Snowflake Inc. (SNOW) 2027 Q1 法說會逐字稿

Good day, and welcome to the Q1 FY27 Snowflake earnings conference call. Today's conference is being recorded.

各位好，歡迎參加 Snowflake 2027 會計年度第一季（Q1 FY27）財報電話會議。今天的會議將進行錄音。

At this time, I'd like to turn the conference over to Katherine McCracken, Head of Investor Relations. Please go ahead.

此刻，我想將會議交給投資人關係主管 Katherine McCracken。請開始。

Good afternoon, and thank you for joining us on Snowflake's first quarter fiscal 2027 earnings call. Joining me on the call today are Sridhar Ramaswamy, our Chief Executive Officer; Brian Robins, our Chief Financial Officer; and Christian Kleinerman, our Executive Vice President of Product, who will participate in the Q&A session.

各位下午好，感謝各位參加 Snowflake 2027 會計年度第一季財報電話會議。今天與我一同出席的有：我們的執行長 Sridhar Ramaswamy、財務長 Brian Robins，以及產品執行副總裁 Christian Kleinerman，他將參與問答環節。

During today's call, we will review our financial results for the first quarter fiscal 2027 and discuss our guidance for the second quarter and full year fiscal 2027.

在今天的會議中，我們將回顧 2027 會計年度第一季的財務結果，並討論 2027 會計年度第二季及全年之財測指引。

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.

在今天的會議中，我們將作出前瞻性陳述，包括與我們的業務營運及財務表現相關的陳述。這些陳述受風險與不確定性影響，可能導致實際結果與陳述內容出現重大差異。關於這些風險與不確定性的資訊，載於我們的財報新聞稿、最新的 10-K 與 10-Q 表格，以及其他向美國證券交易委員會（SEC）提交的報告。我們所有陳述均以今日為準，並基於目前可得資訊作出。除法律要求外，我們不承擔更新任何此類陳述之義務。

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 a reconciliation of GAAP to non-GAAP measures and business metric definitions, including customer account and adoption.

在今天的會議中，我們也將討論若干非 GAAP 財務衡量指標。請參閱我們的投資人簡報，以了解非 GAAP 財務衡量指標之定義，以及 GAAP 與非 GAAP 指標之調節表與業務指標定義（包括客戶帳戶與採用情況）。

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.

財報新聞稿與投資人簡報可於我們的網站 investors.snowflake.com 取得。今天會議的重播也將發布於該網站。

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

接下來，我想把電話交給 Sridhar。

Thank you, Katherine, and thank you all for joining us today. AI is fundamentally reshaping how work gets done, and Snowflake is at the center of the transformation. Across industries, organizations are moving toward a future where employees and intelligent agents work side by side to accelerate decisions, automate complex workflows, and unlock entirely new levels of productivity and innovation. At Snowflake, that future is already taking shape.

謝謝你，Katherine，也感謝各位今天加入我們。AI 正在從根本上重塑工作的完成方式，而 Snowflake 正位於這場轉型的核心。各行各業的組織正邁向一個未來：員工與智慧代理（intelligent agents）並肩工作，以加速決策、自動化複雜工作流程，並解鎖全新層級的生產力與創新。在 Snowflake，這個未來已經正在成形。

Our platform brings together the four elements organizations need to become an agentic enterprise: a unified governed data foundation, access to leading AI models, connectivity across enterprise applications and workflows, and a unifying agentic control plane that turns intent into governed action. That control plane is becoming real through Snowflake Intelligence and Cortex Code, or CoCo, as it's affectionately known.

我們的平台整合了組織成為「代理型企業」（agentic enterprise）所需的四大要素：統一且受治理的資料基礎、可存取領先的 AI 模型、跨企業應用與工作流程的連結能力，以及將意圖轉化為受治理行動的統一代理控制平面（agentic control plane）。這個控制平面正透過 Snowflake Intelligence 與 Cortex Code（或大家親切稱之為 CoCo）逐步落地成真。

Snowflake Intelligence gives business users a natural language interface to enterprise data, context, and actions; while CoCo gives builders her language way to create applications, pipelines, agents, and workflows directly on Snowflake. Snowflake is uniquely positioned to help customers become agentic enterprise, as evidenced by our Q1 results.

Snowflake Intelligence 為商務使用者提供以自然語言操作的介面，用於存取企業資料、情境與行動；而 CoCo 則為建置者提供一種更自然的方式，能直接在 Snowflake 上建立應用程式、管線、代理與工作流程。從我們第一季的表現可見，Snowflake 具備獨特優勢，能協助客戶成為代理型企業。

Product revenue came in at $1.334 billion, with growth accelerating to 34% year over year, up from 30% last quarter and 26% a year ago, marking our strongest sequential dollar growth in company history. Our net revenue retention rate increased to 126%. And with our continued focus on executing the discipline and operational rigor, our Q1 non-GAAP operating margin expanded over 300 basis points year over year to 12%.

產品營收為 13.34 億美元，年增率加速至 34%，高於上季的 30% 與一年前的 26%，並創下公司史上最強的連續季度（sequential）美元成長。我們的淨營收留存率提升至 126%。同時，隨著我們持續專注於紀律執行與營運嚴謹度，第一季非 GAAP 營業利益率年增超過 300 個基點，達到 12%。

I want to take a moment to touch on our outlook. Based on a combination of strength and our core data platform business and meaningful uplift from AI capabilities, including CoCo and Snowflake Intelligence, we are increasing our FY27 outlook from 27% to 31% year-over-year growth. Brian will share more details on our guidance in his remarks. Thank you to all of our Snowflake for the hard work and dedication to deliver these results.

我想花一點時間談談我們的展望。基於核心資料平台業務的強勁表現，以及 AI 能力（包括 CoCo 與 Snowflake Intelligence）帶來的顯著提升，我們將 FY27 的展望從年增 27% 上調至年增 31%。Brian 將在他的發言中分享更多指引細節。也感謝所有 Snowflake 同仁的辛勤付出與投入，促成這些成果。

Across our business, AI is strengthening Snowflake on multiple levels, simultaneously. First, AI is accelerating consumption in our core platform as customers migrate workloads to Snowflake faster in order to access the data, context, and governance needed to power AI securely and at scale.

在我們的整體業務中，AI 正在同時從多個層面強化 Snowflake。首先，AI 正在加速我們核心平台的用量（consumption），因為客戶為了能安全且大規模地取得驅動 AI 所需的資料、情境與治理能力，正更快地將工作負載遷移至 Snowflake。

Second, Snowflake Intelligence and CoCo are seeing the fastest adoption of any new products in our history, opening new opportunities for growth as the first major product surfaces of the agentic control plane.

第二，Snowflake Intelligence 與 CoCo 正以公司史上任何新產品中最快的速度被採用，作為代理控制平面的第一批主要產品介面，為成長開啟新的機會。

And third, adoption of these AI products is increasing core platform consumption as customers move from questions to answers, from prompts to pipelines, and from ideas to production workflows on Snowflake. Customers adopting CoCo or growing even faster, and we expect that momentum's continue as adoption expands.

第三，這些 AI 產品的採用也正在提升核心平台用量，因為客戶在 Snowflake 上從「提問到得到答案」、從「提示（prompts）到管線（pipelines）」、從「想法到正式上線的工作流程」不斷推進。採用 CoCo 的客戶成長速度更快，我們預期隨著採用擴大，這股動能將持續。

The strength of our Q1 results reflects the powerful flywheel effect of the agentic enterprise. Importantly, this momentum starts with the strength of our core business. Our 13,912 customers turned to Snowflake because our AI data cloud is easy to use, seamlessly connected for collaboration, and trusted with enterprise-grade governance and security. In fact, 42% of our customers are data sharing on Snowflake with at least one stable edge. This underscores the power of the platform to connect organizations, partners, and obligations around a single governed source of truth.

我們第一季成果的強勁，反映了代理型企業所帶來的強大飛輪效應。重要的是，這股動能始於我們核心業務的扎實表現。我們的 13,912 家客戶選擇 Snowflake，是因為我們的 AI 資料雲易於使用、可無縫連結以促進協作，並具備企業級治理與安全性，值得信賴。事實上，42% 的客戶在 Snowflake 上至少與一個穩定邊界（stable edge）進行資料共享。這凸顯平台能將組織、合作夥伴與義務連結在同一個受治理的單一真實來源（single governed source of truth）之上的力量。

That interconnected foundation becomes even more valuable in the age of AI. Snowflake isn't just software. It is a circulatory system connecting modern enterprises, enabling data, applications, and AI agents to move securely and seamlessly across organizations. This combination of connectivity, governance, and ease of use is why enterprises continue to choose Snowflake as the cornerstone for their data and AI strategies again and again.

在 AI 時代，這個互聯的基礎變得更有價值。Snowflake 不只是軟體；它是一套連結現代企業的循環系統，使資料、應用程式與 AI 代理能在組織之間安全且順暢地流動。正是這種連結性、治理能力與易用性的組合，讓企業一再選擇 Snowflake 作為其資料與 AI 策略的基石。

Take Holiday Inn Club Vacations, a leading vacation ownership company, they chose Snowflake to power their data and AI modernization, citing our simplicity built in AI and machine learning capabilities and strong partnership as reasons for their selection. With Snowflake, they're now positioned to scale analytics and operations across their business.

以 Holiday Inn Club Vacations（領先的度假所有權公司）為例，他們選擇 Snowflake 來推動其資料與 AI 現代化，並指出我們的簡潔性、內建的 AI 與機器學習能力，以及強大的合作夥伴關係，是他們做出選擇的原因。透過 Snowflake，他們如今已具備在全公司範圍擴展分析與營運的能力。

And Houzz, the leading AI-driven platform for construction and design, selected Snowflake to accelerate their next phase of growth, enabling faster access to insights across the business. With Snowflake, Houzz will significantly improve data processing performance, reduce pipeline maintenance, and free up engineering resources to focus on building new products. Going forward, they'll be investing in natural language query processing and self-serve analytics to make data more accessible across the organization. Their existing customers continue to go all in on Snowflake.

