Q4 2026 Snowflake Inc Earnings Call

February 25, 2026

Speaker #1: Good day, ladies and gentlemen. Thank you for joining today's Snowflake Q4, FY26 earnings call. My name is Tia, and I will be your moderator for today's call.

Operator: Good day, ladies and gentlemen. Thank you for joining today's Snowflake Q4 FY 2026 Earnings Call. My name is Tia, and I will be your moderator for today's call. All lines will be muted during the presentation portion of the call, with an opportunity for questions and answers at the end. If you would like to ask a question, please press star 1 on your telephone keypad. I would now like to pass the call over to your host, Katherine McCracken, Head of Investor Relations. Please proceed.

Speaker #1: call over to your host, Katherine McCracken, All lines will be muted during the presentation Head of Investor Relations. Please proceed.

Speaker #2: Good afternoon, and thank you for joining us on Snowflake's fourth quarter fiscal 2026 earnings call. Joining me on the call today are Sridhar Ramaswamy, our Chief Executive Officer; Brian Robbins, our Chief Financial Officer; and Christian Klinerman, our Executive Vice President of Product, who will participate in the Q&A session.

Katherine McCracken: Good afternoon, thank you for joining us on Snowflake's Q4 fiscal 2026 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 Q4 fiscal 2026, and discuss our guidance for Q1 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.

Speaker #2: During today's call, we will review our financial results for the fourth quarter fiscal 2026 and discuss our guidance for the first quarter and full year fiscal 2027.

Speaker #2: 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.

Speaker #2: 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.

Speaker #2: 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.

Katherine McCracken: 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 our 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 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.

Speaker #2: 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 adoption.

Speaker #2: 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.

Speaker #2: With that, I would now like to turn the call over to Sridhar.

Speaker #3: Thank you, Katherine. And thank you all for joining us today. This past year has been transformative for every business. A year ago, we were talking about the promise of AI.

Sridhar Ramaswamy: Thank you, Katherine, and thank you all for joining us today. This past year has been transformative for every business. A year ago, we were talking about the promise of AI. Today, the promise is real, and Snowflake sits at the center of the enterprise AI revolution. Across the market, AI is reshaping the software landscape, redefining categories and competitive dynamics. In our view, this is creating a clear separation between systems that demonstrate intelligence and platforms that can deploy it safely and at scale. The winners will be the platforms that combine trusted enterprise data, govern business metrics, secure execution, and broad model choice, and make all of it easy to use. That's exactly what Snowflake was built to do. We deliver the data foundation enterprises rely on across clouds and across data types, with the performance, reliability, and operational simplicity required for mission-critical workloads.

Speaker #3: Today, the promise is real. And Snowflake sits at the center of the enterprise AI revolution. Across the market, AI is reshaping the software landscape, redefining categories, and competitive dynamics.

Speaker #3: In our view, this is creating a clear separation between systems that demonstrate intelligence and platforms that can deploy it safely and at scale. The winners will be the platforms that combine trusted enterprise data, governed business metrics, secure execution, and broad model choice, and make all of it easy to use.

Speaker #3: That's exactly what Snowflake was built to do. We deliver the data foundation enterprises rely on across clouds and across data types. With the performance reliability and operational simplicity required for mission-critical workloads.

Speaker #3: As AI agents become central to how work gets done, those same capabilities become even more valuable because agents are only as powerful as the data they can access, and the governance and security that surround it.

Sridhar Ramaswamy: As AI agents become central to how work gets done, those same capabilities become even more valuable, because agents are only as powerful as the data they can access and the governance and security that surround it. You can see that leadership in what we shipped this year. With Snowflake Intelligence, we brought enterprise-grade agentic capabilities directly to business teams. With the general availability of Cortex Code, we extended that to builders, accelerating the entire data lifecycle and helping customers move faster from development to production. Most recently, we expanded Cortex Code CLI to encompass data systems as we work towards simplifying how all of them are used in practice. The general-purpose agentic capabilities of Cortex Code CLI, combined with our AI-ready data on Snowflake, are already driving meaningful operational impact just weeks after launch.

Speaker #3: You can see that leadership in what we shipped this year. With Snowflake Intelligence, we brought enterprise-grade agentic capabilities directly to business teams. With the general availability of Cortex Code, we extended that to builders.

Speaker #3: Accelerating the entire data lifecycle and helping customers move faster from development to production. Most recently, we expanded Cortex Code CLI to encompass data systems as we work towards simplifying how all of them are used in practice.

Speaker #3: The general purpose agentic capabilities of Cortex Code CLI combined with our AI-ready data on Snowflake are already driving meaningful operational impact just weeks after launch.

Speaker #3: Snowflake Intelligence and Cortex Code are meaningful steps in Snowflake's evolution. On the platform, where enterprises govern and analyze their data, to the platform where they build and run AI-native applications and workflows.

Sridhar Ramaswamy: Snowflake Intelligence and Cortex Code are meaningful steps in Snowflake's evolution on the platform where enterprises govern and analyze their data to the platform where they build and run AI-native applications and workflows. Turning to our results, product revenue in Q4 grew 30% year-over-year to reach $1.23 billion. Remaining performance obligations totaled $9.77 billion, with year-over-year growth accelerating to 42%. Our net revenue retention was at a healthy 125%. Thanks to AI, we are both scaling revenue and becoming operationally more efficient. Fiscal 2026 non-GAAP operating margin reached 10.5%, expanding more than 400 basis points year-over-year, reflecting our continued focus on operational rigor.

Speaker #3: Turning to our results, product revenue in Q4 grew 30% year over year to reach $1.23 billion. Remaining performance obligations totaled $9.77 billion, with year-over-year growth accelerating to $42%.

Speaker #3: Our net revenue retention was at a healthy $125%. Thanks to AI, we are both scaling revenue and becoming operationally more efficient. Fiscal 26 non-GAAP operating margin reached $10.5%, expanding more than $400 basis points year over year.

Speaker #3: Reflecting our continued focus on operational rigor. Stock-based compensation declined meaningfully from 41% of revenue in fiscal 25 to 34% in fiscal 26, and we expected to further decrease to 27% of revenue in fiscal 27.

Sridhar Ramaswamy: Stock-based compensation declined meaningfully from 41% of revenue in fiscal 2025 to 34% in fiscal 2026. We expect it to further decrease to 27% of revenue in fiscal 2027. This year's results are a testament that the AI Data Cloud continues to deliver tremendous value to our more than 13,300 customers across every stage of the data lifecycle. Built with deep product cohesion, Snowflake is easy to use, seamlessly connected for collaboration, grounded in the security and governance enterprises trust. As we innovate, we remain maniacally focused on driving great business outcomes for our customers. That focus is why leading organizations continue to choose Snowflake as the foundation for their data and AI strategies. We added 2,332 net new customers this year. We are seeing more and more businesses move over to Snowflake.

Speaker #3: This year's results are a testament that the AI data cloud continues to deliver tremendous value to our more than 13,300 customers across every stage of the data lifecycle.

Speaker #3: Built with deep product cohesion, Snowflake is easy to use, seamlessly connected for collaboration, grounded in the security and governance enterprises trust. As we innovate, we remain maniacally focused on driving great business outcomes for our customers.

Speaker #3: That focus is why leading organizations continue to choose Snowflake as the foundation for their data and AI strategies. We added 2,332 net new customers this year, and we are seeing more and more businesses move over to Snowflake.

Speaker #3: Seagate, for example, is modernizing its data foundation to better support its mission of powering data-driven innovation at global scale. By consolidating a massive data environment on Snowflake, the company is moving away from legacy infrastructure onto a platform built for scalability, reliability, and predictable cost—enabling teams across the business to access high-performance, AI-ready analytics and make faster, more informed decisions.

Sridhar Ramaswamy: Seagate, for example, is modernizing its data foundation to better support its mission of powering data-driven innovation at global scale. By consolidating a massive data environment on Snowflake, the company is moving away from legacy infrastructure onto a platform built for scalability, reliability, and predictable cost, enabling teams across the business to access high-performance, AI-ready analytics and make faster, more informed decisions. Our core business remains strong, and AI is expanding workloads across our platforms. Capital One is a great example of how we are deepening our relationships with key customers. As Capital One scales its AI initiatives, they're leveraging Snowflake to unify proprietary data, optimize engineering workloads, and deliver AI-driven analytics across the enterprise. Key to our growth is the strength and momentum around our AI products. This quarter, we delivered the largest sequential increase in accounts using AI, bringing the total to more than 9,100 accounts.

Speaker #3: Our core business remains strong and AI is expanding workloads across our platforms. Capital One is a great example of how we are deepening our relationships with key customers.

Speaker #3: As Capital One scales, its AI initiatives, they are leveraging Snowflake to unify proprietary data. Optimize engineering workloads and deliver AI-driven analytics across the enterprise.

Speaker #3: Key to our growth is the strength and momentum around our AI products. This quarter, we delivered the largest sequential increase in accounts using AI, bringing the total to more than $9,100 accounts.

Speaker #3: And in just three months, Snowflake Intelligence has scaled from a nascent offering to an essential capability for over 2,500 accounts, almost doubling quarter over quarter.

Sridhar Ramaswamy: In just 3 months, Snowflake Intelligence has scaled from a nascent offering to an essential capability for over 2,500 accounts, almost doubling quarter-over-quarter. For example, Toyota Motor Europe, a global automotive leader, is leveraging Snowflake Intelligence to revolutionize its operations. By enhancing enterprise search with easy-to-use knowledge chatbots and streamlining contract management through Document AI, Toyota has fundamentally shifted its development timelines, reducing AI agent deployment from months to weeks, creating a significant competitive advantage. United Rentals, a global leader in equipment rentals, is using Snowflake Intelligence to power a new business intelligence agent that helps teams across more than 1,600 branches get real-time answers from their financial and operational data using natural language. The agent enables faster, more consistent decision-making for frontline managers.

Speaker #3: For example, Paradigm Order Europe, a global automotive leader, is leveraging Snowflake Intelligence to revolutionize its operations. By enhancing enterprise search with easy-to-use knowledge chatbots, and streamlining contract management through document AI, Toyota has fundamentally shifted its development timelines, reducing AI agent deployment from months to weeks, creating a significant competitive advantage.

Speaker #3: And United Rentals, a global leader in equipment rentals, is using Snowflake Intelligence to power a new business intelligence agent that helps teams across more than 1,600 branches get real-time answers from their financial and operational data using natural language.

Speaker #3: The agent enables faster, more consistent decision-making for frontline managers. United Rentals is also using Snowflake's Cortex Code to accelerate the development and testing of additional AI agents scaling trusted intelligence across the business.

