Q1 2025 Snowflake Inc Earnings Call

May 22, 2024

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Hello, everyone.

Sierra: Thank you for attending today's Q1 Fiscal Year 2025 Snowflake Earnings Call. My name is Sierra, and I will be your moderator today.

Thank you for attending today's Q1 fiscal year 2025, Snowflake earnings call.

Sierra: My name is Sierra and I'll be your moderator today.

Sierra: All lines will be muted during the presentation portion of the call.

Sierra: Opportunity for questions and answers at the end.

Sierra: If you would like to ask a question. Please press star followed by one on your telephone keypad.

Sierra: All lines will be muted during the presentation portion of the call. We have an opportunity for questions and answers at the end. If you would like to ask a question, please press star followed by 1 on your telephone keypad. I would now like to pass the conference over to our host, Jimmy Sexton, Head of Investor Relations.

Speaker Change: I would now like to pass the conference over to our host Jimmie section head of Investor Relations.

Jimmy Sexton: Good afternoon, and thank you for joining us on Snowflake's Q1 Fiscal 2025 earnings call. Joining me on the call today is Sridhar Ramaswamy, our Chief Executive Officer, Mike Scarpelli, our Chief Financial Officer, and Christian Kleinerman, our Executive Vice President of Product, who will participate in the Q&A session. During today's call, we will review our financial results for the first quarter of fiscal 2025 and discuss our guidance for the second quarter and full year of fiscal 2025. Additionally, during today's call, we will make forward-looking statements, including statements related to our business operations and financial performance. These statements are subject to risks and uncertainties, which could cause them to differ entirely from actual results.

Speaker Change: Good afternoon, and thank you for joining us on Snowflakes Q1 fiscal 2025 earnings call. Joining me on the call today issued our Ramaswami, our Chief Executive Officer, Mike Scarpelli, Our Chief Financial Officer, and Christian Klein, Our executive Vice President of product, who will participate in the Q&A session.

Speaker Change: During today's call, we will review our financial results for the first quarter of fiscal 2025, and discuss our guidance for the second quarter and full year fiscal 2025.

During today's call, we will make forward looking statements, including statements related to our business operations and financial performance. These statements are subject to risks and uncertainties, which could cause them to differ materially from actual results.

Jimmy Sexton: Information concerning these risks and insurances is available in the attorney's press release. Our most recent form is AIM-10-Q and our other SEC reports. All our statements are made as of today based on information currently available to us, and, except as required by law, we assume no obligation to update any such statement. During today's call, we will also discuss certain non-GAAP financial measures. A reconciliation of GAAP to non-GAAP measures is included in today's earnings press release.

Speaker Change: Permission concerning these risks and uncertainties.

Speaker Change: Earnings press release.

Speaker Change: Our most recent Form 10-Q, and our other SEC reports.

Speaker Change: All our statements are made as of today based on information currently available to us.

Speaker Change: We assume no obligation to update any such statements.

Speaker Change: On today's call. We will also discuss certain non-GAAP financial measures a reconciliation of GAAP to non-GAAP measures is included in today's earnings press release, the earnings press release, and an accompanying investor presentation are available on our website at investors that snowflake Dot com.

Jimmy Sexton: The earnings press release and an accompanying investor presentation are available on our website at investors.snowflake.com. A replay of today's call will also be posted on the website. With that, I would now like to turn the call over to Sridhar.

Speaker Change: A replay of today's call will also be posted on the website with that I would now like to turn the call over to <unk>.

Sridhar Ramaswamy: Thanks Jimmy, and good afternoon everyone. Before we get into it, many of you have given me a warm welcome to my new role over the past few months, and I just wanted to say thank you. I've been focused on three key priorities in my first quarter at CEO: listening to and learning from our customers; driving execution and alignment within our go-to-market team; and fueling our innovation and product delivery. I've been really impressed by how the team has responded and by our overall pace of play.

Speaker Change: Thanks, Jimmy.

Speaker Change: Good afternoon, everyone.

Speaker Change: Before we get into it many of you have given me a warm welcome to my new role over the past few months.

Speaker Change: And I just wanted to say thank you.

Jimmy: I've been focused on three key priorities in my first quarter seal.

Jimmy: Listening to and learning from our customers driving execution and alignment within our go to market teams.

Jimmy: Fueling our innovation and product delivery.

Jimmy: I've been really impressed by how the team has responded and by our overall pace of play we have a lot of opportunity ahead of us and Theres a lot of excitement across our company to go and get it.

Sridhar Ramaswamy: We have a lot of opportunity ahead of us, and there's a lot of excitement across our company to go get it. When I look at the Snowflake growth story, it was first driven by an amazing data product and then by the layers of collaboration and applications that we added on top to make Snowflake a true data cloud. What is exciting about AI is that it can turbocharge our capabilities and growth on all three levels. It also helps democratize access to all the amazing enterprise data in Snowflake, massively increasing our reach. The progress we have made in AI for the last year, culminating in the past quarter, is remarkable.

Jimmy: When I look at the Snowflake growth story. It was first driven by an amazing data product.

Jimmy: And then by the layers of collaboration and applications that we added on top to make snowflake true data flopped.

Jimmy: What is exciting about AI is that you can turbocharge, our capabilities and growth on all three layers.

Jimmy: It also helps Democrat dies access to all the amazing enterprise data in snowflake massively increasing outreach.

Jimmy: The progress we have made in AI over the last year, culminating in the past quarter is.

Jimmy: Remarkable.

Sridhar Ramaswamy: We believe AI is going to continue to fuel our platform, helping our customers perform and Deliver Customer Experiences Better Than Ever, as evidenced by our Q1 results. Our core business is very strong. We are still in the early innings of our plan to bring our world-class data platform to customers around the globe, and in the first quarter alone, we saw some of our largest customers meaningfully increase their usage of our core platform.

Jimmy: We believe AI is going to continue to fuel our platform, helping our customers perform.

Jimmy: In Delaware customer experiences better than ever.

Jimmy: As evidenced by our Q1 results.

Jimmy: Core business is very strong.

Jimmy: Still in the early innings of our plan to bring our world class data platform to customers around the globe.

Jimmy: And in the first quarter alone we saw some of our largest customers meaningfully increase their usage of our core offering.

Sridhar Ramaswamy: The combination of an incredibly strong data cloud now powerfully boosted by AI is the strength and story of Snowflake. I want to touch on our Q1 results, and Mike will get into the details with you. I'm really proud that our team delivered a very strong Q1. Product revenue for the quarter was $790 million, up 34% year-over-year. Remaining performance obligations totaled $5 billion.

Jimmy: The combination also has incredibly strong data cloud now powerfully boosted by AI.

Jimmy: Is the strength and story of Snowflake.

Speaker Change: I wanted to touch on our Q1 results and Mike will get into the details with you.

Speaker Change: I'm really proud that our team delivered a very strong Q1.

Michael P. Scarpelli: Product revenue for the quarter was $790 million up 34% year over year.

Remaining performance obligations totaled $5 billion.

Sridhar Ramaswamy: Year-over-year growth accelerated to 46%. The non-GAAP adjusted free cash flow margin was 44%. Given the strong quarter, we are increasing our product revenue outlook for the year. Working through the second quarter and beyond, our priorities remain the same.

Speaker Change: <unk> growth year over year growth accelerated to 46%.

Michael P. Scarpelli: non-GAAP adjusted free cash flow margin was 44%.

Michael P. Scarpelli: Given the strong quarter, we are increasing our product revenue outlook for the year.

Michael P. Scarpelli: Working through the second quarter and beyond our priorities remain the same.

Sridhar Ramaswamy: I've had conversations with over 100 customers over the past several months, and I'm very optimistic. Snowflake is a beloved platform, and the value we bring comes through in every customer conversation I have. They're critical in helping our customers run their businesses. For example, one of the largest U.S. telcos relies on us to help them close their books every month. We also help a global financial service customer with their counterparty credit risk process. The art of the possible on snowflakes is really incredible.

Michael P. Scarpelli: Yes.

Conversations with over 100 customers over the past several months and I'm very optimistic.

Michael P. Scarpelli: No Blake is a beloved platform and the value we bring comes through in every customer conversation I have.

Michael P. Scarpelli: They are critical in helping our customers run their businesses.

Michael P. Scarpelli: For example, one of the largest U S telcos relies on us to help them close their books every month.

Michael P. Scarpelli: We also help a global financial service customers on their counterparty credit risk process.

Michael P. Scarpelli: The art of the possible on Snowflake is really incredible.

Sridhar Ramaswamy: It's also probably no surprise that AI is top of mind for our customers as well. They want to make all business data in Snowflake available to everyone, not just business analysts. They want us to help drive clarity, value creation, and reliability as they enter this new frontier. Over the last quarter, my time spent with our go-to-market teams has been focused on driving execution and alignment. Internally, we emphasize consumption and new customer acquisition.

It's also probably not surprise that AI is top of mind for our customers as well.

Michael P. Scarpelli: They want to make all business data in snowflake available to everyone not just the business analyst.

Michael P. Scarpelli: They want us to help drive clarity value creation and reliability as they.

Michael P. Scarpelli: We entered this new frontier.

Michael P. Scarpelli: Over the last quarter My time spent with our go to market teams has been focused on driving execution and alignment.

Michael P. Scarpelli: Internally, we emphasized consumption and new customer acquisition.

Sridhar Ramaswamy: And we are developing an end-to-end cadence for both priorities. This includes developing sales motions for specific workloads such as AI and data engineering. We have more to gain as we standardize our consumption mindset and effectively execute it.

Andrew: Andrew developing an end to end cadence for both priorities.

Andrew: This includes developing sales motions and specific workloads, such as AI and data engineering.

Andrew: We have more to gain as we standardize our consumption mindset and effectively execute.

Sridhar Ramaswamy: We expect that this efficiency will contribute to further revenue growth. Those who know me know that I have a relentless focus on product innovation and delivery. Teams across the company are building and delivering at an incredible pace. Earlier this month, we announced that Cortex, our AI layer, is generally available.

Andrew: We expect that this efficiency will contribute to further revenue growth.

Speaker Change: Those are nuomi, nor that I have a relentless focus on product innovation and delivery.

Speaker Change: Teams across the company are building and delivering at an incredible pace.

Speaker Change: This month, we announced that cortex, our AI layer is generally available.

Sridhar Ramaswamy: Iceberg, Snowpark Container Services, and Hybrid Tables will all be generally available later this year. We're investing in AI and machine learning, and our pace of progress in a short amount of time has been fantastic. What is responding most with our customers is that we are bringing differentiation to the market.

Speaker Change: Iceberg Snowball container services and hybrid tables will all be generally available later this year.

We are investing in AI and machine learning and our pace of progress in a short amount of time has been fantastic.