再看 Houzz（領先的 AI 驅動建築與設計平台），他們選擇 Snowflake 以加速下一階段成長，讓全公司能更快取得洞察。透過 Snowflake，Houzz 將大幅提升資料處理效能、降低管線維護成本，並釋放工程資源以專注於打造新產品。展望未來，他們將投資自然語言查詢處理與自助式分析，讓全組織更容易存取資料。他們既有客戶也持續全面採用 Snowflake。

After nearly two years, and one of the most complex data warehouse migrations and financial services, one of the largest banks in the United States completed their Teradata migration onto Snowflakes, since what many legacy platforms they intend to move to Snowflake. This migration represents one of many legacy platforms they intend to move to Snowflake. Their teams are now building AI-powered regulatory intelligence, natural language analytics, and data discovery directly on top of a platform they already run at massive scale.

在歷時近兩年、且屬於金融服務領域最複雜的資料倉儲遷移之一後，美國最大型銀行之一完成了將 Teradata 遷移至 Snowflake 的工作。這次遷移代表他們計畫移轉到 Snowflake 的眾多傳統平台之一。他們的團隊如今正直接在一個已能以超大規模運行的平台之上，建置 AI 驅動的法規智慧、自然語言分析與資料探索能力。

Then there's Nestle, one of the world's largest consumer goods company with more than 2,000 brands globally, operating in 185 countries. They're expanding their use of Snowflake to power their enterprise digital transformation. As part of this, Nestle is reimagining its operations end to end, with data and AI as key enablers, building enterprise data products used by over 50,000 users across 150 global capabilities. This enables a real-time connected view of the business, allowing teams to make faster and more proactive decisions.

還有雀巢（Nestle），全球最大的消費品公司之一，在全球擁有超過 2,000 個品牌，業務遍及 185 個國家。他們正擴大使用 Snowflake 以推動企業數位轉型。作為其中一環，雀巢正以資料與 AI 作為關鍵推動力，端到端重新構想其營運，打造企業資料產品，供超過 50,000 名使用者在 150 項全球能力（capabilities）中使用。這使企業能以即時且互聯的方式掌握業務全貌，讓團隊做出更快速、更具前瞻性的決策。

And one of the world's largest wealth management firms built a Cortex-powered agent called Ask Your Data and deployed it to their entire executive leadership team. Over 60% of business inquiries that were previously routed to analysts for manual data pools are now answered instantly on demand, leveraging their existing data in Snowflake.

此外，全球最大的財富管理公司之一打造了一個由 Cortex 驅動的代理，名為 Ask Your Data，並部署給其整個高階管理團隊。過去超過 60% 需要轉交分析師進行人工資料彙整的業務詢問，如今可即時按需獲得答案，並運用其既有存放於 Snowflake 的資料。

We also saw Global 2000 companies like Global Payments, depository trust and clearing corporation; DDCC; and Blue Yonder expand their use of Snowflake to support growing workloads, accelerate AI-powered insights, and drive further value for their end customers. This continued expansion is reflected in our large customer growth. In Q1, eight customers surpassed $10 million in trailing 12-month revenue. We now have 64 customers spending more than $10 million on a trailing 12-month basis.

我們也看到 Global 2000 企業（如 Global Payments、Depository Trust & Clearing Corporation（DTCC）以及 Blue Yonder）擴大使用 Snowflake，以支援持續成長的工作負載、加速 AI 驅動的洞察，並為其終端客戶創造更多價值。這種持續擴張也反映在我們的大型客戶成長上。第一季有 8 家客戶的過去 12 個月（TTM）營收超過 1,000 萬美元。我們目前共有 64 家客戶在過去 12 個月基礎上支出超過 1,000 萬美元。

As AI strengthens demand for our core platform, it is also expanding Snowflake's opportunities to deliver a new generation of AI-powered products and experience. Snowflake is uniquely positioned to lead in the next phase of enterprise AI because we already sit at the center of our customers' data, business context, AI models, and workflow.

隨著 AI 強化對我們核心平台的需求，它也正在擴大 Snowflake 提供新一代 AI 驅動產品與體驗的機會。Snowflake 具備獨特優勢，能在企業 AI 下一階段中領先，因為我們已位於客戶資料、業務情境、AI 模型與工作流程的中心。

What customers increasingly want is simple, one place to get work done, a place where a business user can ask a question, understand the answer, and trigger the next step, and where a developer and turn an idea into an application, a pipeline, an agent, or a workflow without leaving Snowflake. That is what we mean by the agentic control plane. It's the governed layer where intent becomes action, grounded in the customer's enterprise data, business context, model, applications, and security policies.

客戶愈來愈想要的是簡單、單一入口即可完成工作：一個讓企業使用者能提出問題、理解答案並觸發下一步的地方；同時也讓開發者無需離開 Snowflake，就能把想法變成應用程式、管線、代理（agent）或工作流程。這就是我們所說的代理式控制平面（agentic control plane）。它是一個受治理的層，讓意圖轉化為行動，並以客戶的企業資料、業務情境、模型、應用程式與安全政策為基礎。

Snowflake intelligence is the business user surface of that control plane. CoCo is the builder interface. Together, they help customers move from insight to action and from prompt to production, all within Snowflake's trusted governance model.

Snowflake Intelligence 是該控制平面的企業使用者介面。CoCo 是建置者介面。兩者結合，協助客戶在 Snowflake 可信的治理模型內，從洞察走向行動、從提示（prompt）走向上線（production）。

In fact, accounts using Snowflake Intelligence more than doubled quarter over quarter as more organizations embrace a governed conversational way for business users to ask questions, get answers, and act on enterprise data. And CoCo is already in use with more than 7,100 accounts, giving builders a natural language way to create applications, pipelines, agents, and workflows directly in Snowflake.

事實上，隨著更多組織採用受治理的對話式方式，讓企業使用者能提問、獲得答案並對企業資料採取行動，使用 Snowflake Intelligence 的帳戶數在季度間成長超過一倍。而 CoCo 已在超過 7,100 個帳戶中使用，為建置者提供以自然語言直接在 Snowflake 中建立應用程式、管線、代理與工作流程的方式。

Just recently, our partner, Infinite Lambda, was preparing for a major customer pitch. One of their engineers used CoCo to build a true customer 360 application in just five hours, bringing together customer data, shard insights, recommended actions, and live dashboards into a single experience. And they showed it to the customer. The reaction was immediate. After the meeting, Infinite Lambda CEO called me and said, you are changing this industry.

就在最近，我們的合作夥伴 Infinite Lambda 正在準備一場重要的客戶提案。其中一位工程師使用 CoCo 在短短五小時內建置出真正的 Customer 360 應用程式，將客戶資料、分片（shard）洞察、建議行動與即時儀表板整合為單一體驗，並展示給客戶。客戶的反應立刻出現。會後，Infinite Lambda 的執行長打電話給我說：你正在改變這個產業。

Providence, one of the largest health systems in the United States, is using Snowflake Cortex to surface insights from clinical notes and patient records in seconds. With CoCo, they're now building these workflows directly in Snowflake, enabling care teams to access critical information faster while maintaining privacy standards.

Providence（美國最大的醫療體系之一）正在使用 Snowflake Cortex，在數秒內從臨床筆記與病患紀錄中呈現洞察。透過 CoCo，他們現在直接在 Snowflake 中建置這些工作流程，使照護團隊能更快取得關鍵資訊，同時維持隱私標準。

And Thomson Reuters, the global provider of legal, tax, and regulatory intelligence, uses Snowflake Cortex, including CoCo, to power air-driven legal and compliance workflows. By leveraging CoCo to build and deploy intelligent applications directly within Snowflake, its teams can turn complex regulatory data into actionable insights in seconds while accelerating product development. This approach maintains the fiduciary-grade governance and reliability required for high stake professional uses.

而 Thomson Reuters（全球法律、稅務與法規情報供應商）使用 Snowflake Cortex（包含 CoCo）來驅動 AI 驅動的法律與合規工作流程。透過運用 CoCo 直接在 Snowflake 內建置並部署智慧型應用程式，其團隊能在數秒內將複雜的法規資料轉化為可執行的洞察，同時加速產品開發。此作法維持高風險專業用途所需的受託等級（fiduciary-grade）治理與可靠性。

CoCo's contributing meaningfully AI revenue while also driving increased engagement across the broader platform. This tangible momentum, together with continued strength in our core platform, is reflected in our increased FY27 outlook.

CoCo 正在對 AI 營收做出實質貢獻，同時也帶動更廣泛平台的互動提升。這股具體動能，加上我們核心平台持續強勁的表現，反映在我們上調的 FY27 展望之中。

Today, with the announcement of our intended acquisition of Natoma, we are extending the Snowflake agentic control plane beyond data and development workload into the everyday applications where work happens. With Natoma, users can do things like send emails, summarize Clack conversations, check calendars, and open JIRA tickets without ever leaving Snowflake Intelligence or CoCo.

今天，隨著我們宣布擬收購 Natoma，我們正把 Snowflake 的代理式控制平面從資料與開發工作負載，延伸到日常工作發生的各種應用程式中。透過 Natoma，使用者可以在不離開 Snowflake Intelligence 或 CoCo 的情況下，完成例如寄送電子郵件、摘要 Clack 對話、查看行事曆，以及建立 JIRA 工單等操作。

The important point is not just convenience; it is control. These actions happen from a governed environment with enterprise security, permissions, observability, and policy enforcement built in. This will extend Snowflake's leadership in AI governance by ensuring companies can safely manage not just their data but also the actions AI agents take across business workflows.

