Operator: Good day, and welcome to the Q1 FY ’27 Snowflake Earnings Conference Call. Today’s conference is being recorded. At this time, I’d like to turn the conference over to Katherine McCracken, Head of Investor Relations. Please go ahead.

Katherine McCracken: Good afternoon, and thank you for joining us on Snowflake’s First Quarter Fiscal 2027 Earnings Call. Joining me on the call today are Sridhar Ramaswamy, our Chief Executive Officer; Brian Robins, 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 2027 and discuss our guidance for the second 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 10-K and 10-Q 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 definitions of the non-GAAP financial measures, and a reconciliation of GAAP to non-GAAP 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.

Sridhar Ramaswamy: Thank you, Katherine, and thank you all for joining us today. AI is fundamentally reshaping how work gets done, and Snowflake is at the center of the transformation. Across industries, organizations are moving toward a future bed employees and intelligent agents work side by side to accelerate decisions, automate complex workflows and unlock entirely new levels of productivity and innovation. With Snowflake, that future is already taking shape. Our platform brings together the four elements organizations need to become an agent enterprise, a unified governed data foundation, access to leading AI models, connectivity across enterprise applications and workflows and a unifying agent control plane that turns intent into governed action.

That control plane is becoming real through Snowflake Intelligence and Cortex Code or CoCo, as it’s affectionately known. Snowflake Intelligence gives business users a natural language interface to enterprise data context and actions, while CoCo gives builders a natural language way to create applications, pipelines, agents and workflows directly on Snowflake. Snowflake is uniquely positioned to help customers become agentic enterprises as evidenced by our Q1 results. Product revenue came in at $1.334 billion with growth accelerating to 34% year-over-year, up from 30% last quarter and 26% a year ago, marking our strongest sequential dollar growth in company history. Our net revenue retention rate increased to 126%. And with our continued focus on executing with discipline and operational rigger, our Q1 non-GAAP operating margin expanded over 300 basis points year-over-year to 12%.

I want to take a moment to touch on our outlook. Based on a combination of strength in our core data platform business, a meaningful uplift from AI capabilities, including CoCo and Snowflake Intelligence, we are increasing our FY ’27 outlook from 27% to 31% year-over-year growth. Brian will share more details on our guidance in his remarks. Thank you to all of our Snowflake for the hard work and dedication to deliver these results. Across our business, AI is strengthening Snowflake on multiple levels simultaneously. First, AI is accelerating consumption in our core platform as customers migrate workloads to Snowflake faster in order to access the data context and governance needed to power AI, securely and at scale. Second, Snowflake Intelligence and CoCo are seeing the fastest adoption of any new products in our history, opening new opportunities for growth as the first major product surfaces of the agented control plane.

And third, adoption of these AI products is increasing core platform consumption as customers move from questions to answer from prompts to pipeline and from ideas to production workflows on Snowflake. Customers who are adopting CoCo are growing even faster, and we expect that momentum to continue as adoption expands. The strength of our Q1 results reflects the powerful flywheel effect of the agentic enterprise. Importantly, this momentum starts with the strength of our core business. Our 13,912 customers start to Snowflake because our AI data cloud is easy to use, seamlessly connected for collaboration, entrusted with enterprise-grade governance and security. In fact, 42% of our customers are data sharing on Snowflake with at least one stable edge.

This underscores the part of the platform to connect organizations, partners and applications around a single governed source of truth. That interconnected foundation becomes even more valuable in the age of AI. Snowflake isn’t just software. It is a circulatory system, connecting modern enterprises, enabling data applications and AI agents to move secularly and seamlessly across organizations. This combination of connectivity, governance and ease of use is why enterprises continue to choose Snowflake as the cornerstone for their data and AI strategies again and again. To take Holiday Inn Club Vacations, a leading vacation ownership company, they choose Snowflake to power their data and AI modernization, citing our simplicity, built in AI and machine learning capabilities, a strong partnership as reasons for their selection.

With Snowflake, they are now positioned to scale analytics and operations across their business. And Health, the leading AI-driven platform for construction and design selected Snowflake to accelerate their next phase of growth, enabling faster access to insights across the business. With Snowflake, House will significantly improve data processing performance, reduce pipeline maintenance and free up engineering resources to focus on building new products. Going forward, they’ll be investing in natural language query processing and self-serve analytics to make data more accessible across the organization. On our existing customers, continue to go all in on Snowflake. After nearly 2 years and one of the most complex data warehouse migrations in financial services, one of the largest banks in the United States, completed their Teradata migration on to Snowflake.

