deployed is really on end-user productivity, whether it's developers with code gen tools or business users using tools to summarize documents, to extract data, or things like deflecting tickets from people to, you know, systems with like conversational AI. I think you are starting to see the first steps in people deploying agent-based systems. and I can talk a little bit about that, but that's still very, very early. we're seeing small ISVs, some of them are taking off, who are really driving most of the impact. But the real enduring value will come. You know, when you talk to a customer today, most of them, when you ask them, is AI really transforming your business? They'll say no. Yes, we're seeing some productivity gains here and there, but it's not really transforming my business.
Very helpful. And then maybe if I could just sneak one in for Mike you've been kind.
Kind of saying from I think the first Steve started about how the margin profile of this business, it's not and it's.
It's not an or it's an and and its clearly coming through both the growth acceleration, but also the meaningful margin outperformance as you think about sustaining this kind of accelerating pace.
And investing in things like the Bayer's startup community, how are you finding that balance that and versus or <unk>.
Balance that quite frankly is elusive to a lot of companies.
That are doing what you guys are doing.
Well I think thats the final part of my job quite frankly, so I would give kudos to not only the management team, but everybody of Mongo DB to really jump in if I think that this has been a companywide effort and as we look forward and as we talked about Alex the number one driver of margin expansion for Mongo as their revenue.
Carmen: I think the real enduring value will come when they build custom AI solutions that truly transform their business, whether to drive, you know, new revenue opportunities or dramatically reduce their existing cost structure. But we're really pleased. We, you know, I mentioned this electric car company that's very tech-savvy, that's using MongoDB. I should mention one of the fastest growing startups in the Bay Area has bet big on MongoDB. DevRev, the company going after the help desk space, has built their own agentic platform on MongoDB. So we feel really good about, you know, what this all portends for the future. But as I said, it was a small part of our growth this quarter.
Growth. So those two are directly connected it's a great business model, where we can grow Atlas in the 20% plus range and then keep that VA in that single digit it generates a ton of gross profit that funds a lot and the team has done a really has done a great job of making sure that we have.
<unk>, so we feel really good about.
What this all portend for the future, but as I said it was a small part of our growth this quarter.
Speaker 1: Very helpful. And then maybe if I could just sneak one in for Mike, you know, you've been, kind of saying from, from I think the first day you started about how the the margin profile of this business, it's not an, it's not an or, it's an and, and it's clearly coming through in both the growth acceleration, but also the meaningful margin outperformance. As you think about sustaining this kind of accelerating pace, and investing in things like the, you know, the Bay Area startup community, how are you finding, that balance, that and versus or, balance that quite frankly is elusive to a lot of companies that, that are doing what you guys are doing?
We're investing in growth that we go back and look at what we're doing making sure that it's driving growth. If it's not then we have an open discussion about whether we should reallocate. So I felt good about it when I started I candidly I feel better about it 90 days later.
Very helpful. And then maybe if I could just sneak one in for Mike you've been kind.
Kind of saying from I think the first Steve started about how the margin profile of this business, it's not and it's.
It's not an or it's an and and it's clearly coming through in both the growth acceleration, but also the meaningful margin outperformance as you think about sustaining this kind of accelerating pace.
Excellent. Thank you guys congrats again.
Alright, Thank you Alex.
Thank you. Our next question comes from Kash Rangan with Goldman Sachs. Please proceed.
Investing in things like the bare startup community, how are you finding that balance that and versus or Bally.
It's always tough to go after Alex because you have such a good questions, but that's not going to stop me.
So David and Mike Congratulations on the quarter.
Balance that.
Frankly, as elusive to a lot of companies that.
It's Super interesting you were talking about how some of the Silicon Valley.
That are doing what you guys are doing.
Brian Denyeau: Well, I think it's the funnest part of my job, quite frankly. So, I would give kudos to not only the management team, but everybody at MongoDB to really jump in this. I think that this has been a company-wide effort. And as we look forward, and as we talked about, Alex, the number one driver of margin expansion for Mongo is the revenue growth. So those two are directly connected. It's a great business model where when we can grow Atlas in the 20% plus range and then keep that ARR or VA in that single digit, it generates a ton of gross profit that funds a lot. And the team has done a really, has done a great job of making sure that we are investing in growth, that we go back and look at what we're doing, making sure that it's driving growth.
Startup founders don't have time to think about database, but goodfriend.
Well I think that's the finance part of my job quite frankly so.
I'd give kudos to not only the management team, but everybody of Mongo DB to really jump in if I think that this has been a companywide effort and as we look forward and as we talked about Alex the number one driver of margin expansion for Mongo as the revenue growth. So those two are directly connected it's a great business model.
<unk> seems to have made a wise choice here.
As you.
Set in camp went up in the Bay area and start to evangelize the need for.
Atlas consumption.
AI savvy database, how do you reconcile the type of the fact that enterprise is where we really saw that the bread and butter value proposition our model.
Whereas when we can grow Atlas in the 20% plus range and then keep that VA in that single digit it generates a ton of gross profit that funds a lot and the team has done a really has done a great job of making sure that we are investing in growth that we go back and look at what we're doing making sure.
Resonate so.
What is happening with Debra D, a leading indication of what's going to happen in the enterprise.
Because we have much to your observation not seen much of a productivity impact from the enterprise because of AI the business level and so what could be the unlock what about.
Brian Denyeau: If it's not, then we have an open discussion about whether we should reallocate. So I felt good about it when I started. Candidly, I feel better about it 90 days later.
That it's driving growth if it's not then we have an open discussion about whether we should reallocate. So I felt good about it when I started I candidly I feel better about it 90 days later.
Folks like Peter was doing correctly.
Could be a precursor if it is what is to come in the oil price.
Yes, Kash thanks for the question.
Speaker 1: Excellent. Thank you guys. Congrats again.
