Snowflake Inc. (SNOW) 2027 Q2 法說會逐字稿

Good day and welcome to the second-quarter FY27 Snowflake earnings presentation.

各位好，歡迎收看 Snowflake 2027 財年第二季財報簡報。

Today's conference is being recorded.

今天的會議將進行錄音。

At this time, I would like to turn the conference over to Katherine McCracken. Please go ahead.

此刻，我想將會議交給 Katherine McCracken。請開始。

Good afternoon and thank you for joining us on Snowflake's second-quarter fiscal 2027 earnings call.

各位下午好，感謝各位參加 Snowflake 2027 財年第二季財報電話會議。

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.

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 second-quarter fiscal 2027 and discuss our guidance for the third-quarter and full-year fiscal 2027.

在今天的電話會議中，我們將回顧 2027 財年第二季的財務結果，並討論 2027 財年第三季與全年之財測指引。

During today's call, we will make forward-looking statements, including statements related to our business operations and financial performance.

在今天的電話會議中，我們將作出前瞻性陳述，包括與我們的業務營運與財務表現相關的陳述。

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.

這些陳述受風險與不確定性影響，可能導致其與我們的實際結果出現重大差異。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）提交的報告中。

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.

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 definition of the non-GAAP financial measures and a reconciliation of GAAP to non-GAAP measures and business-metric definitions, including customer count and adoption.

在今天的電話會議中，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 指標的調節表與業務指標定義（包括客戶數與採用情況）。

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網站 investors.snowflake.com 取得。今天電話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，我想把電話會議交給 Sridhar。

Thank you, Katherine. And thank you, all, for joining us today.

謝謝你，Katherine。也謝謝各位今天加入我們。

We're in the midst of a once-in-a-lifetime technology shift. And Snowflake remains at the center of the enterprise-AI revolution.

我們正處於一場一生僅見的科技轉變之中。而 Snowflake 仍然位於企業 AI 革命的核心。

AI is fundamentally changing how enterprises build, operate, and make decisions. To stay competitive, every organization faces a new imperative: become an Agentic Enterprise and do it quickly, safely, and cost efficiently.

AI 正從根本上改變企業建置、營運與決策的方式。為了保持競爭力，每個組織都面臨一項新的當務之急：成為一個 Agentic Enterprise（代理型企業），而且要快速、安全且具成本效益地做到。

Snowflake is making this transformation a reality. We bring together the core elements of an Agentic Enterprise, a governed data foundation, access to leading AI models, deep application workflows, and a unifying agentic-control plane that orchestrates across these elements to turn intent into governed action.

Snowflake 正讓這項轉型成為現實。我們把代理型企業的核心要素整合在一起：受治理的資料基礎、對領先 AI 模型的存取、深度的應用工作流程，以及一個統一的代理控制平面（agentic-control plane），可在這些要素之間進行協同編排，將意圖轉化為受治理的行動。

By putting intelligence to work at scale, our customers are building faster, executing more efficiently, and reimagining their businesses in ways that weren't possible before. Put simply, the Agentic Enterprise runs on Snowflake.

透過在規模化情境下運用智慧，我們的客戶得以更快建置、更有效率執行，並以過去不可能的方式重新想像其業務。簡而言之，代理型企業運行於 Snowflake 之上。

And the traction is translating into strong business performance, as evidenced by our Q2 results. Product Revenue came in at $1.49 billion, with growth accelerating to 37% year over year, marking our second consecutive quarter of record sequential-dollar growth.

而這股動能也轉化為強勁的業務表現，從我們第二季的結果即可見一斑。產品營收為 14.9 億美元，年增率加速至 37%，並創下連續第二季的單季美元增量（sequential-dollar growth）新高紀錄。

After exiting Q4 of last fiscal year at 30% year-over-year growth, we have now added 7 points of acceleration in just two quarters. And with our continued focus on executing with discipline and operational rigor, our Q2 non-GAAP operating margin expanded by more than 400 basis points year over year to 15%.

在上一財年第四季結束時，我們的年增率為 30%；如今僅兩季之內，我們已增加了 7 個百分點的加速幅度。同時，隨著我們持續專注於紀律執行與嚴謹營運，我們第二季非 GAAP 營業利益率年增擴張超過 400 個基點，達到 15%。

Thank you to all of our Snowflakes for the hard work and dedication that made this performance possible.