Speaker Change: What is resonating most with our customers is that they are bringing differentiation to the market Snowflake delivers enterprise AI that is easy efficient and trusted.

Sridhar Ramaswamy: Snowflake delivers enterprise AI that is easy, efficient, and trustworthy. You've seen an impressive ramp in Cortex AI customer adoption since it went generally available. As of last week, over 750 customers are using these capabilities. Cortex can increase productivity by reducing time-consuming tasks.

Speaker Change: You have seen an impressive ramp in <unk> customer adoption since going generally available.

Speaker Change: As of last week over 750 customers are using these capabilities.

Speaker Change: Our next can increase productivity by reducing time consuming tasks. For example, Sigma computing users Kartik language models to summarize and categorize customer communications from their CRM.

Sridhar Ramaswamy: For example, Sigma Computing uses CARTEX language models to summarize and categorize customer communications from their CRM. And in the quarter, we also announced ARTIC, our own language model. Artic outperforms leading open models such as Lama 270B and Mixtrol 8x7B in various benchmarks. We developed our team in less than three months at one-eighth the training cost of peer modeling. AI is the bridge between structured and unstructure

Speaker Change: In the quarter, we also announced Arctic our own language model.

Speaker Change: Arctic or outperform leading open models, such as Lamar to 70 D and mixed roll <unk> in various benchmarks.

Speaker Change: Redeveloped Arctic in less than three months at one eight the training cost of peer models.

Speaker Change: AI is a bridge between structured and unstructured data we see this with document AI customers find value in extracting features on the fly from piles of documents.

Sridhar Ramaswamy: We see this with document AI; customers find value in extracting features on the fly from piles of documents. We're making meaningful progress on Snowpark container services being generally available in the second half of the year, and dozens of partners are already building solutions that will leverage container services to serve their end customers. We view Snowpark and other new features as our emerging business. These are in the early days of revenue contribution, but we're seeing very healthy demand.

Speaker Change: We are making meaningful progress on snowfall container services being generally available in the second half of the year and dozens of partners are already building solutions that are leveraged container services to serve their end customers.

Speaker Change: We view Snow Park and other new features as our emerging businesses.

Speaker Change: These are in the early days of revenue contribution, but we are seeing very healthy demand.

Sridhar Ramaswamy: More than 50% of customers are using Snowpark as of Q1. Revenue from Snowpark is driven by Spark Migration. In Q1, we began the process of migrating several large Global 2000 customers to Snowpark.

Speaker Change: More than 50% of customers are using snow park as of Q1.

Speaker Change: Revenue from Snow Park is driven by spark migrations in Q1, we began the process of migrating several large global 2000 customers.

Speaker Change: No Mark.

Sridhar Ramaswamy: Our collaboration capability is also a key competitive advantage for us. Nearly a third of our customers are sharing data products as of Q1 2025, up from 24% one year ago. Collaboration already serves as a vehicle for new customer acquisition. Through a strategic collaboration with FiveServe, Snowflake was chosen by more than 20 FiveServe financial institutions and merchant clients to enable secure, direct access to their financial data and insights. We announced support for unstructured data over two years ago.

Speaker Change: Our collaboration capability is also a key competitive advantage for us.

Speaker Change: Nearly a third of our customers are sharing data products as of Q1 2025.

Speaker Change: From 24% one year ago.

Speaker Change: Collaboration already serves as a vehicle for new customer acquisition.

Our strategic collaboration with Fiserv Snowflake was chosen by more than 25 sort of financial institutions and merchant clients to enable secure direct access to their financial data and insights.

Speaker Change: Yeah.

Speaker Change: We announced support for unstructured data or two years ago.

Sridhar Ramaswamy: Now, about 40% of our customers are processing unstructured data on Snowflake, and we've added more than 1,000 customers in this category over the last six months. Iceberg is enabling us to play offense and address a larger data set. Many of our largest customers have indicated that they will now leverage Snowflake for more workloads as a result of this functionality. More than 300 customers are using Iceberg in public preview.

Speaker Change: Now about 40% of our customers are processing unstructured data on snowflake.

Speaker Change: We've added more than 1000 customers in this category over the last six months.

Speaker Change: Iceberg is enabling us to play offense and address a larger data footprint.

Speaker Change: For our largest customers have indicated that they will now leveraged snowflake for more workloads as a result of this functionality.

Speaker Change: More than 300 customers are using iceberg in public preview.

Sridhar Ramaswamy: Snowflake has a powerful and unique partner ecosystem. Part of our success is that we have many partners that amplify the power of our platform. They range from big organizations like EY and Deloitte to firms like LTI Mindtree and NextPath.

Snowflake is a powerful and unique partner ecosystem.

Speaker Change: Part of our success is that we have many partners that amplify the power of our platform.

Speaker Change: They range from big organizations, like <unk>, and Deloitte, but also firms like <unk> and next pathway.

Sridhar Ramaswamy: S&P Global sees us as a strong collaborator in their cloud distribution model, and companies like Observe, Blue Yonder, Relational AI, Fivetran, Hex, and Domo have built their software on top of Snowflake. These partners bring entirely new capabilities and unlock new use cases for us and our customers. They also often bring new customers to us.

Speaker Change: S&P global seasons, as a strong collaborator in their cloud distribution model.

Speaker Change: And companies like observed Blue Yonder, relational AI, <unk> hex and Domo.

Speaker Change: Their software on top of Snowflake. These.

Speaker Change: These partners bring an entirely new capabilities and unlock new use cases for us and our customers there.

Speaker Change: They also often bring new customers to us and.

Sridhar Ramaswamy: And they really care about how easy it is to build on Snowflake, how reliable Snowflake is, and also about how we can go to customers. Partners bring enormous power to our data cloud vision, and their success creates success for us and our customers. To wrap it up, Snowflake is the world's best enterprise AI data platform. Combined with our collaboration capability and thriving application platform, we are driving powerful network effects that will fuel our growth. AI vastly amplifies this opportunity both in the near and medium term. Our product philosophy is simple. One platform with all features available.

Speaker Change: And they really care about how easy it is to build on snowflake, how reliable snowflake and also about how we can go to customers jointly.

Speaker Change: The partners bring enormous power through our data cloud vision their success creates success for us and our customers.

Speaker Change: Yeah.

Speaker Change: To wrap it up snowflake is the world's best enterprise AI data platform.

Speaker Change: Combined with our collaboration capability and thriving application platform, we're driving powerful network effects that will fuel our growth.

Speaker Change: Yes, it vastly amplifies this opportunity both in the near and medium terms.

Speaker Change: Our product philosophy is simple.

Speaker Change: One platform with all features available.

Sridhar Ramaswamy: We're turning every analyst and data engineer into a sophisticated AI analyst. The magic of Snowflake is that we make difficult tasks easy. Stay tuned for more to come at the Snowflake Data Cloud Summit coming up in San Francisco, June 3rd through the 6th. I look forward to seeing you all there. Now, I'll turn it over to Mike. Thank you, Sridhar.

We are turning every analyst and data engineered into a sophisticated AI analyst the magic of Snowflake is that we make difficult tasks easy.

Speaker Change: Sapiens are more to come a snowflake data cloud summit coming up in San Francisco June 3rd through the sixth.

Michael P. Scarpelli: I look forward to seeing you all there now I will turn it over to Mike.

Michael P. Scarpelli: Thank you sure either.

Michael P. Scarpelli: Q1 product revenue grew 34% year-over-year to $790 million. Our largest growth contributors included a media and entertainment company and a large retail and consumer goods company. Smaller accounts outside of the Global 2000 were an important source of outperformance.

Michael P. Scarpelli: Q1 product revenue grew 34% year over year to $790 million, our largest growth contributors included a media and entertainment.

Michael P. Scarpelli: And a larger retail and consumer goods company.

Michael P. Scarpelli: Smaller accounts outside of the global 2000, where an important source of our performance.

Michael P. Scarpelli: Interquarter, we saw strong growth in February and March, growth moderated in April, and we view this variability as a normal component of the business. Excluding the impact of leap year, product revenue grew approximately 32% year over year. We continue to see signs of a stable optimization environment.

Michael P. Scarpelli: Intra quarter, we saw strong growth in February and March growth moderated in April we view. This variability is a normal component of the business.

Michael P. Scarpelli: Excluding the impact of leap year product revenue grew approximately 32% year over year.

We continue to see signs of a stable optimization environment.

Michael P. Scarpelli: Seven of our top 10 customers grew quarter over quarter.

Michael P. Scarpelli: Seven of our top 10 customers grew quarter over quarter. Q1 marked the first quarter under our FY25 sales compensation plan. Our sales reps are executing well against their plan. In Q1, we exceeded our new customer acquisition and consumption quotas. Non-Gap Product Gross Margin of 76.9% was down slightly year-over-year.

Michael P. Scarpelli: Q1 marked the first quarter under our FY 'twenty five sales compensation plan, our sales reps are executing well against our plan in Q1, we exceeded our new customer acquisition and consumption quotas.

Michael P. Scarpelli: non-GAAP product gross margin of 76, 9% was down slightly year over year as mentioned on our prior call. We have headwinds associated with GPU related costs as we invest in new AI initiatives.

Michael P. Scarpelli: As mentioned on our prior call, we have headwinds associated with GPU-related costs as we invest in new AI initiatives. Our non-GAAP operating margin of 4% benefited from revenue from performance. Our non-GAAP adjusted free cash flow margin was 44%. As a reminder, Q1 and Q4 are our seasonally strong quarters for non-GAAP adjusted free cash flow. We ended the quarter with $4.5 billion in cash, cash equivalents, short-term and long-term investments. In Q1, we used $516 million to repurchase 3 million shares at an average price of $173.14. We have $892 million remaining under our original $2 billion authorization.

Michael P. Scarpelli: Our non-GAAP operating margin of 4% benefited from revenue outperformance.

Michael P. Scarpelli: non-GAAP adjusted free cash flow margin was 44% as a reminder, Q1 and Q4 are seasonally strong.

Michael P. Scarpelli: For non-GAAP adjusted free cash flow.

Michael P. Scarpelli: We ended the quarter with $4 $5 billion in cash cash equivalents short term and long term investments.

Michael P. Scarpelli: In Q1, we used $516 million to.

Michael P. Scarpelli: 3 million shares at an average price of $173 14.

Michael P. Scarpelli: We have $892 million remaining under our original $2 billion authorization now, let's turn to our outlook.

Michael P. Scarpelli: Now, let's turn to our outlook. As a reminder, we only forecast product revenue based on observed behavior. This means our FY25 guidance includes contributions from Snowpark. FY25 guidance does not include revenue from newer features such as Cortex until we see material consumption. Iceberg will be GA later this year.

Michael P. Scarpelli: As a reminder, we only forecast product revenue based on observed behavior.

Michael P. Scarpelli: This means our FY 'twenty five guidance includes contributions from Snow Park.