Excellent. Thank you guys congrats again.
Obviously, you have so much risk.
Brian Denyeau: Thanks.
Carmen: Thank you, Alex.
With respect to <unk> as he built <unk> into a really great business and he is going to do the same at that Brad I will tell you that the AI cohort as I said earlier is that was not really material to our growth. So I think these are all customers kind of earlier in their journey. So what.
Alright, Thank you Alex.
Dev Ittycheria: Thank you. Our next question comes from Kash Reagan with Goldman Sachs. Please proceed.
Thank you. Our next question comes from Kash Rangan with Goldman Sachs. Please proceed.
Carmen: Wow. It's always tough to go after Alex because he asks such good questions, but that's not going to stop me. so Dave and Mike, congratulations on the quarter. You know, it's super interesting. You were talking about how some of the Silicon Valley AI startup founders don't have time to think about databases, but our good friend Diraj at DevRev seems to have made a wise choice here. So, as you, set encampment up in the Bay Area and start to evangelize the need for a, Atlas consumption AI-savvy database, how do you reconcile that with the fact that at the same time, the enterprise is where we really saw that the bread and butter value proposition of Mongo resonates? So, could, could what is happening with DevRev be a leading indication of what's going to happen in the enterprise?
It's always tough to go after Alex because you have such a good questions, but that's not going to stop me.
What we are seeing what's driving the growth.
So David and Mike Congratulations on the quarter.
Right now as these.
It's Super interesting you were talking about how some of the Silicon Valley.
Large enterprises with workloads that we acquired both last year and this year that are really driving the growth.
Startup founders don't have time to think about database, but a good friend.
<unk> seems to have made a wise choice here so as you.
Especially the Atlas gross that we saw this quarter and what that really confirms is that our move up market made sense. The quality of those workloads. The durability of the growth they've become grow for growth for longer and become bigger than what we've seen in the past is really making us feel good about that decision and come in.
Set in camp went up in the Bay area and start to evangelize the need for a atlas consumption AI.
Hi, Savi database, how do you reconcile the type of the fact that <unk> enterprise is where we really saw that the bread and butter value proposition of Mongo.
And to juxtapose that we also obviously decided to double down on self serve to better serve the.
Isn't it so.
What is happening with Debra D, a leading indication of what's going to happen in the enterprise.
The small and medium sized business market and Thats also become.
Carmen: because we've all, much to your observation, not seen much of a productivity impact from the enterprise because of AI at the business level. And so what could be that unlock is what are the, what are folks like Diraj doing correctly that is a, could be a precursor, if it is, for what is to come in the enterprise?
Obviously, becoming more and more effective and it gets just given the number of customers that we've added over the last six months. So we feel like those motions are working well in concert together.
Because we have much to your observation not seen much of a productivity impact from the enterprise because of AI the business level.
And so what could be the unlock is one of them.
And we feel like this allows us to be much more efficient about how we go to market and is also going to be continued more work to continue to drive that efficiency, even better but we also are investing for the long term and so we're just constantly.
Folks like theaters doing correctly, but is it could be a precursor if it is.
What is to come in the oil price.
Brian Denyeau: Yeah, so Kash, thanks for the question. you know, obviously I have so much respect for Diraj. He built Nutanix into a real great business, and he's going to do the same at DevRev. I will tell you that the AI cohort, as I said earlier, is, you know, was not really material to our growth. So I think, you know, these are all customers kind of earlier in their journey. So I, you know, what we are seeing, what's driving the growth, right now is these, you know, large enterprises with workloads that we acquired both last year and this year that are really driving the growth, especially the Atlas growth that we saw this quarter. And what that really confirms is that our move-up market made sense.
Yes, Kash thanks for the question.
<unk> so much.
Respectful Dara has he built <unk> into a really great business and he is going to do the same at <unk> I will tell you that the AI cohort as I said earlier is not really material to our growth. So I think these are all customers kind of earlier in their journey. So.
Debating those decisions internally, but we feel good about whats working and we feel good that like someone like a <unk>.
As betting early on are all going to be because that's a good signal for other founders were thinking about doing the same.
Awesome well drilled into this more in a couple of weeks when you when we see you in San Francisco absolutely.
What we are seeing what's driving the growth.
Right now as these.
Large enterprises with workloads that we acquired both last year and this year that are really driving the growth, especially.
Thank you one moment for our next question.
Especially the Atlas gross that we saw this quarter and what that really confirms is that our move up market made sense. The quality of those workflows. The durability of the growth they've become grow for growth for longer and become bigger than what we've seen in the past is really making us feel good about that decision and <unk> and.
Is Brad Reback with Stifel. Please proceed.
Brian Denyeau: The quality of those workloads, the durability of their growth, they become, you know, grow for grow for longer and become bigger than what we've seen in the past is really making us feel good about that decision. And conjunctive was that we also obviously decided to double down on self-serve to better serve the, the small and medium-sized business market. And that's also become, you know, obviously becoming more and more effective. And it gets just given the number of customers that we've added over the last six months. So we feel like those motions are working well in concert together. And, and we feel like this allows us to, you know, be much more efficient about how we go to market. And there's also going to be continued more work to, you know, continue to, drive that efficiency even better.
Thanks very much.
The 7% AOR growth seems fine im assuming youre not satisfied with single digit growth there Dave.
David any sense of.
And to juxtapose that we also obviously decided to double down on self serve to better serve the.
Where we should think about that longer term. Thanks.
Clearly <unk>.
The small and medium sized business market and Thats also become you know.
As a large enterprise motion and what we've seen is that it's typically.
Obviously, becoming more and more effective and it gets just given the number of customers that we've added over the last six months. So we feel like those motions are working well in concert together.
Less new customers <unk>, and it's more of our existing customer base, who have a mix of year end and sometimes.
And then also start deploying Atlas.
