Good day and welcome to the second quarter FY27 Snowflake earnings presentation. Today's conference is being recorded. At this time, I would like to turn the conference over to Katherine McCracken.

Please go ahead. Good afternoon and thank you for joining us on Snowflake's second quarter fiscal 2027 earnings call. Joining me on the call today are Sridhar Ramaswamy, our Chief Executive Officer, Brian Robbins, 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 second quarter fiscal 2027 and discuss our guidance for the third quarter and full year fiscal 2027. 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 our actual results. Information concerning these risks and uncertainties is available in our earnings press release, our most recent forms 10K and 10Q, and our other SEC reports. All our statements are made as of today based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During today's call, we will also discuss certain non-GAAP financial measures. See our investor presentation for the definition of the non-gap financial measures and a reconciliation of gap to non-gap measures and business metric definitions, including customer count and adoption. The earnings press release and 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.

Thank you, Catherine. And thank you all. for joining us today. We're in the midst of a once-in-a-lifetime technology shift, and Snowflake remains at the center of the enterprise AI revolution. AI is fundamentally changing how enterprises build, operate, and make decisions. To stay competitive, every organization faces a new imperative, become an agentic enterprise, and do it quickly, safely, and cost-efficiently. Snowflake is making this transformation a reality. We bring together the core elements of an agentic enterprise, a governed data foundation, access to leading AI models, deep application workflows, and a unifying agentic control plane that orchestrates across these elements to turn intent into governed action. By putting intelligence to work at scale, our customers are building faster, executing more efficiently, and reimagining their businesses in ways that weren't possible before. Put simply, the agentic enterprise runs on snowflake. Undertraction is translating into strong business performance as evidenced by our Q2 results. Product revenue came in at $1.49 billion with growth accelerating to 37% year-over-year, marking our second consecutive quarter of record sequential dollar growth. After exiting Q4 of last fiscal year at 30% year-over-year growth, we have now added seven points of acceleration in just two quarters. And with our continued focus on executing the discipline and operational rigor, our Q2 non-GAAP operating margin expanded by more than 400 basis points year-over-year to 15%. Thank you to all of our snowflakes for the hard work and dedication that made this performance possible. As these results convincingly demonstrate, AI is compounding Snowflake's advantage across three reinforcing dynamics. First, AI is bringing new workloads onto the platform. To power their AI initiatives, enterprises need a governed, unified foundation for data and context, and companies across industries are turning to Snowflake to power that foundation. Second, our first-party AI products, Cocoa and Cowork, continue to see rapid adoption, As customers build and deploy agents on Snowflake, we are expanding our role into the agentic control plane and creating new opportunities for growth. Third, AI activation continues to lift overall platform consumption. Customers using AI on Snowflake consume more across the data platform, creating a structural multiplier for our business. Together, these dynamics show how the agentic enterprise has created a powerful flywheel across our business.

and that flywheel is accelerating.