Michael P. Scarpelli: FY 'twenty five guidance does not include revenue from newer features such as cortex until we see material consumption.

Michael P. Scarpelli: We have invested in Iceberg because we expect it to increase our future revenue opportunities. However, for the purpose of guidance, we continue to model revenue headwinds associated with the movement of data out of Snowflake and into Iceberg storage, the negative impact of which is weighted to the back half of the year.

Michael P. Scarpelli: Iceberg will BTA later this year, we have invested a nice bird because we expect it to increase our future revenue opportunity. However for the purpose of guidance. We continue to model revenue headwinds associated with the movement of data out of snowflake and into iceberg storage the negative.

Michael P. Scarpelli: Fact is weighted to the back half of the year.

Michael P. Scarpelli: For Q2, we expect product revenue between $805 and $810 million. We are increasing our FY25 product revenue guidance. We now expect full-year product revenue of approximately $3.3 billion, representing 24% year-over-year growth. Turning to Margin.

Michael P. Scarpelli: For Q2, we expect product revenue between 805 and $810 million.

Michael P. Scarpelli: We are increasing our FY 'twenty five product revenue guidance, we now expect full year product revenue of approximately $3 3 billion.

Michael P. Scarpelli: Representing 24% year over year growth.

Turning to margins, we are lowering our full year margin guidance in light of increased GPU related costs related to our AI initiatives. We are operating in a rapidly evolving market and we view these investments as key to unlocking additional revenue opportunities in the future as era.

Michael P. Scarpelli: We are lowering our full-year margin guidance in light of increased GPU-related costs. As for our AI initiatives, we are operating in a rapidly evolving market, and we view these investments as key to unlocking additional revenue opportunities in the future. As a reminder, we have GPU-related costs in both the cost of revenue and R&D. Additionally, we announced our intent to acquire certain technology assets and hire key employees from Truera. Truera is an AI observability platform that provides capabilities to evaluate and monitor large language modeling apps and machine learning models in production.

Michael P. Scarpelli: Minder, we have GPU related costs in both cost of revenue and R&D.

Michael P. Scarpelli: We announced our intent to acquire certain technology assets and higher key employees from true era.

Speaker Change: <unk> is an AI observe mobility platform that provides capabilities to evaluate and monitor large language model apps and machine learning models in production.

Michael P. Scarpelli: We are excited to welcome approximately 35 employees from Truera to Snowflake. The impact of the transaction is reflected in our outlook. For Q2, we expect a 3% non-GAAP operating margin. For FY25, we expect 75% non-GAAP product gross margin, 3% non-GAAP operating margin, and 26% non-GAAP adjusted free cash flow margin. Finally, we will host our Investor Day on June 4th in San Francisco in conjunction with the Snowflake Data Cloud Summit, our annual user conference. If you are interested in attending, please email ir at snowflake.com. With that, operator, you can now open up the line for questions.

Speaker Change: We are excited to welcome approximately 35 employees from treasurer to Snowflake the impact of the transaction is reflected in our outlook.

Speaker Change: For Q2, we expect 3% non-GAAP operating margin.

Speaker Change: For FY 'twenty, five we expect 75% non-GAAP product gross margin, 3% non-GAAP operating margin and 26% non-GAAP adjusted free cash flow margin. Finally, we will host our Investor day on June 4th in San Francisco in conjunction with the snowfall.

Speaker Change: <unk> data cloud summit, our annual users conference if you're interested in attending please email <unk> and snowflake dot com with that operator, you can now open up the line for questions.

Speaker Change: Thank you.

Operator: We will now begin the Q&A session. If you'd like to ask a question, please press star followed by one on your telephone keypad. If you would like to remove a question, press star followed by 2. And if you are using a speakerphone, please pick up your handset before asking your question. Our first question of the day comes from Keith Weiss with Morgan Stanley. Please proceed.

Speaker Change: We will now begin the Q&A session.

Speaker Change: If you'd like to ask a question. Please press star followed by one on your telephone keypad.

Speaker Change: If you'd like to remove that question press star followed by two.

Speaker Change: And if you are using a speakerphone. Please pick up your handset before asking a question.

Speaker Change: Our first question today comes from Keith Weiss with Morgan Stanley. Please proceed.

Keith Weiss: Excellent. Very nice quarter, guys.

Speaker Change: Excellent.

Keith Weiss: Quarter, guys and thank you for taking the question.

Keith Weiss: Looking at the.

Speaker Change: Front page Investor <unk>.

Speaker Change: Relations page.

Speaker Change: 5 billion queries it looks like your query volume is actually accelerating now again.

Speaker Change: Walk us through some of the drivers of that acceleration is it new products that are driving acceleration or is it the relief of optimization or just like better data sharing so.

Speaker Change: Just a little bit more clarity on what's driving that acceleration and then on the other side.

Sridhar Ramaswamy: And thank you for taking the question. [inaudible] That equation, it looks like there's still pressure on the price per query. Any indications on whether that pressure on the price per query is coming more from the compute side of the equation or the storage side of the equation? Any color there would be super helpful. Thank you. Overall...

Speaker Change: That equation it looks like there's still pressures on like the price per query any.

Speaker Change: Any indications on whether that like pressure on the price per query is coming more from the compute side of equation of the storage side of equation any color there would be super helpful.

Speaker Change: Okay.

Sridhar Ramaswamy: Thank you. Overall, as both Mike and I said, our core business is very strong. And growth is coming from both new customers as well as expansion from existing customers. And as we gain more and different kinds of workloads, for example, AI, and data engineering are increasing quite nicely. They're all contributing to additional query growth, and the relationship between query growth and cost per query is not a simple, straightforward one. And we look for broad growth across the different categories of workloads that we handle, and they've all been doing really well.

Speaker Change: Thank you <unk>.

Speaker Change: Paul.

Speaker Change: Both Mike and I said, our core business is is very strong.

Speaker Change: And growth is coming from both new customers as well as expansion from existing customers.

Speaker Change: And.

Speaker Change: As we.

Speaker Change: To gain more and different kinds of workloads for example, AI data engineering.

Speaker Change: Increasing quite quite.

Speaker Change: Quite nicely, they're all contributing to additional credit growth on.

Speaker Change: The relationship between credit growth and cost per query is not a simple straightforward one.

Speaker Change: And we look for broad growth across.

Speaker Change: The different categories of workloads that.

Speaker Change: That we handle.

And they've all been doing really well.

Mark Ronald Murphy: Our next question today comes from Mark Murphy with J.P.

Speaker Change: Our next question today comes from Mark Murphy with JP Morgan.

Speaker Change: Please proceed.

Sridhar Ramaswamy: Thank you very much. I'll add my congratulations.

Mark Ronald Murphy: Okay. Thank you very much and I'll add my Congratulations Street are you trained Arctic LLM with pretty amazing efficiency could you walk us through the architectural difference in the product that.

Sridhar Ramaswamy: Sridhar, you trained Arctic LLM with pretty amazing efficiency. Can you walk us through the architectural differences in the product that might allow it to run more efficiently than other products out there in the market? And Mike, is there any directional change to the $50 million target for GPU spend this year, just considering the launch of Cortex and Arctic LLM, and it sounds like some snow park traction. Should we think of that trending a little higher?

Speaker Change: Might allow it to run more efficiently than other products out there in the market and Mike.

Speaker Change: Is there any directional change to the $50 million.

Speaker Change: Good for GPU spend this year just considering the.

Speaker Change: The launch of cortex, and Arctic LLM and it sounds like some no park traction should we think of that trending a little higher.

Sridhar Ramaswamy: Thank you. So, absolutely, we did train ARTIC in a remarkably short period of time, a little over three months, on a remarkably small amount of GPU compute. A lot of the training efficiency of these models does come from their architectures. We had a rather unique mixture of experts in architecture. These are increasingly the architectures that are driving impressive gains for all of the other leading AI companies. But what also went into it was just an amazing amount of pre-experimentation in order to figure out things like what are the right data sets, what orders they should be fed in, and how do we make sure that they're actually optimizing for enterprise metrics, the kind of things our customers care about, which are things like, are these models really good at creating SQL queries, for example, so that they can talk to data?

Speaker Change: Thank you so absolutely.

Speaker Change: Did train Arctic in a remarkably short period of time little little over three months.

Speaker Change: On a remarkably small amount of GPU compute.

Speaker Change: A lot of the training efficiency of these models can do come from architectures, we had a rather unique mixture of experts architecture. These are increasingly.

Speaker Change: <unk> architectures that are driving impressive gains for all of the other leading AI companies.

Speaker Change: But.

Speaker Change: What also went into it was just an amazing amount of pre experimentation in order to figure out things like what are the right data sets, what auditors should they be fed in and.

Speaker Change: And how do we make sure that we're actually optimizing Florida enterprise metrics, the kind of things our customers care about which are things like are these models really good.

Sridhar Ramaswamy: And so, we are taking a very different view of how we can make AI much better in an enterprise context because, you know, naturally, that's the place where we have the most value to add. And, you know, our AI budgets are modest in the scheme of things. And so, being creative in how we develop these models is something that the team comes to naturally expect. And I think that kind of discipline and scarcity, to be honest, produce a lot of innovation. And I think that's what you're seeing.

Creating seek liquidity for example, so that they can talk to data and so we are taking very much the view of how do we make AI much better in an enterprise context, because naturally that's the place where we have the most value.

Speaker Change: To add.

Speaker Change: And.

Speaker Change: Our.

Speaker Change: AD budgets are modest in the scheme of in the scheme of things and so being creative in how we develop these models is something that.

The team comes to naturally expect and I think that kind of.

Disciplined on scarcity to be honest produces a lot of innovation and I think that's what.

Sridhar Ramaswamy: And then, in terms of investments, I'll hand over to Mike in a second. But I'm comfortable with the amount of investments that we are making. Part of, you know, what we gain as Snowflake is the ability to fast follow on a number of fronts, the ability to optimize against metrics that we care about, not producing, like, the latest, greatest, biggest model, let's say, for image generation. And so, having that kind of focus lets us operate on a relatively modest budget pretty efficiently.

Speaker Change: Thats, what youre seeing.

Speaker Change: And then in terms of investments I will hand over to Mike in a second.

Speaker Change: But I am comfortable with the amount of investments Scott.

Speaker Change: We are making.

Speaker Change: Art of.

Speaker Change: What we gain.

Speaker Change: Snowflake is a ability to fast follow on a number of fronts has the ability to optimize against metrics that we care about not producing like the latest greatest biggest model, let's say in the generation and.

Sridhar Ramaswamy: And so, the focus very much now is on how do we take all of the products that we have released into production? We have over 750 customers that are busy developing against our AI platform. This is a fast-moving space, but we are very comfortable with both the pace, the investment, and the choices that we are making to make AI effective for Snowflake. Mike? Yeah, and I will add that yes, we are making a lot of investments.