This migration represents one of many legacy platforms they intend to move to Snowflake. Their teams are now building AI-powered regulatory intelligence, natural language analytics and data discovery directly on top of a platform they already run at massive scale. Then there is Nestle, one of the world’s largest consumer goods companies with more than 2,000 brands globally, operating in 185 countries. They’re expanding their use of Snowflake to power their enterprise digital transformation. As part of this, Nestle is reimagining its operations end-to-end with data and AI as key enablers: building enterprise data products used by over 50,000 users across 150 global capabilities. This enables a real-time connected view of the business, allowing teams to make faster and more proactive decisions.

On one of the world’s largest wealth management firms built a Cortex powered agent called Osteo data, and deployed it to their entire executive leadership team. Over 60% of business inquiries that were previously routed to analysts for manual data pools are now answered instantly on demand, leveraging their existing data in Snowflake. We also saw Global 2000 companies like Global Payments, Depository Trust and Clearing Corporation, DTCC and Blue Yonder, expand their use of Snowflake to support growing workloads, accelerate AI-powered insights and drive further value for their end customers. This continued expansion is reflected in our large customer growth. In Q1, 8 customers surpassed $10 million in trailing 12-month revenue. We now have 64 customers spending more than $10 million on a trailing 12-month basis.

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. Snowflake is uniquely positioned to lead in the next phase of enterprise AI because we already sit at the center of our customers’ data, business context, AI models and workflow. What customers increasingly want is simple, one place to get work done. A place where a business user can ask a question, understand the answer and trigger the next step. And where a developer can turn an idea into an application, a pipeline, an agent, auto workflow without leaving Snowflake. That is what we mean by the agentic control plane. It’s the governed layer where intent becomes action, grounded in the customers’ enterprise data, business context, model, applications and security policies.

Snowflake Intelligence is the business user surface of that control plane. CoCo is the builder interface. Together, they help customers move from insight to action and from prompt to production, all within Snowflake trusted governance model. In fact, accounts using Snowflake Intelligence more than doubled quarter-over-quarter as more organizations embrace a governed, conversational way for business users to ask questions, get answered and act on enterprise data. And CoCo is already in use with more than 7,100 accounts, giving builders a natural language way to create applications, pipelines, agents and workloads directly in Snowflake. Just recently, our partner, Infinite Lamda was preparing for a major customer pitch. One of our engineers use CoCo to build a true customer 360 application in just 5 hours, bringing together customer data, churn insights, recommended actions and live dashboards into a single experience, and they showed it to the customer, the reaction was immediate.

After the meeting, in CEO called me and said, you are changing this industry. Providence, one of the largest health systems in the United States is using Snowflake Cortex to surface insights from clinical notes and patient records in seconds. With CoCo, they are now building these workflows directly in Snowflake, enabling care teams to access critical information faster while maintaining privacy standards. And Thomson Reuters, the global provider of legal, tax and regulatory intelligence uses Snowflake Cortex, including cocoa to power AI-driven legal and compliance workflows. By leveraging CoCo to build and deploy intelligent applications directly within Snowflake, its teams can turn complex regulatory data into actionable insights in seconds, while accelerating product development.

This approach maintains the fiduciary grade governance and reliability required for high-stakes professional use. CoCo is contributing meaningful AI revenue while also driving increased engagement across the broader platform. This tangible momentum together with continued strength in our core platform is reflected in our increased FY ’27 outlook. Today, with the announcement of our intended acquisition of Natoma, we are extending the Snowflake agented control plane beyond data envelopment workflows into the everyday applications where work happens. With Natoma users can do things like send e-mails, summarize lack conversations, check calendars and open JIRA tickets without ever leaving Snowflake Intelligence or CoCo. The important point is not just convenience, it is control.

These actions happen from a government environment with enterprise security, permissions, observability and policy enforcement built in. This will extend Snowflake leadership in AI governance by ensuring companies can safely manage not just their data, but also the actions AI agents take across business workflows. As we continue to innovate to support our customers, we are also leading the AI transformation from within. With Snowflake Intelligence and CoCo our teams are revolutionizing how they work. Across our global support organization at Snowflake, CoCo now analyzes incoming customer cases before an engineering engages, surfacing diagnostic insights and likely root causes upfront. Alongside the use of AI accelerated investigations. This has driven over 25% faster case resolution times and a 25% increase in Crete throughput per engineer.

By using CoCo engineering team that runs Snowflake Cloud deployment has freed up capacity and moved resources to product innovation, while reducing complex case resolution time by nearly 30% and cutting engineering time spent per ticket by roughly 40%. Across our data organization, CoCo’s double developer productivity as measured by and line accord per engineer and has automated more than 100 workflows across finance, marketing, sales and HR in just weeks. Through this operational transformation, our teams are moving with greater speed and focus to capture the AI opportunity in front of us. In Q1, we delivered over 20% more product capabilities to market than we did a year ago, underscoring both the pace of our innovation and the breadth of platform expansion underway across Snowflake.