At the heart of this momentum is the continued strength of our core business. Snowflake now provides the data and AI foundation for 14,554 customers around the world. Customers continue to turn to Snowflake because our AI data cloud is easy to use, seamlessly connected for collaboration and trusted with enterprise grade governance and security. This quarter, we added 692 net new customers, including 14 from the global 2000, representing a 32% increase in net new customer additions year over year. At the same time, some of the world's most recognizable enterprises are deepening their relationships with snowflakes. Companies like BlackRock and Block are running more of their mission-critical work on snowflakes, and in several cases, adopting Coco to move faster. The pattern is consistent. The more our customers build on Snowflake, the more they lean in. In fact, 65 customers have now crossed $10 million in trailing 12-month product revenue, demonstrating how our largest customers continue to go all in on Snowflake. Part of our strength is in extending our customers' reach to the critical data that sits outside of their organization. Currently, 43% of our customers share data on Snowflake with at least one stable edge, demonstrating Snowflake's role as the circulatory system of the modern enterprise. We enable data, applications, and AI agents to move securely and seamlessly, not just within, but across organizations. In fact, Credit chose Snowflake for our data sharing capabilities, which now facilitate privacy-safe ads measurement. And as customers move quickly to modernize their data estates and establish a strong context layer for AI, More and more customers are migrating workloads to our platform, a process now massively accelerated with AI. For example, one of the largest Australian banks migrated its financial crime platform to Snowflake, processing 17 billion transactions and delivering 10x faster query performance. Now, they're building AI agents on Snowflake to accelerate the migration of the rest of their data estate and automate legacy data discovery and mapping. As AI strengthens demand for our core platform, it is also expanding Snowflake's opportunity to deliver a new generation of AI-powered products and experience. Because Snowflake sits at the center of our customers' data, business context, AI models, and workflows, you're uniquely positioned to become the governed control plane for the agentic enterprise. Our breakout AI products, CoWork and Coco, bring that vision to life. They provide a governed layer where users across the business, from knowledge workers to builders, can put the full power of their enterprise context to work, all with simple conversational language. With Cowork & Coco, customers are reimagining some of their most critical business processes, from supply chain operations to enterprise-wide sales motion. Sayari, whose risk intelligence supports Fortune 100 enterprises and national security agencies, chose Snowflake to rebuild its global data infrastructure and cut costs by more than half. Its engineers are now using CoCo to accelerate the migration of 12 billion records into an AI-ready foundation. And as more customers see what's possible with this technology, adoption continues to build. CoRec expanded to 5,800 accounts, up nearly 11% quarter over quarter. Meanwhile, CoCo continues to see rapid adoption, surpassing 9,100 accounts and adding more than 2,000 net new accounts in this quarter alone. We have customers like 1Password, the security company trusted by more than 200,000 businesses, which choose Snowflake for our COCO capabilities. COCO enabled their team to move key data pipelines into Snowflake quickly, laying the foundation for their data and AI work. On the world's number one job site, Indeed has rolled out co-work on COCO across its data teams and integrated Snowflake into its core data architecture, citing lower cost and greater efficiency. which compounds at the scale that they operate in over 60 countries and 28 languages. But the opportunity goes beyond adoption. By making it possible to build, collaborate, and interact with enterprise data through conversational language, Cowork and Coco are bringing entirely new users to Snowflake. Within accounts adopting these products, we see a step change in user growth as Snowflake reaches new lines of business and expands its footprint within existing teams. As we continue to develop CoWork and CoCo as agency control planes, we are also building up the broader platform enterprises need to put AI to work at scale. Model choice gives customers the flexibility to select from leading frontier and open models and evolve their approach as the market changes. Post-training lets them adapt models to their specific data and business context, and agent observability and analytics give customers full visibility into what their AI is doing, how it's performing, and what it costs. And to help our customers optimize cost, performance, and speed, we've introduced Cortex AI Gateway, which dynamically routes each task to the right model based on customer-defined policies and real-world performance data with cost and governance controls built in. As those economics improve, Customers can deploy AI more broadly and with greater confidence, creating another catalyst for adoption and consumption on Snowflake. Cortex AI Gateway also extends AI from insight to action through its integration of Natoma. Users can now send emails, summarize Slack conversations, open Jira tickets, and act across their business, all without leaving Cowork or Cocoa. We also continue to advance how our agents understand the unique context of a business. At Snowflake Summit, we introduce Cortex Sense, which captures the business definitions and institutional knowledge an AI agent needs and provides that context at the moment it answers a question. This means Snowflake is giving AI both the context to understand a business and the ability to act on its behalf with enterprise security, governance, and observability built in. As we drive this AI transformation for our customers, we are leading from the front, using Cocoa and Cowork throughout our own business to accelerate productivity and efficiency. For example, in our marketing organization, Cocoa has helped bring search optimization in-house, eliminating $400,000 in annual agency spend, reducing keyword research from approximately 10 hours to 20 minutes, and content production from an estimated 24 hours down to just two. In finance, our long-range planning used to require a three-person team and more than 50 spreadsheets. It now runs with one analyst and a series of models that reflect our pricing structure and consumption dynamics. Within our sales teams, we have automated prospecting for over 125,000 contacts and leads, with 70% of initial outreach emails for inbound leads now being generated automatically before SDR involvement. We are bringing these proven use cases directly to market while applying our operational learnings to continuously upgrade our platform, moving with speed to capture the AI opportunity in front of us. In the first half of this year alone, we have launched over 330 product capabilities to general availability, 35% more than we did in the first half of last year, underscoring both the pace of our innovation and the breadth of platform expansion underway across Snowflake. Our go-to-market organization also continues to execute as reflected in strong new customer growth. We have deployed CoCo and CoWork across the sales team to analyze pipelines, prepare for customer conversations, and accelerate the onboarding of new reps. Our teams are using these products every day, learning firsthand what they can do, and taking those insights directly to our customers. We're seeing the results in how quickly customers are putting Snowflake to work, the number of use cases, individual customer projects deployed on snowflakes increased 89% year-over-year as customers move more workloads into production. At the same time, use cases one per account executive increased 43% year-over-year, demonstrating both growing customer demand and strong sales productivity. And we are pairing this investment in growth with continued operational discipline. We remain on track for GAAP profitability in Q4 fiscal 28 and the operating leverage we build along the way strengthens the durability of that outcome. Taken together, our rapid pace of innovation, fighter go-to-market execution and operational discipline positions us well to capture the huge opportunity ahead. This quarter demonstrated that the transition to the agentic enterprises accelerating and Snowflake is at the center of it. AI agents, are only as powerful as the data and business context they reason from and the governance surrounding them. Snowflake provides that trusted foundation while bringing together model choice and flexibility, access to critical applications, and the agentic control plane that connects intelligence to action across the enterprise. CoWork and CoCo demonstrate what governed architecture makes possible, enabling business users and builders to work with greater speed and intelligence while Snowflake manages the complexity underneath. And importantly, our customers' success with AI translates directly into growth for Snowflake. AI brings new workloads to the platform, extending our reach to new users and drives greater consumption across the business. We are entering the second half of fiscal 27 with strong product momentum and we see a long runway for durable high growth and continued margin expansion. The agentic enterprise runs on snowflake and we're just getting started. With that, I'll pass it to Brian to go through the financial details.