重點不只是便利，而是控制。這些行動是在受治理的環境中發生，內建企業級安全、權限、可觀測性與政策執行。這將延伸 Snowflake 在 AI 治理上的領導地位，確保企業不僅能安全管理資料，也能安全管理 AI 代理在各種業務工作流程中採取的行動。

As we continue to innovate to support our customers, we are also leading the AI transformation from within. With Snowflake intelligence and CoCo, our teams are revolutionizing how they work. Across our global support organization at Snowflake, CoCo now analyzes incoming customer cases before an engineer engages. Surfacing diagnostic insights and likely root causes upfront alongside the use of AI accelerated investigations, this has driven over 25% faster case resolution times and a 25% increase in case throughput per engineer.

在持續創新以支援客戶的同時，我們也從內部引領 AI 轉型。透過 Snowflake Intelligence 與 CoCo，我們的團隊正在革新工作方式。在 Snowflake 的全球支援組織中，CoCo 現在會在工程師介入前先分析進來的客戶案件，提前呈現診斷洞察與可能的根因，並搭配 AI 加速的調查，讓案件解決時間提升超過 25%，且每位工程師的案件處理量提升 25%。

By using CoCo, engineering team that runs Snowflake cloud deployment has freed up capacity and moved resources to product innovation, while reducing complex case resolution time by nearly 30% and cutting engineering time spent per ticket by roughly 40%. Across our data organization, CoCo's double-developer productivity, as measured by pull requests and lines of code per engineer and has automated more than 100 workflows across finance, marketing, sales, and HR in just weeks.

透過使用 CoCo，負責 Snowflake 雲端部署的工程團隊釋放了產能並將資源轉向產品創新，同時將複雜案件的解決時間縮短近 30%，並將每張工單所花的工程時間降低約 40%。在我們的資料組織中，CoCo 使開發者生產力加倍（以每位工程師的 pull request 與程式碼行數衡量），並在短短數週內自動化了財務、行銷、銷售與人資等超過 100 個工作流程。

Through this operational transformation, our teams are moving with greater speed and focus to capture the AI opportunity in front of us. In Q1, we delivered over 20% more product capabilities to market than we did a year ago, underscoring both the pace of our innovation and the breadth of platform expansion underway across Snowflake.

透過這項營運轉型，我們的團隊以更快速度與更高專注度把握眼前的 AI 機會。在 Q1，我們推向市場的產品能力比一年前多出超過 20%，凸顯我們創新的速度，以及 Snowflake 平台擴張的廣度正在加速進行。

We are also strengthening our go-to-market organization to support our next phase of growth. Following a seamless transition, our new Chief Revenue Officer, Jonathan Beaulier, JB, is positioning Snowflake to scale in the AI era. JB brings more than a decade of experience at Snowflake, deep knowledge of our customers and platform, and a strong operational focus as we continue to evolve our go-to-market motion. That strong execution Is translating into continued customer momentum and broader product adoption of the platform.

我們也在強化我們的市場推進（go-to-market）組織，以支援下一階段的成長。在順利交接後，我們的新任首席營收長 Jonathan Beaulier（JB）正讓 Snowflake 在 AI 時代具備可規模化成長的能力。JB 在 Snowflake 擁有超過十年的經驗，對客戶與平台有深厚理解，並具備強烈的營運聚焦，協助我們持續演進市場推進模式。這樣的強勁執行力正轉化為持續的客戶動能，以及平台更廣泛的產品採用。

In the quarter, we added 616 net new customers, up 38% year over year. We're also seeing customers deploy and scale workloads at a faster pace the number of use cases. Individual projects managed on Snowflake deployed in the quarter increased 114% year over year as customers moved more workloads into production on the platform. At the same time, the number of use cases one per account executive increased 86% year over year, underscoring both growing customer demand and improved sales execution across the organization.

本季度我們新增 616 位淨新客戶，年增 38%。我們也看到客戶以更快速度部署並擴大工作負載與使用案例數。季度內在 Snowflake 上管理並部署的個別專案數年增 114%，因為客戶將更多工作負載導入平台的正式環境（production）。同時，每位客戶經理（account executive）所對應的使用案例數年增 86%，凸顯客戶需求持續成長，以及整體組織的銷售執行力提升。

We're also continuing to strengthen our ecosystem as we deepen our strategic partner relationships and extend the reach of our AI data cloud. Just today, we announced an expanded collaboration with AWS through a new $6 billion multi-year agreement to accelerate enterprise AI adoption globally, leveraging Graviton compute and AI services. The announcement comes as Snowflakes surpassed $7 billion in lifetime AWS marketplace sales, reflecting the growing demand for AI and data workloads running on Snowflake.

我們也持續強化生態系，深化策略夥伴關係並擴大我們 AI 資料雲的觸及範圍。就在今天，我們宣布與 AWS 擴大合作，透過一項新的 60 億美元多年期協議，運用 Graviton 運算與 AI 服務，加速全球企業採用 AI。此公告也正值 Snowflake 在 AWS Marketplace 的累計銷售額突破 70 億美元，反映市場對在 Snowflake 上運行的 AI 與資料工作負載需求日益增加。

During the quarter, we also announced an expanding $200 million partnership with OpenAI. And just recently, we brought the joint capability from our landmark partnership with SAP to general availability, enabling customers to unite mission-critical business data across their core data systems within our AI data cloud.

本季度我們也宣布與 OpenAI 擴大合作，合作規模增加 2 億美元。而就在最近，我們也將與 SAP 這項里程碑式合作的聯合能力推向正式可用（general availability），使客戶能在我們的 AI 資料雲中，整合其核心資料系統內的關鍵任務業務資料。

Before I close, I want to acknowledge our Co-Founder and Chief Architect, Benoit Dageville, who will be stepping away from day-to-day operations in mid-June and continuing as a member of Snowflake's Board of Directors. Benoit is one of the greatest technical visionaries of our industry. His leadership and innovation helped invent the modern cloud data platform and laid the foundation for everything Snowflake has become today.

在我結束之前，我想致謝我們的共同創辦人兼首席架構師 Benoit Dageville。他將於 6 月中旬起退出日常營運，並繼續擔任 Snowflake 董事會成員。Benoit 是本產業最傑出的技術遠見者之一。他的領導與創新協助發明了現代雲端資料平台，並奠定了 Snowflake 今日一切成就的基礎。

The impact he's had on this company, our customers, and the broader technology landscape is extraordinary, and we are deeply grateful for his continued guidance as we enter this next chapter. Our product organization will continue to be led by Christian Kleinerman.

他對本公司、我們的客戶，以及更廣泛的科技版圖所帶來的影響非同凡響；在我們邁入下一篇章之際，我們也非常感謝他持續提供指導。我們的產品組織將繼續由 Christian Kleinerman 領導。

For the past several years, you've seen AI emerge as a tailwind for our business. Q1 marks an important shift in this journey. The combination of Snowflake's trusted enterprise data, rich business context, leading AI models, and secure connectivity into enterprise applications creates a unique opportunity. Snowflake Intelligence and Cortex Code are the two primary ways customers experience that opportunity, one for business users, one for builders. Together, they allow customers to move from intent to action in a governed environment, positioning Snowflake to win a new market, the agentic control plane.

過去幾年，你們已看到 AI 成為我們業務的順風。Q1 標誌著這段旅程中的重要轉折。Snowflake 可信的企業資料、豐富的業務情境、領先的 AI 模型，以及與企業應用程式之間的安全連結相結合，創造了獨特機會。Snowflake Intelligence 與 Cortex Code 是客戶體驗這個機會的兩種主要方式：前者面向企業使用者，後者面向建置者。兩者結合，讓客戶能在受治理的環境中從意圖走向行動，使 Snowflake 有能力贏得一個新市場：代理式控制平面。

We are benefiting from AI as a secular tailwind while also monetizing first-party AI capabilities. Through the combination of rapid innovation, strong go-to-market execution, and operational discipline, we are well positioned to deliver accelerating growth and margin expansion.

我們受惠於 AI 作為長期結構性順風，同時也在將第一方 AI 能力變現。透過快速創新、強勁的市場推進執行力與營運紀律的結合，我們已具備良好條件，實現加速成長與利潤率擴張。

With that, I'll turn it over to Brian to walk through the financial details. Brian?

接下來，我把時間交給 Brian，請他說明財務細節。Brian？

Thank you, Sridhar. In Q1, year-over-year product revenue growth accelerated approximately 400 basis points to reach 34%. Growth benefited from a meaningful increase in AI revenue and an acceleration in our core data platform business. AI is a driving force behind our momentum.

謝謝你，Sridhar。Q1 產品營收年增率加速約 400 個基點，達到 34%。成長受惠於 AI 營收的顯著增加，以及我們核心資料平台業務的加速。AI 是推動我們動能的驅動力。

AI serves as a catalyst for our core data platform business. With an AI-first mindset, customers are moving to the cloud and to Snowflake with increasing urgency. This tailwind is evident in the pace of new customer additions. As Sridhar mentioned, our net new customer additions increased 38% year over year. We added 13 Global 2000s compared to 4 in the same period last year.

AI 也扮演我們核心資料平台業務的催化劑。以 AI 優先的思維，客戶正以更迫切的速度移轉到雲端並選擇 Snowflake。這股順風可從新增客戶的速度看出。如 Sridhar 所提，我們的淨新增客戶數年增 38%。我們新增了 13 家 Global 2000 企業，而去年同期為 4 家。

Snowflake's AI workload is now a significant revenue engine in its own right. AI products like Cortex Code are expanding our opportunity with existing customers as CoCo encourages faster, more consumption of the data platform. We now have 79 customers spending more than $1 million on a trailing 12-month basis. 46 customers crossed the $1 million threshold in Q1 compared to 26 in the year-ago period.