And we feel like this allows us to be much more efficient about how we go to market and is also going to be continuing more work to continue to drive that efficiency, even better but we also are investing for the long term and so we're just constantly.
I think.
One thing.
<unk>, that's becoming more and more clear is that customers are becoming much more thoughtful about like how to think about using <unk>.
Brian Denyeau: But we also are investing for the long term. And so we're just constantly, you know, you know, debating those decisions internally, but we feel good about what's working. And we feel good that like someone like a Diraj is, you know, is betting early on MongoDB because that's a good signal for other founders who are thinking about doing the same.
Deployments on premise versus using the cloud I think four or five years ago. There was a belief that everything was going to move to cloud I think large enterprises have become much more sophisticated and nuanced in their thinking and they believe that some workloads make sense to run on Prem in some workloads make sense to run in the cloud and I think that's where the <unk> story becomes really attractive because the same code base can be.
Debating those decisions internally, but we feel good about whats working and we feel good that like someone like a <unk> is.
As betting early on are all going to be because that's a good signal for other founders were thinking about doing the same.
Carmen: Awesome. We'll drill into this more in a couple of weeks when you, when we see you in San Francisco.
Awesome well drilled into this more in a couple of weeks when you when we see you in San Francisco absolutely.
Used and so it also gives them optionality for the future where they can move from on Prem to the cloud and a lot of our customers have done that either with new workloads and some existing workloads and then they can also move from cloud to cloud and.
Brian Denyeau: Absolutely.
Dev Ittycheria: Thank you. One moment for our next question. Is Brad Rehbach with Steefel? Please proceed.
Thank you one moment for our next question.
They can also move back to on premise states choose to do so so that optionality becomes a very powerful.
Is Brad Reback with Stifel. Please proceed.
Michael Berry: Great. Thanks very much. The 7% EA ARR growth seems fine. I'm assuming you're not satisfied with single-digit growth there. Dave, any sense of where we should think about that longer term? Thanks.
Thanks very much.
Our value proposition for our customers.
The 7% EMEA growth seems fine im assuming youre not satisfied with single digit growth there Dave.
Great. Thank you very much.
Thank you Brad.
Thank you. Our next question is from the line of <unk> Kidron with Oppenheimer. Please proceed.
David any sense of.
Where we should think about that longer term. Thanks.
Carmen: You know, clearly, EA is a large enterprise motion. And what we've seen is that it's typically, you know, less new customers choose EA, and it's more of our existing customer base who have a mix of EA, and then sometimes they, they, they then also start deploying Atlas. I think, one, thing that's becoming more and more clear is that customers are becoming much more thoughtful about like how to think about using, you know, deployments on-premise versus using the cloud. I think four or five years ago, there was a belief that everything was going to move to the cloud. I think, large enterprises have become much more sophisticated and nuanced in their thinking, and they believe that some workloads make sense to run on-prem, and some workloads make sense to run in the cloud.
Clearly <unk>.
Okay.
Thanks.
As a large enterprise motion and what we've seen is that it's typically.
Okay, great numbers, and congrats to Jeff and good luck in your new role.
Less new customers <unk>, and it's more of our existing customer base, who have a mix of <unk> and sometimes it's big.
Dave I wanted to dig into there.
AI opportunity again, but take it from a perspective of our go to market motion.
But then also start deploying Atlas.
Clearly you can power a lot of AI use cases that are embedded with bigger platforms through a self serve motion, but it sounded like to really capture.
I think one thing.
I think thats, becoming more and more clear as our customers are becoming much more thoughtful about like how to think about using <unk>.
The big workload opportunities, it's going to have to be more of an enterprise poorer so I'm kind of wondering.
Deployments on premise versus using the cloud I think four or five years ago. There is a belief that everything was going to move to the cloud I think large enterprises have become much more sophisticated and nuanced in their thinking and they believe that some workloads make sense to run on Prem in some workloads make sense to run in the cloud and I think that's where the <unk> story becomes really attractive because the same code base can be.
How do you think about targeting.
AI opportunity from go to market motion does that doesn't just fall into which are big enterprise I'm going to send you to an enterprise salesperson and all the risk coal and our self serve and do it yourself.
Carmen: And I think that's where the MongoDB story becomes really attractive because the same code base can be used. And so it also gives them optionality for the future where they can move from on-prem to the cloud. And a lot of our EA customers have done that either with new workloads and some existing workloads. And then, they can also move from cloud to cloud. and they can also move back to on-prem if they choose to do so. So that optionality becomes a very powerful, value proposition for our customers.
Is it something a little bit more do you think.
Used and so it also gives them optionality for the future where they can move from on Prem to the cloud and a lot of our customers have done that either with new workloads and some existing workloads and then they can also move from cloud to cloud and.
Perhaps but do you need to take care in order to capitalize on this opportunity.
What I would say <unk> is that we've seen this movie before with the cloud, whereas some early stage customers started growing very very quickly and then we.
They can also move back to on premise they choose to do so so that optionality becomes a very powerful value proposition for our customers.
We then put dedicated sales.
Focus on those accounts and they grew than even faster. So we're clearly watching the market and win selsor customers are to a point where.
Michael Berry: Great. Thank you very much.
Great. Thank you very much.
Carmen: Thank you, Brad.
They really need a higher touch.
Thank you Brad.
Dev Ittycheria: Thank you. Our next question is from the line of Itai Kidron with Oppenheimer. Please proceed.
Thank you. Our next question is from the line of <unk> Kidron with Oppenheimer. Please proceed.
Kind of engagement model, then we're more than happy to do that and we have a team that kind of helps transition customers from self serve to more of a direct sales approach and that has worked for US I think what we've learned is that that deadline by which we actually engage a high touch model that can move higher because we've become so sophisticated with self serve that we can really.