Sridhar Ramaswamy: United Rentals is also using Snowflake's Cortex Code to accelerate the development and testing of additional AI agents, scaling trusted intelligence across the business. That's just the start of what Cortex Code can do. It's a truly transformational coding agent that's already helping over 4,400 customers build and scale AI-powered applications and massively accelerating their ability to deploy production-grade AI. The chief technology officer of one of our partners, evolv Consulting, described Cortex Code's impact on their business, saying, quote, "20 days, 21,000 operations, or 600 hours of work delivered. That is 16 workweeks compressed into less than a month. Development cycles that used to require extensive research, trial and error, and debugging now flow naturally through AI-assisted iteration. We're using this capability to accelerate how we bring new workloads onto Snowflake for our customers." End quote.

Speaker #3: And that's just the start of what Cortex Code can do. It's a truly transformational coding agent that's already helping over 4,400 customers build and scale AI-powered applications.

Speaker #3: And massively accelerating their ability to deploy production-grade AI. The chief technology officer of one of our partners, Evolve Consulting, described Cortex Code's impact on their business, saying, "20 days, 21,000 operations, over 600 hours of work delivered.

Speaker #3: That is 16 work weeks compressed into less than a month. Development cycles that used to require extensive research, trial and error, and debugging now flow naturally through AI-assisted iteration.

Speaker #3: We're using this capability to accelerate how we bring new workloads onto Snowflake for our customers." Cortex Code meaningfully expands the surface of AI development on our platform and reinforces Snowflake as the enterprise AI foundation.

Sridhar Ramaswamy: Cortex Code meaningfully expands the surface of AI development on our platform and reinforces Snowflake as the enterprise AI foundation. As we look forward, we continue to see immense opportunity to support enterprises across the data lifecycle, and we are innovating rapidly opportunity. This year, we launched over 430 product capabilities, underscoring the strength of our product velocity. We are broadening how data enters and flows through Snowflake. Snowflake Openflow, now generally available, makes it easier than ever to bring in structured, unstructured, batch, or streaming data into the platform. We've also deepened how applications are built on Snowflake. Now generally available, Snowflake Postgres is a world-class operational database built directly onto the Snowflake platform, enabling developers to build and run production-grade transactional applications with the performance, reliability, and ecosystem of Postgres, fully managed and governed within Snowflake.

Speaker #3: As we look forward, we continue to see immense opportunity to support enterprises across the data lifecycle. And we are innovating rapidly to create opportunity.

Speaker #3: This year, we launched over 430 product capabilities, underscoring the strength of our product velocity. We are broadening how data enters and flows through Snowflake.

Speaker #3: Snowflake Open Flow now generally available makes it easier than ever to bring in structured, unstructured, batch, or streaming data into the platform. We've also deepened how applications are built on Snowflake.

Speaker #3: Now generally available, Snowflake Postgres is a world-class operational database built directly onto the Snowflake platform. Enabling developers to build and run production-grade transactional applications with the performance, reliability, and ecosystem of Postgres fully managed and governed within Snowflake.

Speaker #3: This transforms Snowflake from a system you analyze with into a platform that you build on. And our recent acquisition of Observe, a market-leading observability platform, extends the value that Snowflake can deliver.

Sridhar Ramaswamy: This transforms Snowflake from a system you analyze with into a platform that you build on. Our recent acquisition of Observe, a market-leading observability platform, extends the value that Snowflake can deliver. By integrating observability directly with data and AI products, we reduce complexity and enable faster, more reliable operations at scale. This expands our opportunity into the $50 billion IT operations market and positions Snowflake to lead in next-generation AI-powered observability. At the same time, we are strengthening the ecosystem around the platform. Our landmark partnership with SAP is delivering incredible value, helping customers like Xpand Energy unite mission-critical business data across their core systems within our AI Data Cloud. Our deepened partnership with Anthropic is already helping customers like Intercom see significant impact. Snowflake provides the secure, governed data foundation that Intercom's AI is built on.

Speaker #3: By integrating observability directly with data and AI products, we reduce complexity and enable faster, more reliable operations at scale. This expands our opportunity into the $50 billion IT operations market and positions Snowflake to lead in next-generation AI-powered observability.

Speaker #3: At the same time, we are strengthening the ecosystem around the platform. Our landmark partnership with SAP is delivering incredible value, helping customers like expand energy and unite mission-critical business data across their core systems within our AI data cloud.

Speaker #3: Our deepened partnership with Anthropic is already helping customers like Intercom see significant impact. Snowflake provides the secure, governed data foundation that Intercom's AI is built on.

Speaker #3: By applying direct AI capabilities to this data, including their use of Anthropic's cloud models, Intercom automates customer support at scale. This allows it to handle significantly higher support volumes with greater consistency and lower operational burden, especially for large complex customers.

Sridhar Ramaswamy: By applying direct AI capabilities to this data, including their use of Anthropic's cloud model, Intercom automates customer support at scale. This allows it to handle significantly higher support volumes with greater consistency and lower operational burden, especially for large, complex customers. We also recently announced a $200 million expanded partnership with OpenAI. It brings OpenAI's models natively into Snowflake to help our customers innovate faster while keeping their data secure and governed. Through our partnership with Google Cloud, customers now have access to the latest Gemini models natively within Snowflake, further expanding model choice and availability. As we innovate, we are scaling efficiently. Work is fundamentally changing, and we are leading this transformation both within Snowflake and across the industry. In many cases, we are creating entirely new AI-native systems built directly on Snowflake.

Speaker #3: We also recently announced a $200 million expanded partnership with OpenAI. It brings OpenAI's models natively into Snowflake to help our customers innovate faster, while keeping their data secure and governed.

Speaker #3: And through our partnership with Google Cloud, customers now have access to the latest Gemini models natively within Snowflake, further expanding model choice and availability.

Speaker #3: As we innovate, we are scaling efficiently. Work is fundamentally changing and we are leading this transformation both within Snowflake and across the industry. In many cases, we are creating entirely new AI-native systems built directly on Snowflake.

Speaker #3: Across our business, Snowflake Intelligence and Cortex Code are already delivering measurable results. Our service delivery team can complete customer projects up to five times faster improving response accuracy by more than 25% and compress implementation cycles from days to hours.

Sridhar Ramaswamy: Across our business, Snowflake Intelligence and Cortex Code are already delivering measurable results. Our service delivery team can complete customer projects up to 5 times faster, improving response accuracy by more than 25%, and compress implementation cycles from days to hours, to drive 40% to 50% higher project margins and enabling customers to go live more than 40% faster. We have seen our site reliability engineering investigations that once required hours across multiple engineers, now resolved in minutes, dramatically reducing resolution times and further strengthening Snowflake's reliability. We have built agentic capabilities that help our sellers prioritize accounts, automate research, and generate personalized outreach, projected to recoup the equivalent of 90 full-time engineers of productivity this year. Our finance team is working on automating travel and expenses analysis, proactively curbing out-of-policy behavior, an initiative that is expected to drive millions in annual savings.

Speaker #3: To drive 40% to to 50% higher project margins and enabling customers to go live more than 40% faster. We have seen our site reliability engineering investigations that once required hours across multiple engineers now result in minutes.

Speaker #3: Dramatically reducing resolution times and further strengthening Snowflake's reliability. And we have built agentic capabilities that help our sellers prioritize accounts, automate research, and generate personalized outreach—projected to recoup the equivalent of 90 full-time engineers of productivity this year.

Speaker #3: Our finance team is working on automating travel and expenses analysis, proactively curbing out-of-policy behavior in initiatives that are expected to drive millions in annual savings.

Speaker #3: And we are seeing this transformation within our customers as well. They are leveraging agents not just to analyze information, but to automate complex workflows and, in some cases, retiring entire categories of previously used software systems.

Sridhar Ramaswamy: We're seeing this transformation within our customers as well. They are leveraging agents, not just to analyze information, but to automate complex workflows, and in some cases, retiring entire categories of previously used software systems. Take Sanofi, for example. AI-powered workflows built on Snowflake with partners like Elementum are replacing the traditional software systems used for processes like software license and invoice management. By running these workflows directly in Snowflake, Sanofi is streamlining operations while keeping its data securely within the platform. This is where the enterprise is heading, we believe Snowflake is uniquely positioned to become the control plane for the agentic era. We've built the conditions that make agents safe, scalable, and enterprise-ready, covering a single enterprise-wide source of truth, governed metrics and shared business definitions, cross-cloud and cross-domain interoperability, built-in security, auditability, and governance.

Speaker #3: Take Sanofi, for example. AI-powered workflows built on Snowflake with partners like Elementum are replacing their traditional software systems used for processes like software license and invoice management.

Speaker #3: By running these workflows directly in Snowflake, Sanofi is streamlining operations while keeping its data securely within the platform. This is where the enterprise is heading.

Speaker #3: And we believe Snowflake is uniquely positioned to become the control plane for the agentic era. We've built the conditions that make agents safe, scalable, and enterprise-ready, covering a single enterprise-wide source of truth.

Speaker #3: Governed metrics and shared business definitions, cross-cloud and cross-domain interoperability, built-in security, auditability, and governance. Our continued rapid innovation, tight go-to-market alignment, and operational discipline are all in high gear to capture this opportunity.

Sridhar Ramaswamy: Our continued rapid innovation, tight go-to-market alignment, and operational discipline are all in high gear to capture this opportunity, and we see a long runway of durable, high growth, and continued margin expansion ahead. I'll turn it over to Brian to take us through the financial details.

Speaker #3: And we see a long runway of durable, high growth and continued margin expansion ahead. Now, I'll turn it over to Brian to take us through the financial details.

Speaker #2: Thank you, Sridhar. Q4 was a strong quarter across revenue, bookings, and margin results. Product revenue grew 30% year over year. Our results were driven by stable growth in our core business in a step-up in growth contribution from AI workloads.

Brian Robins: Thank you, Sridhar. Q4 was a strong quarter across revenue, bookings, and margin results. Product revenue grew 30% year-over-year. Our results were driven by stable growth in our core business and a step-up in growth contribution from AI workloads. We saw no decline in our Net Revenue Retention rate, which remains at 125%. Q4 sales execution was outstanding. Remaining Performance Obligations accelerated for the second consecutive quarter. We signed the largest deal in Snowflake's history, greater than $400 million in total contract value, and signed 7 nine-figure contracts, compared to 2 in the same period last year. These strong commitments represent Snowflake's strategic role in our customers' long-term data and AI strategies. We've consistently emphasized durable growth depends on two fundamentals: landing new customers and expanding existing ones. We've delivered on both.