Thank you, Sridhar. In Q2, product revenue once again accelerated to reach 37% year-over-year growth. This marks our third straight quarter of acceleration. Q2 benefited from continued strength in our core data platform business and a meaningful step up in AI revenue. Our AI revenue reflects a broadening portfolio of AI capabilities. COCO delivered another standout quarter. Consumption of co-workers scaling and driving revenue contribution alongside diverse set of AI tools from AI functions and document processing to machine learning and notebooks. Our go-to-market teams continue to execute well against a strong demand environment. As Sridhar mentioned, net new customer additions increased 32% year-over-year. We added 14 net new Global 2000 customers, bringing our total to 829. Our AI Data Cloud now supports over 41% of the Global 2000. Within our existing base, customer expansion is healthy, as evidenced by our net revenue retention rate of 126%. This expansion is underpinned by growth in both migrations and AI use cases. In Q2, 48 net new customers surpassed 1 million in trailing 12-month spend. We now have 828 customers spending above the 1 million threshold. Remaining performance obligations grew 30% year-over-year, totaling 9 billion. As a reminder, we continue to see customers favor Q4 renewals. As a result, we expect bookings to be increasingly weighted towards the fourth quarter. Of the 9 billion RPO, we expect approximately 54% to be recognized as revenue in the next 12 months. This represents approximately 42% year-over-year growth compared to our estimate in the same quarter last year. Our Q2 results reinforce our commitment to delivering both growth and margin expansion. In Q2, non-GAAP operating margin expanded over 400 basis points year-over-year to reach 15%. Our outperformance was driven by strong revenue growth and disciplined headcount management. Year-to-date, we've added 334 employees, which includes 173 from our Observe acquisition. This compares to 935 added in the year-ago period. We ended the quarter of $4.3 billion in cash, cash equivalents, short-term and long-term investments.

Moving to our outlook.

As always, our forecast is based on observed consumption patterns. There are no changes to our forecast methodology or our guidance philosophy. Given the strength we've observed, both in our core data platform business and AI business, we're raising our product revenue guidance for the year. For FY27, we now expect product revenue of $6.07 billion, representing 36% year-over-year growth. This includes approximately one percentage point of growth from observed, consistent with our previous outlook. In Q3, We expect product revenue between 1.588 and 1.593 billion, representing 37 to 38% year-over-year growth. Turn into margins. For FY27, we now expect 74% non-GAAP product gross margin. This revised outlook includes a higher revenue mix from fast-growing AI workloads, which carry a lower contribution margin today. We're delivering continued operating margin expansion as we offset growing cloud costs with slowing headcount expense. We are increasing our FY27 non-GAAP operating margin guidance from 13.5 to 14.5%. For Q3, we expect non-GAAP operating margin of 15.5%. We're reiterating our full year non-GAAP adjusted free cash flow margin guide of 23%. I'd like to close with my two key goals for the year. help the business to deliver growth and margin expansion. Second, support ongoing excellence in our go-to-market motion. AI is fundamental to our progress against both goals. As we help our customers modernize their data and business operations, AI is becoming a powerful growth driver. Internally, AI is unlocking greater productivity. Across the organization, from sales to engineering to finance, our use of AI is transforming our daily work. AI is driving greater efficiency and reducing our reliance on headcount growth. Our progress against both priorities is evident in the strength of our Q2 results. With that, I'll pass the call to the operator for Q&A.

Thank you. If you are dialed in via the telephone and would like to ask a question, please signal by pressing star 1 on your telephone keypad. If you are using a speakerphone, please make sure your mute function is turned off to allow your signal to reach our equipment. A voice prompt on the phone line will indicate when your line is open. Please limit yourself to one question to allow everyone an opportunity. And we will take our first question from Sanjit Singh with Morgan Stanley.