Analyst: Thanks. I think great numbers and congrats to Jess and good luck in your new role. Dev, I wanted to dig into the AI opportunity again, but take it from the perspective of a go-to-market motion. Clearly, you can power a lot of AI use cases that are embedded with bigger platforms through a self-serve motion, but it sounds like to really capture the big workload opportunities, it's going to have to be more of an enterprise push. So I'm kind of wondering, how do you think about targeting the AI opportunity from go-to-market motion? Does that, that doesn't just fall into, if you're a big enterprise, I'm going to send you to an enterprise salesperson and all the rest called our self-serve and do it yourself.
Thanks.
Okay, great numbers in <unk>.
<unk> suggests and good luck in your new role.
Dave I wanted to dig into the AI opportunity again, but take it from a perspective of our go to market motion.
Served customers for early stage customers for a long period of time in terms of the enterprise what I'd say is what I've said earlier is that the enterprise is still quite early in their journey to AI. Most of the investments right now are more on end user productivity like developers using co gen tools.
Clearly you can power a lot of AI use cases that are embedded with bigger platforms through a self serve motion, but it sounded like to really capture.
The peak workload opportunities, it's going to have to be more of an enterprise poorer. So I'm kind of wondering how do you think about targeting the AI opportunity from go to market motion does that doesn't just fall into if youre, a big enterprise I'm going to send you to an enterprise sales person and all the rest called our self serve and do it yourself.
And.
What I call low stakes use cases.
In fact, I had two meetings today with two different leaders of two different financial institutions here in New York.
Analyst: is there something a little bit more you think, a target perhaps that you need to take here in order to capitalize on this opportunity?
And they both talked about what theyre doing and AI that both admitted that they've kind of started with low stakes use cases, but their appetite to start doing more is increasing as we get more and more comfortable with the technology.
Just something a little bit more do you think.
Perhaps but do you need to take care in order to capitalize on this opportunity.
Carmen: Yeah, what I would say, Itai, is that, you know, we've seen this movie before with the cloud where some early-stage customers started growing very, very quickly. And then we just, we then put, you know, dedicated sales, you know, focus on those accounts and they grew then even faster. So we're clearly watching the market. And when self-serve customers are to a point where, you know, they really need a higher touch, kind of engagement model, then we're more than happy to do that. And we have a team that kind of helps transition customers from self-serve to, more of a direct sales approach. And that has worked for us.
What I would say <unk> is that we.
We've seen this movie before with the cloud, whereas some early stage customers started growing very very quickly and then we.
And they are quite excited to leverage <unk> as part of that journey, but again, I think thats kind of a microcosm into the enterprise market, where I think there is still.
Then put dedicated sales.
Focus on those accounts and they grew than even faster. So we're clearly watching the market and win selsor customers are to a point where.
Quite early there AI journey and if you remember this is something I've been saying for a while that most customers most people overestimate the impact of our new technologies like AI in the short term, but underestimated the long term and I think we're just in that classic journey.
They really need a higher touch.
Kind of engagement model, then we're more than happy to do that and we have a team that kind of helps transition customers from self serve to more of a direct sales approach and that has worked for US I think what we've learned is that that deadline by which we actually engage a high touch model that can move higher because we've become so sophisticated with self serve that we can really.
Right now.
I appreciate that.
Maybe as a follow up Mike.
Carmen: I think what we learned is that that that line by which we actually engage a high-touch model can move higher because we've become so sophisticated with self-serve that we can really serve customers for early-stage customers for a long period of time. In terms of the enterprise, what I would say is what I've said earlier is that the enterprise is still quite early in their journey to AI. Most of the investments right now are more on end-user productivity, like, you know, developers using code gen tools, and, you know, what I call low-stakes use cases. in fact, I had two meetings today with two different, leaders of two different financial institutions here in New York. and they both talked about what they're doing in AI.
Wanted to make sure digging a little bit into the non <unk> business.
It's predominantly a business can you tell us roughly what percent of your customers here on multiyear deals with suggest annual deals and just kind of curious how where we are now and what was it say a year or two ago and where do you think that mix is going to be a year or two from now.
Served customers for early stage customers for a long period of time in terms of the enterprise what I'd say is what I've said earlier is that the enterprise is still quite early in their journey to AI. Most of the investments right now are more on end user productivity like developers using co gen tools.
Yes.
Thanks for the question, we don't break out the percentage of customers are multiyear versus versus one year. What I would say is in fiscal 'twenty. Five obviously, we saw a lot of larger multi year deals and you see that in the numbers. This year, we will always see multiyear deals they havent been I would call. It as large so it's more widespread.
And.
What I call low stakes use cases.
In fact, I had two meetings today with two different leaders of two different financial institutions here in New York.
And they both talked about what theyre doing and AI that both admitted that they've kind of started with low stakes use cases, but their appetite to start doing more is increasing as we get more and more comfortable with the technology.
So.
Carmen: They both admitted that they've kind of, you know, started with low-stakes use cases, but their appetite to start doing more is increasing as they get more and more comfortable with the technology. and they're quite excited to leverage MongoDB as part of that journey. But again, I think that's a kind of a microcosm into the enterprise market where I think they're still, you know, quite early in their AI journey. And if you remember, this is something I've been saying for a while that, you know, most customers, you know, most people overestimate the impact of a new technology like AI in the short term, but underestimate in the long term. And I think we're just in that classic journey, right now.
Thats really the change that we've seen we haven't broken that out I don't think that it has changed much especially over the year as Dave talked about.
It's going to be a mix of Atlas on on Prem and that mix has stayed relatively consistent.
And they are quite excited to leverage <unk> as part of that journey, but again, I think thats kind of a microcosm into the enterprise market, where I think there is still.
When you look at the customers that are choosing multiyear deals has anything changed in the way they think about the reasoning behind doing that versus not.
