Snowflake Inc (SNOW) 2026 Q4 法說會逐字稿

Good day, ladies and gentlemen. Thank you for joining today. Snowflake Q4 FY26 earnings call. My name is Tia and I will be your moderator for today's call. (Operator Instructions)

女士們、先生們，大家好。感謝您今天的參與。Snowflake 2026財年第四季財報電話會議。我叫蒂婭，我將擔任今天電話會議的主持人。（操作說明）

I would now like to pass the call over to your host, Katherine McCracken, Head of Investor Relations. Please proceed.

現在我將把電話交給主持人，投資人關係主管凱瑟琳麥克拉肯。請繼續。

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 Robins, our Chief Financial Officer; and Christian Kleinerman, our Executive Vice President of Product, who will participate in the Q&A session. During today's call, we will review our financial results for the fourth quarter, fiscal 2026, and discuss our guidance for the first quarter and full year fiscal 2027.

下午好，感謝各位參加 Snowflake 2026 財年第四季財報電話會議。今天與我一起參加電話會議的有：執行長 Sridhar Ramaswamy；財務長 Brian Robins；以及產品執行副總裁 Christian Kleinerman，他們將參加問答環節。在今天的電話會議上，我們將回顧 2026 財年第四季的財務業績，並討論我們對 2027 財年第一季和全年的業績指引。

During today's call, we will make forward-looking statements including statements related to our business operations and financial performance. These statements are subject to risks and uncertainties which could cause them to differ materially from our actual results. Information concerning these risks and uncertainties is available in our earnings press release, our most recent Forms 10-K and 10-Q, and our other SEC reports. All our statements are made as of today based on information currently available to us, except as required by law, we assume no obligation to update any such statements.

在今天的電話會議中，我們將發表一些前瞻性聲明，包括與我們的業務營運和財務表現相關的聲明。這些聲明存在風險和不確定性，可能導致其與我們的實際結果有重大差異。有關這些風險和不確定性的資訊可在我們的獲利新聞稿、最新的 10-K 表格和 10-Q 表格以及我們提交給美國證券交易委員會的其他報告中找到。除法律另有規定外，我們所有聲明均基於我們目前掌握的信息，截至今日發布，我們不承擔更新任何此類聲明的義務。

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 the 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.

在今天的電話會議中，我們還將討論一些非GAAP財務指標，請參閱我們的投資者簡報，以了解非GAAP財務指標的定義，以及GAAP與非GAAP指標和業務指標定義的調節，包括採用情況。獲利新聞稿和投資者簡報可在我們的網站 investors.snowflake.com 上查看。今天電話會議的錄音回放也將發佈在該網站上。

With that, I would now like to turn the call over to Sridhar.

接下來，我將把電話交給斯里達爾。

Thank you, Catherine. 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, that 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.

謝謝你，凱瑟琳。感謝各位今天蒞臨現場。過去一年對所有企業來說都是變革性的一年。一年前我們還在討論人工智慧的前景。今天，這個承諾已經成為現實。Snowflake 處於企業人工智慧革命的核心地位。人工智慧正在重塑整個市場的軟體格局，重新定義軟體類別和競爭態勢。

In our view, this is creating a clear separation between systems that demonstrate intelligence. And platforms that can deploy 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.

我們認為，這清楚地劃分了能夠展現智慧的系統與能夠展現智慧的系統之間的界線。以及能夠安全、大規模部署的平台。最終的贏家將是那些能夠將可信任的企業數據、業務指標、安全執行和廣泛的模型選擇結合起來，並使所有這些易於使用的平台。

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.

這正是 Snowflake 的設計初衷。我們提供企業在雲端和各種資料類型上賴以生存的資料基礎架構，並具備關鍵任務工作負載所需的效能、可靠性和操作簡便性。

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.

隨著人工智慧代理在工作中扮演越來越重要的角色，這些能力也變得更加寶貴，因為代理的能力取決於它們能夠存取的資料以及圍繞這些資料的治理和安全措施。

You can see that leadership in what he shipped this year. With Snowflake Intelligence, we've brought enterprise grade agency capabilities directly to business teams. With the general availability of Cortex Code, we extended that to builders, accelerating the entire data life cycle and helping customers move faster from development to production.

從他今年推出的產品中就能看出他的領導能力。借助 Snowflake Intelligence，我們將企業級代理商的能力直接帶給了業務團隊。隨著 Cortex Code 的全面普及，我們將其擴展到了建構者，加速了整個資料生命週期，並幫助客戶更快地從開發過渡到生產。

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 agent the capabilities of Cortex Code CLI combined with our AI Data on Snowflake are already driving meaningful operational impact just weeks after launch. 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.

最近，我們擴展了 Cortex Code CLI，使其涵蓋資料系統，以便簡化所有這些系統在實踐中的使用方式。Cortex Code CLI 的通用代理功能與我們在 Snowflake 上的 AI 數據相結合，在發布幾週後就已經產生了顯著的營運影響。Snowflake Intelligence 和 Cortex Code 是 Snowflake 從企業管理和分析資料的平台發展到建置和運行 AI 原生應用程式和工作流程的平台流程中的重要一步。

Turning to our results, product revenue in Q4 grew 30% year over year to reach $1.23 billion. Remaining performance obligations total $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.

從業績來看，第四季產品營收年增 30%，達到 12.3 億美元。剩餘履約義務總額為 97.7 億美元，年成長加速至 42%。我們的淨收入留存率達到了健康的 125%。多虧了人工智慧，我們既擴大收入規模，也提高營運效率。

Fiscal '26 non-GAAP operating margin reached 10.5%, expanding more than 400 basis points year over year, 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.

2026 財年非 GAAP 營業利潤率達到 10.5%，年成長超過 400 個基點，反映了我們對營運嚴謹性的持續關注。股票選擇權激勵支出佔收入的比例從 2025 財年的 41% 大幅下降到 2026 財年的 34%，我們預計 2027 財年將進一步下降到 27% 的收入。

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 life cycle. Built with deep product cohesion, Snowflake is easy to use, seamlessly connected for collaboration, grounded in the security and governance enterprises trust.

今年的業績證明，人工智慧資料雲在資料生命週期的各個階段繼續為我們超過 13,300 家客戶帶來巨大的價值。Snowflake 擁有強大的產品凝聚力，易於使用，可無縫連接以實現協作，並以企業信賴的安全性和治理為基礎。

As we innovate, we remain maniacally focused on driving great business outcomes for our customers. That focus is why leading organizations continue to use Snowflake as the foundation for their data and AI strategies. We added 2,332 net new customers this year and we're seeing more and more businesses move over to the Snowflake.

在不斷創新的同時，我們始終專注於為客戶創造卓越的業務成果。正是出於這種關注，領先的組織才會繼續使用 Snowflake 作為其數據和人工智慧策略的基礎。今年我們新增了 2,332 位淨客戶，我們看到越來越多的企業遷移到 Snowflake 平台。

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 analytics and make faster, more informed decisions. Our core business remains strong, and AI is expanding workloads across our platforms.