Speaker #2: We saw no declines in our net revenue retention rate, which remains at 125%. Q4 sales execution was outstanding. Remaining performance obligations accelerated for the second consecutive quarter.

Speaker #2: We signed the largest deal in Snowflake's history, greater than $400 million in total contract value, and signed seven nine-figure contracts compared to two in the same period last year.

Speaker #2: These strong commitments represent Snowflake's strategic role in our customers' long-term data and AI strategies. And we've consistently emphasized durable growth depends on two fundamentals.

Speaker #2: Landing new customers and expanding existing ones. We've delivered on both. We delivered another strong quarter of new customer wins adding 740 net new customers up 40% year over year including 15 global 2,000 organizations.

Brian Robins: We delivered another strong quarter of new customer wins, adding 740 net new customers, up 40% year-over-year, including 15 Global 2000 organizations. At the same time, we're proving that we can drive meaningful customer expansion. We now have 733 customers spending more than $1 million on a trailing-twelve-month basis, growing 27% year-over-year, and a record number of customers crossed $10 million in trailing-twelve-month spend, bringing a total of 56 customers above this $10 million threshold, growing 56% year-over-year. Turning to our margin results. FY 2026 non-GAAP product gross margin was 75.8%. We are demonstrating that we can scale while driving efficiency. FY 2026 non-GAAP operating margin was 10.5%, and FY 2026 non-GAAP adjusted free cash flow margin was 25.5%.

Speaker #2: At the same time, we're proving that we can drive meaningful customer expansion. We now have 733 customers spending more than $1 million on a trailing 12-month basis growing 27% year over year.

Speaker #2: And a record number of customers crossed $10 million in trailing 12-month spend, bringing a total of $56 customers above this $10 million threshold, growing 56% year over year.

Speaker #2: Turning to our margin results. FY26 non-GAAP product gross margin was 75.8%. We are demonstrating that we can scale while driving efficiency. FY26 non-GAAP operating margin was 10.5%.

Speaker #2: And FY26 non-GAAP adjusted free cash flow margin was 25.5%. Earlier this month, we closed the acquisition of Observe which we acquired for approximately $600 million in a combination of cash and stock.

Brian Robins: Earlier this month, we closed the acquisition of Observe, which we acquired for approximately $600 million in a combination of cash and stock. With Observe's offering, we're unlocking new expansion opportunities within our customer base. The impact of the acquisition is reflected in our outlook. In Q4, we used $150 million to repurchase approximately 668,000 shares at a weighted average share price of approximately $225. We have $1.1 billion remaining on our repurchase authorization and ended the quarter with $4.8 billion in cash equivalents, short-term, and long-term investments. Before moving to our outlook, I'd like to share my priorities for FY 2027. First, I see a clear opportunity to drive both growth and operating margin expansion. We are investing in our key growth drivers.

Speaker #2: With Observe's offering, we're unlocking new expansion opportunities within our customer base. The impact of the acquisition is reflected in our outlook. In Q4, we used $150 million to repurchase approximately 668,000 shares at a weighted average share price of approximately $225.

Speaker #2: We have $1.1 billion remaining on our repurchase authorization and ended the quarter with $4.8 billion in cash equivalents short-term and long-term investments. Before moving to our outlook, I'd like to share my priorities for FY27.

Speaker #2: First, I see a clear opportunity to drive both growth and operating margin expansion. We are investing in our key growth drivers. As Sridhar relayed, we deployed more than 430 product capabilities to market this year.

Brian Robins: As Sridhar relayed, we deployed more than 430 product capabilities to market this year. We'll continue to expand operating margins as we drive greater efficiency across the business. Second, it's clear that our go-to-market motion is working. My focus for this next year is on ensuring stability and ongoing excellence. We've established a financial framework to support continued product velocity and sales execution. Now, let's look to our outlook for FY 2027. In Q1, we expect product revenue between $1.262 to 1.267 billion, representing 27% year-over-year growth. For FY 2027, we expect product revenue of approximately $5.66 billion, representing 27% year-over-year growth. We expect Observe to contribute approximately one percentage point of product revenue growth in FY 2027. As always, our forecast is built on using existing patterns of consumption.

Speaker #2: We'll continue to expand operating margins as we drive greater efficiency across the business. Second, it's clear that our go-to-market motion is working. My focus for this next year is on ensuring stability and ongoing excellence.

Speaker #2: We've established a financial framework to support continued product velocity and sales execution. Now, let's look to our outlook for FY27. In Q1, we expect product revenue between $1.262 and $1.267 billion representing 27% year over year growth.

Speaker #2: For FY27, we expect product revenue of approximately $5.66 billion representing 27% year over year growth. We expect Observe to contribute approximately 1 percentage point of product revenue growth in FY27.

Speaker #2: As always, our forecast is built on using existing patterns of consumption. There are no changes to our forecast process or our guidance philosophy. Our outlook is supported by continued strength in our core business and further growth in AI workloads.

Brian Robins: There are no changes to our forecast process or our guidance philosophy. Our outlook is supported by continued strength in our core business and further growth in AI workloads. We expect FY 2027 non-GAAP product gross margin of 75%. We're guiding Q1 non-GAAP operating margin of 9% and FY 2027 non-GAAP operating margin of 12.5%. Our hiring this year will be weighted to Q1, reflecting the addition of 178 employees from Observe. We expect non-GAAP adjusted free cash flow margin of 23%. This includes an approximate 150 basis point headwind related to our acquisition. As in prior years, we expect our bookings will continue to be weighted to Q4, and we expect next year's non-GAAP adjusted free cash flow seasonality to mirror FY 2026.

Speaker #2: We expect FY27 non-GAAP product gross margin of 75%. We're guiding Q1 non-GAAP operating margin of 9% and FY27 non-GAAP operating margin of 12.5%. Our hiring this year will be weighted to the first quarter, reflecting the addition of $178 employees from Observe.

Speaker #2: We expect non-GAAP adjusted free cash flow margin of 23%. This includes an approximate $150 basis point headwind related to our acquisition. As in prior years, we expect our bookings will continue to be weighted to the fourth quarter and we expect next year's non-GAAP adjusted free cash flow seasonality to mirror FY26.

Speaker #2: Finally, we'll host an investor day in conjunction with our summit conference the week of June 1st in San Francisco. If you're interested in attending, please email ir@snowflake.com.

Brian Robins: Finally, we'll host an Investor Day in conjunction with our Snowflake Summit conference the week of June first in San Francisco. If you're interested in attending, please email ir@snowflake.com. With that, I'll pass the call to the operator for Q&A.

Speaker #2: With that, I'll pass the call to the operator for Q&A.

Speaker #3: We would now begin the Q&A session. If you would like to ask a question, please press star followed by 1 on your touch tone keypad.

Operator: We will now begin the Q&A session. If you would like to ask a question, please press star followed by 1 on your touch tone keypad. If for any reason you would like to remove that question, please press star followed by 2. Again, to ask a question, press star 1. As a reminder, if you are using a speakerphone, please remember to pick up your handset before asking your question. We will pause here briefly to allow questions to generate in the queue. The first question comes from the line of Sanjit Singh with Morgan Stanley. Please proceed.

Speaker #3: If for any reason you would like to remove that question, please press star followed by 2. Again, to ask a question, press star 1.

Speaker #3: As a reminder, if you are using a speakerphone, please remember to pick up your handset before asking your question. We will pause here briefly to allow questions to generate in queue.

Speaker #3: The first question comes from the line of Sanjit Singh with Morgan Stanley. Please proceed.

Speaker #4: Yeah, thank you for taking the question. Thank you, Russ, on reasserting 30% product revenue growth in Q4. I had two questions. Starting with Brian and then hopefully for you, Sridhar.

Sanjit Singh: Yeah, thank you for taking the questions, and congrats on reasserting 30% product revenue growth in Q4. I had two questions, starting with Brian and then, hopefully for you, Sridhar. Brian, on the guide for fiscal year 2027, it basically implies sustained growth around 27% throughout the year. Just sort of just want to get your perspective on the durability of that 27%, given that it's a consumption model, sort of a sustained growth off of a really good year this year. Just, just sort of the confidence in that.

Speaker #4: Brian, on the guide for fiscal year 27, basically implies sustained growth around 27% throughout the year. And just wanted to get your perspective on the durability of that 27% given that it's a consumption model sort of sustained growth off of a really good year this year.

Speaker #4: So just sort of the confidence in that. And then for Sridhar, as we go into the first four years of Snowflake Intelligence and an expanded product portfolio, I was wondering if you can give a sense of where we are in terms of momentum with the areas of the business outside of the core.

Sanjit Singh: For Sridhar, as we go into the first full year of Snowflake Intelligence and an expanded product portfolio, I was wondering if you can give us a sense of where we are in terms of momentum with the areas of the business outside of the core. I think we got an update on the data engineering revenue run rate or growth rate, you know, several quarters ago. Once we get an update on that and where we sort of stand with the AI portfolio, exiting this year and going into fiscal year 2027. Thanks.

Speaker #4: I think we got an update on the data engineering revenue run rate or growth rate several quarters ago. So once we get an update on that and where we sort of stand with the AI portfolio exiting this year and going into fiscal year 27.

Speaker #4: Thanks.

Speaker #2: Thanks, Angie. I'll go first. From a guidance perspective, we guide based on observing customer behavior up until really the point of earnings. And the guidance, if you sort of double-click into it this year, is really based on the high, stable growth that we see in our core business.

Brian Robins: Thanks, Sanjit. I'll go first. From a guidance perspective, we guide based on the observed customer behavior up until really the point of earnings. The guidance, if you sort of double-click into it this year, it's really based on the high, stable growth that we see in our core business. It's also the growing contribution from AI workloads. Finally, we called out in the prepared remarks, there's one percentage point of growth from our Observe acquisition. I'll turn it over to Sridhar for the second part.

Speaker #2: It's also the growing contribution from AI workloads. Then finally, we called out in the prepared remarks. There's 1 percentage point of growth from our Observe acquisition.

Speaker #2: I'll turn it over to Sridhar for the second part.

Sridhar Ramaswamy: To just reiterate on top of that, our overall guidance philosophy hasn't really changed. We continue to be very stable with respect to that. I see products like Snowflake Intelligence now with 2,500 customers, as a major driver of growth across all aspects of the data lifecycle. I think what products like Snowflake Intelligence, and I never tire of showing every single CXO and CEO that I meet, Snowflake Intelligence on my phone. The ready access that it offers is truly magical to critical business information, and that reinforces the need for enterprises to adopt Snowflake to get their data estates in gear so that they can bring the transformative power of things like Snowflake Intelligence to that data. The really important thing also to remember about Snowflake Intelligence is that it works fine on all open data.