Speaker Change: And so having that kind of focus lets us operate on a relatively modest budget pretty efficiently.

Speaker Change: And so the focus very much now is on how do we take all of the products that we have released into into production. We have over 750 customers that are busy Delaware being against our against our AI platform. This is a fast moving space, but we are very comfortable with both the <unk>.

Speaker Change: As the investments.

And the choices that we're making to make effective first snowflake Mike.

Michael P. Scarpelli: And I will add that yes, we think we may be spending a little bit more on GPUs, but it's also people that we're hiring specifically in AI. We talked about the acquisition of Truera. Those people all fall into that organization. As I mentioned, the world of AI is rapidly evolving, and we're investing in that because we do think there's a massive opportunity for Snowflake to play in that. And it will have a meaningful impact on future revenue.

Speaker Change: And I will add that.

Speaker Change: Yes, we think we may be spending a little bit more on Gpus, but it's also people that we're hiring specifically in AI, we talked about the acquisition of <unk>. Those people all fall into that organization. So as I mentioned the world of AI is rapidly evolving and we are.

Speaker Change: We're investing in that because we do think there is a massive opportunity for snowflakes to play there and it will have.

Speaker Change: Meaningful impact on future revenues.

Speaker Change: Yeah.

Thank you very much.

Kirk Materne: Our next question today comes from Kirk Materne with Evercore. Please proceed.

Speaker Change: Our next question today comes from Kirk Martin with Evercore.

Speaker Change: Please proceed.

Sridhar Ramaswamy: Yeah, thanks very much. And congrats on the quarter. Sridhar, can you just talk a little bit about how we should think about your customers' time to value with Cortex? Meaning, you know, how long do you think it takes them to start using the technology before it can start to translate into a little bit faster consumption patterns? And then just one for Mike. Mike, can you just talk a little bit about deferred revenue? This quarter is down perhaps a little bit more sequentially than we've seen in prior years. I don't know if there's any one-time in nature there, but if you just touch upon that, that'd be great.

Unknown Attendee: Yes, thanks, very much and congrats on the quarter treater or can you just talk a little bit about how we should think about your customers' time to value with cortex, meaning.

Speaker Change: How long do you think it takes them to start using the technology before it can start to translate into a little bit faster consumption patterns and then just one for Mike Mike can you just talk a little bit about deferred this quarter is down perhaps a little bit more sequentially than we've seen in prior years I don't know if there's anything onetime in nature, there, but could you just touch upon that that'd be great. Thank you all.

Michael P. Scarpelli: Thank you. One of the cool things about Cortex AI, and our AI products in general, in the context of the consumption model, is that our customers don't have to make big investments to see what value they're going to get. You know, because they don't have to make commitments to how many GPUs that they're going to be renting, for example. They just use Cortex AI, for example, from SQL, which is very, very easy to do without a pre-commitment.

Speaker Change: Thank you.

Speaker Change: One of the cool things about.

Speaker Change: Cortex AI.

Speaker Change: And our AI products in general in the context of the consumption model is that our customers don't have to make big investments to see what value that theyre going to get.

Speaker Change: Because they don't have to make commitments to <unk>.

Speaker Change: How many gpus that they are going to be renting for example.

Speaker Change: Youth cortex AI for example from sequel, which is very very easy.

Speaker Change: To do.

Speaker Change: Without a without a pre comment and this means that they can focus very much on value creation on the structure of cortex. AI is also so that anybody that can write sequel can now begin to do really interesting things for example.

Michael P. Scarpelli: And this means that they can focus very much on some sort of value creation. And the structure of Cortex AI is also such that anybody that can write SQL can now begin to do really interesting things. For example, look at how often, let's say, a particular product was mentioned in an earnings transcript or be able to go from other kinds of unstructured information, like whether it is text or whether it is images, to structured information, which Document AI, our AI product there, does.

Speaker Change: Look at how often let's say a particular product was mentioned in an earnings transcript or being able to go from other kinds of unstructured information.

Speaker Change: Like whether it is tax Todd whether it is images to structured.

Speaker Change: Information on which document AI hour.

Speaker Change: Our <unk> products it does and so we very much want to structure all of these efforts as one in which our customers are able to iterate very quickly.

Michael P. Scarpelli: And so we very much want to structure all of these efforts as ones in which our customers are able to iterate very quickly, take things to production, get value out of them, and then make bigger commitments on top. And that's one of the benefits that you get from making the technology super easy to adopt. There's not a massive learning curve, and neither is there a GPU commitment or other kinds of software engineering that needs to happen in order to use AI with Snowflake. Yeah, and your question...

Speaker Change: Take things to production get value out of it and then make bigger commitments on.

Speaker Change: On top and Thats part of the benefit that you get.

Speaker Change: Making the technology Super easy to adopt there's not a massive learning curve neither does that GPU commitment.

Speaker Change: All other kinds of software engineering that needs to happen in order to use AI with snowflake.

Michael P. Scarpelli: Yeah, on your question on deferred, Kurt, if you're referring to January to today, the end of the year, Q4 is always a very, very big billing quarter. Q1 is not as big of a billing quarter. So you have that flowing through on the deferred revenue. However, RPO, and you can see RPO, as Sridhar mentioned, is up 46% year over year. And we do have, for instance, we signed a $100 million deal this quarter with a customer who pays us monthly in arrears, so it doesn't show up in deferred revenue. We've signed a number of deals with big companies that pay monthly in arrears that don't show up in deferred revenue, but they're in RPO.

Speaker Change: Question on deferred Kurt if youre, referring to.

Speaker Change: <unk> January to today the end of the year Q4 is always a very very big Bill linked quarter Q1 is not as big of a billing quarter. So you have that flowing through.

Speaker Change: The deferred revenue however, our Poe and you can see <unk> as <unk> mentioned is up 46% year over year and we do have for instance, we signed a $100 million deal this quarter with a customer who pays us monthly in arrears. So it doesn't show up in deferred revenue, we signed a number of deals with big companies.

Speaker Change: That pay us monthly in arrears that don't show up in deferred revenue, but they are in our appeal.

Karl Emil Keirstead: That's helpful. Thanks, Mike. Thanks, Sridhar. I appreciate it. Our next question today comes from Karl Keirstead.

Speaker Change: That's helpful. Thanks, Mike Thanks, Rich I appreciate it.

Speaker Change: Okay.

Karl Emil Keirstead: Our next question today comes from Karl Keirstead with UBS. Please proceed. Carl, your line is now open.

Speaker Change: Our next question today comes from Karl Keirstead with UBS.

Karl Emil Keirstead: Please proceed.

Karl Emil Keirstead: Okay.

Speaker Change: Your line is now open.

Michael P. Scarpelli: I'm sorry. Mike, could you elaborate on the comment that usage growth moderated in April? Maybe you could unpack that and explain why it usually does. And then also, when I look at your 2Q and Fiscal 25 revenue guidance... It's actually pretty solid, so that would lead one to believe that whatever moderation there might be in April, it doesn't feel like it, according to your guidance, rolled into May. Just curious if that's the correct interpretation. Thank you.

Speaker Change: Oh I'm sorry.

Michael P. Scarpelli: Mike could you elaborate on the comment that.

Speaker Change: Usage growth moderated in April maybe you could unpack that and explain why it usually does and then also when I look at your <unk> in fiscal 'twenty five revenue guidance.

It's actually pretty solid so that would lead one to believe that whatever moderation there might be an April it doesn't feel like it according to your guidance.

Speaker Change: Bold into May just curious if thats the correct.

Speaker Change: <unk>. Thank you.

Michael P. Scarpelli: Well, what I would say is February and March were very strong, and I'm saying April was more muted. April, just as a reminder, and it really impacts you in Europe and some others because it is Ascension Day or the Easter holiday. And in Europe, they take a long time off. That does have an impact on consumption. Remember, this is a daily consumption model. And the guidance we gave is based upon what we're seeing from our customers as of this week.

Speaker Change: Well, what I would say as February and March were very strong.

Speaker Change: And I'm, saying April was more muted April just as a reminder, in it really impacts you in Europe, and some others that is essentially de or Easter holiday and in Europe. They take a long time off that does have an impact on consumption. Remember this is a daily consumption model and the guidance. We gave is based upon.

Speaker Change: What we're seeing through our customers as of this week.

Michael P. Scarpelli: Okay, and Mike, if I could ask a follow-up question, you had mentioned previously, including at a conference in March, that your efforts around that tiered storage side whereby we could see some roll-off on the storage revenues could begin to impact the P&L in the April quarter. Was that the case? And would you be able to approximate what impact, maybe, the roll-off on the storage reps had? Thank you.

Michael P. Scarpelli: Okay, and Mike if I could ask a follow up you had mentioned previously including I think.

Speaker Change: Conference in March that your efforts around that tiered storage side, whereby we could see some roll off on the storage revenues could begin to impact the P&L in the.

Speaker Change: April quarter was that the case.

Speaker Change: Would you be able to approximate what impact maybe the roll off on the storage reps had thank you.

Michael P. Scarpelli: Sure. We did roll it out to all of our customers, and we started, by the way, doing it at the end of last year, whereby, depending on the amount of commitment you're making on an annual basis, you get tiered storage pricing. So, in essence, you get your storage discounted from the list price of $23 per terabyte. We started rolling that out, and that actually impacted us somewhere between $6 and $8 million in the quarter. I forget exactly what that is.

Speaker Change: Sure.

Speaker Change: We did rollout to all of our customers. When we started by the way of doing it at the end of last year, whereby depending on the amount of commitment you are making on an annual basis, you can tiered storage pricing. So in essence your storage discounted from the list price of $23 per terabytes, we started rolling that out and that actually in the quarter.

Impact of somewhere between $6 million to $8 million I forget exactly what that is that is pure margin impact that's not to say there are other customers big customers, where we've always discounted storage given their size that is just the pure because of the tiered storage. This rolled out to everyone that we will continue to have an impact.

Michael P. Scarpelli: That is pure margin that that impacted. That's not to say there are other customers, big customers, where we've always discounted their storage given their size. That is just the pure pure because of the tiered storage that's rolled out to everyone, and that will continue to have an impact as people continue to renew their contracts. But the storage mix, as a percent of revenue, has remained pretty much consistent at 11% of our revenue is associated with storage. That did not change. We're actually seeing growth in storage in Snowflake.

Speaker Change: As people.

Speaker Change: Continue to renew their contracts, but storage mix as a percent of revenue has remained pretty much consistent at 11% of our revenue is associated with storage that did not change.

Speaker Change: Okay, Mike Thanks.

Speaker Change: Mhm go ahead, we're actually seeing growth storage and snowflake.

Michael P. Scarpelli: Got it. Okay. Thank you for both answers. Super helpful. Our next question comes from Raimo Lenschow with Barclays. Please proceed. Thank you. Sridhar, like, thank you for all your comments around the AI evolution for you. But where is there a kind of a vision for you?