Snowflake 的 AI 工作負載如今本身已成為重要的營收引擎。像 Cortex Code 這類 AI 產品正在擴大我們與既有客戶的機會，因為 CoCo 會鼓勵更快、更多地消耗資料平台。我們目前有 79 位客戶在過去 12 個月（TTM）基礎上支出超過 100 萬美元。Q1 有 46 位客戶跨越 100 萬美元門檻，相較去年同期為 26 位。

Remaining performance obligations grew 38% year over year compared to 34% in Q1 of last year. We continue to see customers favor Q4 renewals. As a result, we expect bookings to be increasingly weighted towards the fourth quarter.

剩餘履約義務（RPO）年增 38%，相較去年 Q1 的 34%。我們持續看到客戶偏好在 Q4 續約。因此，我們預期訂單（bookings）將愈來愈集中在第四季。

We remain committed to delivering both growth and margin expansion. In Q1, non-GAAP operating margin expanded over 300 basis points year over year to reach 12%. Strong revenue growth and disciplined hiring both contributed to the outperformance in non-GAAP operating margin. We added 190 employees this quarter compared to approximately 400 added in the year-ago period. Of these 190 employees, 173 joined Snowflake through the Observe acquisition. Excluding Observe, organic hiring was limited to 17 people in the quarter.

我們仍致力於同時實現成長與利潤率擴張。Q1 非 GAAP 營業利潤率年增超過 300 個基點，達到 12%。強勁的營收成長與有紀律的招募共同促成非 GAAP 營業利潤率的優於預期。本季我們新增 190 名員工，相較去年同期約新增 400 名。在這 190 名員工中，有 173 名是透過收購 Observe 加入 Snowflake。排除 Observe 後，本季自然招募僅 17 人。

In Q1, we used approximately $300 million to repurchase 1.7 million shares. We have approximately $800 million remaining of our original $4.5 billion repurchase authorization. We ended the quarter with $4.4 billion in cash, cash equivalents, short-term and long-term investments.

Q1 我們使用約 3 億美元回購 170 萬股。我們原先 45 億美元的回購授權中，尚餘約 8 億美元。本季末我們持有 44 億美元的現金、約當現金、短期與長期投資。

During the quarter, we entered into a five-year $6 billion contract with AWS, more than doubling our prior contract signed in FY23. With this agreement, AWS has committed to an expanded go-to-market investment and collaboration. This agreement marks an important milestone in our ongoing partnership with AWS, and its impact is fully incorporated into our outlook.

本季我們與 AWS 簽訂一份為期五年、金額 60 億美元的合約，較 FY23 簽署的先前合約金額增加逾一倍。依據此協議，AWS 承諾擴大上市（go-to-market）投資與合作。此協議是我們與 AWS 持續夥伴關係中的重要里程碑，其影響已完整納入我們的展望。

Moving to our outlook. As always, our forecast is based on existing consumption patterns. There are no changes to our forecast methodology or guidance philosophy. Given the strength we've observed in both our core data platform business and AI business, we are raising our guidance for the year. For FY27, we now expect product revenue of $5.84 billion, representing 31% year-over-year growth. In Q2, we expect product revenue between $1.415 billion and $1.42 billion, representing 30% year-over-year growth.

接著談展望。如同以往，我們的預測係基於既有的消耗（consumption）模式。我們的預測方法或指引理念沒有任何改變。鑑於我們在核心資料平台業務與 AI 業務兩方面觀察到的強勁表現，我們上調全年指引。FY27 我們目前預期產品營收為 58.4 億美元，年增 31%。Q2 我們預期產品營收介於 14.15 億至 14.20 億美元，年增 30%。

Our Observe acquisition is progressing well, consistent with our initial expectations. Observe contributed less than 1 percentage point of product revenue growth in Q1, and we continue to expect the acquisition to add approximately 1 percentage point of revenue growth -- product revenue growth for the full year.

我們對 Observe 的收購進展順利，符合最初預期。Observe 在 Q1 對產品營收成長的貢獻不到 1 個百分點，我們仍預期該收購將為全年產品營收成長增加約 1 個百分點。

Turning to margins. We expect 75% non-GAAP product gross margin for FY27. We expect Q2 non-GAAP operating margin at 12.5%, and we are increasing our full-year non-GAAP operating margin guidance from 12.5% to 13.5%. We are reiterating our non-GAAP adjusted free cash flow margin guide of 23%.

再來談利潤率。我們預期 FY27 非 GAAP 產品毛利率為 75%。我們預期 Q2 非 GAAP 營業利潤率為 12.5%，並將全年非 GAAP 營業利潤率指引由 12.5% 上調至 13.5%。我們重申非 GAAP 調整後自由現金流利潤率指引為 23%。

Our full-year outlook for both non-GAAP operating margin and non-GAAP adjusted free cash flow margin continues to include approximately 150-basis-point headwind related to our Observe acquisition. This impact is unchanged from last quarter. Our intended acquisition of Natoma will bring 20 employees to Snowflake.

我們對全年非 GAAP 營業利潤率與非 GAAP 調整後自由現金流利潤率的展望，仍包含與收購 Observe 相關、約 150 個基點的不利因素（headwind）。此影響與上季相同，未有變動。我們擬收購 Natoma，將為 Snowflake 帶來 20 名員工。

Before turning to Q&A, I'd like to briefly revisit my priorities for FY27. Last quarter, I outlined two key priorities. First, driving growth and margin expansion; second, supporting ongoing excellence in our go-to-market motion. We are executing well on both fronts as AI strengthens every element of our business.

在進入問答之前，我想簡要回顧我對 FY27 的優先事項。上季我概述了兩項關鍵優先事項：第一，推動成長與利潤率擴張；第二，支持我們上市動能（go-to-market motion）的持續卓越。隨著 AI 強化我們業務的每個環節，我們在這兩方面都執行得很好。

Since last quarter, we've seen a step function change in our AI revenue opportunity led by Cortex Code. AI is only transforming how we operate internally, enabling greater productivity through a combination of slower hiring and more cloud spend.

自上季以來，在 Cortex Code 的帶動下，我們看到 AI 營收機會出現階躍式（step function）的變化。AI 不僅在改變我們對外的產品，也正在改變我們的內部運作方式，透過「放緩招募」與「增加雲端支出」的組合，提升生產力。

On the go-to-market side, we're incredibly pleased with the response to our new CRO. JB brings a wealth of experience and a proven track record of success at Snowflake. He understands how to deliver great outcomes and win with individual customers. More importantly, he knows how to drive that success across the broader organization.

在上市（go-to-market）方面，我們對新任 CRO 的回饋感到非常滿意。JB 擁有豐富經驗，並在 Snowflake 有經過驗證的成功紀錄。他了解如何交付優異成果並在單一客戶層面取勝。更重要的是，他知道如何把這種成功推動到更廣泛的組織層面。

Finally, next week, we'll host our Investor Day in conjunction with Snowflake Summit, conference in San Francisco. If you're interested in attending, please e-mail ir@snowflake.com.

最後，下週我們將在舊金山與 Snowflake Summit 大會同時舉辦投資人日（Investor Day）。若您有興趣參加，請寄電子郵件至 ir@snowflake.com。

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

接下來，我把電話交給接線員進行問答。

(Operator Instructions) And we'll go ahead and take the first question.

（接線員指示）我們將開始進行第一個問題。

This is Sanjit Singh from Morgan Stanley. Sridhar, I've been covering consumption software companies, consumer software companies for a long time. In a normal year, we typically don't see the sequential dollar growth that you guys are posting up. Typically, you don't see raises through the full year or Q2 guides the way we're seeing with this set of results.

我是 Morgan Stanley 的 Sanjit Singh。Sridhar，我長期追蹤以消耗計價的軟體公司、以及消費者軟體公司。在一般年份，我們通常不會看到你們目前呈現的這種連續季度（sequential）的美元成長。通常也不會像這次結果一樣，看到全年或 Q2 指引被上調。

But the simple question is like what sort of inflected in the quarter on like two fronts, I would say, maybe from a market backdrop demand perspective and then from like a -- within the Snowflake portfolio, between, let's say, maybe vibrations, organic customer expansion in the core data platform and then the AI story. Can you talk about where specifically you're seeing the inflection? Thank you very much.

但簡單的問題是：本季到底在哪些方面出現了轉折（inflection）？我想從兩個面向來看：一是市場背景下的需求面；二是在 Snowflake 產品組合內部，例如核心資料平台的自然客戶擴張（organic customer expansion）與 AI 故事之間。你能談談你們具體在哪裡看到這個轉折嗎？非常感謝。

Absolutely. So I would break this up into three parts. First, AI is accelerating the value that people can get from the data that they have put into Snowflake or that they can put into Snowflake. So we saw like a healthy secular tailwind for our core data platform.

當然。我會把它拆成三個部分。第一，AI 正在加速人們從已放入 Snowflake 的資料、或可放入 Snowflake 的資料中取得價值的速度。因此，我們看到核心資料平台出現健康的長期結構性順風（secular tailwind）。

And part two is really that agentic products, the control plane products like Snowflake Intelligence, and Cortex Code, CoCo, came into their own in Q1. Recall that CoCo went into GA on February 5, so just as we were opening up the quarter. And we've seen very strong traction with both the products.

第二部分是，代理式（agentic）產品、控制平面（control plane）產品，例如 Snowflake Intelligence，以及 Cortex Code、CoCo，在 Q1 真正開始展現實力。回想一下，CoCo 在 2 月 5 日進入 GA（正式可用），也就是我們剛開始本季時。我們看到這兩項產品都取得非常強勁的進展（traction）。

And the really interesting thing with Cortex Code is that it, in turn, drives more consumption on the core data platform simply because it's much easier to get projects done, whether it's a pipeline or creating a new agent or setting up a new dynamic table or even honestly, a migration. So it's driving the second-order effect as well. But it's really -- this is the 1, 2, 3, and that's why I like to think of this as AI compounding Snowflake strength in data.