Speaker #5: And to just reiterate on top of that, our overall guidance philosophy hasn't really changed. We continue to be very stable, respect that. I see products like Snowflake Intelligence now with 2,500 customers.

Speaker #5: As a major driver of growth across all aspects of the data lifecycle. I think what products like Snowflake Intelligence and I never tire of showing every single CXO and CEO that I meet Snowflake Intelligence on my phone.

Speaker #5: They already access that it offers is truly magical to critical business information. And that reinforces the need for enterprises to adopt Snowflake, to get their data estates in gear so that they can bring the transformative power of things like Snowflake Intelligence to that data.

Speaker #5: Really important thing also to remember about Snowflake Intelligence is that it works fine on all open data. You can build Snowflake an amazing agent with using Snowflake Intelligence on data that is sitting in S3 managed by Glue or sitting in other places.

Sridhar Ramaswamy: You can build Snowflake, a amazing agent with using Snowflake Intelligence on data that is sitting in S3, managed by Glue, or sitting in other places. Any open data ecosystem is supported by Snowflake Intelligence, that's really very powerful. Cortex Code is the real game changer for us because it is a massive accelerant for every part of the data lifecycle. What I mean by that is, we can build Openflow pipelines to bring in data from complex systems into Snowflake at a fraction of the time that it used to take before. Similarly, building dbt pipelines to run data engineering on that data, or to build Dynamic Tables or debug performance issues with either of these, now is again, 10x plus faster. What's magical about Coco is also the ability to actually build Snowflake Intelligence agents faster.

Speaker #5: Any open data ecosystem is supported by Snowflake Intelligence. And that's really very powerful. But CartXcode is the real game changer for us because it is a massive accelerant for every part of the data lifecycle.

Speaker #5: What I mean by that is we can build open flow pipelines to bring in data from complex systems into Snowflake at a fraction of the time that it used to take before.

Speaker #5: Similarly, building DBD pipelines to run data engineering on that data, or to build dynamic tables, or debug performance issues with either of these, now is again 10x-plus faster.

Speaker #5: And what's magical about Coco is also the ability to actually build Snowflake Intelligence agents faster. I think that's the unlock of AI using AI to make things go faster.

Sridhar Ramaswamy: I think that's the unlock of AI using AI to make things go faster. We see this, as I said, of having transformative effects on our business. I'll give you folks an anecdote. One of our partners wrote to us after using Cortex Code CLI and said that, you know, all this time they had been using shovels to dig, and we just gave them bulldozers.

Speaker #5: And we see this, as I said, of having transformative effects on our business. I'll give you folks an anecdote. One of our partners wrote to us after using CartXcode CLI and said, "All this time, they had been using Shauls to dig, and we just gave them bulldozers." Let's go to the next question.

Operator: Let's go to the next question, Mark Murphy.

Speaker #5: Mark Murphy.

Speaker #3: Thank you. The next question comes from the line of Mark Murphy with JPMorgan. Please proceed.

Operator: Thank you. The next question comes from the line of Mark Murphy with J.P. Morgan. Please proceed.

Operator: Thank you. The next question comes from the line of Mark Murphy with JPMorgan. Please proceed.

Speaker #2: Yeah, thank you so much. So the bookings and RPO figures look very robust once again. It looks like the biggest bookings figure in the history of the company actually by a pretty wide margin.

Mark Murphy: Thank you so much. The bookings and RPO figures look very robust once again. It looks like the biggest bookings figure in the history of the company, actually, by a pretty wide margin. I just want to ask first, can you describe the $400 million deal in terms of the customer type? Because it's a gigantic contract. I just don't think we've heard anything like that. Second, I'm curious if you see some sustainable new drivers kicking in there for bookings, like maybe, thinking back on, you know, achieving a faster product GA cadence is something you've done, or what, you know, is this a little more temporary, one-time?

Speaker #2: I just want to ask first, can you describe the 400 million dollar deal in terms of the customer type? Because I don't it's a gigantic contract.

Speaker #2: I just don't think we've heard anything like that. And second, I'm curious if you see some sustainable new drivers kicking in there for bookings.

Speaker #2: Maybe thinking back on achieving a faster product GA, cadence is something you've done. Or is this a little more temporary one-time? You had the hiring surge several quarters ago, and I think you've been incentivizing reps a little more, a little more heavily on bookings this year.

Mark Murphy: You know, you had the hiring surge, several quarters ago, and I think you've been incentivizing reps a little more, a little more heavily on bookings this year. Just wondering if you can comment on this.

Speaker #2: So I'm just wondering if you can comment on this.

Speaker #4: I can start, Brian, and you can add on. Bookings and multi-year contracts are a clear indication of the trust that our partners have in their future with Snowflake.

Sridhar Ramaswamy: I can start, Brian can add on. Bookings and multi-year contracts are a clear indication of the trust that our partners have in their future with Snowflake. Yes, the product acceleration and velocity goes a lot towards convincing customers that we are a platform for the future. We didn't do anything particularly special in the quarters. Yes, we did adjust the compensation plan to also take bookings into account last year, but in many ways, that represents a reversion back to how things were two years ago. We plan to continue that this year, it's very much business as usual. I do think that the $400 million, $400+ million deal that we signed is an indication of the importance that we deliver to that large financial services customers.

Speaker #4: And yes, the product acceleration and velocity goes a lot towards convincing customers that we are a platform for the future. We didn't do anything particularly special in the quarters.

Speaker #4: Yes, we did adjust the compensation plan to also take bookings into account last year. But in many ways, that represents a reversion back to how things were two years ago.

Speaker #4: So we plan to continue that this year. So it's very much business as usual. I do think that the 400 million, 400 plus million dollar deal that we signed is an indication of the importance that we deliver to that large financial services customers.

Sridhar Ramaswamy: I do think that the $400 million, $400+ million deal that we signed is an indication of the importance that we deliver to that large financial services customers. We have previously talked about deals in the $250 million range. I think it represents a maturity of Snowflake as a durable provider, not just today, of data services, but also into the future. Brian?

Speaker #4: We have previously talked about deals in the 250 million dollar range. I think it represents a maturity of Snowflake as a durable provider not just today, of data services, but also into the future.

Sridhar Ramaswamy: We have previously talked about deals in the $250 million range. I think it represents a maturity of Snowflake as a durable provider, not just today, of data services, but also into the future. Brian?

Speaker #4: Brian?

Speaker #2: Well said, Sridhar. I would say one of the big contracts, over $400 million, was with an existing customer. So it was already built into the run rate.

Brian Robins: Well said, Sridhar. You know, I would say when the big contract over $400 million, it was an existing customer, so it's already built into the run rate. We did sign seven, nine-figure deals as well. Just to re-echo what Sridhar says.

Speaker #2: We did sign seven, nine-figure deals as well. And so just to re-echo what Sridhar says, it's. Yeah, just Q4. And just to re-echo what Sridhar says, it's really a buy-in from our customers on our product roadmap and AI strategy in the positive business outcomes that we're delivering for their business.

Mark Murphy: Just Q4.

Brian Robins: Yeah, just Q4. Just to re-echo what Sridhar says, it's really a buy-in from our customers on our product roadmap and AI strategy, and the positive business outcomes that we're delivering for their business.

Speaker #4: Wonderful. Congratulations and thank you.

Mark Murphy: Wonderful. Congratulations, and thank you.

Speaker #2: Thanks, Mark.

Brian Robins: Thanks, Mark.

Speaker #3: Thank you. The next question comes from the line of Brad Zelnick with Deutsche Bank. Please proceed.

Operator: Yeah. The next question comes from the line of Brad Zelnick with Deutsche Bank. Please proceed.

Speaker #2: Great. Thanks so much. And I'll echo my congrats. Sridhar, I guess this one's for you. Just coming away from sales kickoff and now the first full year with go-to-market under Mike Gannon's command, what are you going to do differently in the field to win and drive upside in fiscal 27?

Brad Zelnick: Great. Thanks so much, I'll echo my congrats. Sridhar, I guess this one's for you. Just coming away from sales kickoff and now the first full year with go-to-market under Michael Scarpelli's command, what are you going to do differently in the field to win and drive upside in fiscal 2027?

Speaker #4: Well, Mike's had a year. He is had a very positive influence on the sales team. But I think what drives momentum for the whole company and absolutely the sales team are great products that let our sellers, our solution engineers deliver value for our customers.

Sridhar Ramaswamy: Mike's had a year. He is had a very positive influence on the sales team. I think what drives momentum for the whole company, and absolutely, the sales team, are great products that let our sellers, our solution engineers, deliver value for our customers. I have never seen more excitement from our sales force about the products that we create. We have had multiple people, I'll let Christian chime in because he gets a lot of, a lot of these accolades. We have had multiple people come and tell us how Cortex Code is absolutely transformational in what people can do with Snowflake. Many folks come and tell us that they've never felt as much excitement about a product that we have created since when the original product was created.

Speaker #4: And I have never seen more excitement from our sales force about the products that we create. We have had multiple people. I'll let Christian chime in because he gets a lot of a lot of these accolades.

Speaker #4: We have had multiple people come and tell us how CartXcode is absolutely transformational in what people can do with Snowflake many folks come and tell us that they've never felt as much excitement about a product that we have created since when the original product was created.

Sridhar Ramaswamy: We have had multiple people come and tell us how Cortex Code is absolutely transformational in what people can do with Snowflake. Many folks come and tell us that they've never felt as much excitement about a product that we have created since when the original product was created. Christian had a section of Cortex Code heroes that highlighted their experience. I'll let him say it, since he was the one that ran that section.

Speaker #4: And Christian had a section of CartXcode heroes that highlighted their experience. I'll let him say it since he was the one that ran that section.

Sridhar Ramaswamy: Christian had a section of Cortex Code heroes that highlighted their experience. I'll let him say it, since he was the one that ran that section.

Speaker #5: Yeah, super quickly, partners, customers, and our internal field are all incredibly excited about the results we're seeing with CartXcode. The original value prop of Snowflake, which is change what's possible in terms of ease of use, is just gone like 10x with CartXcode.

Operator: Yeah, super quickly, like.

Christian Kleinerman: Yeah, super quickly, like orders, customers and our internal field are all incredibly excited about the results they're seeing with Cortex Code. The original value prop of Snowflake, which is change what's possible in terms of ease of use, it's just gone like 10x with Cortex Code. We showcase a number of instances where people are building pipelines faster, transformation faster, insights faster, and I think we're only at the beginning of what is possible.

Christian Kleinerman: Customers and our internal field are all incredibly excited about the results they're seeing with Cortex Code. The original value prop of Snowflake, which is change what's possible in terms of ease of use, it's just gone like 10x with Cortex Code. We showcase a number of instances where people are building pipelines faster, transformation faster, insights faster, and I think we're only at the beginning of what is possible.