感謝所有 Snowflake 同仁的辛勤付出與投入，讓這樣的表現成為可能。

As these results convincingly demonstrate, AI is compounding Snowflake's advantage across three reinforcing dynamics.

如這些結果有力證明，AI 正透過三個相互強化的動態，持續放大 Snowflake 的優勢。

First, AI is bringing new workloads onto the platform. To power their AI initiatives, enterprises need a governed, unified foundation for data and context. And companies across industries are turning to Snowflake to power that foundation.

第一，AI 正把新的工作負載帶上平台。為了推動 AI 計畫，企業需要一個受治理、統一的資料與情境（context）基礎。各行各業的公司都正轉向 Snowflake 來支撐這個基礎。

Second, our first-party AI products, CoCo and CoWork, continue to see rapid adoption. As customers build and deploy agents on Snowflake, we are expanding our role into the agentic-control plane and creating new opportunities for growth.

第二，我們的第一方 AI 產品 CoCo 與 CoWork 持續快速被採用。隨著客戶在 Snowflake 上建置與部署代理（agents），我們正將角色延伸至代理控制平面，並創造新的成長機會。

Third, AI activation continues to lift overall platform consumption. Customers using AI on Snowflake consume more across the data platform, creating a structural multiplier for our business.

第三，AI 啟用（activation）持續推升整體平台用量。在 Snowflake 上使用 AI 的客戶，會在資料平台上消耗更多資源，為我們的業務創造結構性的乘數效應。

Together, these dynamics show how the Agentic Enterprise has created a powerful flywheel across our business. And that flywheel is accelerating.

綜合而言，這些動態顯示代理型企業如何在我們的業務中形成強大的飛輪效應。而這個飛輪正在加速。

At the heart of this momentum is the continued strength of our core business. Snowflake now provides the data and AI foundation for 14,554 customers around the world.

這股動能的核心，是我們核心業務持續展現的強勁力道。Snowflake 目前為全球 14,554 位客戶提供資料與 AI 基礎。

Customers continue to turn to Snowflake because our AI Data Cloud is easy to use, seamlessly connected for collaboration, and trusted, with enterprise-grade governance and security.

客戶持續選擇 Snowflake，因為我們的 AI Data Cloud 易於使用、可無縫連結以利協作，且值得信賴，具備企業級治理與安全性。

This quarter, we added 692 net new customers, including 14 from the Global 2000, representing a 32% increase in net new customer additions year over year.

本季我們淨新增 692 位客戶，其中包含 14 家 Global 2000 企業，淨新增客戶數較去年同期增加 32%。

At the same time, some of the world's most recognizable enterprises are deepening their relationships with Snowflake. Companies like BlackRock and Block are running more of their mission-critical work on Snowflake and, in several cases, adopting CoCo to move faster.

同時，全球一些最具代表性的企業也正在加深與 Snowflake 的合作關係。像 BlackRock 與 Block 這樣的公司，正把更多關鍵任務工作放在 Snowflake 上運行，並在若干情況下採用 CoCo 以加速推進。

The pattern is consistent. The more our customers build on Snowflake, the more they lean in. In fact, 65 customers have now crossed $10 million in trailing 12-month Product Revenue, demonstrating how our largest customers continue to go all in on Snowflake.

這個趨勢一貫且明確。客戶在 Snowflake 上建置得越多，就越會加大投入。事實上，目前已有 65 位客戶的過去 12 個月（TTM）產品營收超過 1,000 萬美元，顯示我們最大型客戶持續全面投入 Snowflake。

Part of our strength is in extending our customers' reach to the critical data that sits outside of their organization. Currently, 43% of our customers share data on Snowflake with at least one Stable Edge, demonstrating Snowflake's role as the circulatory system of the modern enterprise.

我們實力的一部分，在於將客戶的觸角延伸至其組織外部的關鍵資料。目前有 43% 的客戶在 Snowflake 上與至少一個 Stable Edge 進行資料分享，展現 Snowflake 作為現代企業循環系統的角色。

We enable data, applications, and AI agents to move securely and seamlessly, not just within but across organizations. In fact, [Credit] chose Snowflake for our data-sharing capabilities, which now facilitate privacy-safe ads measurement.