We are also strengthening our go-to-market organization to support our next phase of growth. Following a seamless transition, our new Chief Revenue Officer, Jonathan Boulier, J.B., is positioning Snowflake to scale in the AI era. J.B. brings more than a decade of experience at Snowflake, deep knowledge of our customers and platform and a strong operational focus as we continue to evolve our go-to-market motion. That strong execution is translating into continued customer momentum and broader product adoption of the platform. In the quarter, we added 616 net new customers, up 38% year-over-year. We’re also seeing customers deploy and scale workloads at a faster pace. The number of new cases, individual projects managed on Snowflake deployed in the quarter increased 114% year-over-year as customers move more workloads into production on the platform.

At the same time, the number of use cases on per account executive increased 86% year-over-year, underscoring both growing customer demand and improved sales execution across the organization. We’re also continuing to strengthen our ecosystem as we deepen our strategic partner relationships and extend the reach of our AI data cloud. Just today, we announced an expanded collaboration with AWS through a new $6 billion multiyear agreement to accelerate enterprise AI adoption globally, leveraging Graviton compute and AI services. The announcement comes as Snowflake surpassed $7 billion in lifetime AWS Marketplace sales, reflecting the growing demand for AI and data workloads running on Snowflake. During the quarter, we also announced an expanding $200 million partnership with Open AI.

And just recently, we brought the joint capability from our landmark partnership with SAP to general availability, enabling customers to unite mission-critical business data across their core data systems within our AI data clock. Before I close, I want to acknowledge our Co-Founder and Chief Architect, Benoit Dageville, who will be stepping away from day-to-day operations in mid-June and continuing as a member of Snowflake’s Board of Directors. Benoit is one of the greatest technical visionaries of our industry. His leadership and innovation helped invent the modern cloud data platform and laid the foundation for everything Snowflake has become today. The impact he’s had on this company, our customers and the broader technology landscape is extraordinary, and we are deeply grateful for his continued guidance as we enter this next chapter.

Our product organization will continue to be led by Christian Kleinerman. For the past several years, you’ve seen AI emerge as a tailwind for our business. Q1 marks an important shift in this journey. The combination of Snowflake’s trusted enterprise data, rich business context, leading AI models, and secure connectivity into enterprise applications creates a unique opportunity. Snowflake Intelligence and Cortex Co are the two primary ways customers experience that opportunity, one for business users, one for builders. Together, they allow customers to move from intent to action in a governed environment positioning Snowflake to win a new market, the agentic control plane. We are benefiting from AI as a secular tailwind while also monetizing first-party AI capabilities.

Through the combination of rapid innovation, strong go-to-market execution and operational discipline. We are well positioned to deliver accelerating growth and margin expansion. With that, I’ll turn it over to Brian to walk through the financial details. Brian?

Brian Robins: Thank you, Sridhar. In Q1, year-over-year product revenue growth accelerated approximately 400 basis points to reach 34%. Growth benefited from a meaningful increase in AI revenue and an acceleration in our core data platform business. AI is a driving force behind our momentum. AI serves as a catalyst for our core data platform business. With an AI-first mindset, customers are moving to the cloud and to Snowflake with increasing urgency. This tailwind is evident in the pace of new customer additions. As Sridhar mentioned, our net new customer additions increased 38% year-over-year. We added 13 Global 2000s compared to 4 in the same period last year. Snowflake’s AI workload is now a significant revenue engine in its own right.

AI products like Cortex Code are expanding our opportunity with existing customers as CoCo encourages faster, more consumption of the data platform. We now have 79 customers spending more than $1 million on a trailing 12-month basis. 46 customers crossed the 1 million threshold in Q1 compared to 26 in the year ago period. Remaining performance obligations grew 38% year-over-year compared to 34% in Q1 of last year. We continue to see customers favor Q4 renewals. As a result, we expect bookings to be increasingly weighted towards the fourth quarter. We remain committed to delivering both growth and margin expansion. In Q1, non-GAAP operating margin expanded over 300 basis points year-over-year to reach 12%. Strong revenue growth and disciplined hiring both contributed to the outperformance in non-GAAP operating margin.

We added 190 employees this quarter compared to approximately 400 added in the year ago period. Of these 190 employees, 173 joined Snowflake through the Observe acquisition. Excluding Observe, organic hiring was limited to 17 people in the quarter. In Q1, we used approximately $300 million to repurchase 1.7 million shares. We have approximately $800 million remaining of our original $4.5 billion repurchase authorization. We ended the quarter with $4.4 billion in cash, cash equivalents, short-term and long-term investments. During the quarter, we entered into a 5-year $6 billion contract with AWS more than doubling our prior contract signed in FY ’23. With this agreement, AWS has committed to an expanded go-to-market investment in collaboration.