例如，希捷正在對其資料基礎架構進行現代化改造，以更好地支持其在全球範圍內推動資料驅動創新的使命。透過在 Snowflake 上整合海量資料環境。該公司正在從傳統基礎設施轉向為可擴展性、可靠性和可預測成本而構建的平台，使公司各團隊能夠存取高效能 AI 分析並做出更快、更明智的決策。我們的核心業務依然強勁，人工智慧正在不斷擴大我們各個平台上的工作負載。

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.

Capital One 是我們如何加深與重要客戶關係的一個很好的例子。隨著 Capital One 擴大其人工智慧計畫的規模，他們正在利用 Snowflake 來統一專有數據、優化工程工作負載，並在整個企業範圍內提供人工智慧驅動的分析。我們發展的關鍵在於我們人工智慧產品的實力和發展勢頭。本季度，我們使用人工智慧的帳戶數量實現了最大的環比增長，總數超過 9100 個帳戶。

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. 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.

短短三個月內，Snowflake Intelligence 就從新興產品發展成為 2500 多個帳戶的必備功能，幾乎每季都翻了一番。例如，全球汽車領導者豐田汽車歐洲公司正在利用 Snowflake Intelligence 來徹底改變其營運方式。豐田透過易於使用的知識聊天機器人增強企業搜尋功能，並透過文件人工智慧簡化合約管理，從根本上改變了其開發時間表，將人工智慧代理的部署時間從數月縮短到數週，從而創造了顯著的競爭優勢。

And United Rentals, the 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.

全球領先的設備租賃公司 United Rentals 正在使用 Snowflake Intelligence 來驅動一個新的商業智慧代理，該代理商可以幫助遍布 1600 多個分支機構的團隊使用自然語言從其財務和營運數據中獲得即時答案。

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. 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. And massively accelerating their ability to deploy production grade AI.

此代理程式能夠幫助第一線管理人員更快、更一致地做出決策。United Rentals 也正在使用 Snowflake 的 Cortex Code 來加速開發和測試其他 AI 代理，從而在整個業務範圍內擴展可信任智慧。而這只是Cortex Code功能的冰山一角。它是一款真正具有變革意義的編碼代理，目前已幫助超過 4400 家客戶建立和擴展 AI 驅動的應用程式。並大幅提升他們部署生產級人工智慧的能力。

The Chief Technology Officer of one of our partners, Evolve Consulting, described Cortex's impact on their business, saying, quote, 20 days, 21,000 operations, over 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.

我們的合作夥伴之一 Evolve Consulting 的首席技術官描述了 Cortex 對其業務的影響，他說：“20 天，21,000 次操作，超過 600 小時的工作量，相當於 16 個工作週壓縮到不到一個月的時間裡。”過去需要大量研究、反覆試驗和調試的開發週期，現在透過人工智慧輔助迭代自然而然地完成了。我們正在利用這項功能來加快將新工作負載遷移到 Snowflake 上的速度，以便更好地為我們的客戶服務。

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 life cycle, and we're innovating rapidly with opportunity. This year we launched over 430 product capabilities underscoring the strength of our product velocity.

Cortex Code 大幅擴展了我們平台上 AI 開發的面貌，並鞏固了 Snowflake 作為企業 AI 基礎的地位。展望未來，我們仍然看到在資料生命週期的各個階段為企業提供支援的巨大機遇，我們正在快速創新以掌握這些機會。今年我們推出了超過 430 項產品功能，凸顯了我們產品迭代速度的強大。

We're broadening how data enters and flows through Snowflake. Snowflake Openflow, not 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 fully managed and governed within Snowflake.

我們正在拓展資料進入 Snowflake 和在 Snowflake 中流動的方式。Snowflake Openflow（目前尚未正式發布）讓結構化、非結構化、大量或串流資料引入平台變得前所未有的容易。我們還深入研究如何在 Snowflake 上建立應用程式。現已全面上市。Snowflake Postgres 是一個世界級的操作型資料庫，直接建立在 Snowflake 平台上，使開發人員能夠建立和運行生產級事務應用程序，並享受 Snowflake 完全管理和控制的效能、可靠性和生態系統。

This transforms Snowflake from a system you analyze it 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. 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.

這使得 Snowflake 從一個用於分析的系統轉變為一個用於構建的平台。我們最近收購了市場領先的可觀測性平台 Observe，進一步擴展了 Snowflake 所能提供的價值。透過將可觀測性直接與數據和人工智慧產品整合。我們降低了複雜性，並實現了更快、更可靠的大規模營運。這將使我們有機會進入價值 500 億美元的 IT 營運市場，並使 Snowflake 在下一代 AI 驅動的可觀測性領域佔據領先地位。

At the same time, we're strengthening the ecosystem around the platform. Our landmark partnership with SAP is delivering incredible value, helping customers like Expand 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 govern data foundation that Intercom's AI is built on.

同時，我們正在加強圍繞該平台的生態系統。我們與 SAP 的里程碑式合作正在創造巨大的價值，幫助像 Expand Energy 這樣的客戶將核心系統中的關鍵業務資料整合到我們的 AI 資料雲中。我們與 Anthropic 深化的合作關係已經幫助像 Intercom 這樣的客戶看到了顯著的影響。Snowflake 為 Intercom 的人工智慧提供了安全可靠的資料治理基礎。

By applying direct AI capabilities to this data, including the use of Anthropic's Claude 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.

透過將人工智慧功能直接應用於這些數據，包括使用 Anthropic 的 Claude 模型，Intercom 實現了大規模的客戶支援自動化。這使得它能夠以更高的一致性和更低的營運負擔處理顯著更高的支援量，尤其對於大型、複雜的客戶而言更是如此。

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. 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.

我們最近也宣布與 OpenAI 擴大合作，投資額達 2 億美元。它將 OpenAI 的模型原生整合到 Snowflake 中，幫助我們的客戶更快地進行創新，同時確保其資料的安全性和可控性。透過與 Google Cloud 的合作，客戶現在可以在 Snowflake 中原生存取最新的 Gemini 模型，進一步擴大了模型選擇範圍和可用性。

As we innovate, we're 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. Across our business, Snowflake Intelligence and Cortex Code are already delivering measurable results.

隨著我們不斷創新，我們也在有效率地擴大規模。工作方式正在發生根本性的變化，而我們正在引領 Snowflake 內部以及整個產業的這種變革。在許多情況下，我們正在創建完全基於 Snowflake 構建的全新原生 AI 系統。在我們的業務中，Snowflake Intelligence 和 Cortex Code 已經取得了可衡量的成果。

Our service delivery team can complete customer projects up to 5 times faster. Improving response accuracy by more than 25% and compressed 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 time and further strengthening Snowflake's reliability.

我們的服務交付團隊可以以最高 5 倍的速度完成客戶專案。回應準確率提高 25% 以上，實施週期從幾天縮短到幾小時。推動專案利潤率提高 40% 至 50%，並使客戶上線速度提高 40% 以上。我們發現，以前需要多名工程師花費數小時才能解決的站點可靠性工程調查，現在只需幾分鐘即可解決，大大縮短了解決時間，並進一步增強了 Snowflake 的可靠性。

And we have built agency 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.