Yeah, thank you for taking the last question and congrats on the second quarter of a pretty material acceleration. The spirit of my question is around the quality of the acceleration that you're seeing and just sort of as a backdrop around the time the company went public, you know, growth was being driven by a lot of investment in cloud, cloud native companies that may have been unprofitable. And so I wanted to ask the question on the quality of the acceleration on sort of two levels. First, on the right to win in the script, you guys mentioned supply chain use cases and finance use cases. The question here is why is Cocoa, along with the platform, the right mousetrap for these use cases that kind of extend beyond classic kind of business analytics use cases. And then on sort of the durability of the growth, like are you seeing any sort of irrational behavior or poor operational hygiene when it comes to consuming both cocoa and co-work? So really sort of a question on the quality of the acceleration you're seeing.

This is Sridhar. Let me take a first cut at this. Other folks can add on since it's a pretty broad question. First, I think we see the acceleration come from a very broad swath of customers. It is not concentrated, for example, with, let's say, AI native companies. They continue to be. A small and a small part of our overall revenue stream. And I think the thing that's also materially different this time around with folks that are investing is that products like Coco make optimization far, far easier than before. You can point Coco at a Thank you very much. We have learned the lessons of the pandemic and one thing that we stress with each and every one of our customers is the need to drive spend in an efficient way. And this is also a mantra that our sales team itself adopts pretty aggressively because they know that every such case where they go to a customer and point out things that they could be doing better is a trust building exercise. that is going to more than pay for itself in new projects that customers will implement on Snowflake. So overall, I'm pretty happy with both the fact that our growth is coming from a very broad swath of our customers without a whole lot of concentration in any one particular sector and also about the fact that the very tools that make it possible to do things quickly also come with a set of functions that make it pretty easy to optimize. And the final point, as I said, others will add on to it, the final point about our right to win for the kind of business use cases that perhaps we previously were not there in the conversation for, AI, as you know, has massively shrunk the distance between data and value. I'm sure all of you live it in your day-to-day life, but certainly I, as the CEO, can get a whole lot of value out of data a lot faster because of tools like COCOA and COWORK. And the agentic harness is indeed a very powerful weapon for solving many different kinds of problems. And it is our ability to take these powerful tools and drive our own transformation, whether it is in making SDRs more efficient or in making account planning work much more effectively at scale or in letting our sales leaders inspect and run their businesses a lot more effectively or our finance team under Brian to be a lot more effective with what they do. We are able to go to our customers and not just preach, but also demonstrate what we have shown for ourselves internally. That just gives us a lot of credibility going into these conversations about transformation.

I'll add just a little into what Sridhar said. From a durability perspective, we give our guidance-based observed behavior. So we've seen a couple quarters of this behavior. Our sales team is doing a great job with proving the business value of the use cases. and we're continuing to see great new logo additions. Cocoa, when we look at Cocoa, the accounts that are using Cocoa are consuming more of the core as well. And so there's this flywheel effect that we talk about. We had 9,100 Cocoa accounts this quarter. That's up significantly from last quarter. And the gross retention rate has been relatively flat across the last several quarters. And then just want to emphasize what Sridhar said as well is We're actually selling into way more personas today. So in a given week, I have three to five conversations with CFOs of existing customers of ours or customers that want to be. And so the CFOs are now making the purchase decision, the CRO, CMO, CEOs. And so there's a lot more personas that we're selling into this broader portfolio of products.

Appreciate the thoughts. Thank you.

Thank you.

and we will take our next question from Kirk Maturin with Evercore ISI.

Yeah, thanks very much for taking the question. Congrats on a great start to the year. I was wondering if you guys could try to separate out a little bit or give us a little bit of color on how we should think about what portion of the acceleration is coming from these newer products that are obviously getting really rapid adoption versus sort of the flywheel of those newer products on the core. I assume just Given the size of the core, it's the core growing faster is probably the bigger factor. But I was wondering if there's any way for us to sort of distill down, you know, what these products are, you know, these newer products are having maybe on their own account. Thanks.

I would roughly call it even our AI products, which is a pretty broad swath at this point. Absolutely, it's COCO and CoWork, but it's also things like AI functions that make data operations proceed faster. at an impressive scale, or even newer products like the AI Gateway. They contributed approximately half of the acceleration that we are seeing. But there are a lot of other products that are also demonstrating robust growth, and Brian touched on some of them, whether it's notebooks or applications written in Streamlit or React that are deployed into Snowflake. and of course migrations themselves going faster. I have talked pretty much in every single earnings call over the past six quarters about migrations and that is an area where we continue to get faster and faster and some of the recent advances both in models and harnesses are letting us run long duration tasks of a scale and complexity that we haven't been able to do before. And the rate at which workloads are coming onto Snowflake is also an important factor. And one anecdotal example is that a big network equipment manufacturer is doing a teradata migration in less than three quarters This is something that would have taken probably two to three years in any previous time. So these are some of the things that are contributing to our acceleration and beat.