This agreement marks an important milestone in our ongoing partnership with AWS and its impact is fully incorporated into our outlook. Moving to our outlook. As always, our forecast is based on existing consumption patterns. There are no changes to our forecast methodology or guidance philosophy. Given the strength we’ve observed in both our core data platform business and AI business, we are raising our guidance for the year. For FY ’27, we now expect product revenue of $5.84 billion, representing 31% year-over-year growth. In Q2, we expect product revenue between $1.415 and $1.42 billion, representing 30% year-over-year growth. Our Observe acquisition is progressing well, consistent with our initial expectations, Observe contributed less than 1 percentage point of product revenue growth in Q1, and we continue to expect the acquisition to add approximately 1 percentage point of revenue growth — product revenue growth for the full year.

Turning to margins. We expect 75% non-GAAP product gross margin for FY ’27. We expect Q2 non-GAAP operating margin at 12.5%, and we are increasing our full year non-GAAP operating margin guidance from 12.5% to 13.5%. We are reiterating our non-GAAP adjusted free cash flow margin guide of 23%. Our full year outlook for both non-GAAP operating margin and non-GAAP adjusted free cash flow margin continues to include approximately 150 basis point headwind related to our Observe acquisition. This impact is unchanged from last quarter. Our intended acquisition of Natoma will bring 20 employees to Snowflake. Before turning to Q&A, I’d like to briefly revisit my priorities for FY ’27. Last quarter, I outlined two key priorities. First, driving growth and margin expansion; second, supporting ongoing excellence in our go-to-market motion.

We are executing well on both fronts as AI strengthens every element of our business. Since last quarter, we’ve seen a step function change in our AI revenue opportunity led by Cortex Code. AI is only transforming how we operate internally enabling greater productivity through a combination of slower hiring and more cloud spend. On the go-to-market side, we’re incredibly pleased with the response to our new CRO. J.B. brings a wealth of experience and a proven track record of success at Snowflake. He understands how to deliver great outcomes and win with individual customers. More importantly, he knows how to drive that success across the broader organization. Finally, next week, we’ll host our Investor Day in conjunction with Snowflake Summit, conference in San Francisco.

If you’re interested in attending, please e-mail ir@snowflake.com. With that, I’ll pass the call to operator for Q&A.

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Operator: [Operator Instructions] And we’ll go ahead and take the first question.

Sanjit Singh: This is Sanjit Singh from Morgan Stanley. Sridhar, I’ve been covering consumption software companies, consume software companies for a long time. In a normal year, we typically don’t see the sequential dollar growth that you guys are posting up. Typically, you don’t see raises through the full year or Q2 guides the way we’re seeing with this set of results. But the simple question is like what sort of inflected in the quarter on like two fronts, I would say, maybe from a market backdrop demand perspective? And then from like a — within the Snowflake portfolio, between, let’s say, maybe vibrations, organic customer expansion in the core data platform and then the AI story. Can you talk about where specifically you’re seeing the inflection?

Sridhar Ramaswamy: Absolutely. So I would break this up into three parts. First, AI is accelerating the value that people can get from the data that they have put into Snowflake or that they can put into Snowflake. So we saw like a healthy secular tailwind for our core data platform. And part two is really that agentic products, the control plane products like Snowflake Intelligence, and Cortex Code, CoCo, came into their own in Q1. Recall that CoCo went into GA on February 5. So just as we were opening up the quarter. And we’ve seen very strong traction with both the products. And the really interesting thing with Cortex Code is that it, in turn, drives more consumption on the core data platform simply because it’s much easier to get projects done, whether it’s a pipeline or creating a new agent or setting up a new dynamic table or even honestly, a migration.

So it’s driving the second order effect as well. But it’s really — this is the 1, 2, 3, and that’s why I like to think of this as AI compounding Snowflake strength in data. And I’ll hand it off to Brian for the mechanics of how these came together in our forecast for the quarter and the year. Brian?

Brian Robins: Yes. Thanks, Sridhar. I’ll impact that a little in terms of impact. And so Cocoa had the largest driver to the increase in our forecast. As a reminder, when we forecast, we only forecast Observe behavior. And as Sri mentioned, that just happened in the quarter. And so this quarter, we had a very unique opportunity to layer CoCo in the model, and that’s reflected throughout the remainder of the year. We also saw acceleration in our core business, and that informs our outlook as well. And so there’s no change to our guidance philosophy, where 3% we view as a really strong beat.

Operator: And we’ll take the next question.