Speaker Change: Got it okay. Thank you for both answers Super helpful.

Raimo Lenschow: Our next question comes from Raimo Lenschow with Barclays. Please proceed. Thank you.

Speaker Change: Our next question comes from Raimo <unk> with Barclays. Please proceed.

Raimo Lenschow: Thank you.

Speaker Change: Thank you for all your comments around the AI evolution for you guys.

Speaker Change: It kind of vision for you.

Speaker Change: <unk> lighting.

Speaker Change: You want to play versus where you don't want to play in this kind of new AI world.

Speaker Change: Obviously like.

Speaker Change: There is like how many MCU.

Christian Klein: <unk> do you need to own the acquisition today Christian. Thank you do you need to do of stability or is that more people hire with kind of knowledge can you just kind of.

Speaker Change: How is your thinking there evolving thank you.

Sridhar Ramaswamy: This is a fabulous question. First and foremost, I think it is important for all of us to acknowledge that AI language models are going to have an impact at multiple levels of what you can think of as a data stack. So, for example, the way in which people are going to be migrating from an old system, an on-premises system, to something like Snowflake is going to be aided by the presence of a co-pilot that can do much of the translation.

Speaker Change: This is <unk>.

Speaker Change: <unk> question.

Speaker Change: Like first and foremost I think it is important for all of us to acknowledge.

Speaker Change: That.

Hi language models are going to have an impact at multiple levels of what you can think of as the data stack.

Speaker Change: For example, the way in which people are going to be migrating from.

Speaker Change: From an old system that on Prem system to something like Snowflake.

Speaker Change: He is going to be aided by the presence of a co pilot that can do much of the translation. We already have such a translation product everything else is going to make that go even faster.

Speaker Change: But in other areas like data cleansing data engineering that are perhaps not as sexy, but nevertheless require a huge amount of investment in order to make sure that the data is enterprise grade. We think AI is going to play a big role both in the creation of those pipelines, but also in things like how does one make sure.

Speaker Change: That the data is clean for example, if VII.

Speaker Change: Currently slips into a table or a distribution goes very wonky.

Speaker Change: Language models can help detect deviations from from patterns.

Sridhar Ramaswamy: We already have such a translation product, and we think AI is going to make that go even faster. And then going up the stack, we have a very acclaimed product for writing SQL, our co-pilot within our user interface that can significantly accelerate an analyst's ability to get to know a dataset and be productive with it. And then, of course, to something like a data API, which now begins to put enterprise data into the hands of a business user, but with a very high degree of reliability.

Speaker Change: And then going up the stack.

Speaker Change: Have a very a claimed product for writing sequel, our co pilot within our user interface that can significantly accelerate.

And analysts the ability to get to know a dataset and be productive.

Speaker Change: With it and then of course to something like a data API.

Speaker Change: Now begins to put enterprise data into the hands of a business user, but with a very high degree of reliability.

Sridhar Ramaswamy: And so my point is that there is broad impact, and I think things like automating some of the work that an analyst has to do, for example, to troubleshoot problems will be things that a language model can do. Having said that, for a variety of problems, small models, which we are perfectly capable of developing from scratch, like we did for Document AI, or more of a mid-sized model, like what we did with Arctic, actually suffice for the vast majority of the applications that I'm talking about.

Speaker Change: And so my point is that that is that is that has broad impact and I think things like <unk>.

Speaker Change: <unk> some of the work that an analyst has to do for example to troubleshoot problems will be things that a language model can do.

Speaker Change: Having said that.

Speaker Change: For a variety of problems small models, which we are perfectly capable of developing from scratch likely have done far document AI automotive mid sized model like what we did with Arctic.

Actually suffices for the vast majority of the applications that.

Speaker Change: I'm talking about.

Sridhar Ramaswamy: And so there are academic benchmarks, like there's one called MMLU. It's a notoriously difficult benchmark and depends very much on model size and how many dollars people are throwing at training those models. We can get a huge amount done with a small team under modest investment without needing to play at that level where companies are talking about spending billions of dollars. I don't think we need to be there.

Speaker Change: And so that our academic benchmarks like there's one call MMO user notoriously difficult benchmark and depends very much on model size and how many dollars people are drawing.

Speaker Change: Training those models.

Speaker Change: We can get a huge amount done with a small team under modest investment without needing to play at that.

Speaker Change: At that level or you are talking companies are talking about spending billions of dollars I don't think we need to be there I think being very focused on what we need to Delaware for our customers will take us a long way with the amount of investments that we're making and finally I will add that we have amazing partnerships with a ton of people.

Brent John Thill: I think being very focused on what we need to deliver for our customers will take us a long way with the amount of investment that we are making. And finally, I would add that we have amazing partnerships with a ton of people. Even today, I wrote about how we are collaborating with Landing.AI, Andrew Ng's company, but we have partnerships with Mistral, with Reka, with a ton of other companies. The field of AI is so large that I don't think there's going to be one company that makes every model that every person is going to use.

Speaker Change: Even today I wrote about how we're collaborating with land landing that AI <unk> company, but we have partnerships with Mr. All of the <unk> with a ton of other companies. The field of AI is so large that I don't think theres going to be one company that is going to make every model that every person is going to use.

Brent John Thill: We are very good at developing the models that we need in our core, and we actively collaborate with a large set of players for other kinds of models. And obviously, they see value in the 10,000 customers we have and being able to go to market together. And so I think this is likely to continue for the indefinite future in terms of what we need to do. Our next question today comes from Brent Thill with Jeffries.

Speaker Change: Good <unk> the models that.

Speaker Change: That we need in our core.

Speaker Change: And we actively collaborated with a large set of players for other kinds of models and obviously they see value in the 10000 customers, we have and being able to go to market together.

Speaker Change: So I think this is likely to continue for the indefinite future in terms of what we need to do.

Speaker Change: Okay perfect. Thank you.

Michael P. Scarpelli: Our next question today comes from Brent Thill with Jeffries. Please proceed. Mike, on the acceleration of RPO up 46%, I know you mentioned the

Speaker Change: Our next question today comes from Brent Thill with Jefferies. Please proceed.

Sridhar Ramaswamy: Remember that 46% is up year-over-year, but the year-ago comparison didn't have the $250 million deal we signed in Q4 that went into there. There was another $100 million deal that was signed subsequent to that too. But what I will say is, as I mentioned, we're very pleased with the number of Cap Ones in our bookings in Q1. And there are, as I mentioned, we did a $100 million deal in Q1, and we will do another $100 million deal this quarter, potentially too. So we're very pleased with our business and the commitment that our customers are making to Snowflake long term. And quickly, for Sridhar.

Michael P. Scarpelli: Mike on the acceleration of our 46% I know you mentioned the $100 million deal, but was there anything else that was surprising to you and.

Speaker Change: In the quarter that helped in this re acceleration any any other notable trends that maybe you havent seen are you starting to see now.

Speaker Change: Yes, remember that 46% is up year over year.

The year ago comparison, Didnt have the $250 million deal. We signed in Q4 that went into there. There was another $100 million deal that was signed subsequent to that too so but what I will say is as I mentioned, we're very pleased with the number of <unk> in our bookings in Q.

Speaker Change: One.

Speaker Change: And there are.

Speaker Change: As I mentioned, we did a $100 million deal in Q1, and we will do another $100 million deal this quarter potentially too.

Speaker Change: So we're very pleased with their business and the more of the commitment that our customers are making and snowflake long term.

Speaker Change: And quickly for <unk> I know you mentioned the priorities are the same but you are the new CEO I guess from your perspective, where are your top priorities for the rest of 'twenty four.

Sridhar Ramaswamy: I trust them. Driving product innovation faster is definitely way up there on the list. And you see this coming to fruition with things like how fast our AI platform, Cortex AI, came to market or what we did with Arctic. But I want to stress again that we see incredible potential across our AI data cloud. The AI layer is one part, but support for Iceberg is actually an exciting new chapter for all players in data.

Speaker Change: Driving product innovation faster is definitely way up there.

Speaker Change: In the list and you see this coming to fruition.

Speaker Change: Things like how fast.

Speaker Change: <unk> platform cortex, AI came to market or what we did with <unk>.

Speaker Change: With Arctic, but I want to stress again that we see incredible potential.

Speaker Change: Across our AI data cloud.

Speaker Change: AIG is one part but support for iceberg is actually an exciting new chapter for all players in data.

Sridhar Ramaswamy: You know, we had an announcement yesterday and today at the Build conference. But the general theme is, you know, we are able to bring Snowflake to bear on more of the data that is sitting in Data Lake. And then beyond that, we have things like hybrid tables that are kind of coming out, container services, which massively expand the kind of applications that can run on top of Snowflake.

Speaker Change: <unk> had an announcement.

Speaker Change: Yesterday and today.

Speaker Change: At the build conference, but the general theme is.

Speaker Change: We are able to bring snowflake the bet on more of the data that is sitting in data Lake and then beyond that we have.

Speaker Change: Have things like hybrid tables that are coming out container services, which massively expand the kind of applications that can run on top of snowflake. So product innovation is is one focus.

Sridhar Ramaswamy: So product innovation is one focus. But just as equally important, helping our go-to-market teams take these products to market, having the specialization to be able to zone in on the applications that deliver the most value for our customers, upping the game on just enablement within, you know, within Snowflake, and also doing a great job of enablement with the many partners that we work with. That broad suite of taking products to market, I would say, is my other, like, priority insight.

Speaker Change: Just as equally importantly.

Speaker Change: Helping our go to market team take these products to market, having the specialization to be able to zone in on the applications that deliver the most value Florida.

Speaker Change: Our customers.

Speaker Change: Upping the game on just enablement within.

Speaker Change: Within within Snowflake, and also doing a great job of enablement with many partners.

Speaker Change: We work with that broad suite.

Speaker Change: Taking products to market I would say is my.

Sridhar Ramaswamy: I also spend a substantial amount of time on the road talking to customers. I would say, on average, I'm out traveling every other week. That's kind of how you get to meet over 100 customers in, what, 70 odd days. But that's a rough breakdown of my priorities, make sure that I'm in front of customers and with folks in the field, focus on product execution, and also on just go-to-market efficiency.

Speaker Change: Rather like priority inside I also spent a substantial amount of time.

Speaker Change: On the road talking to customers I would say on average I'm.

Speaker Change: Im travelling every other week, that's kind of how you get to meet over 100 customers and was $77 70 odd days, but thats rough rough breakdown of my priority is make sure that I'm in front of customers and the folks in the field focus on product execution and also on just go to market efficiency.

Speaker Change: Thank you.

Matthew George Hedberg: Our next question today comes from Matt Hedberg with RBC.

Speaker Change: Our next question today comes from Matt Hedberg with RBC.