Speaker #5: We showcase a number of instances where people are building pipelines faster, transformation faster, insights faster. And I think we're only at the beginning of what is possible.

Speaker #4: One of our sales leaders, who I assure you, would be the last person to declare himself to be a software engineer built a Streamlit application, deployed it on Snowflake, and had his team use it.

Sridhar Ramaswamy: One of our sales leaders, who I assure you, would be the last person to declare himself to be a software engineer, built a Streamlit application, deployed it on Snowflake, and had his team use it. That's how easy Cortex Code makes it to use data from Snowflake.

Speaker #4: That's how easy CartXcode makes it to use data from Snowflake.

Speaker #2: Exciting stuff. Thanks, guys.

Operator: Exciting stuff. Thanks, guys.

Brad Zelnick: Exciting stuff. Thanks, guys.

Speaker #3: Thank you. The next question comes from the line of Kirk Mattern with Evercore ISI. Please proceed.

Operator: Thank you. The next question comes from the line of Kirk Materne with Evercore ISI. Please proceed.

Speaker #6: Hey, this is Srug on for Kirk. Thanks for taking the question. Sridhar, observability is a big market, right? How does Observe fit into that topography?

Chirag Ved: Hey, this is Chirag on for Kirk. Thanks for taking the question. Sridhar, observability is a big market, right? How does Observe fit into that topography, and what were you seeing in the market and in the company that it made sense to bring them in-house? Thank you.

[Analyst] (Evercore ISI): Hey, this is Chirag on for Kirk. Thanks for taking the question. Sridhar, observability is a big market, right? How does Observe fit into that topography, and what were you seeing in the market and in the company that it made sense to bring them in-house? Thank you.

Speaker #6: And what were you seeing in the market and in the company that it made sense to bring them in-house? Thank you.

Speaker #4: Observability, especially in the world of AI, is a big deal. As you point out, it's a very large market, a $50 billion plus market, which means that it has many different angles of expertise that go into it.

Sridhar Ramaswamy: Observability, especially in the world of AI, is a big deal. As you point out, it's a very large market, a 50 billion-plus market, which means that it has many different angles of expertise that go into it. AI observability, in particular with agents, is a big, big deal. I'm sure many of you use agents, and no one is ever going to accuse a coding agent of not being chatty. There's just volumes upon volumes of text that then need to be distilled into things like skills, into things like what went right and what went wrong. We see this as a critical data problem, and we also see it as a natural extension of our overall role as a data platform.

Speaker #4: And AI observability in particular with agents is a big, big deal. I'm sure many of you use agents. And no one is ever going to accuse a coding agent of not being chatty.

Speaker #4: There's just volumes upon volumes of text that then need to be distilled into things like skills, into things like what went right and what went wrong.

Speaker #4: And so we see this as a critical data problem. And we also see it as a natural extension of our overall role as a data platform.

Speaker #4: Observe was built on top of Snowflake. So it inherits the excellent data and compute foundation that Snowflake has. And for a lot of our customers, especially ones with very large volumes of data, observability as traditionally done has become a little bit of a sore point with respect to just the sheer cost of it.

Sridhar Ramaswamy: Observe was built on top of Snowflake, so it inherits the excellent data and compute foundation that Snowflake has. For a lot of our customers, especially ones with very large volumes of data, observability, as traditionally done, has become a little bit of a sore point with respect to just the sheer cost of it. This is where Observe is able to offer a value prop that is factors away, not like 10, 20%, factors more efficient. I think those are the kinds of customers that are going to benefit enormously. There is a huge overlap between potential customers of Observe and customers of Snowflake, and it's really that one-two punch of Observe is built on Snowflake, so our job of integrating it is very simple. Observe has an excellent value prop for a large set of customers that also happen to be Snowflake customers.

Speaker #4: And this is where Observe is able to offer a value prop that is factors away, not like 10, 20 percent. Factors, more efficient, and I think those are the kinds of customers that are going to benefit enormously.

Speaker #4: There is a huge overlap between potential customers of Observe and customers of Snowflake. And it's really that one-two punch of Observe's built on Snowflake—our job of integrating it is very simple.

Speaker #4: Observe has an excellent value prop for a large set of customers that also happen to be Snowflake customers. That was ultimately the thing that made both Jeremy and the Observe team want to be part of Snowflake.

Sridhar Ramaswamy: That was the, ultimately, the thing that made both Jeremy and the Observe team want to be part of Snowflake. We are very excited for what's ahead. Christian, anything to add? No. That's great. Let's move on to the next question.

Sridhar Ramaswamy: That was the, ultimately, the thing that made both Jeremy and the Observe team want to be part of Snowflake. We are very excited for what's ahead. Christian, anything to add?

Speaker #4: We are very excited for what's ahead. Christian, anything to add? Let's move on to the next question.

Christian Kleinerman: No. That's great. Let's move on to the next question.

Speaker #3: Thank you. The next question comes from the line of Raymond. Michelle with Barclays. Please proceed.

Operator: Thank you. The next question comes from the line of Raimo Lenschow with Barclays. Please proceed.

Operator: The next question comes from the line of Raimo Lenschow with Barclays. Please proceed.

Speaker #7: Hi, this is Sheldon McMean for Raimo. Thanks for taking our question. As you keep making the Snowflake platform more accessible to users and your solutions, you certainly have an exciting opportunity to expand users and consumption.

Sheldon McMeans: Hi, this is Sheldon McMeans on for Raimo. Thanks for taking our question. As you keep making the Snowflake platform more accessible to users and your solutions, you certainly have an exciting opportunity to expand users and consumption. You know, there is also risk, of, you know, maybe sticker shock as AI agents proliferate or new users create, you know, more applications and workloads on your platform. How are you working with customers to, you know, help reduce the risk of cycles of strong growth and optimization? Just a little bit on, do you feel like customers truly understand, kind of the potential consumption uplift they can have as they leverage your agents more?

Speaker #7: But there is also a risk of maybe sticker shock as AI agents proliferate or new users create more applications and workloads on your platform.

Speaker #7: And so how are you working with customers to help reduce the risk of cycles of strong growth and optimization and just a little bit on do you feel like customers truly understand kind of the potential consumption uplift they can have as they leverage your agents more?

Speaker #4: It's a great question, but one that we've spent a lot of time thinking about. Let's make sure we examine the counterfactual for some of the early agent products.

Sridhar Ramaswamy: It's a great question, but one that we've spent a lot of time thinking about. Let's make sure we examine the counterfactual for some of the early agent products. Several of them were launched as part of subscription bundles. Many companies that offer agent platforms see them as an extension of their existing subscription model. At Snowflake, we charge based on consumption. We therefore offer a very predictable model. I'm also of the firm belief that products have to show value right out of the gate, and I can quote you our personal example, where our sales agent replaced a legacy dashboarding system that we were paying close to $5 million for. It delivered ROI out of the gate, because that moved to be a set of Streamlit and Snowflake Intelligence.

Speaker #4: Several of them were launched as part of subscription bundles. And many companies that offer agent platforms see them as an extension of their existing subscription model.

Speaker #4: At Snowflake, we charge based on consumption, and we therefore offer a very predictable model. I'm also of the firm belief that products have to show value right out of the gate.

Speaker #4: And I can quote you our personal example, where our sales agent replaced a legacy dashboarding system that we were paying close to $5 million for.

Speaker #4: And so it delivered ROI out of the gate, because that moved to be a set of Streamlits, and Snowflake Intelligence. And this is where we feel like we are very, very value-aligned, but we are not stopping there.

Sridhar Ramaswamy: This is where we feel like we are very, very value-aligned. We are not stopping there. We know that our customers will want price predictability, even with Snowflake Intelligence. We will be launching features like a per-user cap on top of Snowflake Intelligence. They can feel like there is a clear upper limit to how much they can get charged with an agent. We think models like this, that are consumption-based with clear user caps and account caps offer the best of both worlds, which is consumption pricing with price predictability. We'll continue to innovate rapidly in this area because we think these agents can deliver huge value. Absolutely, we don't want our customers to have sticker shock.

Speaker #4: The know that our customers will want price predictability even with Snowflake Intelligence. So we will be launching features like a per-user cap on top of Snowflake Intelligence so they can feel like there is a clear upper limit to how much they can get charged with an agent.

Speaker #4: We think models like this that are consumption-based with clear user caps and account caps offer the best of both worlds, which is consumption pricing with price predictability.

Speaker #4: And we'll continue to innovate rapidly in this area, because we think these agents can deliver huge value and absolutely, we don't want our customers to have sticker shock.

Speaker #4: We want to be predictable. And we will provide the controls that are necessary to make for wide deployments of Snowflake Intelligence. We've also done things like integrate Snowflake as a whole with identity providers so that even the task of things like configuring users to be able to use our products like Snowflake Intelligence is a whole lot simpler than ever before.

Sridhar Ramaswamy: We want to be predictable, and we will provide the controls that are necessary to make for wide deployment of Snowflake Intelligence. We've also done things like integrate Snowflake as a whole with identity providers, so that even the task of things like configuring users to be able to use our products, like Snowflake Intelligence, is a whole lot simpler than ever before. Christian and my vision is effectively that every single employee of every enterprise customer we have, should have access to a set of agents that provide them with all the key business details that they need to run their part of the business.

Speaker #4: Christian and my vision is effectively that every single employee of every enterprise customer we have should have access to a set of agents that provide them with all the key business details that they need to run their part of the business.

Speaker #5: And only get billed for what they use, which is always correlated with amazing outcomes.

Brian Robins: Only get billed for what they use, which is always correlated with amazing outcomes.

Christian Kleinerman: Only get billed for what they use, which is always correlated with amazing outcomes.

Speaker #7: Very clear. Thank you. And a quick follow-up. So you've certainly talked about your robust AI agent strategy progressing well. But there's also the idea of other agentic workflows leveraging Snowflake for critical steps in their process.

Sheldon McMeans: Very clear. Thank you. A quick follow-up. You've certainly talked about your robust AI agent strategy progressing well, but there's also the idea of other agentic workflows leveraging Snowflake for critical steps in their process. Can you speak to this latter area and how that's evolving for you? Do you see that as a fiscal year 27 growth opportunity? Do you see it mainly going through your zero copy partnerships, or would there be another pattern that would emerge there?

Speaker #7: Can you speak to this latter area and how that's evolving for you? And do you see that as a fiscal year '27 growth opportunity?

Speaker #7: And do you see it mainly going through your zero-copy partnerships? Or would there be another pattern that would emerge there?