S. Kirk Materne: It’s Kirk Materne with Evercore ISI. Congrats on the quarter. Sridhar, I want to dive a little bit more into CoCo just in terms of — how does that sort of change your customers’ ability to get more data out of the platform at a faster rate? Can you just dive into that a little bit more? And then can you also just talk about how having a product like CoCo maybe changes the go-to-market model a little bit. You said, obviously, JV had a great first quarter. Just wondering how having these genic products also sort of shapes your thinking around the go-to-market efforts as we go through the rest of the year.

Sridhar Ramaswamy: Yes. So CoCo is a is a general-purpose coating agent that has a set of features that are specialized for Snowflake and data platforms. We have published benchmarks that show that CoCo can outperform even the frontier model when it comes to doing operations within Snowflake. And over the past quarter, we’ve actually expanded it to support other data platforms like Amazon Glue or Airflow or DVT cloud, and in fact, even Databricks. So it’s incredibly powerful. And in terms of how it impacts our customers’ ability, our ability, our partners’ ability to get things done faster, is any kind of coding transformation and the migration is one such example can be made faster with CoCo. We have a migration team that is busy creating, we call them harnesses, they are ways of structuring the process so that a complex migration can be broken down and attacked methodically.

We work very closely with both partners and customers and help them get these migrations done faster. And I previously talked about how many — some of our partners are even switching their entire business models from charging for time and material to being able to charge for outcomes. In addition, something like creating an agent to run insight snowflake intelligence, just goes a whole lot faster because we have created workflows within CoCo for the entirety of the agent creation pipeline. In fact, this has gotten so demystified that even somebody like me can go from a data set to things like Cortex Analysts and search instances to creating an agent to running an eval on it. That’s the life cycle of creating an agent. It’s like an automation platform for everything having to do with Snowflake.

There is a ton of activity within Snowflake and outside by partners, for example, to build even more complicated skills and processes on top of this. I very much think that this is early. And in terms of how it’s affecting our go-to-market, first and foremost, I think this products like Snowflake Intelligence have made the entirety of our go-to-market team, AI native in a way that honestly would not have been even — like we could not even imagine it a year ago because we have a lot of government data. And so our solution engineers, our sales — our account executives even can show the power of Snowflake. There’s nothing like pulling your phone out to show what Snowflake Intelligence can do as every CEO that’s met me in the last 9 months. Now that’s one of the things that I always do.

Our solution engineers are able to build much more realistic demos and prototypes and even actually get projects done for their customers very, very quickly, showing our customers what is possible with CoCo. And similarly, our internal teams, whether it’s the support team that I talked about are our SRE team, our site reliability engineering team that runs our production systems are our services team. They have 95-plus percent adoption of CoCo, which leverages them enormously when they are creating they’re creating products. And CoCo and counting agents have also changed things like enablement. It’s a lot easier to learn, and you can literally ask a coating agent how to do something, have you right have it right to example for you for you to examine it, thinker with it and then write a more complicated example.

A professor a friend of mine called coating agent self-categogical. They come with that learning built-in which means that a product feature released in CoCo can be used by someone in services literally the same week. And it’s that rapid iteration that’s also benefiting us. And so that’s the virtuous loop that we are on. We think we can get projects done faster. We think we are also, honestly, very early in the world of agentic development that are new techniques being developed, honestly, every week. And our ability to get more and more complex projects done on top of these coating agents is just enormously powerful. And I think we’re setting the standard for what data work and more is going to be like, both with CoCo, but also with Snowflake Intelligence and things like MCP are a further unlock into what is possible with these agents.

Operator: And we’ll take another question.

Karl Keirstead: It’s Karl Keirstead with UBS. I’d love to continue the conversation on CoCo, if that’s okay, for a question to both Sridhar and Brian. Sridhar, it’s pretty evident that customer spend on Cortex Code and even broadly models like Claudia bending spending higher given the token or usage-based pricing. I think a lot of investors are worried that it’s going to reach a point where customers might try to govern or throttle the use of these tools to try to contain spend. I’m just curious, are you anticipating that to happen? Perhaps the answer is that the value add is such that you are unlikely to see that? And then maybe for Brian, I think there might also be a perception that products like Cortex Code come at generally a lower gross margin than the rest of the business.

But one thing I noted from your guidance for the full year is that you stuck with the product gross margin guidance of 75% despite a big apparent uptick in Cortex Code, which suggests perhaps that the gross margin drag is minimal, if any. And I’d love to ask you to comment a little bit on that.

Sridhar Ramaswamy: I’ll start, and then I’ll hand off to Brian. Cost is always an issue that we pay attention to. This is true in Snowflake Intelligence. This is also true in Cortex Code. But what helps significantly is the fact that these are products in which you can either get things done that you are never able to before or get things done 10 times and sometimes like more than that faster. Those are not normal things. And to give you concrete examples, a very large bank that we work with has told me that while they spend several hundred million dollars on data systems as a whole, it’s a very large bank. The amount of money that they spend on the human capital that powers all of these various pieces of software and link them up is 3 to 4x that.