Thanks so much, Rina.

Thank you.

And we will take our next question from Carl Kirstead with UBS.

Okay, great. Maybe I'll direct this to Sridhar and Christian. I'd love to ask about model neutrality and model choice. I'm guessing the bulk of tasks completed by COCO are being directed to frontier labs, but I'm just curious during the quarter, did you detect any interesting behavioral shift, let's say a mix shift from open class models to sonnet class models? And if that happens, Brian, is there any effect potentially positive on gross margins to Snowflake's financials. And Sridhar, is being model neutral, is that becoming a competitive advantage in cases where Snowflake competes directly with the prospect of a customer using one of the Frontier Labs standalone? Thanks so much.

I'll start. Christian will add on. As models have gotten more powerful, cost has absolutely become a concern. And all of you know this, at least as far as the frontier labs go, there used to be somewhat of a dichotomy where Anthropic was available extensively on AWS, while the OpenAI models tended to be more on Azure. The material change that's happened is that both the companies are deploying substantial capacity of their own, but it's also the case that they are available in other clouds than the ones that they started with. And we are absolutely seeing a lot of interest in being able to switch between different models and also to optimize cost. and this is also where open source models come in. There's obviously been several generations of these open source models and we support many of them within Snowflake. And yes, we have pretty different economics when it comes to open source models since we run the inference ourselves. So that offers a lot of potential for future optimization. And within our harnesses, Many of the requests that we get from customers come in this mode that we call auto, where we can pair up the task with the model that is most appropriate for that particular task. And that gives us a lot of leeway in being able to optimize tasks for our customers.

Yeah, Carl, in addition to what Sridhar said, another interesting trend that I would call it early, but we're hearing from a number of customers, is the desire to Post-train open models, which the training itself is an opportunity for us and we're starting to see a lot of interest. And to your question on whether neutrality is a competitive advantage, absolutely it is. We have heard from many, many customers that they made large commitments to one specific model company and later on are saying, oh, I should have wanted to do a different model. whereas the commitment to Snowflake gives them that flexibility and as Sridhar said, automatic routing into what is the right model for the right task. So definitely a very strong advantage for us.

This is a theme that clearly Christian and early Snowflake pioneered in terms of being able to offer really great capability across the cloud service providers. To quote Yogi Berra, it feels like deja vu all over again when it comes to model neutrality.

Okay, very helpful. Thank you.

Thank you.

Carl, just real quickly, I just wanted to hit on the margin aspect of your question. Yeah, thank you, Brian. Going back to when we develop products, the number one thing is we want to develop a great product. That is the key thing that we want to do. Secondly, we want to make sure that we have massive adoption through use cases and driving benefit to then in turn drive revenue. and then we'll work on sort of the margin implication. Sridhar and I are very committed to driving overall operating margin leverage in the business and so you saw our non-GOT product gross margin go down to 74% because we've increased our guidance so much and so the mix between our AI products and the course changed a little but we're still committed as we guided to increase our overall operating margin. And so as we go through and do model choice and use different models, The best thing for us right now is to give our customers the best answer with the best business outcome, and then we'll continue to work on margins as we go forward. But we're committed to driving operating leverage in the model.

And one more thing on this one, Carl, which is even the frontier models have been revising prices down on a regular basis and have been introducing additional models to their families, which have kept costs somewhat in check relative to the usage of organizations. Thank you.

Thank you. We will take our next question from Ramo Lenshaw with Barclays.

Thank you. from me as well.

If I look at the organization and if I look at where revenue is coming from at the moment, you're very, you're still relatively indexed towards the U.S., North America. Can you talk a little bit about what you're seeing in other regions like Europe, Asia, because it does seem there's like a big opportunity to expand the footprint there. Thank you.

Yeah, absolutely. You know, I think there's, this isn't region specific. sat in the sales QVR just a month ago and looked at sort of the performance and all regions are performing and the outlook for our regions are factored into our guidance, but all regions are operating very well.

Thank you.

Thank you. We will take our next question from Ryan McWilliams with Wells Fargo.

Hey, thanks for taking the question.

This really seems like the AI moment for the data space. What would you say is the biggest change on why AI is accelerating Snowflake revenues now? Is it Cortex code helping users get activated on AI faster? Has it been some of your other product improvements in conjunction with better AI models now making AI use cases more attractive or customers just more ready for AI? What do you think has led to this AI moment?