Speaker #4: Could you clarify your question, please? What did you have in mind?

Sridhar Ramaswamy: Could you clarify your question, please?

Sheldon McMeans: Yeah.

Sridhar Ramaswamy: What did you have in mind?

Speaker #7: The idea of an agentic.

Sheldon McMeans: The idea of an agent. Yes, an agentic workflow that's done in a different platform that maybe needs to leverage some data in Snowflake for a step of the process.

Speaker #5: Yes, an agentic workflow that's done in a different platform that maybe needs to leverage some data in Snowflake for a step of the process.

Speaker #4: Well, interoperability has always been a key part of how we operate. And over the past two years, Christian and I are very proud of the fact that we have executed flawlessly on an interoperable data strategy.

Sridhar Ramaswamy: Well, interoperability has always been a key part of how we operate, and over the past 2 year, Christian and I are very proud of the fact that we have executed flawlessly on an interoperable data strategy. We support Iceberg as a first-class construct within Snowflake. We support Iceberg, where we manage the writes. In fact, we recently announced we support Iceberg, where we even manage the block storage, so that our customers get the best of all worlds. They get the manageability that they get with Snowflake, while feeling confident that another engine can read that data. What we have done over the past year is use interoperability to drive additional workloads for Snowflake, because as I said earlier, we can run SQL queries on any open data through things like catalog link databases.

Speaker #4: We support Iceberg as a first-class construct within Snowflake. We support Iceberg where we manage the rights. In fact, we recently announced we support Iceberg where we even manage the block storage.

Speaker #4: So that our customers get the best of all worlds. They get the manageability that they get with Snowflake, while feeling confident that another engine can read that data and what we have done over the past year, is use interoperability to drive additional workloads for Snowflake, because as I said earlier, you can we can run SQL queries on any open data through things like catalog-linked databases.

Speaker #4: You can also create agents that are sitting on any open data. And this kind of interoperability is really key for Snowflake to succeed. No customer wants to get into a situation where they cannot where they do not have options.

Sridhar Ramaswamy: We can also create agents that are sitting on any open data. This kind of interoperability is really key for Snowflake to succeed. No customer wants to get into a situation where they do not have options. We offer interoperability at the storage level. Certainly, people can write SQL and access the data, so we offer interoperability at the JDBC level. One level above that, we make semantic models available to others. We introduce Semantic Views, but anyone can read Semantic Views. Finally, our Snowflake Intelligence agents also double up and can be MCP servers that can be used by other agents as well. Offering interoperability at every layer of the stack is central to what we do.

Speaker #4: So we offer interoperability at the storage level. Certainly, people can write SQL and access the data. So we offer interoperability at the JDBC level.

Speaker #4: And one level above that, we make semantic models available to others. We introduce semantic views, but anyone can read semantic views. And finally, our Snowflake Intelligence agents also double up and can be MCP servers that can be used by other agents as well.

Sridhar Ramaswamy: Finally, our Snowflake Intelligence agents also double up and can be MCP servers that can be used by other agents as well. Offering interoperability at every layer of the stack is central to what we do. We also focus on creating world-class products that lead the way, that are easy to use and set up, that make all of this way simpler than what anyone else can do. We don't see any contradiction between the two.

Speaker #4: And so, offering interoperability at every layer of the stack is central to what we do. But we also focus on creating world-class products that lead the way, that are easy to use and set up, that make all of this way, way simpler than what anyone else can do.

Sridhar Ramaswamy: We also focus on creating world-class products that lead the way, that are easy to use and set up, that make all of this way simpler than what anyone else can do. We don't see any contradiction between the two.

Speaker #4: We don't see any contradiction between the two.

Speaker #7: Understood. Thank you.

Sheldon McMeans: Understood. Thank you.

Speaker #8: Thank you. The next question comes from Dolanas Kozileva, the Bank of America. Please proceed.

Operator: Thank you. The next question comes from the line of Koji Ikeda, Bank of America. Please proceed.

Speaker #9: Yeah. Hey, guys. Thanks so much for taking the question. I wanted to ask about the $9.8 billion in RPO. Which is growing 42%. I mean, really, really nice there.

Koji Ikeda: Yeah. Hey, guys. Thanks so much for taking the question. I wanted to ask about the $9.8 billion in RPO, which is growing 42%. I mean, really, really nice there. Instead of asking you where you saw strength, I'm most curious if you could talk about any air pockets where you were surprised that they didn't contribute more, why you think that happened, and how you think those pockets get better from here?

Speaker #9: And so instead of asking you where you saw strength, I'm most curious if you could talk about any air pockets where you were surprised that they didn't contribute more.

Speaker #9: Why you think that happened and how you think those pockets get better from here.

Speaker #7: Hey, Koji. This is Brian. There wasn't any—we called out the big contract in the quarter for over $400 million, and the seven 9-figure deals.

Brian Robins: Hey, Koji, this is Brian. You know, there wasn't. You know, we called out the big contract in the quarter for over $400 million in the seven, nine-figure deals, but there wasn't anything in the quarter that happened where I thought there's areas that we over exceeded or underperformed. Overall, we had a good sales execution quarter. You know, the RPO, as we talked about a little earlier, is just really points to the business outcomes that we're driving for our customers, and them buying into Snowflake long term.

Speaker #7: But there wasn't anything in the quarter that happened where I thought there's areas that we overexceeded or underperformed. Overall, we had a good sales execution quarter.

Speaker #7: And the RPO, as we talked about a little earlier, is just really points to the business outcomes that we're driving for our customers. And then buying into Snowflake long-term.

Speaker #4: Overall, I'm just I have to add. I have to add that I'm incredibly proud of our sales team for delivering both across consumption in terms of driving use cases both the wins and our services team for driving more and more of them to production.

Sridhar Ramaswamy: Overall, I'm just...

Koji Ikeda: Got it.

Sridhar Ramaswamy: I have to add that I'm incredibly proud of our sales team for delivering both across consumption in terms of driving use cases, both the wins and our services team, for driving more and more of them to production. Of course, what the sales teams got done in terms of these monumental contracts. Overall, it was a stellar year by those folks, we are all very grateful.

Speaker #4: And of course, what the sales teams got done in terms of these monumental contracts overall—it was a stellar year by those folks. And we are all very grateful.

Speaker #7: Well said.

Brian Robins: Well said.

Speaker #9: Yep. Yep. Thank you for that. And maybe just a quick follow-up here. I wanted to ask about platform usage visibility and predictability. Maybe compare and contrast today versus a year ago.

Koji Ikeda: Yep. Thank you for that. Maybe just a quick follow-up here. I wanted to ask about platform usage, visibility, and predictability. Maybe compare and contrast today versus a year ago, if that has changed at all, and if it has, what has been driving that change? Thanks, guys.

Speaker #9: If that has changed at all, and if it has, what has been driving that change? Thanks, guys.

Speaker #4: Could you clarify your question? What did you mean by platform usage and visibility?

Sridhar Ramaswamy: Could you clarify your question? What did you mean by platform usage and visibility?

Brian Robins: ... the usage of your platform by your customers, you know, how much more predictable is it today versus a year ago, if at all?

Koji Ikeda: ... the usage of your platform by your customers, you know, how much more predictable is it today versus a year ago, if at all?

Speaker #9: The usage of your platform by your customers. How much more predictable is it today versus a year ago, if at all?

Sridhar Ramaswamy: We continue to have among the most sophisticated systems for consumption prediction. We obviously calibrate ourselves on how well we do. Something like a 0.5% deviation is 1 part in 200, and for us, that's sort of a big deal. That's the level of sophistication that there is. There is similar methodology that is being applied for contract prediction, the TACV prediction as well. It's an area where I expect us to see, where I expect us to get better and better over time. Another area that we are actively working on, which has a little bit less predictability, is one that goes from use cases to consumption. It's an active topic for us. It's a little bit of a research project because we are not always privy to what our customers do.

Speaker #4: We continue to have among the most sophisticated systems for consumption prediction. And we obviously calibrate ourselves on how well we do. Something like a 0.5% deviation is one part in 200.

Speaker #4: And for us, that's sort of a big deal. That's the level of sophistication that there is. And there is similar methodology that is being applied for contract prediction, the TACV prediction as well.

Speaker #4: And it's an area where I expect us to see where I expect us to get better and better over time. And another area that we are actively working on, which has a little bit less predictability, is one that goes from use cases to consumption.

Speaker #4: It's an active topic for us. It's a little bit of a research project because we are not always privy to what our customers do.

Speaker #4: But we feel very good overall about our ability to model the business and be able to see where it goes. Of course, you also want the surprises that are not part of your models.

Sridhar Ramaswamy: We feel very good overall about our ability to model the business and be able to see where it, where it goes. Of course, you also want the surprises that are not part of your models. There is no model that would, divine the birth of Cortex Code or its adoption by 4,400 customers. We are happy when, things like that happen, but, when it comes to the core, we are very, very buttoned up among the best teams that I've worked with, and I've worked with a lot of them at Google and other places, when it comes to predictability of our business.

Speaker #4: There is no model that would divine the birth of Cortex Code or its adoption by 4,400 customers. We are happy when things like that happen.

Speaker #4: But when it comes to the core, we have a very, very buttoned-up team—among the best teams that I've worked with. And I've worked with a lot of them at Google and other places.

Speaker #4: When it comes to when it comes to predictability of our business.

Speaker #8: Thank you. The next question comes from Dolanas, Matt Hedberg. Would RBC Capital Markets? Please proceed.

Operator: Thank you. The next question comes from the line of Matthew Hedberg with RBC Capital Markets. Please proceed.

Operator: Thank you. The next question comes from the line of Matt Hedberg with RBC Capital Markets. Please proceed.

Speaker #7: Great. Thanks for taking my question, guys. Congrats from me as well. You guys are checking a lot of boxes. You're accelerating at scale. Shridhar, you went through a number of new AI product announcements.

Matthew Hedberg: Great. Thanks for taking my question, guys. Congrats from me as well. You guys are checking a lot of boxes. You know, you're accelerating at scale. Sridhar, you went through a number of new AI product announcements, and it looks to me like you're starting fiscal 2027, organically, a couple points higher than you did at this point last year. I guess, you know, investors want to know, is AI-related products, is that some or all of the upside that you're starting to see in this model? It certainly feels like you guys are well positioned, you know, from these trends. I'm just wondering, is it starting to inflect in the model?

Matt Hedberg: Great. Thanks for taking my question, guys. Congrats from me as well. You guys are checking a lot of boxes. You know, you're accelerating at scale. Sridhar, you went through a number of new AI product announcements, and it looks to me like you're starting fiscal 2027, organically, a couple points higher than you did at this point last year. I guess, you know, investors want to know, is AI-related products, is that some or all of the upside that you're starting to see in this model? It certainly feels like you guys are well positioned, you know, from these trends. I'm just wondering, is it starting to inflect in the model?