And anything that makes that part of the labor force 10x more effective is always incredibly welcome. . Having said that, when we want to roll, for example, Snowflake Intelligence out to 10,000 users, cost governance is absolutely an issue just like it’s an issue in Snowflake when I want to roll products out at scale. And so we are doing things like cost limits at an account level or at a particular agent level or you want to be able to restrict how much tokens a particular user can be spending. Of course, it quickly comes back to having exceptions for very talented users that are actually worth the tokens that they are using. And that’s the kind of infrastructure that we are really good at creating. And so we feel very good about being able to do that.

Plus there is a lot of innovation that we are driving within these coating agent products themselves. As I said, they handle very complicated task, but not everything is complicated. If you want to summarize, for example, I mean, I did something a couple of days ago, to summarize black threads. And perfectly small models from this raw are enough for that. You don’t need the latest and greatest focus models for summarizing slack threat. We are building those kinds of capabilities natively into Snowflake, so that it can be efficient in what kind of models that it uses. But my short answer to your question is they’re creating incredible value, but we are not resting on that. We are creating the controls that one needs in order to keep costs manageable as things continue expanding.

Then I’ll let Brian take the AIN margin question.

Brian Robins: Yes, Karl, thanks for the question. You’re absolutely right. Our AI products have a lower gross margin than our core platform. The one thing that we want to do with our AI products when we launch a new product like CoCo is make sure that we develop a great product that we get massive adoption. And we’ve seen really good adoption with CoCo. We’re up to roughly about a little over 7,000 accounts have adopted CoCo. With that said, we’re offsetting that and keeping the same product gross margin, 75% for the full year. in lower bandwidth cost, i.e., I talked about the AWS contract. And so we’re offsetting it there. So that’s how we’re able to do that. We’re committed to find efficiency to be able to maintain that 75% gross margin.

Operator: And we’ll take the next question.

Raimo Lenschow: Raimo Lenschow from Barclays. Congrats from me as well, that’s an amazing quarter. The question I have is more for Brian. Brian, like if you think about the last couple of quarters, you’ve been telling us about like the beat cadence that you think about. Obviously, this quarter, you beat by much more and CoCo is helping there, but it’s also a consumption model. Like how do you think about this going forward? And how should we think about the guidance philosophy that you have here? Maybe you can help us there, but congrats from me as well, amazing quarter.

Brian Robins: Thank you. Let me emphasize that there’s been no change in guidance philosophy and we view a 3% beat is a very solid beat. The difference that happened this quarter was CoCo was launched in the quarter. And we base guidance on Observe behavior, and so we didn’t have any Observe behavior for guidance for CoCo. And so we — we had a unique opportunity now since we’ve been able to watch that for a quarter to layer that in now for the full year, and that’s what we’ve done. And then we also saw the acceleration of the Core, and we’ve included that for the full year based on what we’ve seen.

Operator: And we’ll take the next question.

Matthew Hedberg: This is Matt Hedberg from RBC. Congrats from me as well. I had a question. There’s been a lot of talk, especially from an autonomous AI perspective, the importance of context engineering and harness engineering. And Sridhar, you mentioned that in your prepared remarks. I guess I’m wondering what role does Snowflake have in that? And how do we think about that from a moat perspective from some of the AI labs?

Sridhar Ramaswamy: Yes. The data that is stored in Snowflake is among the most valuable pieces of data for a particular company. This is the — it’s called the gold layer and typically has the most important information at Snowflake, for example, all of our revenue information or consumption information and information about the different departments are all kept in Snowflake. But on top of that, the dashboarding platforms that are written on top of Snowflake have an amount of additional context as well. And we see what they do. So our ability to provide context to AI is exceptional. And we are also busy creating products that can use this to make the act of getting value from AI even faster. I talked earlier about how we have workflow automation for the entire life cycle of creating an agent.

We want to do more than that. We want CoCo to be the place where it is fastest to get value from the data investments that you have made. And Christian is working on a key effort on this side as well. Christian, do you want to add additional context.

Christian Kleinerman: Yes. And briefly, Matt, I think your question is insightful. We have a track record of using metadata and activity inside of Snowflake to drive better results. Oftentimes, it used to be query optimization and performance. And we are now using that same type of civil activity in Snowflake to provide better context to AI. We will be showcasing at Summit some of the differences of how out-of-the-box results are better with CoCo and Snowflake Intelligence as opposed to other agents.