Speaker Change: Please proceed.

Sridhar Ramaswamy: My questions, Sridhar, you know, we spend a lot of time focused on the investments you're making in R&D and GPUs, but I'm wondering about your sales and marketing progress and maybe what you've learned from your time there, especially when you mentioned expanding your reach. And I guess specifically, does your sales process need to change or evolve when talking to, say, data scientists, for example?

Speaker Change: So my questions Street are.

Speaker Change: We spent a lot of time focused on.

Speaker Change: Vestments, youre, making in R&D and Gpus, but I'm wondering about your sales and marketing Chromecast and maybe what you've learned from your time, there, especially when you noted expanding your reach and I guess, specifically does your sales motion needs to change or fall.

Speaker Change: When talking to say data Sciences for instance.

Sridhar Ramaswamy: This is a great question, and I touched on this in the answer to my previous question. Absolutely not. I think the kind of product offerings that are needed to be able to effectively have a conversation with a data science team are a little bit different from, say, the team that's running warehouses. What is exciting, and I can tell you that today from many conversations that I've had with customers, is that applications written on top of Snowflake, something we call managed applications where our customers write applications on top of it and then use things like our collaboration to actively share data with their customers.

Speaker Change: This is a great question and I touched on this in answer to my previous question absolutely.

Speaker Change: I think the kind of.

Speaker Change: Product offerings that are needed to be able to effectively have.

Speaker Change: Have a conversation with a data science team.

A little bit different from say the team that's running warehouses.

Speaker Change: What is exciting and I can tell you that today from many conversations that I've had.

Speaker Change: With customers.

Speaker Change: Is that.

Speaker Change: Obligations written on top of Snowflake, something we called managed applications that our customers write applications on top and then using things like our collaboration to actively share data with their customers.

Sridhar Ramaswamy: That actually puts us in conversation directly with business leaders in these companies because we have now become a part of their top line of actually helping them generate revenue. And yes, so there are different product movements that are needed for different products and the different people that are going to benefit from these. We created a specialized partner organization, for example, that is focused explicitly on data providers, on, you know, who can bring additional data to Snowflake and then how do we drive revenue opportunities for them.

Speaker Change: That is actually puts us in conversation directly with business leaders in these companies because we now become a part of their topline of actually helping them generate generate revenue.

Speaker Change: And yes. So there are different products motions that are needed for that.

Speaker Change: Different products and the different people that are going to benefit from these we created a specialized partner organization. For example that is focused exclusively on data providers on.

Speaker Change: Who can bring additional data there's snowflake and then how do we drive revenue opportunities for them.

Sridhar Ramaswamy: And similarly, with AI, for example, we need people who are much more comfortable in the world of language models. Our magic is also that we make AI available to all analysts, and that's a big boost that they are going to get from, you know, how they use Snowflake. Absolutely, there is change going into our go-to-market strategy, but as you know, it is a gradual change. We are constantly looking for the best way to take a particular product to market or how to solve a specific customer problem. And you can see that reflected in how our field organizations are organized and managed.

Speaker Change: And similarly with <unk>.

Speaker Change: With AI for example, we need people that are much more comfortable in the world of language model. Our magic is also that we make.

Speaker Change: Available to all analysts and that's a big booth that they are going to get.

Speaker Change: From how they used snowflake.

Speaker Change: <unk> that has changed going into our go to market motion, but as you know it is a it is a gradual change we are constantly looking for what's the best way to take a.

Speaker Change: Particular price the market or how to solve a specific customer problems.

Speaker Change: Youll see that reflected in our field organizations are organized and managed.

Michael P. Scarpelli: That's great. That's great. Maybe just a quick one for Mike.

Speaker Change: That's great that's great and maybe just a quick one for Mike I appreciate the color on consumption trends at Super helpful. I know you said you based your guidance on what you've seen this week I guess, maybe just a question on May have you seen Megan bounce back a bit versus what sounds like a seasonally slow April traditionally.

Michael P. Scarpelli: Appreciate the color on consumption trends. That's super helpful. I know you said you based your guidance on what you've seen this week. I guess, you know, maybe just the question on May. Have you seen May bounce back a bit versus what sounds like a seasonally slow April traditionally? As I said, our guidance is based...

Michael P. Scarpelli: As I said our guidance is based upon consumption patterns, we're seeing in the quarter and thats reflected inside there.

Michael P. Scarpelli: As I said, our guidance is based upon consumption patterns we're seeing in the quarter, and that's reflected inside there.

Speaker Change: Yeah.

Speaker Change: Thanks.

Brent Alan Bracelin: Our next question comes from Brent Bracelin with Piper Sandler. Please proceed.

Speaker Change: Our next question comes from Brent <unk> with Piper Sandler.

Speaker Change: Please proceed.

Sridhar Ramaswamy: Thank you. Good afternoon, Sridhar. In your opening remarks, you flagged Iceberg as a potential unlock that could accelerate growth. Maybe that's a longer-term view, but can you just walk through how or why spending could actually go up for Snowflake in an environment where customers move to Iceberg? Thanks.

Speaker Change #100: Thank you good afternoon should all in your opening remarks, you flagged iceberg.

Speaker Change #101: Central unlocked that can accelerate growth.

Maybe that's a longer term view, but could you just walk through how or why spending could actually go up for snowflake in an environment where customer moves iceberg.

Sridhar Ramaswamy: So, first of all, Iceberg is a capability, and it is a capability to be able to read and write files in a structured, interoperable format. And yes, there will be some customers that will move a portion of their data from Snowflake into an Iceberg format because, say, they have an application that they want to run on top of the data. But the fact of the matter is that data lakes, or cloud storage in general for most customers, has data that is often 100 or 200 times the amount of data that is sitting inside Snowflake.

Speaker Change #102: So first of all.

Speaker Change #102: Iceberg as a capability.

Speaker Change #102: And it is a capability to be able to read and to write files in a structured interoperable pharmacy.

Speaker Change #102: And yes, there will be some customers that.

Speaker Change #102: That will move a portion of their data.

Speaker Change #102: From snowflake into an iceberg format, because they have an application that they want to run on top of the data, but the fact of the matter is that.

Speaker Change #102: Data lakes, our cloud storage in general for most customers has data that is often 100 or 200 times somewhat of data that is sitting inside snowflake.

Sridhar Ramaswamy: And now, with Iceberg as a format and our support for it, all of a sudden, you can run workloads with Snowflake directly on top of this data. And we don't have to wait for some future time in order to be able to pitch and win these use cases, whether it's data engineering or whether it's AI. Iceberg becomes a seamless pipe into all of this information that existing customers already have, and that's the unlock that I'm talking about. I'll also have Christian say a word. He's been at this for a very long time and has a lot of insight into it.

Speaker Change #102: And now with iceberg as a fee.

Speaker Change #102: <unk> and our support for.

Speaker Change #102: All of a sudden you can run workloads at snowflake directly on top of this of this data and we don't have to wait for some.

Speaker Change #102: Some future time in order to be able to pitch and then these use cases, whether it's data engineering or whether it is AI iceberg becomes a seamless.

Pipe into all of this information that existing customers already have and that's the unlocked that I'm talking about and I'll also have Christian.

Speaker Change #103: Say a word he has been at this sort of a very long time and has a lot of insight.

Christian Kleinerman: Yeah, I would just add to what Sridhar said; we have many of our existing customers, echoing what Sridhar just described; they have lots of data. Tens of petabytes of data, ready to be analyzed. They don't think that it makes sense for that data to be copied or ingested into Snowflake, but they have use cases where they want to combine data in Snowflake with that existing data. So the opportunity is very real.

Speaker Change #104: Yes, I would just add to what you just said we have many of our existing customers echoing what was reduced they have.

Christian Kleinerman: And what Sridhar also alluded to, the announcement we made with Microsoft in the last two days is entirely about that. How do we take the data that is available in and through Iceberg, make it available to Snowflake. So the opportunity is not a long-term one. It's not framed as something we'll have to wait a lot for.

Speaker Change #105: Lots of data tend to paid up license data ready to be analyzed.

They don't want paying that it makes sense for that they have to be copied or injected into snowflake, but they have use cases, where they want to combine data and probably with that existing data. So the opportunity is very real and whats going on also alluded to the announcement, we made with Microsoft in the last two days is it partly about that how do we take the data that is.

Speaker Change #105: Available in my office.

Speaker Change #105: And through iceberg maintain available because the openings. So so the opportunity is is not a long term one is not claimed.

Speaker Change #105: We'll have to wait a lot part.

Michael P. Scarpelli: Quick clarification for Mike here, knocking down some big deals, another $100 million deal in Q1, sounds like another one in Q2, last I checked, the macro is pretty tough. What's driving that? Is the AI roadmap helping?

Speaker Change #106: Quick clarification for Mark here.

Speaker Change #107: <unk> done some big deals another $100 million.

Speaker Change #108: Deal in Q1, it sounds like you have another one in Q2.

Speaker Change #109: Last I checked the macro is pretty tough whats driving that.

Speaker Change #110: Hi roadmap healthy.

Michael P. Scarpelli: You know, these are all existing customers and large customers, and it still is core data warehousing, but they're all interested and want to have a discussion around what we're doing in AI. But many of these, both the one in Q1, we are core to their business, and the one that's going to do in Q2, the current quarter now, we are core to how they run their business. And that is what's really driving these customers to make these big, long-term commitments with us.

Speaker Change #111: These are all existing customers large customers and it still is core data warehousing, but theyre all interested and wanted to have a discussion around what we're doing in AI, but many of these.

Speaker Change #111: Both the one in Q1, we are core to their business and the one that's going to do in Q.

Speaker Change #111: Current quarter now we are core to how they run their business.

Speaker Change #112: And that is what's really driving these customers to make these big long term commitments with US and then several of these deals not the one that Mike mentioned, but in several other very large one.

Sridhar Ramaswamy: And in several of these deals, not the one that Mike mentioned, but in several other very large ones, collaboration or actually having Snowflake be the conduit by which these large customers monetize their data by having their customers access this data serves as a very powerful catalyst. And absolutely, AI is a help in all of these. And these are the folks that are leaning into and creating AI applications on top of Snowflake. But at its core, you should see these very large investments as a bet on Snowflake as the AI data platform. Should we go to the next question? Operator, next question.

Speaker Change #112: Collaboration.

Speaker Change #112: Or actually having snowflake be the conduit by which these large customers.

Speaker Change #112: Monetize their data by having their customers access this data serves as a very powerful catalyst.

Speaker Change #112: And.

Speaker Change #112: Absolutely.

Speaker Change #112: Is it health in all of these and these are the folks that are leaning into and creating applications on top of snowflake.

Speaker Change #112: But at its core you should see these very large investments.

Speaker Change #112: Is that on snowflake as the AI data platform.

Speaker Change #113: So we go to the next question.