Speaker #7: And it looks to me like you're starting fiscal '27 organically a couple of points higher than you did at this point last year. So I guess investors want to know, is AI-related products, is that some or all of the kind of the upside that you're starting to see in this model?

Speaker #7: Because it certainly feels like you guys are well-positioned from these trends. I'm just wondering, is it starting to inflect in the model?

Speaker #4: Well, the other side of this is that our models predict based on observed behavior. And we think that there is a lot of upside.

Sridhar Ramaswamy: The other side of this is that our models predict based on observed behavior. We think that there is a lot of upside. As I said, there's no way that they can take into account the impact of Coco, because the historical data simply is not there. We see the benefit of things like Coco vividly because we can see how quickly projects finish when they're being done by our services team. We also see when our partners take these products and are able to do truly transformative things. You know, as you can ask me, am I overusing that word?

Speaker #4: As I said, there's no way that they can take into account the impact of Cocoa. Because the historical data simply is not there. We see the benefit of things like Cocoa vividly because we can see how quickly projects finish when they're being done by our services team.

Speaker #4: We also see when our partners take these products and are able to do truly transformative things. And you can ask me, am I overusing that word?

Speaker #4: I point you to a blog post that one of our partners James Dinkel wrote. Where he said that they were basically moving their business model as a whole from charging for time to offering fixed-fee services.

Sridhar Ramaswamy: I point you to a blog post that one of our partners, James Dinkel, wrote, where he said that they were basically moving their business model as a whole, from charging for, you know, time, to offering fixed-fee services. A lot of that predictability came because they used Cortex Code to drive the vast majority of the migration. We see a lot of upside to where the business can go. On top of this, part of what we have learned, even over the past few weeks with Cortex Code, is the impact that it can have on every function within Snowflake.

Speaker #4: And a lot of that predictability came because they use Cortex Code to drive the vast majority of the migration. So we see a lot of upside to where the business can go.

Speaker #4: And on top of this, part of what we have learned even over the past few weeks with Cortex Code is the impact that it can have on every function within Snowflake or product managers now have their own version of this to be able to predict to be able to look at everything from what are the launches coming out next week or what are the bugs that have been filed against their products.

Sridhar Ramaswamy: Our product managers now have their own version of this, to be able to predict, to be able to look at everything from what are the launches coming out next week, or what are the bugs that have been filed against their products. There's even someone that wrote a Christian feedback bot to give them feedback about how Christian would react to a product proposal. The level of innovation that we are seeing across the company is pretty inspiring. That gives us a lot of confidence about how we approach the year.

Speaker #4: There's even someone that wrote a Christian feedback bot to give them feedback about how Christians would react to a product proposal. The level of innovation that we are seeing across the company is pretty inspiring.

Speaker #4: And that gives us a lot of confidence about how we approach the year.

Speaker #7: And Matt, I'll just.

Brian Robins: Matt, I'll just add.

Speaker #9: I think let's just squeeze a quick one.

Matthew Hedberg: Just squeeze a quick one in.

Matt Hedberg: Just squeeze a quick one in.

Speaker #7: Please, go ahead. I was just going to add on to what Shridhar talked about prior. Go ahead, Matt.

Sridhar Ramaswamy: Please, go ahead.

Brian Robins: I was just going to add on to what Sridhar talked about prior. Go ahead, Matt.

Speaker #9: You can finish your answer, Brian. I was just going to wonder. It looked like gross margins are down about a point this year. And I'm curious, with all the investments that you're making, do you feel like mid-'70s is kind of a stable place for kind of gross margins, especially as we look at a couple of years forward?

Matthew Hedberg: You can finish your answer, Brian. I was just going to wonder, it looked like gross margins are down about a point this year. I'm curious, with all the investments that you're making, do you feel like mid-70s is kind of a stable place for kind of gross margins, especially as we look a couple of years forward?

Matt Hedberg: You can finish your answer, Brian. I was just going to wonder, it looked like gross margins are down about a point this year. I'm curious, with all the investments that you're making, do you feel like mid-70s is kind of a stable place for kind of gross margins, especially as we look a couple of years forward?

Speaker #4: Yeah. Great question. One of our objectives when we launched new products is really, first and foremost, is to build great products. Two, we want to make it easy to use.

Brian Robins: Yeah, you know, great question. You know, one of our objectives when we launch new products is really, first and foremost, is to build great products. 2, we want to make it easy to use, and 3, we want to drive revenue after that. Once we get there, we'll look at optimizing the margins for that. We have launched a lot of new AI products. The margin profile for those right now aren't as high as the core business, but we're offsetting that by finding more efficiencies in the core business. That's really sort of the component of that. We'll do what's right to drive growth, and we'll balance it all the way down to the line at the operating margin level.

Speaker #4: And three, we want to drive revenue after that. Once we get there, we'll look at optimizing the margins for that. We have launched a lot of new AI products.

Speaker #4: The margin profile for those right now aren't as high as the core business. But we're all setting that by finding more efficiencies in the core business.

Speaker #4: And so, that's really sort of the component of that. We'll do what's right to drive growth, and we'll balance it all the way down the line at the operating margin level.

Speaker #5: And things like margin improvements are coming both at the gross margin level, but definitely also at the company level. To just tell you folks about a couple of projects that we did that have had a big impact, one of the folks basically optimized all our freepools across all our deployments using AI because they got way better visibility into that data.

Sridhar Ramaswamy: Things like margin improvements are coming both at the gross margin level, but definitely also at the company level. To just tell you folks about a couple of projects that we did that have had a big impact. One of the folks basically optimized all our free pools across all our deployments using AI because they got way better visibility into that data.

Speaker #5: That actually. Yeah. Freepools, basically, we have to maintain freepools of compute so that our customers don't have to wait when they want to spin up a new warehouse.

Brian Robins: Pools are computes.

Sridhar Ramaswamy: Yeah, free pools. Basically, we have to maintain free pools of compute so that our customers don't have to wait when they want to spin up a new warehouse. Somebody found out a very clever way to look at the data and to optimize it. We've done a number of things around things like storage life cycle policies. When does a table need to be in nearline storage versus more, you know, I, like, glacial storage and things like that. There are a lot of wins to be had with AI, both above the gross margin line, but definitely at an operating margin line as well. To be honest, it's a matter of prioritizing what you put your time into because the world is so rich with opportunity.

Speaker #5: And somebody found out a very clever way to look at the data and to optimize it. Or we've done a number of things around things like storage lifecycle policies.

Speaker #5: When does a table need to be in narrow line storage versus more like glacial storage and things like that? So there are a lot of wins to be had with AI.

Speaker #5: Both above the gross margin line, but definitely at an operating margin line as well. To be honest, it's a matter of prioritizing what you put your time into because the world is so rich with opportunity.

Brian Robins: Matt, just to emphasize that point, just in Q4, we saw a lot of benefit with AI, that we had a small reduction in force, and the about 200 people in the company were impacted. If you look at our Q4 net adds on a headcount basis, we only added 37 people. AI has really changed the framework for investing in growth. It's no longer tied to headcount.

Speaker #7: And Matt, just to emphasize that point, just in fourth quarter, we saw a lot of benefit with AI. That we had a small reduction force and about 200 people in the company were impacted.

Speaker #7: So if you look at our fourth quarter net adds on the headcount basis, we only added 37 people. So AI has really changed the framework for investing in growth.

Speaker #7: It's no longer tied to headcount.

Speaker #9: I see.

Brad Zelnick: Thanks, Chris.

Brad Zelnick: Thank you.

Speaker #1: Thank you, Brett Dill with Jefferies. Please proceed.

Operator: Thank you. The next question comes from the line of Brent Thill with Jefferies. Please proceed.

Brent Thill: Thanks, Sridhar. All the stock things are selling off on the big AI labs taking the stack, as you know. I guess when you think about the advantage you have with the platform of having Gemini, OpenAI, and Anthropic available natively, first, do you think your customers understand that yet? Second, I guess, are you seeing that show up in demand, given that you have all three of the top supported natively?

Speaker #10: Thanks, Sridhar. All the SaaS teams are selling off on the big AI labs taking the stack, as you know. I guess when you think about the advantage you have with the platform of having Gemini, OpenAI, and Anthropic available natively—first, do you think your customers understand that yet?

Speaker #10: And second, I guess, are you seeing that show up in demand given that you have all three of the top supported natively?

Speaker #4: I think it's useful to step back and look at the impact that AI as a whole is having on software. We spend a lot of time looking at this.

Sridhar Ramaswamy: I think it's useful to step back and look at the impact that AI as a whole is having on software. We spend a lot of time looking at this, we live this, and our take is that overall, the winners are going to be the companies that provide that single source of enterprise truth. No AI model is going to help you if there are four sources of the truth. Similarly, having built-in security, auditability, trust, or even governance over access, who can access what data set, is critical. Obviously, you do need the best models, but there are at least three, if not four, best model providers right now, and we work with all of them.

Speaker #4: We live this. And our take is that overall, the winners are going to be the companies that provide that single source of enterprise truth.

Speaker #4: No AI model is going to help you if there are four sources of that truth. Similarly, having built-in security, auditability, trust, or even governance over access—who can access what data set—is critical.

Speaker #4: Obviously, you do need the best models, but there are at least three, if not four, best model providers right now. And we work with all of them.

Speaker #4: And I think our secret SaaS, which has existed since the beginning of the company, is packaging all of this into a cohesive product that is easy to use.

Sridhar Ramaswamy: I think our secret sauce, which has existed since the beginning of the company, is packaging all of this into a cohesive product that is easy to use. You see this play out with things like Snowflake Intelligence and Cortex Code working together. which is Snowflake Intelligence is a pretty cool product, but Cortex Code makes it 4 to 10 times faster to be able to deploy those agents. I think we are really seeing a lot of nice synergies come together as we go into this journey of agentic AI. It is this combination of capabilities, plus the fact that we have always been trustworthy stewards of all enterprise information, that I think make us a great party for every single enterprise to be working with.

Speaker #4: And you see this play out with things like Snowflake Intelligence and Cortex Code working together because Snowflake Intelligence is a pretty cool product. But Cortex Code makes it 4 to 10 times faster to be able to deploy those agents.

Speaker #4: I think we are really seeing a lot of nice synergies come together as we go into this journey of agentic AI. And it is this combination of capabilities plus the fact that we have always been trustworthy stewards of our of all enterprise information that I think make us a great party for every single enterprise to be working with.