而 Cortex Code 真正有趣之處在於，它反過來也會帶動核心資料平台的更多消耗，因為它讓專案更容易完成，不論是建立一條 pipeline、打造一個新代理（agent）、設定新的動態表（dynamic table），甚至老實說，做遷移（migration）。所以它也帶來第二階效應（second-order effect）。但重點是——這就是 1、2、3，也是為什麼我喜歡把它視為 AI 讓 Snowflake 在資料領域的優勢產生複利效應（compounding）。

And I'll hand it off to Brian for the mechanics of how these came together in our forecast for the quarter and the year. Brian?

我把電話交給 Brian，請他說明這些因素如何在我們本季與全年預測中結合在一起的機制。Brian？

Yeah. Thanks, Sridhar. I'll unpack that a little in terms of impact. And so CoCo had the largest driver to the increase in our forecast. As a reminder, when we forecast, we only forecast Observe behavior. And as Sridhar mentioned, that just happened in the quarter. And so this quarter, we had a very unique opportunity to layer CoCo in the model, and that's reflected throughout the remainder of the year.

好的，謝謝你，Sridhar。我會從影響的角度再拆解一下。因此，CoCo 是我們上調預測的最大驅動因素。提醒一下，我們在做預測時，只會預測已觀察到（Observe）的行為。正如 Sridhar 提到的，這是在本季才發生的。因此本季我們有一個非常獨特的機會，把 CoCo 納入模型，而這也反映在今年剩餘期間的預測之中。

We also saw acceleration in our core business, and that informs our outlook as well. And so there's no change to our guidance philosophy, where 3% we view as a really strong beat.

我們也看到核心業務加速成長，這同樣影響了我們的展望。因此，我們的指引理念沒有改變；我們仍然把 3% 視為非常強勁的超預期（beat）。

And we'll take the next question.

我們來進行下一個問題。

It's Kirk Materne with Evercore ISI. Congrats on the quarter. Sridhar, I want to dive a little bit more into CoCo just in terms of how does that sort of change your customers' ability to get more data out of the platform at a faster rate? Can you just dive into that a little bit more?

我是 Evercore ISI 的 Kirk Materne。恭喜本季表現。Sridhar，我想更深入談談 CoCo：它如何改變客戶以更快速度從平台取用更多資料的能力？你能再多談一點嗎？

And then can you also just talk about how having a product like CoCo maybe changes the go-to-market model a little bit? You said, obviously, JV had a great first quarter. Just wondering how having these agentic products also sort of shapes your thinking around the go-to-market efforts as we go through the rest of the year. Thanks.

另外，你也能談談像 CoCo 這樣的產品，是否會在某種程度上改變你們的上市（go-to-market）模式？你提到顯然 JV 的第一季表現很出色。我想了解，這些代理式產品如何影響你們對今年剩餘時間 go-to-market 推進的思考。謝謝。

Yeah. So CoCo is a general-purpose coding agent that has a set of features that are specialized for Snowflake and data platforms. We have published benchmarks that show that CoCo can outperform even the frontier model when it comes to doing operations within Snowflake. And over the past quarter, we've actually expanded it to support other data platforms like Amazon Glue or Airflow or dbt Cloud, and in fact, even Databricks. So it's incredibly powerful.

好的。CoCo 是一個通用型的程式碼代理（coding agent），具備一組針對 Snowflake 與資料平台專門設計的功能。我們已發布基準測試（benchmarks），顯示 CoCo 在 Snowflake 內部操作方面，甚至能勝過前沿模型（frontier model）。而在過去一季，我們也把它擴展到支援其他資料平台，例如 Amazon Glue、Airflow 或 dbt Cloud，事實上甚至也支援 Databricks。因此它非常強大。

And in terms of how it impacts our customers' ability, our ability, our partners' ability to get things done faster, is any kind of coding transformation and the migration is one such example can be made faster with CoCo. We have a migration team that is busy creating. We call them harnesses. They are ways of structuring the process so that a complex migration can be broken down and attacked methodically. We work very closely with both partners and customers and help them get these migrations done faster. And I previously talked about how some of our partners are even switching their entire business models from charging for time and material to being able to charge for outcomes.

就其對我們客戶的能力、我們的能力、以及我們合作夥伴更快完成事情的能力之影響而言，任何形式的程式碼轉型與遷移——遷移就是其中一個例子——都可以透過 CoCo 更快完成。我們有一個遷移團隊正忙著建立我們稱之為 harnesses 的東西。它們是用來結構化流程的方法，讓複雜的遷移可以被拆解並以有系統的方式逐步攻克。我們與合作夥伴和客戶都非常緊密合作，協助他們更快完成這些遷移。我先前也談到，我們的一些合作夥伴甚至正在把整個商業模式，從按工時與材料收費，轉為能夠按成果收費。

In addition, something like creating an agent to run insight Snowflake Intelligence just goes a whole lot faster because we have created workflows within CoCo for the entirety of the agent creation pipeline. In fact, this has gotten so demystified that even somebody like me can go from a data set to things like Cortex analysts and search instances to creating an agent to running an eval on it. That's the life cycle of creating an agent. It's like an automation platform for everything having to do with Snowflake.

此外，像是建立一個代理（agent）來執行洞察 Snowflake Intelligence 之類的事情，也會快上許多，因為我們已在 CoCo 內為整個代理建立管線（pipeline）打造了工作流程。事實上，這件事已經被去神祕化到連像我這樣的人，都能從一個資料集出發，做到像是 Cortex 分析師（analysts）與搜尋實例（search instances），再到建立代理並對它跑評估（eval）。這就是建立代理的生命週期。它就像是一個針對所有與 Snowflake 相關事項的自動化平台。

And there is a ton of activity within Snowflake and outside by partners, for example, to build even more complicated skills and processes on top of this. I very much think that this is early.

而在 Snowflake 內部以及外部的合作夥伴之間，都有大量活動在進行，例如在此之上打造更複雜的技能與流程。我非常認為這仍處於早期階段。

And in terms of how it's affecting our go-to-market, first and foremost, I think this -- products like CoCo and Snowflake Intelligence have made the entirety of our go-to-market team, AI native, in a way that honestly would not have been even -- like we could not even imagine it a year ago because we have a lot of governed data. And so, our solution engineers, our sales, our account executives even, can show the power of Snowflake.

就它如何影響我們的 go-to-market 而言，首先我認為——像 CoCo 與 Snowflake Intelligence 這類產品，讓我們整個 go-to-market 團隊在某種程度上成為 AI 原生（AI native），坦白說這在一年前甚至——我們根本難以想像，因為我們擁有大量受治理（governed）的資料。因此，我們的解決方案工程師、銷售、甚至客戶經理（account executives），都能展示 Snowflake 的威力。

There's nothing like pulling your phone out to show what Snowflake Intelligence can do as every CEO that's met me in the last nine months. No, that's one of the things that I always do.

沒有什麼比拿出手機來展示 Snowflake Intelligence 能做什麼更有說服力了——過去九個月裡每一位見過我的 CEO 都知道。不，這就是我總會做的其中一件事。

Our solution engineers are able to build much more realistic demos and prototypes and even actually get projects done for their customers very, very quickly, showing our customers what is possible with CoCo. And similarly, our internal teams, whether it's the support team that I talked about or our SRE team, our site reliability engineering team that runs our production systems, or our services team. They have 95%-plus adoption of CoCo which leverages them enormously when they're creating products.

我們的解決方案工程師能夠打造更貼近真實情境的 demo 與原型，甚至能非常、非常快速地為客戶把專案真正做出來，向客戶展示使用 CoCo 的可能性。同樣地，我們的內部團隊，不論是我提到的支援團隊、或是我們負責營運生產系統的 SRE 團隊（site reliability engineering，網站可靠性工程）、或是我們的服務團隊。他們對 CoCo 的採用率超過 95%，這在他們打造產品時帶來極大的槓桿效益。

And CoCo and coding agents have also changed things like enablement. It's a lot easier to learn when you can literally ask a coding agent how to do something, have it right to example for you for you to examine it, thinker with it, and then write a more complicated example.

而 CoCo 與程式碼代理（coding agents）也改變了像是賦能（enablement）這類事情。當你可以直接問一個程式碼代理要怎麼做某件事，讓它為你寫出範例供你檢視、調整，然後再寫出更複雜的範例時，學習就容易得多。

A professor friend of mine called coding agent self-categorical. They come with that learning built-in, which means that a product feature released in CoCo can be used by someone in services literally the same week. And it's that rapid iteration that's also benefiting us. And so, that's the virtuous loop that we are on. We think we can get projects done faster.

我有位教授朋友把程式碼代理稱為「自我分類式」（self-categorical）。它們把學習能力內建其中，這意味著在 CoCo 中釋出的某個產品功能，服務團隊的人在同一週就能直接使用。這種快速迭代也正在讓我們受益。因此，這就是我們所處的正向循環。我們認為我們能更快完成專案。

We think we are also, honestly, very early in the world of agentic development that are new techniques being developed, honestly, every week. And our ability to get more and more complex projects done on top of these coding agents is just enormously powerful. And I think we're setting the standard for what data work and more is going to be like, both with CoCo, but also with Snowflake Intelligence. And things like MCP are a further unlock into what is possible with these agents.

我們也認為，坦白說，在代理式開發（agentic development）的世界裡我們仍非常早期——新的技術幾乎每週都在出現。而我們在這些程式碼代理之上完成越來越複雜專案的能力，力量非常巨大。我認為我們正在為資料工作以及更多領域將會是什麼樣子樹立標準——不僅透過 CoCo，也透過 Snowflake Intelligence。而像 MCP 這類東西，會進一步解鎖這些代理所能做到的可能性。

And we'll take another question.