Sridhar Ramaswamy: This also points to the overall strategic value of Cortex Code because if a number of data users from within an enterprise are using these agentic coating platforms, in order to create end-user products. It could be skills, it could be dashboards, it could be agents. We also then have the ability to essentially learn across these. And so we have created memory concepts where use of these products within Snowflake makes Cortex Code itself much better for future use. That is part of the flywheel effect that one gets from having great agentic coating products.

Operator: And moving on to another question.

Brent Thill: It’s Brent Thill at Jefferies. Sridhar, just on the sales and marketing side, if you the backlog observed, we didn’t have a really big S&M hiring quarter. And I’m just curious, based on the demand and everything you’re seeing, why not lean a little harder in the go-to-market side. Maybe you are behind the scenes. And I think this maybe also ties into the transformation that took place in the quarter with the new head of sales. So maybe if you could just tie it all together in a go-to-market view from your perspective, that would be great.

Sridhar Ramaswamy: I think part of what we need to understand right now is that there are many, many places in which AI is making Snowflake a lot more efficient. I thought in my prepared remarks about how we have greatly increased the number of use cases that we have won, which is primarily an account executive driven activity. We’ve also had significant increases in individual productivity year-on-year. This is because of — because of AI, their ability to learn faster, pitch products that are more relevant to their customers, and also have solution engineers create prototypes that are directly relevant and in the context of the customer. So as an organization, we are just becoming a lot more effective. We will continue to invest in all of the key functions that are responsible for driving Snowflake forward, the supply exchange ring.

This applies also on the sales and solution engineering side. But it is counterbalanced by the large amount of efficiencies that we are getting in a number of other functions that are very amenable to AI automation like support like Saudi, like technical documentation. Basically, a lot of information functions and information exchange functions have gotten a whole lot easier — and this is also where teams are being very, very effective in deploying things like CoCo on Snowflake Intelligence for these kinds of use cases. We will absolutely continue to invest wherever we get strong leverage.

Operator: And we’ll take the next question.

Aleksandr Zukin: Alex Zukin from Wolfe Research. Just congrats on an amazing quarter. Sridhar, maybe for you, actually, both of them probably for you. If you think about the profile of a customer a year ago versus now a customer that’s using Cortex Code. What are you seeing in terms of the uplift on spend? And with the acquisition of Natoma that you announced, it seems to me that Cortex Code was just the beginning. Maybe it’s the first agent that you’re kind of going to launch. And you’re not stopping there. Maybe there’s a number of other ones that are coming. So can you just help us think about how that changes the potential spend profile of the customer over time?

Sridhar Ramaswamy: I mean, among the biggest like impact that products like CoCo have with our customers is simply one that of expectation. I talked again in my remarks about how we did a 2-year Teradata migration. We are engaged in more migrations, but the time lines for doing those now run between a quarter and 2 quarters. Why both my team and the customer expects and demand state. And we have the ability to deliver against that. I think the — both the inpatient and hunger or what people can do with data along with the expectation for how quickly we can get them done. I would say that’s like that’s a huge sea change. Christian?

Christian Kleinerman: With what you’re saying, Sridhar, there’s a massive backlog — what customers want to do — so just helping them do it faster, just as they get to the next set of work sooner.

Sridhar Ramaswamy: That’s right. Even our own data teams, for example, typically had backlogs that ran into multiple years. In fact, the standard request, all of you know, this is sort of funny, but not. If you had a request of a data team, the answer usually is like that’s nice, take a ticket and waited. But we are now in a situation where they can actually crack through that backlog just a whole lot more quickly unlocking value. And to go back to the question about Natoma and it’s important and coding agents, it is important to understand that Snowflake Intelligence and Cortex Code are built on the same underlying technology with just different tools having different capabilities that are exposed to end users. They use the model garden underneath that powers all of these models.

They share what’s called the harness. This is the one that is working on top of the model, deciding what tools to call. And increasingly, they are also going to be sharing the same run time. We have a cloud run time product that is in public preview. It means that all of the power that you expect from running CoCo locally can now be executed in the cloud in a governed manner. And I’m already running agents in this cloud agent platform on that ability to launch things, for example, autonomous agents because you no longer need to have your laptop open for something to run is pretty remarkable. It’s all being built on the same infrastructure for the harness, further on time as well as things like session memory. And Cortex Code and Snowflake Intelligence are just two manifestations of the same product.

And the reason MCP and Natoma are a big deal is they now bring the context entirety of SaaS application context into these products. And so I’ve done deep research reports, for example, that I’ve shown Christian that can now look for information from Snowflake, from the web, from Google Docs also from Slack and synthesize that into something that is astoundingly meaningful. And these also let you take action instantly. You can slack somebody, you can compose e-mails and send it and you can take actions on the underlying applications, and that’s the promise. We basically have a builder version and an end user version of these products. Obviously, the names make them sound more different than they are, that’s something that we are working on.