Speaker #1: Thank you. The next question comes from the line of Ryan Weiss with Wells Fargo. Please proceed.

Operator: Thank you. The next question comes from the line of Ryan McWilliams with Wells Fargo. Please proceed.

Speaker #9: Excellent. Thanks for taking the question. Just excited to see the progress around Cortex Code. And it seems like you're combining the best of what AI can do today along with the best of Snowflake.

Ryan McWilliams: Thanks for taking the question. Just excited to see the progress around Cortex Code. It seems like you're combining the best of what AI can do today, along with the best of Snowflake. As it makes it a lot easier to build agents on the Snowflake platform, it seems like there's a lot of different vendors that are trying to be the place for users to build agents. From a technical perspective, what do you think are some of the advantages that Snowflake has to be the best place for users to build agents? Have you seen any increase in query volumes from Cortex Code users today? Thanks.

Speaker #9: As it makes it a lot easier to build agents on the Snowflake platform, it seems like there's a lot of different vendors that are trying to be the place for users to build agents.

Speaker #9: So from a technical perspective, what do you think are some of the advantages that Snowflake has to be the best place for users to build agents?

Speaker #9: And then have you seen any increase in query volumes from Cortex Code users today? Thanks.

Speaker #4: Our mission for a number of years has been to be that data platform that makes data easy to get value from. This is what we did.

Sridhar Ramaswamy: Our mission for a number of years has been to be that data platform that makes data easy to get value from. This is what we did when Snowflake first came out. This is what we've always been doing. In fact, our motto always has been easy, connected, and trusted, so that data within an enterprise is easy to use, but also present wherever you need it to be, wherever you need it to be present. It's that thing that I think it's that quality that gives us an advantage when it comes to creating agents. As I said earlier, we are also believers in interoperability. It is perfectly fine if someone wants an agent and be able to use MCP to call into a Snowflake Intelligence agent.

Speaker #4: When Snowflake first came out, this is what we have always been doing. In fact, our motto always has been easy, connected, and trusted. So that data within an enterprise is easy to use but also present wherever you need it to be, wherever you need it to be present.

Speaker #4: And it's that thing that I think it's that quality that gives us an advantage when it comes to creating agents. As I said earlier, we are also believers in interoperability.

Speaker #4: It is perfectly fine if someone wants an agent and is able to use MCP to call into a Snowflake Intelligence agent. But I think we are uniquely positioned to be that central place where that 360-degree view is possible for a number of our customers.

Sridhar Ramaswamy: I think we are uniquely positioned to be that central place where that 360-degree view is possible. For a number of our customers, we are stewards of their most important data, the gold layer, as it is called in analytics. I think that positions us exceptionally well to also be the ones that are, you know, providing agents for accessing that data. We are heavily leaned into technologies like MCP. MCP works both ways. You can use MCP to read from an agent, but we can use MCP to read data from other systems, and we are beginning to see use cases like that come alive as well. We have done a number of studies. Snowflake Intelligence absolutely drives more usage, more queries. We tend to focus on what's the value that we are creating.

Speaker #4: We are stewards of their most important data, the goal layer as it is called, in analytics. I think that positions us exceptionally well to also be the ones that are providing agents for accessing the data and we are heavily leaned into technologies like MCP.

Speaker #4: MCP works both ways. You can use MCP to read from an agent, but we can use MCP to read data from other systems. And we are beginning to see use cases like that come alive as well.

Speaker #4: And we have done a number of studies. Snowflake Intelligence absolutely drives more usage, more queries. And but we tend to focus on what's the value that we are creating at this point.

Sridhar Ramaswamy: At this point, I'm slightly indifferent about whether we, you know, get more of Snowflake Intelligence revenue from running a query or from running the model. It's all about creating amazing experiences and making it easy to do so. Christian?

Speaker #4: I'm slightly indifferent about whether we get more of Snowflake Intelligence revenue from running a query or from running the model. It's all about creating amazing experiences and making it easy to do so.

Speaker #4: Christian?

Christian Kleinerman: We've definitely seen the telemetry activity on the platform being increased based on the ease of use that both Snowflake Intelligence and Cortex Code brings.

Speaker #3: We definitely see in the telemetry, activity on the platform being increased based on the ease of use that both Snowflake Intelligence and Cortex Code bring.

Christian Kleinerman: We've definitely seen the telemetry activity on the platform being increased based on the ease of use that both Snowflake Intelligence and Cortex Code brings.

Speaker #9: Excellent. Push it together. Thank you.

Brian Robins: Excellent. Appreciate the color. Thank you.

Ryan McWilliams: Excellent. Appreciate the color. Thank you.

Speaker #1: Thank you. The next question comes from the line of Alex Sunkin with Wolf Research.

Operator: Thank you. The next question comes from the line of Alex Zukin with Wolfe Research.

Speaker #10: Hey, guys. Thanks for taking the question. Maybe, Sridhar, a quick one for you, and then a follow-up for Brian. Last quarter, you spoke to how January and February consumption trends would be the most important to determine the fiscal year guide.

Alex Zukin: Hey, guys. Thanks for taking the question. Maybe, Sridhar, a quick one for you, and then a follow-up for Brian. Last quarter, you spoke to kind of how January and February consumption trends would be the most important to determine the fiscal year guide. Maybe just talk specifically about kind of what you saw post-holiday in January and specifically even coming out of February, that give you the confidence to, on what looks like a stronger guide this time versus last year. I've got a quick follow-up for Brian.

Speaker #10: Maybe just talk specifically about kind of what you saw post-holiday in January and specifically even coming out of February, that give you the confidence to on what looks like a stronger guide this time versus last year?

Speaker #10: And then I've got a quick follow-up for Brian.

Speaker #4: Well, Brian did say earlier that when we guide, we try to take every ounce of data possible into that guide. That is what we have—that's what we have done.

Sridhar Ramaswamy: Well, Brian did say earlier that when we guide, we try to take every ounce of data possible into that guide. That is what we have, that's what we have done. We also clarified that the guidance process is a pretty strict one that focuses on historical information and our ability to reliably predict the future. In that sense, it is taking everything into account. If you were to ask me, what's the difference between last year and this year? At the beginning of last year, Snowflake Intelligence was a glimmer in our eye. 1 year later, not only did we launch Snowflake Intelligence and get it adopted, we are also being at the forefront of how you use agentic AI to massively accelerate how a data platform is being used.

Speaker #4: And we also clarified that the guidance process is a pretty strict one that focuses on historical information and our ability to our ability to reliably predict the future.

Speaker #4: So in that sense, it is taking everything into account. And if you were to ask me what's the difference between last year and this year, at the beginning of last year, Snowflake Intelligence was a glimmer in our eye.

Sridhar Ramaswamy: At the beginning of last year, Snowflake Intelligence was a glimmer in our eye. 1 year later, not only did we launch Snowflake Intelligence and get it adopted, we are also being at the forefront of how you use agentic AI to massively accelerate how a data platform is being used. I think all of that is going to culminate into how we perform this year. As far as the guide is concerned, it is very much about using every bit of data that we have until this moment. Brian?

Speaker #4: And one year later, not only did we launch Snowflake Intelligence and get it adopted, we've also been— we are also at the forefront of how you use agentic AI to massively accelerate how a data platform is being used.

Speaker #4: I think all of that is going to culminate in how we perform this year. But as far as the guide is concerned, it is very much about using every bit of data that we have until this moment, right?

Sridhar Ramaswamy: I think all of that is going to culminate into how we perform this year. As far as the guide is concerned, it is very much about using every bit of data that we have until this moment. Brian?

Speaker #3: 100% correct. What was your second follow-up question?

Brian Robins: 100% correct. What was your second follow-up question?

Speaker #10: Yeah, Brian. I was just going to ask if any update on the Snowflake AI ARR and then the free cash flow margin guide. Obviously, digesting the Observe acquisition maybe just to puts and takes there and how we should think about that trajectory.

Alex Zukin: Yeah, Brian, I was just going to ask if any update on the Snowflake AI ARR and then the non-GAAP adjusted free cash flow margin guide, obviously, digesting the Observe acquisition, maybe just the puts and takes there and how we should think about that trajectory?

Speaker #3: Yeah, just on free cash flow, overall, the seasonality will follow prior years. We collect the majority of our cash in the fourth quarter—it's been greater than 60% in the fourth quarter.

Brian Robins: Yeah, just on free cash flow, overall, you know, the seasonality will follow prior years. We collect the majority of our cash in Q4. It's been greater than 60% in Q4 for the last two years. Observe, we guided to 23%. Observe was a 150 basis point headwind. That's included in our numbers, the revenues included, the op margins included, as well as the free cash flow. Then we just wanted to give guidance that we felt comfortable with, that we can perform against.

Speaker #3: For the last two years, Observe, we guided to 23%. Observe was a 150 basis point headwind. That's included in our numbers. The revenues included, the op margins included, as well as the free cash flow.

Speaker #3: And then we just wanted to get guidance that we felt comfortable with, that we can perform against.

Speaker #1: Thank you. That concludes today's Q&A session. I will now hand the call back over to Sridhar for closing remarks.

Operator: Thank you. That concludes today's Q&A session. I will now hand the call back over to Sridhar for closing remarks.

Speaker #4: Thank you, everyone. Snowflake remains at the center of the enterprise AI revolution. And we see significant opportunity ahead. To recap, AI has moved from promise to reality.

Sridhar Ramaswamy: Thank you, everyone. Snowflake remains at the center of the enterprise AI revolution. We see significant opportunity ahead. To recap, AI has moved from promise to reality. Snowflake is built to win this era by combining trusted enterprise data, governed metrics, secure execution, and broad model choice, so that customers can deploy AI and agents safely at scale. We are rapidly transforming from the platform for governing and analyzing data into the platform where customers build and run AI-native applications and workflows, making it easier for both business users and builders to go from idea to production. This strategy is working. Our rapid pace of innovation and strong go-to-market execution are driving continued product revenue growth. We see a long runway of sustained, durable growth ahead. Thank you.

Speaker #4: And Snowflake is built to win this era by combining trusted enterprise data, governed metrics, secure execution, and broad model choice. So that customers can deploy AI and agents safely at scale.

Speaker #4: We are rapidly transforming from the platform for governing and analyzing data into the platform where customers build and run AI-native applications and workflows. Making it easier for both business users and builders to go from ideas to production.

Speaker #4: This strategy is working. Our rapid pace of innovation and strong go-to-market execution are driving continued product revenue growth. And we see a long runway of sustained durable growth ahead.

Speaker #4: Thank you.

Operator: That concludes today's conference call. Thank you. You may now disconnect your line.

Q4 2026 Snowflake Inc Earnings Call

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