我們再來回答下一個問題。

It's Karl Keirstead with UBS. I'd love to continue the conversation on CoCo, if that's okay, for a question to both Sridhar and Brian.

我是 UBS 的 Karl Keirstead。我想延續關於 CoCo 的討論，如果可以的話，這個問題想同時問 Sridhar 和 Brian。

Sridhar, it's pretty evident that customer spend on Cortex Code and even broadly models like Claude is -- or bending spending higher given the token or usage-based pricing. I think a lot of investors are worried that it's going to reach a point where customers might try to govern or throttle the use of these tools to try to contain spend. I'm just curious, are you anticipating that to happen? Perhaps the answer is that the value add is such that you are unlikely to see that.

Sridhar，很明顯客戶在 Cortex Code、甚至更廣泛像 Claude 這類模型上的支出——在以 token 或用量計價的模式下——正在上升或呈現上彎。我想很多投資人擔心，最終會到一個點，客戶可能會試著對這些工具的使用進行治理或節流，以控制支出。我很好奇，你是否預期會發生這種情況？也許答案是其附加價值非常高，因此你不太可能看到那樣的行為。

And then maybe for Brian, I think there might also be a perception that products like Cortex Code come at generally a lower gross margin than the rest of the business. But one thing I noted from your guidance for the full year is that you stuck with the product gross margin guidance of 75% despite a big apparent uptick in Cortex Code, which suggests perhaps that the gross margin drag is minimal, if any. And I'd love to ask you to comment a little bit on that. Thank you.

然後可能問 Brian，我想也有人認為像 Cortex Code 這類產品的整體毛利率通常低於公司其他業務。不過我注意到你們對全年指引仍維持產品毛利率 75% 的指引，儘管 Cortex Code 顯然大幅成長，這似乎暗示毛利率拖累很小、甚至沒有。我想請你對此稍作評論。謝謝。

I'll start, and then I'll hand off to Brian. Cost is always an issue that we pay attention to. This is true in Snowflake Intelligence. This is also true in Cortex Code. But what helps significantly is the fact that these are products in which you can either get things done that you are never able to before or get things done 10 times and sometimes like more than that faster. Those are not normal things.

我先回答，然後再交給 Brian。成本永遠是我們會關注的議題。這在 Snowflake Intelligence 如此，在 Cortex Code 也同樣如此。但很重要的一點是，這些產品要嘛能讓你完成以前根本做不到的事情，要嘛能讓你把事情做得快 10 倍，有時甚至遠超過 10 倍。這些都不是一般性的提升。

And to give you concrete examples, a very large bank that we work with has told me that while they spend several hundred million dollars on data systems as a whole, it's a very large bank, the amount of money that they spend on the human capital that powers all of these various pieces of software and link them up is 3 to 4x that. And anything that makes that part of the labor force 10x more effective is always incredibly welcome.

舉個具體例子，我們合作的一家非常大型的銀行告訴我，雖然他們在整體資料系統上的花費是數億美元——畢竟是很大的銀行——但他們在支撐所有這些不同軟體並把它們串接起來的人力資本上的花費，是其 3 到 4 倍。任何能讓那部分勞動力效率提升 10 倍的東西，永遠都非常受歡迎。

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

話雖如此，當我們想把例如 Snowflake Intelligence 推廣到 10,000 名使用者時，成本治理絕對是一個議題，就像我想在 Snowflake 內把產品大規模推廣時也會遇到一樣。因此，我們正在做一些事情，例如在帳戶層級或特定代理層級設定成本上限，或是限制某個使用者能花多少 token。當然，很快就會回到需要為非常有才華、確實值得使用那些 token 的使用者提供例外。這正是我們非常擅長打造的基礎設施。因此我們對能做到這點感到很有信心。

Plus there is a lot of innovation that we are driving within these coding agent products themselves. As I said, they handle very complicated task, but not everything is complicated. If you want to summarize, for example, I mean, I did something a couple of days ago, to summarize Slack threads. And perfectly small models from NIST-ROL are enough for that. You don't need the latest and greatest focus models for summarizing Slack threat. We are building those kinds of capabilities natively into Snowflake, so that it can be efficient in what kind of models that it uses.

另外，我們也在這些程式碼代理產品本身推動大量創新。正如我所說，它們能處理非常複雜的任務，但並非所有事情都很複雜。比如你只是想做摘要——我前幾天就做過，去摘要 Slack 討論串——那麼來自 NIST-ROL 的小型模型就完全足夠。你不需要最新、最強的前沿模型來摘要 Slack 討論串。我們正在把這類能力原生地建到 Snowflake 裡，讓它能在使用何種模型上更有效率。

But my short answer to your question is they're creating incredible value, but we are not resting on that. We are creating the controls that one needs in order to keep costs manageable as things continue expanding.

但對你問題的簡短回答是：它們正在創造驚人的價值，但我們不會因此停下來。我們正在建立所需的控管機制，讓在持續擴張的同時，成本仍然可控。

Then I'll let Brian take the AI and margin question.

接下來我讓 Brian 回答 AI 與毛利率的問題。

Yeah, Karl, thanks for the question. You're absolutely right. Our AI products have a lower gross margin than our core platform. The one thing that we want to do with our AI products when we launch a new product like CoCo is make sure that we develop a great product that we get massive adoption.

好的，Karl，謝謝你的問題。你完全正確。我們的 AI 產品毛利率低於核心平台。我們在推出像 CoCo 這樣的新 AI 產品時，最想做的一件事，就是確保我們打造出很棒的產品並獲得大量採用。

And we've seen really good adoption with CoCo. We're up to roughly about a little over 7,000 accounts have adopted CoCo. With that said, we're offsetting that and keeping the same product gross margin, 75% for the full year in lower bandwidth cost, i.e., I talked about the AWS contract. And so we're offsetting it there. So that's how we're able to do that. We're committed to find efficiency to be able to maintain that 75% gross margin.

我們看到 CoCo 的採用情況非常好。目前大約有超過 7,000 個帳戶採用了 CoCo。話雖如此，我們透過較低的頻寬成本來抵消影響，並讓全年產品毛利率仍維持在 75%——也就是我提到的 AWS 合約。因此我們在那裡做了抵消。這就是我們能做到的方式。我們致力於找出效率提升，以維持 75% 的毛利率。

Okay, great. Congratulations to both of you.

好的，很棒。恭喜你們兩位。

And we'll take the next question.

接下來我們來接下一個問題。

Raimo Lenschow from Barclays. Congrats from me as well. That's an amazing quarter. The question I have is more for Brian. Brian, like if you think about the last couple of quarters, you've been telling us about like the beat cadence that you think about. Obviously, this quarter, you beat by much more and CoCo is helping there, but it's also a consumption model.

我是來自 Barclays 的 Raimo Lenschow。我也要恭喜你們，這是一個非常驚人的季度。我這個問題比較是問 Brian。Brian，如果你回顧過去幾個季度，你一直在跟我們談你所思考的那種超預期（beat）節奏。顯然這一季你們超預期的幅度大得多，CoCo 在其中也有幫助，但它同時也是一種用量（consumption）模式。

Like how do you think about this going forward? And how should we think about the guidance philosophy that you have here? Maybe you can help us there. But congrats from me as well, amazing quarter.

你如何看待未來這件事？以及我們應該如何理解你們在這裡的財測（guidance）哲學？也許你可以在這方面幫我們釐清一下。我也再次恭喜你們，這是一個很棒的季度。

Thank you. Yeah, let me emphasize that there's been no change in guidance philosophy, and we view a 3% beat is a very solid beat. The difference that happened this quarter was CoCo was launched in the quarter. And we base guidance on Observe behavior, and so we didn't have any Observe behavior for guidance for CoCo.

謝謝。是的，我先強調一下，我們的財測哲學沒有任何改變，而且我們認為 3% 的超預期是一個非常扎實的超預期。這一季之所以不同，是因為 CoCo 是在本季推出的。而我們是以 Observe 的行為（behavior）來制定財測，因此在針對 CoCo 做財測時，我們沒有任何 Observe 行為可供參考。

And so we had a unique opportunity now since we've been able to watch that for a quarter to layer that in now for the full year, and that's what we've done. And then we also saw the acceleration of the Core, and we've included that for the full year based on what we've seen.

因此，現在我們有一個獨特的機會：因為我們已經能觀察一個季度的表現，所以可以把這些因素納入全年預期，而這正是我們所做的。另外，我們也看到核心業務（Core）的加速成長，並且根據我們所觀察到的情況，把這一點也納入全年預期。

And we'll take the next question.

接下來我們來接下一個問題。

This is Matt Hedberg from RBC. Congrats from me as well. I had a question. There's been a lot of talk, especially from an autonomous AI perspective, the importance of context engineering and harness engineering. And Sridhar, you mentioned that in your prepared remarks.

我是來自 RBC 的 Matt Hedberg。我也要恭喜你們。我有一個問題。最近有很多討論，尤其是從自主式 AI（autonomous AI）的角度來看，情境工程（context engineering）與 harness 工程的重要性。Sridhar，你在事先準備的發言中也提到了這點。

I guess I'm wondering what role does Snowflake have in that? And how do we think about that from a moat perspective from some of the AI labs?

我想問的是，Snowflake 在其中扮演什麼角色？以及從護城河（moat）的角度，我們應該如何看待你們相對於一些 AI 實驗室的定位？

Yeah. The data that is stored in Snowflake is among the most valuable pieces of data for a particular company. This is the -- it's called the gold layer and typically has the most important information at Snowflake, for example, all of our revenue information or consumption information and information about the different departments are all kept in Snowflake. But on top of that, the dashboarding platforms that are written on top of Snowflake have an amount of additional context as well. And we see what they do.