But the amount of power and flexibility that these coating agent products offer is pretty remarkable. And in my mind, the right analogy here is that a coding agent, yes, can write code, but at its core, it’s an abstraction agent. It can let you do things at a high level that previously you sort of had to sequence out one by one. And I think that’s the power that comes from them. Christian?

Christian Kleinerman: And one other comment on very important to highlight that it does that tool visibility with governance and auditability because our mission is to help every organization leverage AI in the context of the data but with governance, with security and trustworthiness. So that fits entirely into our mission.

Operator: And we’ll take another question.

Brad Reback: Brad Reback, Stifel. Sridhar, with the success you’re having here with CoCo and Snowflake Intelligence, is that fundamentally changing the competitive landscape when you’re going into new customers? Are you now seeing LOMs more than some of the older competitors?

Sridhar Ramaswamy: We come with a unique value proposition. As you folks know, even in the world of data, that cloud service providers have had products. We have very successful partnerships with them. In fact, we just announced a $6 billion partnership with one of them. Our value prop has always been very clear. We are about customer choice. We are also about a certain amount of independence from the mechanics of the cloud providers, a snowflake implementation works fine on AWS. But it can also work on Azure. We have similar really good partnerships with the leading AI labs, both Anthropic and OpenAI. We collaborate very closely with them to create great AI products, but also to create safe AI products. And similarly, Cortex Code and Snowflake Intelligence provide model choice.

We run fine on both the models. We also host a whole cities of other models ourselves and as things like open source models become more important. We always act on behalf of what is right for the customer, which I think positions us in very good stead with all our customers.

Brian Robins: And I’ll just add on to that. Just from a sales execution perspective within the quarter, the achievement was great in all geographies and all industry verticals. This was the most net new customer adds that we had in company history. And so it’s just really a solid quarter all around.

Operator: And we’ll go ahead and take the next question.

Unknown Analyst: This is Koji Kada from Bank of America. So when I talk with partners and customers of Snowflake, I hear the same thing over and over again. Snowflake is my trusted enterprise data and AI vendor with governance and security guardrails as key differentiators. I think about that a lot. But the AI world is moving so fast. And assuming the competition out there gets better with all this. What makes you confident that Snowflake’s position as the trusted enterprise data and AI partners secure over the long term?

Sridhar Ramaswamy: Because there are a set of deep infrastructure capabilities that just take a lot of time to develop, whether it is role-based access control and role-level access control at massive scale. — our world-class replication that provides for things like disaster recovery, amazing organization support and there are dozens that I’m missing, Christian, do you want to add something?

Christian Kleinerman: No, I think that, that piece on data masking and role-level policies, all of the government security configuration identity makes it such that customers have already configured Snowflake to have trusted access to the data. and AI just amplifies that as opposed to alternatives are just going to get them to reinvent the wheel and rebuild all of this, which doesn’t make much sense.

Sridhar Ramaswamy: And it’s also important to understand that we are also not sitting still our ability to create products like Snowflake Intelligence and Cortex Code, but also all of the second order effects imagine having autonomous agents that can automatically figure out if there are anomalies in your data so that you don’t have to be running those jobs outside are to be able to do governance not with endless tedious sets of SQL statements that you write, but more with the policy that specifies that this is how you want your enterprise governance to be done, and we take care of all the details and the mechanics of running these things behind or creating new classes of applications that sit on a substrate of Snowflake data powered by AI.

These are all things that we make possible. And I think, honestly, that is also what we have to do. Your core thesis that people will be able to add these features or stitch them together is true, but we are also developing great new capabilities at brick neck speed also powered by AI. I think that is what it takes to succeed today.

Christian Kleinerman: We have lots of new controls and policies will be showcasing next week, including amazing mechanisms to simplify them.

Operator: And that does conclude the question-and-answer session. I’ll now turn the conference back over to Snowflake for closing remarks.

Sridhar Ramaswamy: Thank you, everyone. To recap, AI is accelerating consumption across our core platform and our native AI products, Snowflake Intelligence and Cortex Code are scaling rapidly, already contributing meaningfully to revenue in their own right. These AI capabilities are establishing Snowflake as the agentic control plane for the enterprise, connecting data, models, applications and workflows in a trusted environment where intent becomes governed action. We are continuing to execute accelerating growth, expanding margins and deepening our customer relationships while winning many new ones. We believe that Snowflake is uniquely positioned to lead in the era of the agentic enterprise and continue to see enormous opportunity ahead. AI is compounding Snowflake’s advantage in data.

Operator: Thank you. That does conclude today’s conference. We do thank you for your participation, and have an excellent day.

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SNOWFLAKE INC

Q1 2027

Earnings Transcript

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