我們已經建立了代理能力，可以幫助我們的銷售人員優先處理客戶、自動進行研究並產生個人化的推廣活動，預計今年將恢復相當於 90 名全職工程師的生產力。

Our finance team is working on automating travel and expenses analysis, proactively curbing auto policy behavior, an initiative that is expected to drive millions in annual savings. And 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.

我們的財務團隊正在努力實現差旅和費用分析的自動化，主動遏制汽車保險行為，這項舉措預計每年可節省數百萬美元。我們也看到我們的客戶正在發生這種轉變。他們利用智能體不僅分析訊息，而且實現複雜工作流程的自動化，在某些情況下甚至淘汰了以前使用過的整個軟體系統類別。

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. And we believe Snowflake is uniquely positioned to become the control plane for the agentic era.

以賽諾菲為例。基於 Snowflake 和 Elementum 等合作夥伴建立的 AI 驅動型工作流程正在取代用於軟體許可和發票管理等流程的傳統軟體系統。透過在 Snowflake 中直接運行這些工作流程，賽諾菲簡化了操作，同時確保其資料安全地保留在平台內。這就是企業的發展方向。我們相信 Snowflake 擁有得天獨厚的優勢，可以成為智能體時代的控制平台。

We've built the conditions that make agents safe, scalable, and enterprise ready, covering. A single enterprise-wide source of truth, govern 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, and we see a long runway of durable high growth and continued margin expansion ahead.

我們已經建構了使代理商安全、可擴展且能夠滿足企業需求的條件，涵蓋了以下方面。單一的企業級資料來源、治理指標和共享業務定義、跨雲端和跨域互通性、內建安全性、可審計性和治理功能。我們持續快速的創新、緊密的市場協調以及嚴謹的運營，都在全力以赴地抓住這一機遇，我們看到未來將迎來長期的可持續高增長和利潤率持續擴張。

Now I'll turn it over to Brian to take us through the financial details.

現在我將把發言權交給布萊恩，讓他為我們詳細介紹財務細節。

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%.

謝謝你，斯里達爾。第四季在營收、訂單量和利潤率方面均表現強勁。產品營收年增30%。我們的業績得益於核心業務的穩定成長以及人工智慧工作負載對成長貢獻的提升。我們的淨收入留存率沒有下降，仍維持在 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 seven nine-figure contracts compared to two in the same period last year. These strong commitments represent Snowflake's strategic role in our customers' long-term data and AI strategies.

第四季銷售業績表現優異。剩餘履約義務連續第二季加速履行。我們簽署了 Snowflake 歷史上最大的一筆交易，合約總價值超過 4 億美元，並簽署了七份九位數合同，而去年同期只有兩份。這些堅定的承諾體現了 Snowflake 在客戶長期數據和人工智慧策略中的戰略地位。

And we've consistently emphasized durable growth depends on two fundamentals 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.

我們一直強調，永續成長取決於兩個基本要素：獲得新客戶和擴大現有客戶群。兩項我們都做到了。我們又迎來了一個強勁的新客戶成長季度，淨新增客戶 740 家，年增 40%，其中包括 15 家全球 2000 強企業。

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, and a record number of customers crossed $10 million in trailing 12 month span, bringing a total of 56 customers above this $10 million threshold, growing 56% year to year.

同時，我們也證明了我們能夠推動客戶數量的顯著成長。過去 12 個月，我們有 733 位客戶的消費額超過 100 萬美元，年增 27%；過去 12 個月，消費額超過 1,000 萬美元的客戶數量創下歷史新高，達到 56 位，年增 56%。

Turn into our margin results. FY26 non-GAAP product gross margin was 75.8%. We're demonstrating that we can scale while driving efficiency. FY26 non-GAAP operating margin was 10.5% and FY26 non-GAAP adjusted free cash flow margin was 25.5%.

轉化為我們的利潤率結果。2026財年非GAAP產品毛利率為75.8%。我們正在證明，我們可以在提高效率的同時擴大規模。2026 財年非 GAAP 營業利益率為 10.5%，2026 財年非 GAAP 調整後自由現金流利潤率為 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. With Observe's offering, we're unlocking new expansion opportunities within our customer base. The impact of the acquisition is reflected in our outlook.

本月初，我們完成了對 Observe 的收購，收購價格約為 6 億美元，以現金和股票相結合的方式支付。借助 Observe 的產品，我們正在為我們的客戶群開拓新的發展機會。此次收購的影響已反映在我們的展望中。

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, cash equivalents, short-term and long-term investments.

第四季度，我們用 1.5 億美元回購了約 66.8 萬股股票，加權平均股價約為每股 225 美元。我們還有 11 億美元的回購授權額度，本季末我們有 48 億美元的現金、現金等價物、短期和長期投資。

Before moving to our outlook, I'd like to share my priorities for FY27. First, I see a clear opportunity to drive both growth and operating margin expansion. We are investing in our key growth drivers. At 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.

在展望未來之前，我想先分享一下我對 2027 財年的工作重點。首先，我認為存在著推動成長和擴大營業利潤率的明確機會。我們正在投資我們的關鍵成長驅動因素。今年，Sridhar Relayed 已向市場推出了 430 多項產品功能。我們將繼續提高營運效率，從而擴大營業利潤率。

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.

其次，很明顯，我們的市場推廣策略是有效的。我明年的工作重點是確保穩定和持續卓越。我們已建立財務框架，以支援產品持續快速迭代和銷售執行。

Now, let's look to our outlook for FY27. In Q1, we expect product revenue between $1.262 billion and $1.267 billion, representing 27% year over year growth. For FY27, we expect product revenue of approximately $5.66 billion representing 27% year to year growth. We expect observed to contribute approximately 1 percentage point of product revenue growth in FY27.

現在，讓我們展望一下2027財年。第一季度，我們預計產品營收在 12.62 億美元至 12.67 億美元之間，年增 27%。預計 2027 財年產品營收約 56.6 億美元，年增 27%。我們預計觀察值將在 2027 財年為產品收入成長貢獻約 1 個百分點。

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. 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%.

和以往一樣，我們的預測是基於現有的消費模式進行的。我們的預測流程和指導理念沒有任何改變。我們核心業務的持續強勁成長以及人工智慧工作負載的進一步成長支撐了我們的前景。我們預期 2027 財年非 GAAP 產品毛利率為 75%。我們預期第一季非GAAP營業利益率為9%，2027財年非GAAP營業利益率為12.5%。

Our hiring this year will be weighted to the first quarter, 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 the fourth quarter, and we expect next year's non-GAAP adjusted free cash flow seasonality to mirror FY26.

今年我們的招募工作將主要集中在第一季度，因為從 Observe 公司引進了 178 名員工。我們預期非GAAP調整後的自由現金流利潤率為23%。這其中包括與我們的收購相關的約 150 個基點的不利因素。與往年一樣，我們預計我們的預訂量將繼續集中在第四季度，我們預計明年的非GAAP調整自由現金流季節性將與2026財年類似。

Finally, we'll host an investor day in conjunction with our summit conference the week of June 1, 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.

最後，我們將於 6 月 1 日當週在舊金山舉辦投資者日活動，與我們的峰會同期舉行。如果您有興趣參加，請發送電子郵件至IR@snowflake.com。之後，我會將電話轉接給接線生進行問答環節。

Sanjit, Singh, Morgan Stanley.