是的。儲存在 Snowflake 裡的資料，往往是某家公司最有價值的資料之一。這被稱為「黃金層」（gold layer），通常包含最重要的資訊；以 Snowflake 為例，我們所有的營收資訊或用量資訊，以及不同部門的相關資訊，都保存在 Snowflake 裡。不過除此之外，建置在 Snowflake 之上的儀表板平台也會帶來額外的情境資訊，而我們能看到它們在做什麼。

So our ability to provide context to AI is exceptional. And we are also busy creating products that can use this to make the act of getting value from AI even faster.

因此，我們為 AI 提供情境（context）的能力非常出色。我們也正積極打造產品，利用這些能力，讓從 AI 中取得價值的速度更快。

I talked earlier about how we have workflow automation for the entire life cycle of creating an agent. We want to do more than that. We want CoCo to be the place where it is fastest to get value from the data investments that you have made. And Christian is working on a key effort on this side as well.

我先前提到，我們針對建立代理（agent）的整個生命週期提供工作流程自動化。我們想做的不只如此。我們希望 CoCo 成為一個地方：讓你能以最快速度，從你已經投入的資料投資中取得價值。Christian 也正在這方面推動一項關鍵工作。

Christian, do you want to add additional context?

Christian，你要不要補充一些背景？

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

好的，好的。Matt，我簡短回應一下，我覺得你的問題很有洞見。我們過去就有利用 Snowflake 內部的中繼資料（metadata）與活動（activity）來帶來更好的結果的紀錄。以前多半用在查詢最佳化與效能上。現在我們把同樣類型的 Snowflake 內部活動訊號，用來為 AI 提供更好的情境。我們會在 Summit 上展示一些差異：相較於其他代理（agents），CoCo 與 Snowflake Intelligence 的開箱即用（out-of-the-box）結果為何更好。

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

這也凸顯了 Cortex Code 的整體策略價值：如果企業內有大量資料使用者透過這些代理式程式開發平台（agentic coding platforms）來建立終端使用者產品，可能是技能（skills）、可能是儀表板（dashboards）、也可能是代理（agents），那麼我們就有能力在這些使用情境之間進行學習。因此我們建立了所謂的記憶（memory）概念：在 Snowflake 內使用這些產品，會讓 Cortex Code 本身在未來使用時變得更好。這就是擁有優秀代理式程式開發產品所帶來的飛輪效應（flywheel effect）的一部分。

And moving on to another question.

接著我們來看下一個問題。

It's Brent Thill at Jefferies. Sridhar, just on the sales and marketing side, given the backlog observed, we didn't have a really big S&M hiring quarter. And I'm just curious, based on the demand and everything you're seeing, why not lean a little harder into the go-to-market side?

我是 Jefferies 的 Brent Thill。Sridhar，關於銷售與行銷（sales and marketing）這一端，考量到我們看到的積壓需求（backlog），這一季並沒有出現很大規模的 S&M 招募。我很好奇，基於需求與你們所看到的一切，為什麼不在 go-to-market 端更用力一些？

Maybe you are behind the scenes. And I think this maybe also ties into the transformation that took place in the quarter with the new head of sales. So maybe if you could just tie it all together in a go-to-market view from your perspective, that would be great. Thanks.

也許你們其實在幕後有在做。另外我想這也可能與本季發生的轉型有關，包括新的銷售主管上任。所以如果你能從你的角度，把這些串起來，談談 go-to-market 的整體觀點，那就太好了。謝謝。

I think part of what we need to understand right now is that there are many, many places in which AI is making Snowflake a lot more efficient. I thought in my prepared remarks about how we have greatly increased the number of use cases that we have won, which is primarily an account executive driven activity. We've also had significant increases in individual productivity year on year. This is because of AI, their ability to learn faster, pitch products that are more relevant to their customers, and also have solution engineers create prototypes that are directly relevant and in the context of the customer.

我認為我們現在需要理解的一部分是：AI 正在許多方面讓 Snowflake 變得更有效率。我在事先準備的發言中提到，我們大幅提高了贏下的使用案例（use cases）數量，而這主要是由客戶經理（account executive）驅動的活動。我們也看到個人生產力（individual productivity）年對年顯著提升。這是因為 AI：他們能更快學習、能向客戶推介更相關的產品，也能讓解決方案工程師（solution engineers）打造與客戶高度相關、且符合客戶情境的原型（prototypes）。

So as an organization, we are just becoming a lot more effective. We will continue to invest in all of the key functions that are responsible for driving Snowflake forward, the supply exchange ring. This applies also on the sales and solution engineering side. But it is counterbalanced by the large amount of efficiencies that we are getting in a number of other functions that are very amenable to AI automation like support, like SRE, like technical documentation.

因此，從組織層面來看，我們正變得更有效。我們會持續投資所有推動 Snowflake 前進的關鍵職能，也就是「供應、交換、環」（the supply exchange ring）。這同樣適用於銷售與解決方案工程端。但另一方面，這會被我們在其他許多職能上取得的大量效率所平衡，因為那些職能非常適合 AI 自動化，例如支援（support）、SRE、技術文件（technical documentation）。

Basically, a lot of information functions and information exchange functions have gotten a whole lot easier, and this is also where teams are being very, very effective in deploying things like CoCo on Snowflake Intelligence for these kinds of use cases. We will absolutely continue to invest wherever we get strong leverage.

基本上，許多資訊職能與資訊交換職能都變得容易得多；而這也正是團隊非常有效地在這類使用案例上部署像 CoCo 或 Snowflake Intelligence 的地方。我們絕對會在能取得強大槓桿（leverage）的地方持續投資。

And we'll take the next question.

接下來我們來接下一個問題。

Alex Zukin from Wolfe Research. Just congrats on an amazing quarter. Sridhar, maybe for you -- actually, both of them probably for you. If you think about the profile of a customer a year ago versus now a customer that's using Cortex Code, what are you seeing in terms of the uplift on spend?

我是 Wolfe Research 的 Alex Zukin。先恭喜你們有個非常驚人的季度。Sridhar，可能問你——其實兩個問題大概都問你。如果你比較一年前的客戶輪廓與現在使用 Cortex Code 的客戶，你們在支出提升（uplift on spend）方面看到了什麼？

And with the acquisition of Natoma that you announced, it seems to me that Cortex Code was just the beginning. Maybe it's the first agent that you're kind of going to launch. And you're not stopping there. Maybe there's a number of other ones that are coming. So can you just help us think about how that changes the potential spend profile of the customer over time?

另外，隨著你們宣布收購 Natoma，看起來 Cortex Code 只是個開始。也許它是你們要推出的第一個代理（agent），而你們不會止步於此，可能還會有其他很多個。你能否幫我們理解，這會如何隨時間改變客戶的潛在支出輪廓？

I mean, among the biggest impact that products like CoCo have with our customers, it's simply one that of expectation. I talked again in my remarks about how we did a two-year Teradata migration. We are engaged in more migrations, but the timelines for doing those now run between a quarter and two quarters. Why both my team and the customer expects and demands that?

我的意思是，像 CoCo 這類產品對客戶最大的影響之一，純粹是「期待」的改變。我在發言中也再次提到，我們曾做過一個為期兩年的 Teradata 遷移。我們正在進行更多遷移，但現在完成這些遷移的時間線大約介於一個季度到兩個季度之間。為什麼？因為無論是我的團隊還是客戶，都期待並要求做到這樣。

And we have the ability to deliver against that. I think both the impatience and hunger or what people can do with data along with the expectation for how quickly we can get them done, I would say that's like -- that's a huge sea change. Christian?

而我們也有能力交付。我認為，人們對資料能做什麼的那種急迫感與渴望，加上對我們能多快完成的期待，我會說這是一個——巨大的典範轉移（sea change）。Christian？

Algin with what you're saying, Sridhar, there's a massive backlog. What customers want to do, so just helping them do it faster just as they get to the next set of work sooner.

呼應你所說的，Sridhar，現在有大量積壓需求。客戶想做的事情很多，所以只要幫他們更快完成，他們就能更快進入下一批工作。

That's right. Even our own data teams, for example, typically had backlogs that ran into multiple years. In fact, the standard request, all of you know, this is sort of funny, but not. If you had a request of a data team, the answer usually is like that's nice, take a ticket and wait. But we are now in a situation where they can actually crack through that backlog just a whole lot more quickly unlocking value.

沒錯。甚至我們自己的資料團隊也是如此，例如以往的積壓需求通常會延續好幾年。事實上，標準的回覆——你們都知道，這有點好笑但也不好笑——如果你向資料團隊提出需求，通常得到的答案會是：很好，請開一張工單（ticket）然後等。但我們現在的情況是，他們真的能更快地清掉這些積壓，進而釋放價值。

And to go back to the question about Natoma and it's important and coding agents, it is important to understand that Snowflake Intelligence and Cortex Code are built on the same underlying technology with just different tools having different capabilities that are exposed to end users. They use the model garden underneath that powers all of these models. They share what's called the harness. This is the one that is working on top of the model, deciding what tools to call.

回到關於 Natoma 以及其重要性、以及程式開發代理（coding agents）這個問題，重要的是要理解：Snowflake Intelligence 與 Cortex Code 建構在相同的底層技術之上，只是不同工具向終端使用者暴露的能力不同。它們底層都使用所謂的 model garden，來驅動所有這些模型。它們也共享所謂的 harness，也就是在模型之上運作、決定要呼叫哪些工具的那一層。

And increasingly, they are also going to be sharing the same run time. We have a cloud run time product that is in public preview. It means that all of the power that you expect from running CoCo locally can now be executed in the cloud in a governed manner. And I'm already running agents in this cloud agent platform on that ability to launch things, for example, autonomous agents because you no longer need to have your laptop open for something to run is pretty remarkable. It's all being built on the same infrastructure for the harness, for the run time as well as things like session memory.