I spoke earlier about Flywheel. It's a lot of things coming together. What products like Cowork firmly demonstrated was the ability to get really flexible and quick value from data. The demo that I have unfailingly showed every CEO that I've met is the one in which I look up their company as a customer on Snowflake. It really brings alive the power of data in ways that abstract expressions never can. And there's this growing realization that AI is a massive unlock for getting the data to the right person. And most data teams are embracing this moment because they see this as a way to get past the unending backlogs that they've had, you know, pretty much since time immemorial. That's a little bit of effect number one. And what Coco has done for us in a super native way is it's made the entirety of Snowflake, absolutely our sales team, AI native. They feel a lot more confident about being able to support any use case on Snowflake because the answer to most problems that a customer or you run into is to simply ask Coco how you solve the problem. And in most cases it can solve it by itself. And so we see a lot of customers, a lot of partners take on migrations, get projects done that honestly we would not even have conceived of when we originally wrote Cortex-Co. That's the magic of these coding agents. And you know, in a funny kind of way, Koch also makes it far easier to create agents and get value from the data itself. And this is the combination that makes Snowflake so attractive. And it's not just acquiring customers. We track this metric called like time to 80% of purchased consumption for new logos that we acquire. and we measure it cohort by cohort. Basically, of the customers that you acquired, let's say in January, what fraction of them are consuming more than 80% of their purchase capacity? Call it three months after their purchase month. And this metric has very, very visibly improved for the newest cohorts of customers that we are acquiring. That's the power of AI. It's faster to get projects done. It's faster to get value from data. And that's the flywheel that we think is really driving the acceleration in our overall business. And as models continue to get smarter, as our ability to run more long duration things, agents in the cloud continue to mature, we expect this flywheel to accelerate even more.

Thank you.

We will take our next question from Matt Hedberg with RBC Capital Markets.

Great. Thanks for taking my question. Congrats from me as well. I wanted to piggyback on the CoCo co-work. It just seems increasingly that both products are really well positioned to agentify the modern enterprise. And Sridhar, you mentioned you use it every day, your sales team's using it every day. And I'm just kind of curious, how deep within your knowledge worker base is cocoa being used, like things like procurement? as an example. And is the right way to think about, you know, COCO being more of a sandbox, as some of these use cases become more repeatable, that these can be brought over to co-work as more turnkey use cases of agents?

This is Christian's favorite question, so I'll let him answer it.

Absolutely. Like, the pattern that we're seeing is we're leveraging COCO and co-work throughout pretty much every function and every key business process throughout Snowflake. And we're leveraging that not only to inform the quality and completeness of our products, but also go in and engage with our customers and tell them, this is how you become AI native. This is how you go and drive efficiencies. And that continues to accelerate and inform one another.

and you're talking about sort of how deep it's used by knowledge workers. Like just in my organization, we're using it in deal desk and tax and accounting and internal audit, FP&A, treasury. So we have over 150 snowflake on snow within the organization where people are using COCO to fundamentally change the way that they do work. And so the adoption within the finance organization is almost at 100%.

and it's true across functions. Absolutely.

Thank you.

We will take our next question from Koji Ikeda with Bank of America.

Hey guys, thanks so much for taking the question. So you described AI as a structural multiplier because customers using AI consume more across the broader Snowflake platform. And so what is the consumption uplift for AI adopters relative to comparable non-adopters? How is that developed across the earliest cohorts? And what evidence are you seeing or maybe what is giving you the confidence that all of this reflects higher lifetime consumption rather than projects just being pulled forward? Thank you.

Yeah, I'll take a first cut and Brian will add on. At this time, we aren't ready to share the exact uplift numbers, but we do measure cohort behavior. And as cocoa adoption gets deeper, more users within an account adopting, and more accounts and more customers themselves adopting, The effect is pretty noticeable for all the different cohorts that we have worked with. And what gives us confidence that this is not merely projects being pulled forward is both the breadth and depth of use cases that are coming our way in terms of what people are doing with COCO and CoWork. It is allowing people to do fairly sophisticated actions that previously would have required things like applications. Our own sales leadership teams, for example, have been experimenting a lot with their inspection process, how they can drive their business forward, and something like that would have required a specialized piece of software, a multi-quarter implementation cycle, and then a staged rollout. Things like that are literally now a matter of a pretty smart sales leader saying things in English and having co-work translate that into what looks like a product. This combined with the fact that we are now having conversations with our customers about a set of use cases that honestly we would not have been considered before. This is everything from supply chain optimization are much better support systems in the case of Sanofi are much better fraud and risk detection systems. This is what gives us confidence that there is both breadth and depth in what AI is able to do for Snowflake.