桑吉特、辛格、摩根士丹利。

Yeah, thank you for taking a look at the questions and congrats on reasserting 30% product revenue growth in Q4. I have two questions, starting with Brian and then hopefully for you, Sridhar. Brian, on the guide for fiscal year 207. Basically implies sustained growth around 27% throughout the year and just sort of just want 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. So just sort of the confidence in that.

是的，感謝您查看這些問題，並祝賀您在第四季度再次實現了 30% 的產品收入成長。我有兩個問題，先問 Brian，然後希望也能問 Sridhar 你。Brian，關於 207 財政年度指南。基本上意味著全年將保持 27% 左右的持續成長，我只是想了解您對這 27% 的成長能否持續的看法，因為這是一個消費模式，而且是在今年非常好的一年之後實現的持續成長。所以就是對這一點抱持信心。

And then 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, 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 to fiscal year '27, thanks.

那麼對於 Sridhar 來說，隨著 Snowflake Intelligence 進入第一個完整的年度，並且產品組合不斷擴展，我想知道您能否讓我們了解一下，在核心業務之外的其他業務領域，我們目前的進展勢頭如何。我認為我們幾個季度前就獲得了關於數據工程、收入運行率或增長率的最新資訊。所以，一旦我們得到關於人工智慧產品組合的最新進展，以及我們今年退出並進入 2027 財年的情況，謝謝。

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, and 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. And finally we called out in the prepared remarks, there's 1 percentage point of growth from our observed acquisition. I'll turn it over to Sridhar for the second part.

謝謝你，桑吉特。我先來。從業績指引的角度來看，我們根據觀察到的客戶行為，直到獲利公佈之時，來制定業績指引。如果你仔細查看今年的業績指引，你會發現它實際上是基於我們核心業務所看到的穩定高成長。人工智慧工作負載的貢獻也在不斷增長。最後，我們在準備好的發言稿中指出，我們觀察到的收購增加了 1 個百分點。第二部分將交給斯里達爾。

And 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 life cycle. 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, that any 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.

此外，我還要重申一點，我們的整體指導理念並沒有真正改變。在這方面，我們依然保持著非常穩定的狀態。我認為像 Snowflake Intelligence 這樣目前擁有 2500 位客戶的產品，是資料生命週期各個方面成長的主要驅動力。我認為像 Snowflake Intelligence 這樣的產品非常棒，我總是樂此不疲地向我遇到的每一位 CXO 和 CEO 展示我手機上的 Snowflake Intelligence，它提供的任何訪問權限對於獲取關鍵業務信息來說都堪稱神奇，這也進一步強調了企業採用 Snowflake 來使其數據資產運轉起來的必要性。這樣他們就可以將 Snowflake Intelligence 等技術的變革力量應用到這些數據中。

The really important thing also to remember about Snowflake Intelligence is that it works fine on all open data. You can build snowflake amazing agents 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, and that's really very powerful.

關於 Snowflake Intelligence，真正需要記住的一點是，它適用於所有開放資料。你可以使用 Snowflake Intelligence 來建構 Snowflake Amazing Agent，資料可以儲存在由 glue 管理的 S3 中，也可以儲存在其他位置。Snowflake Intelligence 為任何開放資料生態系統提供支持，這非常強大。

But Cortex Code is the real game changer for us because it is a massive accelerant for every part of the daily life cycle. 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. Similarly, building DVD pipelines to run data engineering on that data or to build dynamic tables or debug performance issues with either of these now is again NX block faster.

但 Cortex Code 對我們來說才是真正的變革者，因為它大大加速了日常生活的各個環節。我的意思是，我們可以建立開放流管道，將複雜系統中的資料導入 Snowflake，所需時間僅為以前的一小部分。同樣，建立 DVD 管道來運行資料工程或建立動態表或調試效能問題，現在也比以前快得多。

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 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 cortex Cortex Code CLI and said that, all this time they had been using shovels to dig and we just gave them bulldozers.

CoCo 的神奇之處還在於它能夠更快地建立 Snowflake Intelligence 代理程式。我認為這就是人工智慧的解鎖，利用人工智慧讓事情進展得更快，正如我所說，我們看到了它對我們的業務產生的變革性影響。我給大家講個小故事。我們的一位合作夥伴在使用 Cortex Cortex Code CLI 後給我們寫信說，他們一直以來都在用鏟子挖土，而我們只是給了他們推土機。

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

馬克墨菲，我們來看下一個問題。

Mark Murphy, JPMorgan.

馬克墨菲，摩根大通。

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. I just want to ask first, can you describe the $400 million deal, in terms of the customer type, because it, I don't -- it's a gigantic contract. I just don't think we've heard anything like that.

非常感謝。所以，預訂量和 RPO 數據看起來非常不錯。再次展現強勁勢頭，預訂量似乎將創下公司歷史上的最高紀錄，而且漲幅相當大。我首先想問一下，您能否從客戶類型的角度描述一下這筆價值 4 億美元的交易，因為，我不知道——這是一份巨額合約。我認為我們之前從未聽說過類似的事情。

And second, I'm curious if you see some sustainable new drivers kicking in there for bookings like maybe, thinking back on, achieving a faster product GA, cadence is something you've done or what 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 heavily on bookings this year. So I'm just wondering if you can comment on this.

其次，我很好奇您是否看到一些可持續的新驅動因素開始發揮作用，例如，回想一下，加快產品正式發布速度，或者說，這是否只是暫時的，比如幾個季度前你們的招聘熱潮，而且我認為你們今年一直在加大對銷售代表的激勵力度，讓他們更多地參與到預訂中來。所以我想問您是否可以對此發表一下看法。

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. 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. 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.

我可以開始了。Brian 可以增加預訂量，而多年合約清楚地表明了我們的合作夥伴對 Snowflake 未來發展的信任。沒錯，產品的加速發展和迭代速度對於說服客戶我們是一個面向未來的平台至關重要。我們在宿舍裡並沒有做什麼特別的事。是的，我們去年確實調整了補償方案，將預訂情況也考慮在內，但在許多方面，這代表著回到了兩年前的狀態。

And we plan to continue that this year, so it's very much business as usual. I do think that the $400 million , $400 million-plus 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.

我們計劃今年繼續這樣做，所以一切照常進行。我認為，我們簽署的這份價值 4 億美元甚至超過 4 億美元的合同，表明了我們為大型金融服務客戶提供服務的重要性。我們之前討論過金額在 2.5 億美元左右的交易。Brian，我認為這代表了 Snowflake 作為一家持久的資料服務提供者的成熟，不僅體現在當今的資料服務領域，也體現在未來的發展中。

Well said, Sridhar. 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. And so just to re-echo what Sridhar says, it's just Q4, 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 and the positive business outcomes that we're delivering for their business.

說得好，斯里達爾。我認為，當簽訂超過 4 億美元的大合約時，由於是現有客戶，所以這筆費用已經計入日常營運成本了。我們還簽了七份價值數億美元的合約。所以，我再次重申 Sridhar 的話，就是第四季度，是的，就是第四季。正如 Sridhar 所說，這實際上是我們的客戶對我們的產品路線圖和人工智慧策略以及我們為他們的業務帶來的積極業務成果的認可。

Wonderful. Congratulations and thank you. Thanks, Mike.