我們讓資料、應用程式與 AI 代理能夠安全且無縫地流動，不僅在組織內，也能跨組織。事實上，[Credit] 選擇 Snowflake 正是因為我們的資料分享能力，而這項能力如今可促成兼顧隱私安全的廣告衡量。

And as customers move quickly to modernize their data estates and establish a strong contact layer for AI, more and more customers are migrating workloads to our platform, a process now massively accelerated with AI.

而當客戶快速推進資料資產現代化，並為 AI 建立強健的聯絡層（contact layer）之際，越來越多客戶正將工作負載遷移到我們的平台；在 AI 的加持下，這個過程如今大幅加速。

For example, one of the largest Australian banks migrated its financial-crime platform to Snowflake, processing 17 billion transactions and delivering 10x faster query performance. Now, they're building AI agents on Snowflake to accelerate the migration of the rest of their data estate and automate legacy data discovery and mapping.

例如，澳洲最大的銀行之一將其金融犯罪平台遷移至 Snowflake，處理 170 億筆交易，並帶來快 10 倍的查詢效能。現在，他們正於 Snowflake 上建置 AI 代理，以加速其其餘資料資產的遷移，並自動化舊有資料的探索與對應（mapping）。

As AI strengthens demand for our core platform, it is also expanding Snowflake's opportunity to deliver a new generation of AI-powered products and experience.

隨著 AI 強化對我們核心平台的需求，它也正在擴大 Snowflake 的機會，讓我們得以提供新一代由 AI 驅動的產品與體驗。

Because Snowflake sits at the center of our customers' data, business context, AI models, and workflows, we are uniquely positioned to become the governed control plane for the Agentic Enterprise.

由於 Snowflake 位於客戶資料、業務脈絡、AI 模型與工作流程的核心，我們具備獨特優勢，能成為 Agentic Enterprise 的受治理控制平面。

Our break-out AI products, CoWork and CoCo, bring that vision to life. They provide a governed layer where users across the business, from knowledge workers to builders, can put the full power of their enterprise context to work, all with simple conversational language.

我們的突破性 AI 產品 CoWork 與 CoCo，正將這個願景化為現實。它們提供一個受治理的層，讓企業內各類使用者——從知識工作者到建置者——都能以簡單的對話式語言，充分運用其企業脈絡的全部力量。

With CoWork and CoCo, customers are reimagining some of their most critical business processes, from supply-chain operations to enterprise-wide sales motion.

透過 CoWork 與 CoCo，客戶正在重新想像其最關鍵的一些業務流程，從供應鏈營運到全企業的銷售推進。

Sayari, whose risk intelligence supports Fortune 100 enterprises and national-security agencies, chose Snowflake to rebuild its global data infrastructure and cut costs by more than half. Its engineers are now using CoCo to accelerate the migration of 12 billion records into an AI-ready foundation.

Sayari 的風險情報支援《財富》100 大企業與國家安全機構；該公司選擇 Snowflake 來重建其全球資料基礎架構，並將成本削減超過一半。其工程師目前正使用 CoCo，加速將 120 億筆紀錄遷移至可支援 AI 的基礎。

And as more customers see what's possible with this technology, adoption continues to build. CoWork expanded to 5,800 accounts, up nearly 11% quarter over quarter. Meanwhile, CoCo continues to see rapid adoption, surpassing 9,100 accounts and adding more than 2,000 net new accounts in this quarter alone.

隨著更多客戶看見這項技術的可能性，採用度持續攀升。CoWork 擴展至 5,800 個帳戶，較上一季成長近 11%。同時，CoCo 仍維持快速採用，帳戶數突破 9,100 個，且僅本季就新增超過 2,000 個淨新帳戶。

We have customers like 1Password, the security company trusted by more than 200,000 businesses, which choose Snowflake for our CoCo capabilities. CoCo enables their team to move key data pipelines into Snowflake quickly, laying the foundation for their data and AI work.

我們的客戶包括 1Password——一家獲超過 200,000 家企業信賴的資安公司——其選擇 Snowflake 正是看中我們的 CoCo 能力。CoCo 讓他們的團隊能快速將關鍵資料管線移入 Snowflake，為其資料與 AI 工作奠定基礎。

And the world's number 1 job site, Indeed, has rolled out CoWork and CoCo across its data teams and integrated Snowflake into its core data architecture, citing lower cost and greater efficiency, which compounds at the scale that they operate in, over 60 countries and 28 languages.