Speaker Change #114: Operator next question.

Speaker Change #114: Yes.

Speaker Change #114: Okay.

Operator: I think we have audio issues. Yeah, we have a little audio glitch. Please be patient. We can't hear them. We can't hear the operator.

Speaker Change #115: I think we have audio issues, yes, we have a little audio glitch please be patient.

Speaker Change #116: We can't hear them.

Speaker Change #117: We can't hear the operator.

Speaker Change #117: Yes.

Operator: Apologies. Can you guys hear me now? We hear you now. Okay, I'm so sorry about that. Yes, I did say our next question today comes from Patrick Colville. Your line is actually open. I apologize.

Speaker Change #118: I apologize can you guys hear me now.

Speaker Change #119: We hear you now.

Speaker Change #119: Yes.

Speaker Change #119: Okay. So sorry about that yes, I did say our next question today comes from Patrick Colville Youre line is actually open I apologize.

Speaker Change #119: This is Joe Andrew on for Patrick Colville, Thanks for taking our question.

Speaker Change #120: I know you joined Snowflake about a year ago, but you've now been CEO for about three months. So I'm. Just wondering if there is anything that surprised you or that's worth calling out that you've learned since stepping into the CEO role and then also curious of your view on a few other products Streamlet and unit store.

Speaker Change #121: If you could talk a bit about customer engagement you are.

Speaker Change #122: There thanks.

Patrick Edwin Ronald Colville: Yeah, I've been here at Snowflake for close to a year, and as I said, I have, I've had a lot, and I have a lot of customer conversations.

Speaker Change #123: Yes, I've been.

Speaker Change #123: Snowflake close to a year and as I said I have I've had a lot and I have a lot of customer conversations.

Speaker Change #123: The amount of love and respect that our customers have for the core product how easy it is to use how efficient it is and how it maintenance III lowered dramatically lowering total cost of ownership. It is is the thing that continues to pleasantly surprise me is also obviously.

Sridhar Ramaswamy: The amount of love and respect that our customers have for the core product, how easy it is to use, how efficient it is, and how maintenance-free, dramatically lowering total cost of ownership it is, is the thing that, you know, continues to pleasantly surprise me. It's also obviously an important quality for us to preserve while we are releasing new products, and we take the trouble to do that.

Speaker Change #123: An important quality.

Speaker Change #123: To preserve.

Speaker Change #123: While we are releasing new products and we take the trouble to do that uniformly the feedback that we get about cortex, which is our AI layer.

Speaker Change #123: Pretty tough decorate tech reviewers is that yes, we truly make the hard easy because anybody that can write sequel, I was able to do some pretty nifty things with.

Speaker Change #123: With AI.

Speaker Change #123: I think that combination of simplicity and ease of use.

Sridhar Ramaswamy: Uniformly, the feedback that we get about Cortex, which is our AI layer, from, you know, pretty tough tech reviewers, is that, yes, we truly make the hard easy because anybody that can write SQL is now able to do some pretty nifty things with AI. I think that combination of simplicity and ease of use is an incredibly powerful quality for Snowflake. And while I knew it, I think it was still a surprise, a pleasant surprise, every time customers brought it up.

Speaker Change #123: Is an incredibly powerful quality for our snowflake and while I knew it I think it is still a surprise pleasant surprise every time customers bring it up.

Speaker Change #123: And then in terms of streaming.

Speaker Change #123: Streamlet Streamlet is for those that don't know is the rapid prototyping environment, it's a little bit like being able to write an obligation and have it be hosted on snowflake without having to do any other work you don't have to bring up October <unk> cluster, you don't have to fly a binary none of that stuff.

Sridhar Ramaswamy: And then in terms of Streamlet, Streamlet is, for those that don't know, a rapid prototyping environment. It's a little bit like being able to write an application and have it be hosted on Snowflake without having to do any other work. You don't have to bring up a Kubernetes cluster, you don't have to deploy a binary, none of that stuff.

Speaker Change #123: A little application and it just runs.

There are a ton of obligations inside Snowflake for example, whether it's our compensation information or whether it is finance information our forecast or even jackpots that I personally have created these all run on stream lit.

Speaker Change #123: But with just incredible operational efficiency, because they just Ron as part of our Snowflake.

Sridhar Ramaswamy: You know, you write a little application, and it just runs. There are a ton of applications inside Snowflake, for example, whether it's our compensation information, or whether it is finance information, or forecasts, or even chatbots that I personally have created. These all run on Streamlet, but with just incredible operational efficiency because they just run as part of our Snowflake instance that is already running in the customer deployment. There are folks that have adopted it very, very broadly.

Speaker Change #123: Incidence that is already running in the customer deployment. There are folks that have adopted it very very broadly.

Speaker Change #123: And we think of this as really like highlighting showcasing snowflake functionality, making it super easy to distribute these things to snowflake users.

Sridhar Ramaswamy: And we think of this as really like highlighting, showcasing Snowflake functionality, making it super easy to distribute these things to Snowflake users. And from that perspective, it's been a hugely, hugely positive experience. And the team has also been the one, for example, that's been working on Notebooks, which is going to be an important priority going forward, so lots of positive things on that side. And then on Unistore, or as we call them, hybrid tables, these are really meant to address a different kind of workload that is more transactional in nature than the analytic workload that often runs on top of Snowflake. It is in public preview now. It will be in GA later this year.

Speaker Change #123: And in that in that perspective, it's been a hugely hugely positive obligation and the team has also been the one for example, that's been working on notebooks, which is going to be an important priority going going forward. So lots of positive things on that side.

Speaker Change #123: Then on unit store or as we call them hybrid tables and these are really meant to address a different kind of workload that is more transactional in nature than the analytic workload that often runs on top of a snowflake. It isn't public preview it'll be an <unk> later this year I think it.

Sridhar Ramaswamy: I think it opens up several new classes of applications that can run very effectively on top of Snowflake. It's the same sort of magic, which is that you don't need to stand up servers. You don't need to do a whole lot of work on top of them or deal with Kubernetes clusters. And we see, I think it's close to 300 customers that are actively using hybrid tables. We can absolutely expect that number to go up by a lot.

Speaker Change #123: Opens up several new classes of applications that can run very effectively on top of a snowflake keeps the same snowflake sort of magic, which is you don't need to standup servers, you don't need to go to a whole lot of work on.

Speaker Change #123: On top of them are deal with kubernetes clusters.

Speaker Change #124: And we see I think its close to 300 customers that are actively using hybrid tables. We can absolutely expect that number to go up by a lot Christian has any other thoughts on these two.

Sridhar Ramaswamy: No, Streamlit is now generally available on all three clouds, and that has given a lot of interest in adoption. And for Hybrid Cables, many of our customers have liked the evaluation, and they are actually waiting for general availability later this year. Thank you. Our next question of the day comes from Brad Areva.

Speaker Change #125: <unk> is now generally available in all three clouds.

Speaker Change #125: A lot of interest in our Boston and the.

Speaker Change #125: Hi.

Speaker Change #125: Many of our customers have likely evaluation and they are actually waiting for the general availability later this year.

Thank you.

Sridhar Ramaswamy: Our next question of the day comes from Brad Reback with FIFO.

Speaker Change #126: Our next question today comes from Brad Reback with Stifel.

Speaker Change #127: Please proceed.

Rob: Hi, This is Rob on for Brian. Thanks for taking the question for Krishna is $3 over the past few months, including yesterday.

Speaker Change #129: <unk> been investing in a few obstacles.

<unk> logging.

Speaker Change #130: And I'm wondering what's the underlying strategy is with visibility tough investment maybe there is some big opportunities that you're trying to address.

Speaker Change #130: Okay.

Christian Kleinerman: Christian here. Observability is very important for our customers. One is data observability, and being able to understand things like data quality and variations on data itself. But also, as we have evolved Snowflake into being able to host business logic and be an application platform, there's also observability for code. How do I know what my Snowpark container service is doing? Or how do I troubleshoot and monitor and get other things on Snowpark? That is the context for observability. It's an important priority for us.

Speaker Change #131: Thanks, Chris and here I'm sorry.

Speaker Change #132: For our customers fronts, one is data observer ability and be able to understand things like data quantity and variations on stater itself, but also as we have evolved snowflake into being able to close business logic and being an application platform.

Speaker Change #133: Observable Newport code, how do I know, what my Snow Park container service is doing or how do I troubleshoot and monitor.

Speaker Change #133: On Snow Park that is the context for offshore revenue is an important priority for us both.

Christian Kleinerman: We will continue to partner with all the rich ecosystems that will help us go and understand what's happening with data and code. The general comment that I will make is that

Speaker Change #133: As data as well.

Speaker Change #133: And we will continue to partner with.

Speaker Change #133: All the rich ecosystem that will help us go in.

Speaker Change #133: I understand what's happening data and code.

Sridhar Ramaswamy: The general comment that I will make is that Snowflake is a great platform to develop applications on top of, and we end up collaborating with, and sometimes investing in, a lot of companies that build interesting applications on top of Snowflake.

Speaker Change #133: The general comment that I'll make.

Speaker Change #133: Is that.

Speaker Change #133: Snowflake is a great platform to develop applications on top of.

Speaker Change #133: And we end up collaborating sometimes investing.

Speaker Change #133: A lot of companies that build interesting applications on top of Snowflake zero.

Speaker Change #133: <unk> one area, but just to give another example.

Speaker Change #133: Close partnerships with several customer data platforms and that sort of keeps going on and on because we want there to be a vibrant ecosystem on top of snowflake.

Speaker Change #134: Great. Thank you.

Tyler Radke: Our next question today comes from Tyler Radke with Citi.

Speaker Change #135: Our next question today comes from Tyler Radke with Citi. Please proceed.

Speaker Change #134: Okay.

Michael P. Scarpelli: Thank you very much. Mike, you talked about some upside from smaller customers during the quarter. Could you just talk about the nature of those small customers, the startups, maybe Gen AI companies?

Speaker Change #134: Thank you very much Mike you talked about some upside from smaller customers during the quarter.

Tyler Radke: Could you just talk about the nature of those small customers with startups may be Gen. AI companies and was this more of a one off or do you expect this to persist throughout the rest of the year.

Michael P. Scarpelli: It was very much broad-based, and it's across all industries. It's the non-G2K I'm talking about, and some of these are very large companies. There are private companies in there too, and it's across the board.

Michael P. Scarpelli: And was this more of a one-off, or did you expect the strength to persist? Oh, it was, it was very much so.

Michael P. Scarpelli: It was very much broad based and it's across all industries as the non Q2 K I'm talking about in some of these are very large companies.

Private companies in there too and it's across the board.

Okay.

Michael P. Scarpelli: Got it. And then quick follow-up on the sales and marketing side. So both the expenses and headcount increased quite a bit sequentially. Is that primarily quota-carrying hires? Is this, you know, marketing folks, just give us a sense on exactly what's driving that hiring. Well, first of all, on the expense side, we mentioned at the end that we...