精彩的。恭喜並感謝。謝謝你，麥克。

Brad Zelnick, Deutsche Bank.

布拉德‧澤爾尼克，德意志銀行。

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?

太好了，非常感謝，我也要向你表示祝賀。Sridhar，我想這個問題是問你的，你剛剛結束了銷售啟動會，現在是在 Mike Gannon 的領導下進入市場的第一個完整年度，你將在 2027 財年採取哪些不同的措施來贏得市場並推動業績增長？

Well Mike's had a Mike's had a year. He's 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 sale, our sellers, our solution engineers deliver value for our customers, and I have never seen more excitement from our sales force about the products that we create.

嗯，麥克已經度過了一年了。他對銷售團隊產生了非常積極的影響，但我認為真正推動整個公司甚至整個銷售團隊發展的，是優秀的產品，這些產品能夠讓我們的銷售人員、解決方案工程師為客戶創造價值。我從未見過我們的銷售團隊對我們創造的產品如此充滿熱情。

We have had multiple people. I'll let Christian chime in because he gets 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 then the original product was created. And 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.

我們接待過好幾個人。我讓克里斯蒂安說幾句，因為他經常獲得這類讚譽。許多人都來告訴我們，Cortex Code 徹底改變了人們使用 Snowflake 的方式。很多人來告訴我們，自從最初的產品問世以來，他們從未對我們創造的任何產品感到如此興奮。克里斯蒂安還專門闢出一塊「Cortex Code英雄」區，重點介紹他們的經歷。就讓他來說吧，畢竟是他負責的。

Super quickly, like partners, customers, and our internal field are all incredibly excited about the results we'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 showcased the 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.

很快，合作夥伴、客戶以及我們內部的銷售人員都對 Cortex Code 所取得的成果感到無比興奮。Snowflake 最初的價值主張是改變易用性方面的可能性。使用 Cortex Code 後，速度提升了 10 倍。我們展示了人們建立管道、加快轉型、更快獲得洞察的諸多實例，我認為我們僅僅觸及了所有可能性的開始。

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

我們的一位銷售主管，我向你保證，他絕對不會自稱是軟體工程師，但他構建了一個 Streamlet 應用程序，將其部署在 Snowflake 上，並讓他的團隊使用它。Cortex Code 讓使用 Snowflake 的資料變得如此簡單。

Exciting stuff. Thanks guys.

真令人興奮。謝謝各位。

Kirk Materne. Would Evercore ISI.

柯克·馬特恩。Evercore ISI 會怎麼樣？

Hey, this is Rag 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?

嘿，這裡是柯克的拉格。感謝您回答這個問題。Sridhar，可觀測性是一個巨大的市場，對吧？觀察是如何融入這種格局的？你在市場和公司中看到了什麼，才使得將觀察納入公司內部成為可能？

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. And AI observability in particular with agents is a 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, and so we see this as a critical data problem.

正如你所指出的，可觀測性，尤其是在人工智慧領域，非常重要。這是一個非常大的市場，超過 500 億美元，這意味著它涉及許多不同的專業知識角度。而人工智慧的可觀測性，特別是對於智能體而言，至關重要。我相信你們很多人都使用智能代理，而且沒有人會指責一個編碼智能代理不健談。大量的文本需要被提煉成技能、哪些方面做得對、哪些方面做得不對等等，因此我們認為這是一個關鍵的數據問題。

And we also see it as a natural extension of our overall role as a data platform. 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, and this is where Observe is able to offer a value prop that is factors away, not like 10% to 20% factors more efficient.

我們也將其視為我們作為數據平台整體角色的自然延伸。Observe 建構於 Snowflake 之上，因此它繼承了 Snowflake 出色的資料和運算基礎。對於我們的許多客戶，尤其是那些擁有大量資料的客戶而言，傳統的可觀測性實現方式由於其高昂的成本而成為一個令人頭痛的問題。而 Observe 的價值主張恰恰在於此，它能夠大幅降低成本，而不僅僅是提高 10% 到 20% 的效率。

And 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 12 punch of Observe's built on Snowflakes. 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. That was the ultimately the thing that made both Jeremy and the Observe team want to be part of Snowflake.

我認為這類客戶將會從中受益良多。Observe 的潛在客戶和 Snowflake 的客戶之間存在巨大的重疊，這實際上是 Observe 基於 Snowflake 構建的 12 個關鍵因素。我們的整合工作非常簡單。Observe 對大量同時也是 Snowflake 的客戶來說，具有極佳的價值主張。最終，正是這一點讓 Jeremy 和 Observe 團隊都想加入 Snowflake。

We are very excited for what's ahead. Christian, anything to add? That's great. Let's move on to the next question.

我們對未來充滿期待。克里斯蒂安，還有什麼要補充的嗎？那太棒了。我們來看下一個問題。

Raimo Lenschow, Barclays.

雷莫·倫肖，巴克萊銀行。

Hi, this is Sheldon McMeans on for Raimo. Thanks for taking our questions. 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, but You know there's also risk of you know maybe sticker shock as AI agents proliferate or new users create, more applications and workloads on your platform 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.

大家好，我是謝爾頓·麥克米恩斯，為您報道雷莫。謝謝您回答我們的問題。隨著您持續提升 Snowflake 平台及其解決方案對使用者的易用性，您無疑擁有拓展用戶群和提升消費量的絕佳機會。但您也知道，隨著 AI 代理的普及或新用戶在您的平台上創建更多應用程式和工作負載，可能會出現價格過高的風險。那麼，您是如何與客戶合作，幫助他們降低高速成長和優化週期帶來的風險的呢？另外，您是否認為客戶真正理解，當他們更多地利用您的代理商時，他們可以獲得的潛在消費提升？

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, and many companies that offer agent platforms see them as an extension of their existing subscription model.

這是一個很好的問題，也是我們花了很多時間思考的問題。讓我們確保對一些早期代理產品的反事實情況進行檢驗。其中一些產品是作為訂閱套餐的一部分推出的，許多提供代理平台的公司將它們視為現有訂閱模式的延伸。

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, 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, and so it delivered ROI out of the gate because that moved to be a set of streamlets and Snowflake Intelligence.

Snowflake 採用以使用量收費的方式，因此我們提供的是非常可預測的收費模式。我也堅信產品必須從一開始就展現價值，我可以舉個我們自己的例子，我們的銷售代理替換了一個我們之前花費近 500 萬美元購買的舊式儀錶板系統，因此它從一開始就帶來了投資回報，因為它遷移到了 Streamlets 和 Snowflake Intelligence 的集合上。

And this is where we feel like we are very badly aligned, but we are not stopping there. We 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 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.

我們覺得我們在這方面有很大的分歧，但我們不會就此止步。我們知道，即使有了 Snowflake Intelligence，我們的客戶仍然希望價格可預測。因此，我們將在 Snowflake Intelligence 的基礎上推出諸如每個用戶上限之類的功能，以便他們可以清楚地感受到代理商收費的上限。我們認為，像這樣基於消費、具有明確用戶上限和帳戶上限的模型，能夠兼顧消費定價和價格可預測性，從而實現兩全其美。

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. We want to be We want to be predictable and we will provide the controls that are necessary to make for wide deployments of Snowflake Intelligence.