Quite early there AI journey and if you remember this is something I've been saying for a while that most customers most people overestimate the impact of our new technologies like AI in the short term, but underestimated the long term and I think we're just in that classic journey.
No reasons are.
Are the same it's typically there.
If it aligns with our long term strategy that I want to be able to lock in the pricing.
Right now.
Analyst: I appreciate that. And maybe as a follow-up, Mike, I just want to make sure to dig in a little bit into the non-Atlas business, the EA, the predominantly EA business. Can you tell us roughly what percent of your customers here are on multi-year deals versus just annual deals? I'm just kind of curious how where we are now and what was it, say, a year or two ago, and what do you think that mix is going to be a year or two from now?
As everybody knows a data has gravity moving data around is not fun for everybody. So they want to be able to lock in a guarantee their prices for that period of time.
I appreciate that.
Maybe as a follow up Mike.
Wanted to make sure digging a little bit into the non asset business.
It's predominantly a business can you tell us roughly what percent of your customers here on multiyear deals with suggested annual deals and just kind of curious how where we are now and what was it say a year or two ago and where do you think that mix is going to be a year or two from now.
I appreciate it.
You bet. Thank you.
Our next question comes from the line of Sandy.
<unk> <unk> with Mizuho. Please proceed.
Brian Denyeau: Yeah, thanks for the question. We don't break out the percentage of customers on multi-year versus one year. What I would say is in fiscal 25, obviously we saw a lot of larger multi-year deals, and you see that in the numbers. This year, we will always see multi-year deals. They haven't been, I would call it as large. So it's more widespread. So we, that's really the change that we've seen. We haven't broken that out. I don't think that it has changed much, especially over the year, as Dave talked about. It's going to be a mix of Atlas and on-prem, and that mix has stayed relatively consistent.
Thanks for taking my question and <unk> I think some of the comments you were talking about the AI slowdown and you heard about <unk>.
Yes.
Thanks for the question, we don't break out the percentage of customers on multiyear versus versus one year. What I would say is in fiscal 'twenty. Five obviously, we saw a lot of larger multi year deals and you see that in the numbers. This year, we will always see multiyear deals they havent been I would call. It as large so it's more widespread.
Report about 95%.
AI implementation not getting any kind of.
Our return.
How do you see what's kind of do team the inflection point when we think we'll start seeing some of the adoption of this AI like you say, they're testing back what can trigger I know you have been talking about.
So.
That's really the change that we've seen we haven't broken that out I don't think that it has changed much especially over the year as Dave talked about.
Year ago, and a broadly we a few years out.
It's going to be a mix of Atlas on on Prem and that mix has stayed relatively consistent.
But it's good to see some of the traction. So how do you first of all characterize what are the your view on that report and how should we think about.
Analyst: When you look at the customers that are choosing multi-year deals, has anything changed in the way they think about the reasoning behind doing that versus not?
When you look at the customers that are choosing multiyear deals has anything changed in the way they think about the reasoning behind doing that versus not.
In terms of revenue contribution.
Brian Denyeau: No, reasons are the same. It's typically there. If it aligns with their long-term strategy, they want to be able to lock in the pricing. And, you know, as everybody knows, hey, data has gravity. Moving data around is not fun for everybody. So they want to be able to lock in and guarantee their prices for that period of time.
No reasons are the same it's typically there.
Okay and from AI.
Yes, so I think it just comes down to.
If it aligns with our long term strategy that I want to be able to lock in the pricing.
The fundamental principles I think customers need to feel one that the quality of the output of these AI systems as high obviously AI systems are probabilistic in nature non deterministic in nature. So you can't always guarantee the output you can hope that you've trained them.
As everybody knows a data has gravity moving data around is not fun for everybody. So they want to be able to lock in a guarantee their prices for that period of time.
Analyst: Appreciate it.
Brian Denyeau: You bet. Thank you.
I appreciate it.
You bet. Thank you.
Models, well you'd hope that you have given at the right information, but you can't always guarantee output. So as I mentioned I had meetings with two financial services customers earlier today and both of them are still hesitant to rollout and end user facing applications for those specific reasons was going to take a little bit of time for people to really get comfortable that they can really.
Dev Ittycheria: Our next question comes from the line of Siddhi Panigari with Mizuho. Please proceed.
Our next question comes from the line of Phil at Citi.
City Penny car he with Mizuho. Please proceed.
Dev Ittycheria: Thanks for taking my question. And Dave, I think some of the comments you were talking about the AI slowdown, and you heard about the recent MIT report about 95% AI implementation not getting any kind of, you know, return. how do you see, what's kind of, do you think the inflection point when we think we'll start seeing some of the adoption of this AI, like you say, they're testing, but what can trigger? I know you have been talking about, a year ago, you know, probably we are a few years out, but it's good to see some of the traction. So how do you, first of all, what would be your view on that report and how should we think about the, you know, in terms of revenue contribution, material contribution from AI?
Thanks for taking my question and therefore, I think some of the comments you were talking about the AI slowdown and you heard about.
Deal with the last mile issues and make sure that they don't have any.
Report about 95%.
Errors that potentially could be impacted as the brand are really call. It caused a lot of customer problems. So that's point number one then there's issues around obviously.
AI implementation not getting any kind of you know.
Return.
How do you see what's kind of do team the inflection point when we think we'll start seeing some of the adoption of this AI like you say theyre testing back what can trigger I know you have been talking about a year ago and a broadly we a few years out but.
Security of these systems, the stability and reliability of these systems. The scalability of these systems as I mentioned some of these early stage companies are running into scaling issues with existing architecture, which is why they are coming to us. So I think we're just in that learning journey I don't know if theres going to be some massive tipping point I think what we're seeing with the frontier models is.
But it's good to see some of the traction. So how do you first of all characterize what would be your view on that report and how should we think about.