Speaker Change #137: Got it and then a quick follow up on the sales and marketing side. So.

<unk> expenses and head count increased quite a bit sequentially is that primarily quota carrying hires.

Speaker Change #138: Marketing just to give us a sense on exactly what's driving that that higher investment.

Michael P. Scarpelli: Well, first of all, on the expense side, we mentioned at the end of last quarter that because of our change in comp plan, you were going to see more commission expense being expensed immediately versus deferred and amortized. As I said, it doesn't really change the cash flow, but it did add to the expense.

Speaker Change #139: Well first of all on the expense side, we mentioned at the end of last quarter because of our change in comp.

We're going to see more commission expense being expensed immediately versus deferred and amortized as I said it doesn't really change the cash flow, but it did add to the expense and we are adding a number of.

Speaker Change #139: Reps, principally a lot in the acquisition team in the commercial space.

As well as on the business development, the SDR side as well too within the company, but we are adding people throughout the sales organization, including FCS. This year, you will see us and I think we feel pretty good about our business we hit our numbers in the first quarter were constantly looking at head Count then.

Michael P. Scarpelli: And we are adding a number of reps, principally a lot in the acquisition team in the commercial space, as well as on the business development, the SDR side, as well as within the company, but we are adding people throughout the sales organization, including SEs this year. You will see us. And I think we feel pretty good about our business. We hit our numbers in the first quarter, and we're constantly looking at headcount, and we will continue to invest in the sales organization as we see that we can close them. Thank you. Our final question today comes from Aleks Zukin, who says, Hey guys, I apologize for the background noise.

Speaker Change #139: We will continue to invest in the sales organization as we see that.

Speaker Change #140: We can wrap up.

Speaker Change #141: Thank you.

Brad Robert Reback: Our final question today comes from Aleksandr Zukin with Wolf Research. Please proceed. Hey guys, I apologize for the background noise and congrats on a great quarter. Maybe just first, for Sridhar, you mentioned some really interesting core tech news pieces from Sigma, I think, on the prepared remark. Can you maybe dig in a bit more, share some of them?

Speaker Change #142: Our final question today comes from Alex Zukin with Wolfe Research. Please proceed.

Sridhar Ramaswamy: I think I got the gist of your question. I'll definitely address it.

Speaker Change #141: Hi.

Hey, guys apologize to Biopharma congrats.

Great quarter.

Speaker Change #143: Welcome screen.

Speaker Change #144: You mentioned.

Speaker Change #144: Sure.

Speaker Change #144: Thanks.

Speaker Change #145: On the prepared remarks can you maybe dig in a bit more sure some of them.

Speaker Change #145: Some of your larger customers are deploying for example, mobile art.

Speaker Change #146: Oh boy.

Brian: Thanks, Brian.

Speaker Change #146: Alright.

Speaker Change #146: Yes.

Speaker Change #146: And more productive.

Speaker Change #146: Yes.

Sridhar Ramaswamy: What Snowflake makes easy is the ability to analyze, for example, unstructured text information for things like sentiment or even categories of feedback or by using things like vector embedding. And soon, the cortex index will be able to figure out what are the most related support cases for a new question that comes in and auto generate a response. Increasingly, I think of this as the AI stack, where there's a central repository of, let's say, a bunch of previously answered questions, and then when a new question comes in, you're able to generate an answer for the new customer problem simply based on your history.

Speaker Change #146: I think I got the gist of your question.

Speaker Change #146: I will definitely address it.

Speaker Change #146: What snowflake makes easy.

Speaker Change #146: Is the ability to analyze for example on structured text information.

Speaker Change #146: Things like sentiment or even like categories of feedback or.

Speaker Change #146: By using things like Victor embedding.

Speaker Change #146: And soon the cortex index.

Speaker Change #146: Able to do be able to figure out what are the most related support cases, let's say for a new question that came in and auto generate a revpar.

Recently I think of this as the AI stack there is a central repository, let's say a bunch of previously answered questions and then a new question comes in we're able to generate an answer for the new customer problems simply based on your history. There is a little bit like what companies do in perfectly today, where they will let you search or.

Sridhar Ramaswamy: This is a little bit like, you know, what companies do imperfectly today, where they will let you search through, let's say, a forum. Snowflake has a forum for you to figure out, well, has this question already been answered? The magic of language models is that they can automate this process. So the truly new questions can get dispatched to a customer service rep to answer from scratch because, you know, the company does not know about them.

Speaker Change #146: Let's say a forum snowflake as a forum for you to figure out well has this question's already been answered the magic of language models is that they can automate. This process. So the truly new questions can get dispatched to a customer service rep to answer from scratch because the company does not know about it but to me that is a prototype.

Sridhar Ramaswamy: But to me, that is a prototype, which is there is a central repository that's sitting in Snowflake, there's a language model that is basically getting requests from outside routed in, and control logic that decides what to do with this. And obviously, something like just a pure chatbot, where you can just interact; we have one deployed for all of our IT questions internally at Snowflake, for example, just so you can have a quick conversation about a problem that somebody has already solved. We make things like this trivial.

Speaker Change #146: That is a central repository that sitting in snowflake Theres a language model that is basically getting requests from outside routed in and control logic that decides what to do with this.

Speaker Change #146: And obviously something like just a pure chat bot that he can just interact we have one deployed on all of our questions internally a snowflake. For example is just so you can have a like a quick conversation about a problem that somebody has already sorry.

Speaker Change #146: We make things like this trivial, but perhaps what is really interesting about cortex is basically language transformation I talked about sentiment detection, but there's also other stuff like summarization are extracting like data from Jason.

Sridhar Ramaswamy: But perhaps what is really interesting about Cortex is basically language transformation. I talked about sentiment detection, but there's also other stuff, like summarization or extracting data from JSON, or more complicated extracting information from, let's say, images. We automate all of those things. And the beauty of our model is all of this is driven by consumption; there is no precommitment to spend; these applications get deployed, if they get a lot of usage, that generates consumption. And so it's almost Darwinian in how great applications come up and drive usage.

More complicated extracting information from let's say images.

Speaker Change #146: We automate all of those things.

Speaker Change #146: And the beauty of our model is all of this is driven by consumption. There is no pre commit to spend these obligations get deployed if they've got a lot of usage that generates consumption and so it's almost darwinian in how like great obligations come off and drive usage.

Sridhar Ramaswamy: And obviously, making it this simple also means that complex tasks that required software engineering before just become a little pipeline that runs in Snowflake every hour, every two hours, that's acting on all of the data that is coming into Snowflake anyway. So I would say, the use cases that I'm talking about, these are just like things that you could do with Snowflake that are massively accelerated by the presence of language models. This is just one category.

And obviously, making making of Dissemble also means that complex tasks that required software engineering before just become a little pipeline that runs in Snowflake every hour every two hours that's acting on all of the data that is coming into snowflake anyway. So I would say the use cases that I'm talking about these are just like things that you could do.

Speaker Change #146: Do with Snowflake that are massively accelerated by the presence of language model. This is one category. The second one really is in how to language models make it much easier to access data that is structured data that is installed.

Michael P. Scarpelli: The second one really is how language models make it much easier to access data that is structured data that is in Snowflake. You heard me refer to it as a data API. But the idea basically is that it's currently quite hard; you have to go through an analyst, perhaps a BI tool, to get any new pieces of information. What we are working on, this is not yet in public preview, but it will be soon, is a product whereby, by giving semantic information about a Snowflake schema, you essentially make it possible for people to have a conversation with it.

<unk> heard me refer to it as like a data API that idea basically is that it is currently quite hard you have to go through an analyst, perhaps a tool to get any new pieces of information.

Speaker Change #146: What we are working on this is not yet in public preview it'll be soon is a product by which by.

Speaker Change #146: Giving semantic information about a snowflake schema, you essentially make it possible for people to have a conversation with it.

Michael P. Scarpelli: We aren't quite there yet, but I'd like to give Mike Scarpelli an app that knows about finance information that he's able to query, but actually trust the information that is coming out of it. Obviously, the big unlock there is that any business user now has access to data within Snowflake, authorized and governed, of course, but it's a much larger user base that can directly interact with Snowflake. And that's the complement, where there is direct access to data for a much larger user base. There's lots more. This is a topic that I'm super passionate about.

Speaker Change #146: We arent quite here, yet, but I would like to give Mike Scarpelli Anna.

Speaker Change #148: <unk> knows about finance information that he's able to query, but actually trust. The information that is that is coming out of it obviously the big unlock there is that any business user now has access to data within snowflake authorized and governance of course, but it's a much larger user base that can directly interacts with.

Speaker Change #148: With Snowflake and that's a compliment that is a direct access to data to a much larger user base.

Speaker Change #148: Lots more this is a topic that I'm super passionate about I can keep going on and on for hopefully you get a feel for the kinds of obligation in the first classes on structured data. The second class of structured data. Our vision is to bring all of these together into like a single box for the enterprise.

Sridhar Ramaswamy: I can keep going on and on, but hopefully, you get a feel for the kinds of applications. The first class is unstructured data. The second class is structured data. Our vision is to bring all of these together into a single box for the enterprise, where you can ask any question and be able to get an answer to it.

Speaker Change #148: You can ask any question and be able to get an answer to it.

Speaker Change #149: Makes sense and then Mike you talked about consumption.

Speaker Change #149: Exceeding expectations exceeding quarters.

Speaker Change #150: I just wanted to maybe dig.

Speaker Change #151: Rig into you talked about the broad based driver it wasn't like specifics or any maybe.

Speaker Change #152: Customer size, but is there anything around any verticals or any geos that were specifically stronger did snow park momentum contributed to that.

Speaker Change #152: Strength or anything more you can give us there.

Michael P. Scarpelli: It's really the strength in our core business, and it was across all verticals. Financial services continues to be our biggest. With that said, though, we did see some pretty good growth in the technology and healthcare space. Their growth outperformed a number of the other groups in the company, but it's broad-based.

Speaker Change #153: It's really the strength in our core business and it was across all verticals financial services continues to be our biggest with that said, though we did see some pretty good uptick in the technology.

Speaker Change #153: Health care space.

Speaker Change #153: Their growth outperformed.

Speaker Change #153: A number of the other groups in the company.

Speaker Change #153: But as broad base.

Operator: Perfect, thank you guys. Okay, thank you everyone.

Speaker Change #154: Perfect. Thank you guys.

Speaker Change #155: Okay. Thank you everyone for your questions.

Speaker Change #154: Yes.

Operator: That will conclude today's conference call. Thank you all for your participation. You may now disconnect your line.

Okay.

Speaker Change #154: That will conclude today's conference call.

Speaker Change #156: You all for your participation you may now disconnect your lines.

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