我們將繼續在這個領域快速創新，因為我們認為這些代理商可以創造巨大的價值，而且我們絕對不希望我們的客戶對價格感到震驚。我們希望保持可預測性，我們將提供必要的控制措施，以實現 Snowflake Intelligence 的廣泛部署。

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. It 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.

我們也做了一些事情，例如將 Snowflake 整體與身分識別提供者集成，這樣即使是配置使用者以使用我們的產品（如 Snowflake Intelligence）這樣的任務也能輕鬆完成。現在的情況比以往任何時候都簡單得多。克里斯蒂安，我的願景是，我們每個企業客戶的每一位員工都應該能夠接觸到一組代理，這些代理會向他們提供運營其業務部分所需的所有關鍵業務細節。

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

而且他們只需為實際使用的量付費，這總是能帶來極佳的效果。

Very clear, thank you. And a quick follow-up, so you 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, and, do you see that as a fiscal year '27 growth opportunity? And do you see it mainly going through your zero copy partnerships, or would there be another pattern that would emerge there?

非常清楚，謝謝。還有一個簡短的後續問題，所以當然可以。談到您強大的 AI 代理策略進展順利，但還有其他代理商工作流程利用 Snowflake 來實現流程中的關鍵步驟。您能否談談後一個領域以及它對您而言的發展？您是否認為這是 2027 財年的成長機會？你認為這種現象主要透過零複製合作關係出現嗎？還是會出現其他模式？

Could you clarify your question, please? Yeah, what did you have an idea of.

請問您能否把問題說得更清楚？是啊，你有什麼想法？

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.

在不同平台上執行的代理工作流程，可能需要在流程的某個步驟中利用 Snowflake 中的一些資料。

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. 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 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.

互通性一直是我們的營運方式的關鍵組成部分，在過去的兩年裡，我和克里斯蒂安都非常自豪地表示，我們完美地執行了互通資料策略。我們支持 Iceberg 作為 Snowflake 中的一流組件。我們支持冰山。我們負責管理版權。事實上，我們最近宣布支援 Iceberg，我們甚至管理區塊存儲，以便我們的客戶獲得最佳體驗。他們既能獲得 Snowflake 的可管理性，又能確信其他引擎也能讀取這些資料。

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. You can also create agents that are sitting on any open data.

過去一年，我們利用互通性為 Snowflake 驅動了額外的工作負載，因為正如我之前所說，我們可以透過目錄連結資料庫等方式在任何開放資料上執行 SQL 查詢。您也可以建立基於任何開放資料的代理程式。

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. 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.

這種互通性對於 Snowflake 的成功至關重要。沒有任何顧客願意陷入自己別無選擇、沒有其他選擇的境地。因此，我們在儲存層面提供了互通性。當然，人們可以編寫 SQL 並存取數據，因此我們在 JDBC 層面上提供了互通性。

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.

再往上一層，我們將語意模型提供給其他人。我們引入了語義視圖，但任何人都可以閱讀語義視圖。最後，我們的 Snowflake Intelligence 代理程式還可以兼作 MCP 伺服器，供其他代理程式使用。

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 simpler than what anyone else can do. We don't see any contradiction between the two.

因此，在技術堆疊的每一層提供互通性是我們工作的核心，但我們也專注於創造世界一流的產品，引領潮流，易於使用和設置，使這一切比任何其他人都能做到的都更簡單。我們認為這兩者之間沒有任何矛盾。

Understood. Thank you.

明白了。謝謝。

Koji Ikeda, the Bank of America. Please proceed.

池田浩二，美國銀行。請繼續。

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 nice there. 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. Why do you think that happened and how you think those pockets get better from here?

是的，嘿，各位，非常感謝你們回答這個問題。我想問一下價值 98 億美元的 RPO（招聘流程外包）市場，該市場正以 42% 的速度成長。我的意思是，那裡真的很不錯。因此，與其問你你認為優勢在哪裡，我更想知道你是否能談談你認為哪些方面存在不足，讓你感到驚訝的是，這些不足之處並沒有做出更大的貢獻。你認為造成這種情況的原因是什麼？你覺得這些地區的狀況如何改善？

Hey Koji, this is Brian. There wasn't, any, we called out the big contract in the quarter for over $400 million in the 7 nine-figure deals, but there wasn't anything in the quarter that happened where I thought there's areas that we overe xceeded or underperformed. Overall, we had a good sales execution quarter and 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 then buying into Snowflake long-term.

嘿，Koji，我是Brian。本季度我們確實簽下了一筆超過 4 億美元的大合同，這是本季度 7 筆九位數交易中的一筆，但本季度沒有任何事情讓我覺得我們在某些​​方面表現超出預期或低於預期。總的來說，我們本季的銷售執行情況良好，正如我們之前討論過的 RPO，它真正體現了我們正在為客戶推動的業務成果，以及客戶對 Snowflake 的長期投入。

Overall I'm just -- 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 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're all very grateful.

總而言之，我必須補充一點，我為我們的銷售團隊感到無比自豪，他們在推動用例方面取得了全面成功，服務團隊也推動了越來越多的用例投入生產，當然，銷售團隊在這些意義重大的合約方面也取得了巨大成就，對他們來說，這是輝煌的一年，我們都非常感激。

Awesome, yes, 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, if that has changed at all? And if it has, what has been driving that change? Thanks guys.

太棒了，謝謝你。這裡或許還需要補充一點。我想了解一下平台的使用、可見性和可預測性，或許可以比較一下現在的情況和一年前的情況，看看這些面向是否有所改變？如果確實發生了變化，那麼是什麼因素推動了這種變化？謝謝各位。

Could you clarify your question, what did you mean by platform usage and visibility?

您能否更詳細地說明一下您的問題，您所說的平台使用情況和可見度具體指的是什麼？

The usage of your platform by your customers, how much more predictable is it today versus a year ago, if at all.

客戶對您平台的使用情況，與一年前相比，如今的可預測性是否有所提高（如果有的話）。

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, and for us that's sort of a big deal. That's the level of sophistication that there is.

我們仍然擁有最先進的消費預測系統之一。我們顯然會根據自身的表現來衡量自己。0.5% 的偏差相當於 200 分之一，對我們來說，這可是件大事。這就是它所達到的複雜程度。

And there is similar methodology that is being applied for contract prediction, the TACB prediction as well, 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. 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, but we feel very good overall about our ability to model the business and be able to see where it goes.

類似的預測方法也應用於合約預測，以及TACB預測，我預計在這個領域我們會隨著時間的推移變得越來越好。我們正在積極研究的另一個領域，其可預測性稍低，那就是從用例到消費的轉變。這是一個我們正在積極關注的話題。這有點像一個研究項目，因為我們並不總是了解客戶在做什麼，但我們總體上對建立業務模型並了解其發展方向的能力感到非常滿意。

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 buttoned up among the best teams that I've worked with. I've worked with a lot of them at Google and other places when it comes to when it comes to predictability of our business.