All of these frontier models, the kind of clustering around the same ballpark in terms of performance and the efficacy of their models. So I think what's going to start happening is how people start leveraging these insights to build.
In terms of revenue contribution.
Okay Krewson from AI.
Carmen: Yeah, so, I think it just comes down to, you know, the fundamental principles. I think customers need to feel, one, that the quality of the output of these AI systems, is high. Obviously, AI systems are probabilistic in nature, not deterministic in nature. So you can't always guarantee the output. You can hope that you've trained, the models well. You've hoped that you've given it, the right information, but you can't always guarantee the output. So, as I mentioned, I had meetings with two financial services customers earlier today, and both of them are still hesitant to roll out end-user-facing AI applications for those specific reasons.
Yes, so I think it just comes down to.
The fundamental principles I think customers need to feel one that the quality of the output of these AI systems as high obviously AI systems are probabilistic in nature non deterministic in nature. So you can't always guarantee the output you can hope that you have trained them.
What I call a scaffolding around these frontier miles to address the needs of their.
Their business, obviously everyone's talking about agents and people are very very focused on essentially.
Models, well you'd hope that you've given at the right information, but you can almost guarantee the output. So as I mentioned I had meetings with two financial services customers earlier today and both of them are still hesitant to rollout an end user facing applications for those specific reasons. There is going to take a little bit of time for people to really get comfortable that they can really.
Using agents to drive a lot of work agents require if you think about if you're using agents agents will use your systems much more intensely.
Then humans will because they can do things much more quickly. So you need platforms that can massively scale up and down which is again, a good sign and support indicator.
Carmen: So it's going to take a little bit of time for people to really get comfortable that they can really, you know, deal with the last mile issues and make sure that they don't have any, errors that potentially could be, you know, impacting their brand or really cause a lot of customer problems. So that's point number one. Then there's issues around, obviously, the the security of these systems, the stability and reliability of these systems, the scalability of these systems. As I mentioned, some of these early-stage companies are running into scaling issues with existing architecture, which is why they're coming to us. So I think we're just in that learning journey. I mean, I don't know if there's going to be some massive tipping point.
Indicator for <unk>. So I think it's going to take a little bit of time, it's going to take time to be comfortable of technology is going to take time, where people start with low stakes use cases are gravitating to higher state use cases, so I don't think theres going to be some seminal inflection point I think it's just going to take time, but I think that time is coming.
Deal with the last mile issues and make sure that they don't have any.
Errors that potentially could be impacted as a brand or really calls caused a lot of customer problems. So that's point number. One then there's issues around obviously.
Security of these systems, the stability and reliability of these systems. The scalability of these systems as I mentioned some of these early stage companies are running into scaling issues with existing architecture, which is why they are coming to us. So I think we're just in that learning journey I don't know if theres going to be some massive tipping point I think what we're seeing with the frontier models is there.
That's great color. Thank you.
Thank you.
Okay.
Carmen: I think what we are seeing with the frontier models is that all these frontier models are kind of clustering around the same ballpark in terms of performance and the efficacy of their models. So I think what's going to start happening is how people start leveraging these insights to, you know, build, you know, what I call, scaffolding around these frontier models to address the needs of their, of their business. Obviously, everyone's talking about agents, and people are very, very focused on, on essentially, you know, using agents to drive a lot of work. Agents require, you know, if you think about, if you're using agents, agents will use your systems much more intensely, than humans will because they can do things much more quickly. So you need platforms that can massively scale up and down, which is again a good sign and support, indicator for MongoDB.
Our next question is from Brad Sills with Bank of America. Please proceed.
All of these frontier models, the kind of clustering around the same ballpark in terms of performance and the efficacy of their models. So I think what's going to start happening is how people start leveraging these insights to build.
Oh, great. Thank you so much I wanted to ask about some of the investments that you alluded to earlier that youre, making in R&D.
How are you thinking about that is it incremental investments in some of these newer.
Offerings like vector and streaming are there are there new workloads that youre thinking of addressing here.
What I called a scaffolding around these frontier miles to address the needs of their.
We'd love to get some color on just where youre investing in the stock. Thank you.
Their business, obviously everyone's talking about agents and people are very very focused on essentially.
Yes, sure. So we've talked about the fact that R&D is a big part of our investment focus for this year. One we came out with hei, though which is the most performance release ever. So we are already starting to see dividends.
Using agents to drive a lot of work agents require if you think about if you're using agents agents will use your systems much more intensely.
Then humans will because they can do things much more quickly. So you need platforms that can massively scale up and down which is again a good sign as support indicator.
Of our investments in our platform.
$8, one is even better.
Carmen: So I think it's going to take a little bit of time. It's going to take, you know, time of being comfortable with technology. It's going to take time where people start with low-stakes use cases and start gravitating to higher-stake use cases. So, I don't think there's going to be some seminal inflection point. I think it's just going to take time. but I think that time is, is, is coming.
And then we're also making investments in the expansion parts of our platform. What I'll say is we're going to go into a lot more detail around this at Investor day. So if you can hold until September 17th will go into a lot of things that we're doing on the R&D side as well as what we're doing on application modernization and the tooling that we are building there that will really speak to.
Indicator for <unk>. So I think it's going to take a little bit of time, it's going to take time to be comfortable of technology is going to take time, where people start with low stakes use cases are gravitating to higher state use cases, so I don't think theres going to be some seminal inflection point I think it's just going to take time.
But I think that time is coming.
Analyst: That's great color, Dave. Thank you.
That's great color. Thank you.
Those investments that we're making.
Carmen: Thank you.
I'll give you a lot more color.
Thank you.
Got it great. Thanks for that David one more if I may please.
Okay.
Theres been an effort to focus on driving higher quality workloads and that larger account base I mean to what extent would you attribute some of this upside to that effort and then maybe just an update on that effort.
Dev Ittycheria: Our next question is from Brad Sales with Bank of America. Please proceed.