而全球第一大求職網站 Indeed，已在其資料團隊全面部署 CoWork 與 CoCo，並將 Snowflake 整合進其核心資料架構；其指出成本更低、效率更高，而在其營運規模下——涵蓋 60 多個國家與 28 種語言——這些優勢會持續累積放大。

But the opportunity goes beyond adoption. By making it possible to build, collaborate, and interact with enterprise data through conversational language, CoWork and CoCo are bringing entirely new users to Snowflake.

但機會不僅止於採用。透過讓使用者能以對話式語言建置、協作並與企業資料互動，CoWork 與 CoCo 正為 Snowflake 帶來全新的使用者族群。

Within accounts adopting these products, we see a step-change in user growth, as Snowflake reaches new lines of business and expands its footprint within existing teams.

在採用這些產品的帳戶中，我們看到使用者成長出現躍升式變化，因為 Snowflake 觸及新的業務線，並在既有團隊內擴大其覆蓋範圍。

As we continue to develop CoWork and CoCo as agency-control planes, we are also building out the broader platform enterprises need to put AI to work at scale.

在我們持續將 CoWork 與 CoCo 發展為代理控制平面的同時，也正建構企業所需的更廣泛平台，以便在規模化情境下落地 AI。

Model choice gives customers the flexibility to select from leading frontier and open models and evolve their approach, as the market changes. Post-training lets them adapt models to their specific data and business context. And agent observability and analytics give customers full visibility into what their AI is doing, how it's performing, and what it costs.

模型選擇讓客戶能彈性地在領先的前沿模型與開源模型之間做選擇，並隨市場變化演進其方法。後訓練（post-training）讓他們能依其特定資料與業務脈絡調整模型。而代理可觀測性與分析，則讓客戶能全面掌握其 AI 在做什麼、表現如何，以及成本為何。

And to help our customers optimize cost, performance, and speed, we've introduced Cortex AI Gateway, which dynamically routes each task to the right model based on customer-defined policies and real-world performance data, with cost and governance controls built in.

為協助客戶最佳化成本、效能與速度，我們推出了 Cortex AI Gateway；它會依據客戶自訂政策與真實世界效能資料，將每項任務動態路由到合適的模型，並內建成本與治理控管。

As those economics improve, customers can deploy AI more broadly and with greater confidence, creating another catalyst for adoption and consumption on Snowflake.

隨著這些經濟性改善，客戶能更廣泛、也更有信心地部署 AI，進而成為推動 Snowflake 採用與用量的另一個催化劑。

Cortex AI Gateway also extends AI from insight to action through its integration of Natoma. Users can now send e-mails, summarize Slack conversations, open Jira tickets, and act across their business, all without leaving CoWork or CoCo.

Cortex AI Gateway 也透過整合 Natoma，將 AI 從洞察延伸到行動。使用者現在可以寄送電子郵件、摘要 Slack 對話、建立 Jira 工單，並在整個企業中採取行動，而且全程無需離開 CoWork 或 CoCo。

We have also continued to advance how our agents understand the unique context of a business. At Snowflake Summit, we introduced Cortex Sense, which captures the business definitions and institutional knowledge an AI agent needs and provides that context at the moment it answers a question.

我們也持續推進代理如何理解企業的獨特脈絡。在 Snowflake Summit 上，我們推出了 Cortex Sense；它會擷取 AI 代理所需的業務定義與組織知識，並在回答問題的當下提供該脈絡。

This means Snowflake is giving AI both the context to understand a business and the ability to act on its behalf, with enterprise security, governance, and observability built in.

這表示 Snowflake 同時賦予 AI 理解企業所需的脈絡，以及代表企業採取行動的能力，並內建企業級安全性、治理與可觀測性。

As we drive this AI transformation for our customers, we are leading from the front, using CoCo and CoWork throughout our own business to accelerate productivity and efficiency.

在我們推動客戶的 AI 轉型之際，我們也以身作則，在自身業務中全面使用 CoCo 與 CoWork，以加速生產力與效率。

For example, in our marketing organization, CoCo has helped bring search optimization in-house, eliminating $400,000 in annual agency spend, reducing keyword research from approximately 10 hours to 20 minutes and content production from an estimated 24 hours down to just 2.