當然，你也希望看到一些模型以外的驚喜。沒有任何模型可以預測 Cortex Code 的誕生，或者它為何會被 4400 位客戶採用。發生這樣的事情我們當然很高興，但就核心而言，我們是我合作過的最優秀的團隊之一，我們做事非常嚴謹。在谷歌和其他地方，我曾與他們中的許多人合作過，探討我們業務的可預測性問題。

Matt Hedberg, RBC Capital Markets.

Matt Hedberg，加拿大皇家銀行資本市場。

Great, thanks for taking my question, guys. Congrats for me as well. You guys are checking a lot of boxes. 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 a fiscal '27, organically at a couple points higher than you did at this point last year.

太好了，謝謝你們回答我的問題。也恭喜我。你們符合很多條件。你正在大規模加速發展。Sridhar，你發布了一系列新的人工智慧產品，在我看來，你在 2027 財年的開局比去年同期略有起色。

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, 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?

所以我想，投資人想知道，人工智慧相關產品是否是你們在這個模型中開始看到的成長點的一部分或全部，因為從這些趨勢來看，你們顯然已經佔據了有利位置。我只是想知道，這種趨勢是否開始在模型中出現轉折？

So the other side of this is that our models predict based on observed behavior. And 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.

另一方面，我們的模型是根據觀察到的行為進行預測的。我們認為這方面有很大的發展空間。正如我所說，他們根本無法考慮CoCo的影響，因為根本沒有歷史數據。我們能真切地感受到 CoCo 等工具帶來的好處，因為我們可以看到，當我們的服務團隊負責專案時，專案完成的速度有多快。

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? I point you to a blog post that one of our partners, James Dinkle wrote where he said that they were basically moving their business model as a whole from charging for time to offering fixed fee services, 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.

我們也看到，當我們的合作夥伴使用這些產品時，他們能夠做出真正具有變革意義的事情。你可能會問我，我是否過度使用了「變革」這個詞？我向您推薦我們的一位合作夥伴 James Dinkle 撰寫的一篇部落格文章，他在文章中提到，他們基本上正在將整個商業模式從按時間收費轉變為提供固定費用服務，而這種可預測性很大程度上來自於他們使用 Cortex Code 來驅動絕大多數的遷移。所以我們看到這家公司未來發展潛力大。

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. 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.

此外，在過去幾週使用 Cortex Code 的過程中，我們還了解到它會對 Snowflake 中的每個功能產生影響。我們的產品經理現在也有了自己的版本，可以進行預測，可以查看所有信息，例如下週即將推出的產品，或者針對其產品提交的漏洞報告。甚至有人編寫了一個基督教回饋機器人，用來提供關於基督教徒對產品提案的反應的回饋。

The level of innovation that we're seeing across the company is pretty inspiring, and that gives us a lot of confidence about how we approach the year.

公司各部門展現的創新水準令人振奮，這讓我們對今年的工作充滿信心。

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

我只是想補充斯里達爾之前談到的內容。請繼續，馬特。

You can finish your answer, Brian. I was just going to wonder, it looked like gross margins are down about 1 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?

布萊恩，你可以把答案說完了。我正想問一下，今年的毛利率似乎下降了大約 1 個百分點，我很好奇，考慮到你們進行的所有投資，你們覺得 70% 左右的毛利率是一個比較穩定的水平嗎？特別是當我們展望未來幾年的時候？

Yeah, great question. One of our objectives when we launch new products is really first and foremost is to build great products. Two, we want to make it easy to use. 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, 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.

嗯，問得好。我們推出新產品的首要目標之一，就是打造優秀的產品。第二，我們希望它易於使用。第三，我們希望在此之後推動營收成長。一旦達到目標，我們將著手優化利潤率。我們已經推出了許多新的人工智慧產品，這些產品的利潤率目前不如核心業務高，但我們正在透過在核心業務中尋找更多效率來解決這個問題。

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.

所以，這其實就是其中的一個組成部分，我們會做正確的事情來推動成長，並且我們會在營運利潤率層面上保持平衡。

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 free pools across all our deployments using AI because they got way better visibility into that data.

利潤率的提高不僅體現在毛利率層面，也體現在公司層面。我來為大家介紹幾個我們做過的、影響很大的項目，其中一個項目利用人工智慧優化了我們所有部署中的免費池，因為他們對這些數據有了更好的可見性。

That actually (technical difficulty). Yeah, free pool, basically we have to maintain free pools of computes so that our customers don't have to wait when they want to spin up a new warehouse 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 life cycle policies. When does, when does the table need to be in airline storage versus more like glacial storage and things like that.

實際上（技術難題）是的，空閒池，基本上我們必須維護空閒的計算池，這樣我們的客戶在想要啟動一個新的資料倉儲時就不必等待，而且有人發現了一種非常巧妙的方法來查看資料並對其進行優化，或者，我們圍繞儲存生命週期策略等事情做了很多工作。什麼時候，什麼時候需要將桌子存放在航空級倉庫裡，而不是像冰川那樣存放等等。

So there are a lot of winds 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 opportunities.

因此，人工智慧有許多發展空間，不僅在毛利率方面，而且在營業利潤率方面也肯定如此。說實話，關鍵在於如何合理安排時間，因為這個世界充滿了機會。

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

還有馬特，我只是想在第四節強調這一點。人工智慧為我們帶來了許多好處，因此我們進行了小幅裁員，公司約有 200 人受到影響。所以，如果以員工人數來看我們第四季的淨廣告收入，我們只增加了 37 人。因此，人工智慧確實改變了成長型投資的框架。它不再與員工人數掛鉤。

Brent Thill, Jeffries.

布倫特·蒂爾，傑弗里斯。

Franks, tree and our, all the stock names are selling off on the big AI labs taking the stack. I guess when you think about the advantage you have with, the platform of having Gemini, OpenAI, and Anthropic, available natively, do you first, do you think your customers understand that yet? And second, I guess, are you seeing that show up in demand given that you have all three of the top, supported natively?

Franks、Tree 和 Our 等所有股票都在拋售，因為大型人工智慧實驗室正在崛起。我想，當你考慮到擁有 Gemini、OpenAI 和 Anthropic 這三款原生支援的平台所帶來的優勢時，你首先認為你的客戶是否已經理解了這一點？其次，我想問的是，鑑於你們原生支援這三大頂級功能，你們是否看到這方面的需求有所成長？

I think it's useful to step back and look at the impact that AI as a whole is having on software. We spent a lot of time looking at this. We lived 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 4 sources of the truth.

我認為有必要退後一步，檢視一下人工智慧整體對軟體產生的影響。我們花了很多時間研究這個問題。我們親身經歷了這一切，我們的看法是，最終的贏家將是那些能夠提供企業單一真實資料來源的公司。如果真相有 4 個來源，任何人工智慧模型都幫不了你。

Similarly, having built-in security, auditability, trust, or even governance or 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.

同樣，內建安全性、可審計性、信任，甚至治理或存取權限（誰可以存取哪些資料集）都至關重要。顯然，你需要最好的模特，但目前至少有三家，甚至四家最好的模特提供商，我們與他們都有合作。

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, and 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.