Our next question is from Brad Sills with Bank of America. Please proceed.
Speaker 1: Oh, great. Thank you so much. I wanted to ask about some of the investments that you alluded to earlier that you're making in R&D. You know, how are you thinking about that? Is it incremental investments in some of these newer offerings, you know, like vector and streaming? Are there new workloads that you're thinking of addressing here? Would love to get some color on just where you're investing in the stack. Thank you.
Oh, great. Thank you so much I wanted to ask about some of the investments that you alluded to earlier that youre, making in R&D.
I was.
Just trying to be a lot to that effort I would say a big part of this growth is the fact that we are acquiring higher quality workloads that are growing.
How are you thinking about that is it incremental investments in some of these newer offerings like vector and streaming are there are there new workloads that youre thinking of addressing here.
Astronauts and for longer than the workflows required say in earlier years, and I think thats a big part for why you're seeing this growth after now.
We'd love to get some color on just where you are investing in the stack. Thank you.
Great. Thank you.
Carmen: Yeah, sure. So, we talked about the fact that, you know, R&D is a big part of our investment, focus for this year. One, you know, we came out with 8.0, which was the most performant release ever. So we are already starting to see dividends of, of, of our, investments in our platform. 8.1 is even better. and then we're also making investments, you know, in the, you know, expansion parts of our platform. What I would say is we're going to go into a lot more detail around this investor day.
Carmen I think we have time for one more question.
Yes, sure. So we've talked about the fact that R&D is a big part of our investment focus for this year. One we came out with hei, though which is the most performance release ever. So we are already starting to see dividends.
Alright, one moment please.
And we have the line of Rishi <unk> with RBC. Please proceed.
Wonderful Thanks for squeezing me in.
Of our investments in our platform.
That deadline I'll keep myself to one question, Dave really nice to see the early traction with AI native companies.
$8, one is even better.
And then we're also making investments in the expansion parts of our platform, but I will say is we're going to go into a lot more detail around this investor day. So if you can hold until September 17th will go into a lot of things that we're doing on the R&D side as well as what we're doing on application modernization and the tools that we're building there that will really speak to.
It's always made sense to us, especially given your scalability and your ability to work with unstructured data.
Carmen: So if you can hold until September 17th, we'll go into a lot of things that we're doing on the R&D side as well as what we're doing on, you know, application modernization and the tooling that we're building there that will really speak to those investments that we're making and will give you a lot more color.
Fast forward 510 years, and we start to see a real paradigm shift where instead of agents built on kind of the traditional <unk> mobile interface that we've been in for the past 30 years, we actually enter kind of a multiyear gentex our world or maybe the interaction vector may move away from what we've been used to into more natural language.
Those investments that we're making.
I'll give you a lot more color.
Speaker 1: Got it. Great. Thanks for that, Dave. And one more, if I may, please. I know there's been an effort to, focus on driving, you know, higher quality workloads in that larger account base. I mean, to what extent would you attribute some of the upside to that effort and maybe just an update on that effort, as you've mentioned?
Got it great. Thanks for that David one more if I may please.
Theres been an effort to focus on driving higher quality workloads and that larger account base I mean to what extent would you attribute some of this upside to that effort and maybe just an update on that effort.
Can you talk about why Mongo DB still has a strong role and some of the investments that you might be making to position yourself for that world understanding that's at the very least several years away.
Carmen: I would attribute a lot to that effort. I would say a big part of this growth is the fact that we're acquiring higher quality workloads that are growing, faster and for longer than the workloads required, say, in earlier years. And I think that's a big part for why you're seeing this growth happen now.
I was just trying to be a lot to that effort I would say a big part of this growth is the fact that we are acquiring higher quality workloads that are growing.
Yes, sure. So again just to just to make sure. We're all talking at the same language.
We believe that agents essentially do three things one they perceive or understand the state of things we need to.
Faster and for longer than the workloads required say in earlier years, and I think thats, a big part of why you're seeing this growth happened now.
Essentially.
A way to understand the state of what's happening in your business then you need to decide what to do a plan. So basically you have to come up with the planting I want to take this action of these sets of actions and then you have to ask you actually have to go execute those actions right. So why is <unk> going to be good for four four.
Speaker 1: Great. Thank you.
Great. Thank you.
Brian Denyeau: Carmen, I think we have time for one more question.
Carmen I think we have time for one more question.
Dev Ittycheria: All right. One moment, please. And we have, the line of Rishi Jaluria with RBC. Please proceed.
Alright, one moment please.
And we have the line of Rishi <unk> with RBC. Please proceed.
Our agents one is as I said before the Jae song document database is the best of being able to model the real world. The Messiness the complicated nature of the real world does not fit and is.
Analyst: Oh, wonderful. Thanks for, squeezing me in, at the deadline. I'll keep myself to one question. Dave, really nice to see the early traction with, AI-native companies. You know, it's always made sense to us, especially given your scalability and your ability to work with unstructured data. If we were to fast forward five, ten years, and we start to see a real paradigm shift where instead of agents built on kind of the traditional GUI mobile interface that we've been in for the past 30 years, we actually enter kind of a multi-agentic world where maybe the interaction vector may move away from what we've been used to into more natural language.
Wonderful Thanks for squeezing me in.
That deadline I'll keep myself to one question.
Dave really nice to see the early traction with AI native companies.
Easily in rows and columns and Thats why.
It's always made sense to us, especially given your scalability and your ability to work with unstructured data.
Our document database I think is the best way to.
To do that to we obviously support search and vector search. So you can do very sophisticate hybrid search.
Fast forward 510 years, and we start to see a real paradigm shift where instead of agents built on kind of the traditional Gui mobile interface that we've been in for the past 30 years, we actually enter kind of a multi year gentex.
So that becomes a super important and then with memory.