例如，在我們的行銷組織中，CoCo 協助將搜尋最佳化內製化，省下每年 40 萬美元的代理商支出，並將關鍵字研究時間從約 10 小時縮短至 20 分鐘，內容產製時間也從估計 24 小時降至僅 2 小時。

In finance, our long-range planning used to require a three-person team and more than 50 spreadsheets. It now runs with one analyst and a series of models that reflect our pricing structure and consumption dynamics.

在財務方面，我們的長期規劃過去需要三人團隊與超過 50 份試算表。現在只需一位分析師與一系列反映我們定價結構與用量動態的模型即可運行。

Within our sales teams, we have automated prospecting for over 125,000 contacts and leads, with 70% of initial outreach e-mails for inbound leads now being generated automatically before SDR involvement.

在我們的銷售團隊中，我們已為超過 125,000 位聯絡人與潛在客戶自動化開發流程；目前針對入站線索的初次外聯電子郵件，有 70% 會在 SDR 介入前就自動生成。

We are bringing these proven use cases directly to market, while applying our operational learnings to continuously upgrade our platform, moving with speed to capture the AI opportunity in front of us.

我們正將這些已驗證的使用案例直接推向市場，同時把營運上的學習回饋到平台，持續升級，並以速度掌握眼前的 AI 機會。

In the first half of this year alone, we have launched over 330 product capabilities to general availability, 35% more than we did in the first half of last year, underscoring both the pace of our innovation and the breadth of platform expansion underway across Snowflake.

僅今年上半年，我們就已將超過 330 項產品能力推向正式可用（GA），比去年上半年多 35%，凸顯我們創新的速度，以及 Snowflake 全平台擴張的廣度。

Our go-to-market organization also continues to execute, as reflected in strong new customer growth. We have deployed CoCo and CoWork across the sales team to analyze pipelines, prepare for customer conversations, and accelerate the onboarding of new reps.

我們的市場推進（go-to-market）組織也持續落實執行，反映在強勁的新客戶成長上。我們已在銷售團隊全面部署 CoCo 與 CoWork，用於分析管線、準備客戶對話，並加速新業務代表的到職訓練（onboarding）。

Our teams are using these products every day, learning first-hand what they can do, and taking those insights directly to our customers.

我們的團隊每天都在使用這些產品，第一手學習它們能做到什麼，並將這些洞見直接帶給客戶。

We are seeing the results in how quickly customers are putting Snowflake to work. The number of use cases, individual customer projects deployed on Snowflake, increased 89% year over year, as customers moved more workloads into production.

我們從客戶導入 Snowflake 的速度看見成果。使用案例數——亦即客戶在 Snowflake 上部署的個別專案——年增 89%，因為客戶將更多工作負載投入正式環境（production）。

At the same time, use cases won per account executive increased 43% year over year, demonstrating both growing customer demand and strong sales productivity.

同時，每位客戶經理（Account Executive）贏得的使用案例數年增 43%，展現客戶需求持續成長，以及強勁的銷售生產力。

And we are pairing this investment in growth with continued operational discipline. We remain on track for GAAP profitability in Q4 fiscal 2028. And the operating leverage we build along the way strengthens the durability of that outcome.

此外，我們在加大成長投資的同時，也維持持續的營運紀律。我們仍按計畫在 2028 會計年度第 4 季達成 GAAP 獲利。而我們在過程中建立的營運槓桿，也強化了該成果的可持續性。

Taken together, our rapid pace of innovation, tighter go-to-market execution, and operational discipline positions us well to capture the huge opportunity ahead. This quarter demonstrated that the transition to the Agentic Enterprise is accelerating. And Snowflake is at the center of it.

綜合而言，我們快速的創新節奏、更緊密的市場推進執行力，以及營運紀律，使我們具備良好條件去掌握前方龐大的機會。本季顯示，向 Agentic Enterprise 的轉型正在加速。而 Snowflake 正位於其核心。

AI agents are only as powerful as the data and business context they reason from and the governance surrounding them. Snowflake provides that trusted foundation, while bringing together model choice and flexibility, access to critical applications, and the agentic-control plane that connects intelligence to action across the enterprise.