我認為我們成功的秘訣，也是公司自成立以來就存在的秘訣，就是將所有這些功能整合到一個易於使用的整體產品中。這點在 Snowflake Intelligence 和 Cortex Code 的協同工作中體現得淋漓盡致。 Snowflake Intelligence 本身就是一個很棒的產品，但 Cortex Code 讓部署這些代理程式的速度提高了 4 到 10 倍。

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 all enterprise information, that I think make us a great party for every single enterprise to be working with. Thank you.

我認為，隨著我們邁向智慧人工智慧的旅程，我們確實看到了許多良好的協同效應正在匯聚。正是這些能力的結合，加上我們一直以來都是企業所有資訊值得信賴的管理者，我認為使我們成為每個企業都樂於合作的理想夥伴。謝謝。

Ryan Weiss, Wells Fargo.

Ryan Weiss，富國銀行。

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, 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 they're trying to be the place for users to build agents.

太好了，謝謝你回答這個問題。很高興看到 Cortex Code 的進展，它似乎將當今人工智慧的最佳功能與 Snowflake 的最佳功能結合起來，這使得在 Snowflake 平台上建立代理變得更加容易。看起來有很多不同的供應商都試圖成為用戶建立代理的首選平台。

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? And then have you seen any, increase in query volumes from Cortex Code users today.

那麼從技術角度來看，您認為 Snowflake 有哪些優勢使其成為用戶建立代理的最佳平台？那麼，您今天是否注意到來自 Cortex Code 用戶的查詢量增加？

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 present. And it's that thing that I think it's that quality that gives us an advantage when it comes to creating agents.

多年來，我們的使命一直是成為一個讓使用者輕鬆從數據中獲取價值的數據平台。這就是 Snowflake 剛推出時我們所做的。我們一直以來都是這麼做的。事實上，我們的座右銘一直是“簡單、互聯、值得信賴”，這樣企業內部的數據不僅易於使用，而且可以在您需要的任何地方呈現。我認為正是這種特質，讓我們在培養經紀人方面具有優勢。

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, 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, 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 providing agents for accessing the data.

正如我之前所說，我們也相信互通性。如果有人想要一個代理，並且能夠使用 MCP 呼叫 Snowflake Intelligence 代理，那完全沒問題，但我認為我們擁有獨特的優勢，可以成為這樣的中心。對於我們的許多客戶而言，這是一個可以實現 360 度全方位視角的地方，我們是他們最重要數據的守護者。在分析領域，這被稱為黃金層，我認為這使我們能夠非常有效地為資料存取提供代理。

And 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're beginning to see use cases like that come alive as well. And we've 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.

我們非常依賴MCP等技術。MCP是雙向的。您可以使用 MCP 從代理程式讀取數據，但我們也可以使用 MCP 從其他系統讀取數據，而且我們也開始看到這樣的用例逐漸成為現實。我們已經進行了一系列研究。Snowflake Intelligence 的確能帶來更多使用量、更多查詢，但我們更傾向於專注於我們正在創造的價值。

At this point 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. Christian?

目前，我不太在意我們是透過執行查詢還是透過運行模型來獲得更多 Snowflake Intelligence 的收入。一切都是為了創造精彩的體驗，並讓創造精彩體驗變得輕鬆簡單。基督教？

We've definitely seen the telemetry activity on the platform being increased based on the ease of use that both Intelligence and Cortex Code bring.

我們已經明顯看到，由於 Intelligence 和 Cortex Code 的易用性，平台上的遙測活動增加。

Excellent, appreciate the color. Thank you.

太棒了，很喜歡這個顏色。謝謝。

Alex Zukin, Wolfe Research.

Alex Zukin，Wolfe Research。

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, what, on what looks like a stronger guide this time versus last year. And then I've got a quick follow-up for Brian.

各位，謝謝你們回答這個問題。或許斯里達爾，先給你一個簡短的問題，然後再給布萊恩一個後續問題。上個季度，您曾提到一月和二月的消費趨勢對於確定本財年指引最為重要。或許可以具體談談你在一月節後，特別是二月節後所看到的，是什麼讓你有信心，以及為什麼今年的市場指引看起來比去年同期更可靠。接下來我還有一個問題想問布萊恩。

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 done, and 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. So in that sense it is taking everything into account.

布萊恩之前說過，我們在做導遊時，會盡可能地把每一分每一毫的數據納入導遊的講解中。我們已經這樣做了，我們也明確表示，指導過程是一個相當嚴格的過程，專注於歷史資訊和我們可靠預測未來的能力。所以從這個意義上講，它考慮到了所有因素。

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. And one year later, not only did we launch Snowflake Intelligence and get it adopted, we are also being at the forefront of how we 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.

如果你問我去年和今年有什麼不同，那麼在去年年初，Snowflake Intelligence 還只是我們腦海中的想法。一年後，我們不僅推出了 Snowflake Intelligence 並使其應用，而且我們還處於利用智慧 AI 大幅加速資料平台使用方式的最前線。我認為所有這些因素最終都會影響我們今年的表現。

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.

但就指南而言，它非常注重利用我們目前為止所掌握的每一條數據，對吧。

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

完全正確。你的第二個後續問題是什麼？

Yeah, but, 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 put and takes there and how we should think about that trajectory.

是的，不過，我正想問一下 Snowflake AI 的 ARR 和自由現金流利潤率指引是否有任何更新，當然，還要消化 Observable 收購案，也許只是想了解一下這方面的情況以及我們應該如何看待這一發展軌跡。

Yeah, just some free cash. So, 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 for the last two years, we got it to 23%. Observe was 150 basis point headwind. That's included in our numbers. The revenues included, the op margins included, as well as the free cash flow. And then we just wanted to get guidance that we felt comfortable with that we can perform against.

是啊，就是些白給的錢。因此，整體而言，季節性規律將與往年相同。我們大部分現金收入都集中在第四季。過去兩年第四季度，這一比例都超過 60%，而我們已經將其降至 23%。觀測到逆風150個基點。這部分已經包含在我們的統計數據中了。包括收入、營業利益率、自由現金流。然後，我們只是想獲得一些我們覺得合適、能夠按照這些要求行事的指導。

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

謝謝。今天的問答環節到此結束。現在我將把電話交還給斯里達爾，請他作總結發言。

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. 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.

謝謝大家。Snowflake 仍然是企業人工智慧革命的核心，我們看到了未來巨大的發展機會。總而言之，人工智慧已經從設想變成現實。Snowflake 旨在透過結合可信任的企業資料、受監管的指標、安全的執行和廣泛的模型選擇來贏得這個時代，以便客戶可以安全地大規模部署 AI 和代理商。

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. The 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. Thank you.

我們正在迅速從資料管理和分析平台轉型為客戶建置和運行 AI 原生應用程式和工作流程的平台，使業務用戶和建置者都能更輕鬆地將想法轉化為實際產品。這個策略奏效了。我們快速的創新步伐和強大的市場推廣執行力正在推動產品收入的持續成長，我們看到未來將有很長一段時間保持持續、穩定的成長。謝謝。

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

今天的電話會議到此結束。謝謝。現在您可以斷開線路了。