Thank you. Thank you. We will take our next question from Brent, though, with Jefferies.

Thanks, Sridhar. On Coco, good to see 2,000 accounts added. I guess when you start to see now quarter over quarter, is there a difference you're seeing in adoption? Are you getting bigger lands, more users, bigger consumption right out of the gate? Anything that you're seeing that's a trend line since the product has shipped?

Yeah, I work with the team that... basically does go to market. This is the sales team, especially on the solution engineering side, our specialist team, but also the product team. And we have a pretty sophisticated methodology for measuring cocoa penetration from we need to get through legal terms all the way to there are a set of daily users of the product that are living inside cocoa. We have our own pipeline for the different stages of this penetration. But more importantly, we also now have a suite of tools ranging from in-product guidance within Snowsight to hands-on labs that we run for three hours with our customers. And obviously, we have a lot of customers. We can do hands-on labs with each and every one of them. but we are getting much better at matching our actions to the things that are going to drive outcomes. We are also doing a good job of sharing best practices across the different theaters in the globe. All of this is driving just really positive momentum and more importantly, this feels like a problem that is ours to solve and drive at scale for the simple reason that cocoa makes every single thing that a customer does with snowflake go faster and better. So it's among the easiest sales that we have done to our customers. But I'm also pretty happy with how methodical and thorough we are being in driving cocoa adoption.

Thank you.

Thank you.

We will take our next question from Brad Zelnick with Deutsche Bank.

Hey, thanks. And this is Dan on for Brad. Congrats on a great quarter. I wanted to maybe go back to an earlier question on kind of model neutrality or optionality with, you know, with open and frontier models now being offered. Maybe there's a third leg around models of your own like Arctic that might be specifically tuned for the Snowflake platform. I'd just be curious what the latest is in terms of your ambitions here and how that all might kind of fold into the overarching model strategy for Cocoa and Co-Work.

Yeah, so Christian here, Brad. We have not changed the direction we've been on, which is we're not training models to go get into a frontier type of model. But we have continued developing models in the Arctic family for tasks that are more specific, more constrained, that we can provide higher accuracy and more efficiency. We do that in some of the AI functions. We do that for some of the document processing. We do that for embedding, et cetera. So we will continue doing that type of activity. and as you know, the mixing and matching of Frontier, closed models, open-weight models and our own models with fine-tuned models will continue to be part of how we help customers at the end of the day deliver or achieve what they want which is what is the right model for the right task that gives the correct results at the best efficiency.

Thank you. We will take our next question from Alex Zucca with Wolf Research.

Hey guys, thanks for taking the question and congrats on an exceptional quarter.

I guess maybe Sridhar, it feels like we're still very early in the agentic enterprise experience and yet you guys are already seeing a pretty meaningful inflection. And I appreciate that it's too maybe early to share the kind of ARPU expansion at some of these early adopters. You talked about accessing larger kind of strategic priorities, maybe larger budgets. So maybe can you just talk about how much, what is the ambit of opportunity that you are now able to access and see in terms of budget dollars? And maybe weave in, we've heard some really exciting tales of your FDE program and some of the exceptional traction that's getting out there in the marketplace, particularly on the outcome-based selling. So maybe just give us a sneak preview of that as well.

Yeah.

As I was remarking earlier, AI has dramatically lowered the distance between business value that somebody sees, I mean, that a company sees, and the data estate that's next to it. And often it's not as complicated as it sounds. Recently I was talking to an asset manager that manages tens of billions of dollars of assets and they have this problem where they get a very large number of data sets delivered to them every single day They have a large portfolio of assets that they have and a set of decisions that they're in the process of making about new moves that they could be taking. Obviously, this is distributed across hundreds, if not thousands of people. That act of distributing information effectively is basically manual at this place. It's spreadsheets being passed around. Someone has to download a spreadsheet and update a model that's probably sitting on their local PC. and we are talking to them about how do we construct effectively like a multiplexer demultiplexer for the most important information that is coming and that can meaningfully lower both their return and reduce their exposure because models just do a much better job of doing this kind of work and that's just one among many, many, many conversations that I end up having, which is pretty remarkable for a person effectively heading a data infrastructure company. We've also hired a set of exceptional folks that have industry expertise that can answer simple questions around what are the top six things that are going to make the biggest difference to a company's top line and bottom line, and is there a new perspective that we can offer to these. and this is what the Frontier Engineering team is doing. It is combining a knowledge of what is possible with the data platform with harnesses like CoCo and CoWork with the industry-specific knowledge needed to drive meaningful outcomes to our customers. We have talked publicly about working with folks like Sanofi in our Frontier Engineering program. But this is an area where there is breadth and depth of adoption. We are, for example, helping a big financial institution effectively overhaul their digital and data strategy and bring it to the modern world in a way that is very, very sustainable for them. And the confidence that we have going into these kinds of engagements is not just that we commit to delivering the outcome, Obviously, we get paid only when we deliver outcomes in situations like this. But it's also in the fact that Snowflake is an open, well-understood platform. And compared to some pretty proprietary folks out there, where you have to go back to them after you get the first outcome, we can confidently tell them that their data team is very, very capable of driving further engagement with the projects that they have done and building on top of it. It's the combination of these things, our ability to truly talk about business outcomes, commit to delivering them, but deliver it on a clean, open, well-understood architecture that makes the customer looks good and stay good that I'm most excited by.