而且愈來愈多地，它們也將共享相同的執行階段（run time）。我們有一個雲端執行階段產品目前處於公開預覽。這表示你在本機執行 CoCo 時所期待的所有能力，現在都可以在雲端以受治理的方式執行。而我已經在這個雲端代理平台上運行代理，具備啟動各種事物的能力，例如自主代理；因為你不再需要一直開著筆電讓某個東西持續運行，這相當令人驚豔。這一切都建立在相同的基礎架構之上，涵蓋編排（harness）、執行階段，以及像是工作階段記憶（session memory）等功能。

And Cortex Code and Snowflake Intelligence are just two manifestations of the same product. And the reason MCP and Natoma are a big deal is they now bring the context entirety of SaaS application context into these products. And so I've done deep research reports, for example, that I've shown Christian that can now look for information from Snowflake, from the web, from Google Docs, also from Slack, and synthesize that into something that is astoundingly meaningful. And these also let you take action instantly. You can slack somebody, you can compose e-mails and send it, and you can take actions on the underlying applications, and that's the promise.

而 Cortex Code 與 Snowflake Intelligence 只是同一個產品的兩種呈現形式。MCP 和 Natoma 之所以重要，是因為它們現在把 SaaS 應用程式的完整情境（context）帶入這些產品之中。因此我做過一些深度研究報告，例如我曾展示給 Christian 的那些，現在可以從 Snowflake、網路、Google Docs，也可以從 Slack 尋找資訊，並將其綜合成極具意義的成果。而且這些也讓你能立即採取行動。你可以在 Slack 上聯絡某人、你可以撰寫並寄出電子郵件，也可以在底層應用程式上直接執行動作，這就是承諾所在。

We basically have a builder version and an end user version of these products. Obviously, the names make them sound more different than they are. That's something that we are working on. But the amount of power and flexibility that these coding agent products offer is pretty remarkable. And in my mind, the right analogy here is that a coding agent, yes, can write code, but at its core, it's an abstraction agent. It can let you do things at a high level that previously you sort of had to sequence out one by one. And I think that's the power that comes from them.

我們基本上有這些產品的建置者版本（builder version）與終端使用者版本（end user version）。顯然，名稱讓它們聽起來比實際更不同，這也是我們正在改善的地方。但這些程式碼代理產品所提供的能力與彈性之強，確實相當驚人。在我看來，最貼切的類比是：程式碼代理，沒錯，它可以寫程式碼，但其核心其實是一個抽象代理（abstraction agent）。它能讓你在高層次完成事情，而過去你往往必須把步驟一個個拆解並依序執行。我認為這就是它們帶來的力量。

Christian?

Christian？

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

另外再補充一點關於 Natoma。非常重要的是要強調，它在提供工具可視性（tool visibility）的同時，也具備治理與可稽核性（auditability），因為我們的使命是協助每個組織在資料情境下運用 AI，但同時要有治理、安全與可信賴性。因此這完全符合我們的使命。

And we'll take another question.

我們再來回答下一個問題。

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

Stifel 的 Brad Reback。Sridhar，隨著你們在 CoCo 與 Snowflake Intelligence 上取得的成功，這是否從根本上改變了你們在開拓新客戶時的競爭格局？你們現在是否看到 LOMs 的情況比一些較老的競爭對手更多？謝謝。

We come with a unique value proposition. As you folks know, even in the world of data, that cloud service providers have had products. We have very successful partnerships with them. In fact, we just announced a $6 billion partnership with one of them.

我們帶來的是獨特的價值主張。各位都知道，即使在資料領域，雲端服務供應商也一直有自己的產品。我們與他們有非常成功的合作夥伴關係。事實上，我們剛宣布與其中一家達成 60 億美元的合作。

Our value prop has always been very clear. We are about customer choice. We are also about a certain amount of independence from the mechanics of the cloud providers. A Snowflake implementation works fine on AWS, but it can also work on Azure. We have similar really good partnerships with the leading AI labs, both Anthropic and OpenAI. We collaborate very closely with them to create great AI products, but also to create safe AI products.

我們的價值主張一直非常清楚：我們重視客戶選擇。我們也強調在一定程度上不受雲端供應商運作機制的牽制。Snowflake 的部署在 AWS 上運作良好，也同樣可以在 Azure 上運作。我們也與領先的 AI 實驗室（Anthropic 與 OpenAI）建立了同樣非常良好的合作關係。我們與他們緊密合作，打造優秀的 AI 產品，同時也打造安全的 AI 產品。

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

同樣地，Cortex Code 與 Snowflake Intelligence 也提供模型選擇（model choice）。我們在兩種模型上都能良好運行。我們也自行託管大量其他模型，並且隨著開源模型等變得更重要。我們始終以客戶的最佳利益為依歸行事，我認為這讓我們在所有客戶面前都處於非常有利的位置。

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

我再補充一點。就本季的銷售執行而言，我們在所有地區與所有產業垂直領域的表現都很出色。這是公司史上淨新增客戶數最多的一季。因此整體而言，這真的是一個全面穩健的季度。

And we'll go ahead and take the next question.

我們接著進行下一個問題。

This is Koji Ikeda from Bank of America. So when I talk with partners and customers of Snowflake, I hear the same thing over and over again. Snowflake is my trusted enterprise data and AI vendor with governance and security guardrails as key differentiators. I think about that a lot.

我是美國銀行（Bank of America）的 Koji Ikeda。當我與 Snowflake 的合作夥伴與客戶交流時，我一再聽到同樣的說法：Snowflake 是我信賴的企業資料與 AI 供應商，而治理與安全護欄是關鍵差異化因素。我對此想了很多。

But the AI world is moving so fast. And assuming the competition out there gets better with all this. What makes you confident that Snowflake's position as the trusted enterprise data and AI partners secure over the long term? Thank you.

但 AI 世界變化非常快。假設外部競爭者在這一切推動下也變得更強，你們憑什麼有信心 Snowflake 作為值得信賴的企業資料與 AI 夥伴，其地位能在長期內依然穩固？謝謝。

Because there are a set of deep infrastructure capabilities that just take a lot of time to develop, whether it is role-based access control and role-level access control at massive scale or world-class replication that provides for things like disaster recovery, amazing organization support.

因為有一系列深層的基礎架構能力，確實需要很長時間才能打造完成，不論是大規模的以角色為基礎的存取控制（role-based access control）與角色層級存取控制，或是世界級的複寫能力以支援例如災難復原（disaster recovery），以及出色的組織支援。

And there are dozens that I'm missing. Christian, do you want to add something?

還有數十項我沒有一一列舉。Christian，你要補充嗎？

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

不用。我認為你提到的資料遮罩（data masking）與角色層級政策（role-level policies），以及所有治理、安全設定與身分識別（identity）等配置，使得客戶早已把 Snowflake 設定成能夠以可信方式存取資料。AI 只會放大這個優勢；相較之下，其他替代方案只會讓他們必須重新發明輪子、重建這一切，這並不合理。

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

同時也很重要的一點是，我們並沒有停下腳步；我們打造 Snowflake Intelligence 與 Cortex Code 這類產品的能力，以及所有的二階效應。想像一下，有自主代理能自動判斷你的資料是否出現異常，讓你不必在外部執行那些作業；或是治理不再依賴你撰寫無止盡、繁瑣的 SQL 陳述式，而是透過政策來指定你希望企業治理如何進行，我們負責所有細節與背後的運作機制；或是建立新一類應用程式，坐落在以 AI 驅動、以 Snowflake 資料為基底的底層平台（substrate）之上。

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

這些都是我們能夠實現的事情。而且坦白說，這也是我們必須做到的。你提出的核心論點——人們可以新增這些功能或把它們串接起來——是成立的，但我們也在以極快的速度開發全新的能力，同樣由 AI 驅動。我認為這就是在今天取得成功所需要的。

We have lots of new controls and policies which we'll be showcasing next week, including amazing mechanisms to simplify them.

我們有許多新的控制項與政策，將在下週展示，包括用來簡化它們的出色機制。

Thank you. And that does conclude the question-and-answer session. I'll now turn the conference back over to Snowflake for closing remarks.

謝謝。問答環節到此結束。接下來我把會議交回 Snowflake 進行結語。

Thank you, everyone. To recap, AI is accelerating consumption across our core platform. And our native AI products, Snowflake Intelligence and Cortex Code are scaling rapidly, already contributing meaningfully to revenue in their own right. These AI capabilities are establishing Snowflake as the agentic control plane for the enterprise, connecting data, models, applications, and workflows in a trusted environment where intent becomes governed action.

謝謝各位。總結一下，AI 正在加速我們核心平台的使用量（consumption）。而我們的原生 AI 產品——Snowflake Intelligence 與 Cortex Code——正在快速擴張，且已各自對營收做出具體且有意義的貢獻。這些 AI 能力正在把 Snowflake 建立為企業的代理式控制平面（agentic control plane），在可信環境中連結資料、模型、應用程式與工作流程，讓意圖（intent）轉化為受治理的行動（governed action）。

We are continuing to execute accelerating growth, expanding margins, and deepening our customer relationships while winning many new ones. We believe that Snowflake is uniquely positioned to lead in the era of the agentic enterprise and continue to see enormous opportunity ahead. AI is compounding Snowflake's advantage in data.

我們持續執行：加速成長、擴大利潤率，並深化客戶關係，同時贏得許多新客戶。我們相信 Snowflake 具備獨特優勢，能在代理式企業（agentic enterprise）時代領先，並且我們仍看到前方龐大的機會。AI 正在加乘 Snowflake 在資料領域的優勢。

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

謝謝。今天的會議到此結束。感謝各位的參與，祝各位有美好的一天。