Agents and have memory. They would act like goldfish. They can only react to the last thing last piece of information that they saw some memory lets agents connect the dots across time and situations. So you have different kinds of memory things like short term context past experiences knowledge skills et cetera that need to build share quickly you need to be able to orchestrate those agents.
World or maybe the interaction vector may move away from what we've been used to into more natural language can you talk about why Mongo DB still has a strong role and some of the investments that you might be making to position yourself for that world understanding that.
Analyst: Can you talk about why MongoDB still has a strong role and some of the investments that you might be making to position yourself well for that world, understanding that's, you know, at the very least several years away? Thanks.
At the very least several years away.
Because you may have multiple agents doing a certain task you to register and have governance policies around those agents, we think that the underlying platform needs to build to support those things. While there is a lot more work needs to be done the underlying architecture that we have in <unk> is well suited to address those needs and we think that that.
Carmen: Yeah, sure. So again, just to just make sure we're all talking the same language, you know, we believe that agents essentially do three things. One, they perceive or understand the state of things. So you need a, essentially, a way to understand the state of what's happening in your business. Then you need to decide what to do or plan. So basically, you have to come up with a plan saying, I want to take this action or these sets of actions. And then you have to act. You actually have to go execute those actions, right? So why is MongoDB good for, for, for, for agents? One is, as I said before, the JSON document database is the best at being able to model the real world, the messiness, the complicated nature. The real world does not, you know, fit in easily in rows and columns.
Yes, sure. So again just to just to make sure. We're all talking at the same language.
We believe that agents essentially do three things one they perceive or understand the state of things we need to.
Essentially.
A way to understand the state of what's happening in your business then you need to decide what to do a plan. So basically you have to come up with the planting I want to take this action of these sets of actions and then you have to ask you actually have to go execute those actions right. So why is <unk> going to be good for four four.
We will be positioned to be a winner as people deploy more and more agents in their in their enterprise.
Alright very helpful. Thank you so much.
Thank you Ken.
And with that we conclude the Q&A session and I will pass it back to Dev <unk> for his final comments.
For agents one is as I've said before the Jae song document database is the best at being able to monitor real world. The Messiness the complicated nature of the real world does not fit and is.
Sure. Thank.
Thank you again for joining us today in summary, I think it's clear that we delivered another strong quarter of heightened highlighted by the accelerating Atlas growth the continued adoption of.
Carmen: And that's why the, you know, our document database, I think, is the best way to, to, to, to do that. Two, we, we obviously support search and vector search. So you can do very sophisticated hybrid search. so that becomes, super important. And then with memory, you know, if, if agents didn't have memory, they would act like goldfish. They could only react to the last thing or last piece of information that they saw. So memory lets agents connect the dots across time and situation. So you have different kinds of memory, things like short-term context, past experiences, knowledge, skills, et cetera, that you need to be able to share quickly. You need to be able to orchestrate those agents because you may have multiple agents doing a certain task. You need to register and have governance policies around those agents.
Easily in rows and columns and Thats why.
Our document database I think is the best way to.
For AI applications, and our expanding profitability.
To do that to we obviously support search and vector search. So you can do very sophisticate hybrid search.
Our raising our revenue and operating margin guidance for the full year fiscal year 2026 and Israel.
So that becomes a super important and then with memory.
These results really reinforce that <unk> is well positioned to capture the next wave of application development, while driving durable and efficient growth. So with that thank you and we'll talk to you soon take care.
If agents that have memory, there would act like goldfish. They can only react to the last thing last piece of information that they saw some memory lets agents connect the dots across time and situations. So you have different kinds of memory things like short term context past experiences knowledge skills et cetera that used to build share quickly you need to be able to orchestrate those agents.
Thank you and this concludes our conference. Thank you for participating and you may now disconnect.
As you may have multiple agents doing a certain task you need to register and have governance policies around those agents, we think that the underlying platform needs to be able to support those things. While there is a lot more work needs to be done the underlying architecture that we have in mom and DB is well suited to address those needs and we think that that.
Carmen: You know, we think that the underlying platform needs to be able to support those things. While there's a lot more work, you know, needs to be done, the underlying architecture that we have in MongoDB is well suited to address those needs. And we think that, that, you know, we'll be positioned to be a winner as people deploy more and more agents in their enterprise.
We will be positioned to be a winner as people deploy more and more agents are there in their enterprise.
Analyst: All right. Very helpful. Thank you so much.
Alright very helpful. Thank you so much.
Carmen: Thank you.
Dev Ittycheria: Thank you so much. And with that, we conclude the Q&A session, and I will pass it back to Dev Ittycheria for his final comments.
Okay. Thank you so much.
And with that we conclude the Q&A session and I will pass it back to death at this area for his final comments.
Carmen: Sure. Thank you again for joining us today. In summary, I think it's clear that we delivered another strong quarter highlighted by the accelerating Atlas growth, the continued adoption of, for AI applications, and our expanding profitability. We are raising our revenue and operating margin guidance for the full year, fiscal year 2026. And these results, sorry, these results really reinforce that MongoDB is well positioned to capture the next wave of AI application development while driving durable and efficient growth. So with that, thank you, and we'll talk to you soon. Take care.
Sure. Thank.
Thank you again for joining us today in summary, I think it's clear that we delivered another strong quarter heightened highlighted by the accelerating Atlas growth the continued adoption of.
For AI applications, and our expanding profitability, we are raising our revenue and operating margin guidance for the full year fiscal year 2026 and Israel.
These results really reinforce that <unk> is well positioned to capture the next wave of application development, while driving durable and efficient growth. So with that thank you and we'll talk to you soon take care.
Dev Ittycheria: Thank you. And this concludes our conference. Thank you for participating, and you may now disconnect.
Thank you and this concludes our conference. Thank you for participating and you may now disconnect.
Okay.
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