Excellent. Thank you.

Thank you. We will take our next question from Tyler Radke with Citi.

Hey, thank you.

Sridhar, I wanted to ask your take on some of the moves we've seen from traditional SaaS companies partnering with LLMs and sort of becoming more of a database themselves as the LLMs sort of take the UI layer. How do you see this playing out? Does it make sense for Snowflake to take on more of this system of record data? And how do you sort of anticipate that that competitive overlap looks over time?

I mean, the way I think about this is that as software gets easier and easier to create, it's the data and semantics that acquire more and more importance. It isn't lost on any of us that our ability to talk about new value with our customers is driven both by the breadth of the data estates that many, many of our customers have on Snowflake combined with the power of the harness, obviously using the best models. So I've been very, very consistent for now two plus years in my conviction, in our conviction that owning the user experience is critical. And we see cocoa and co-work as fundamental to our future. because they demonstrate to us and to our customers what is possible.

But on the other hand, we understand that we live in a world where we have to play nice.

Snowflake is only a part of the overall software estate that our customers have. We offer interoperability at multiple levels, but we think our flagship products are very important to our future.

Yeah, I'll add maybe that the notion of some of these application providers becoming database players is not a new trend. And what we hear consistently from CIOs and CDOs is if I use three applications, I'm not going to copy my data into three different platforms. It's easier to consolidate in a single central platform like Snowflake, which is why we have bi-directional zero-copy partnerships with many of them, and we see a lot of customers Aligning their data estates with Snowflake.

And our investments in which Christian has pioneered and spearheaded with the team for a very long time around being able to host applications in Snowflake, small and big, also positions us exceptionally well for many applications, not just analytic ones, but also systems of record, you know, operational ones. that can be built right on top of Snowflake. And so internally, we have many projects, some of which Christian and I don't even know, of people that are building interesting applications on top of the analytic data and operational stores that they're setting up within Snowflake. You can definitely expect to hear a lot more about things like hybrid tables and Postgres because they are the foundation, we think, for a new generation of agentic applications, some of which will have UI and some of which won't, on top of Snowflake.

Thank you. Thank you. We will take our next question from Dharmak Jaffaji with JPMorgan.

Hi, thanks for taking my question and congrats from my end on the strong results here. Maybe if I can ask on the full year guide and trying to parse out the increase in the full year guide between core increases on the core versus AI. I think the last quarter you had mentioned most of the full year guide increase was on account of COCO. This quarter, it sounds a lot more balanced between core and AI and your confidence in forecasting acceleration and product revenue growth also seems to be much higher. So just wondering if there's something fundamentally that changed during the quarter in terms of Thank you Brian, thanks for the question. We base our guidance based on observed behavior up until the call that we have and you know what we saw is that you know we talked about sort of you know COCO, COWORK and all the AI functions driving

additional business, but as well as the people who adopt them, they're also increasing business within the core. So it's a reflection of the strength that we're seeing in our AI products, as well as the underlying strength that we're seeing in the core.

Thank you. This concludes today's question and answer session. I will now pass the call back to Snowflake for closing remarks.

Thank you, everyone. The agentic enterprise runs on snowflakes.

We have just achieved 37% year-over-year product revenue growth, marking our third straight quarter of acceleration, while expanding our non-GAAP operating margin 400 basis points year-over-year to 15%. AI has created a powerful flywheel effect across our business, strengthening platform demand, driving adoption of our native AI products, and in turn, fueling greater consumption across the business. And this flywheel is accelerating. Based on this strength, we have increased our fiscal 27 product revenue guidance by over 500 basis points to 36% year-over-year growth.

We are executing with discipline and focus and see enormous opportunity ahead. Thank you.

Thank you.

This does conclude today's call. Thank you for your participation. You may now disconnect