AI 代理的強大程度，取決於其推理所依據的資料與業務脈絡，以及圍繞其周邊的治理。Snowflake 提供這個可信賴的基礎，同時整合模型選擇與彈性、對關鍵應用的存取，以及將智慧連結到企業行動的代理控制平面。

CoWork and CoCo demonstrate what governed architecture makes possible, enabling business users and builders to work with greater speed and intelligence, while Snowflake manages the complexity underneath.

CoWork 與 CoCo 展示了受治理架構所能實現的可能性，使業務使用者與建置者能以更快速度與更高智慧運作，同時由 Snowflake 管理底層的複雜性。

And, importantly, our customers' success with AI translates directly into growth for Snowflake. AI brings new workloads to the platform, extending our reach to new users, and drives greater consumption across the business.

而且重要的是，客戶在 AI 上的成功會直接轉化為 Snowflake 的成長。AI 為平台帶來新的工作負載，將我們的觸角延伸至新使用者，並推動全企業更高的用量。

We are entering the second half of fiscal 2027 with strong product momentum. And we see a long runway for durable high growth and continued margin expansion.

我們正以強勁的產品動能邁入 2027 會計年度的下半年。我們也看到可支撐長期、持久高成長以及持續毛利率擴張的長跑道。

The Agentic Enterprise runs on Snowflake. And we are just getting started.

Agentic Enterprise 以 Snowflake 為運行基礎。而我們才正要開始。

With that, I will pass it to Brian to go through the financial details.

接下來，我會把時間交給 Brian，請他說明財務細節。

Thank you, Sridhar.

謝謝你，Sridhar。

In Q2, Product Revenue, once again, accelerated to reach 37% year-over-year growth. This marks our third straight quarter of acceleration.

在第二季，產品營收再次加速，達到年增 37%。這是我們連續第三個季度加速成長。

Q2 benefited from continued strength in our core data-platform business and a meaningful step-up in AI revenue. Our AI revenue reflects a broadening portfolio of AI capabilities.

第二季受惠於我們核心資料平台業務的持續強勁，以及 AI 營收的顯著提升。我們的 AI 營收反映出 AI 能力組合正在擴大。

CoCo delivered another standout quarter. Consumption of CoWork is scaling and driving revenue contribution, alongside a diverse set of AI tools from AI functions and document processing to machine learning and notebooks.

CoCo 再次交出亮眼的一季。CoWork 的使用量正在擴大並帶動營收貢獻，同時也受惠於多元的 AI 工具組合，涵蓋 AI 功能、文件處理、機器學習與筆記本等。

Our go-to-market teams continue to execute well against a strong demand environment. As Sridhar mentioned, net new customer additions increased 32% year over year.

我們的市場推進團隊在強勁需求環境下持續執行到位。如 Sridhar 所提到，淨新增客戶數年增 32%。

We added 14 net new Global 2000 customers, bringing our total to 829. Our AI Data Cloud now supports over 41% of the Global 2000.

我們淨新增 14 家 Global 2000 客戶，使總數達到 829 家。我們的 AI Data Cloud 現在已支援超過 41% 的 Global 2000。

Within our existing base, customer expansion is healthy, as evidenced by our net revenue retention rate of 126%. This expansion is underpinned by growth in both migrations and AI use cases.

在既有客戶基礎中，客戶擴張維持健康，從我們 126% 的淨營收留存率可見一斑。這項擴張由遷移與 AI 使用案例的成長共同支撐。

In Q2, 48 net new customers surpassed $1 million in trailing 12-month spend. We now have 828 customers spending above the $1 million threshold.

在第二季，有 48 家淨新增客戶過去 12 個月（TTM）支出超過 100 萬美元。目前我們有 828 家客戶的支出高於 100 萬美元門檻。

Remaining performance obligations grew 30% year over year, totaling $9 billion. As a reminder, we continue to see customers favor Q4 renewals. As a result, we expect bookings to be increasingly weighted towards the fourth quarter.

剩餘履約義務（RPO）年增 30%，總額達 90 億美元。提醒一下，我們仍看到客戶偏好在第四季續約。因此，我們預期訂單（bookings）將愈來愈偏重於第四季。

Of the $9 billion RPO, we expect approximately 54% to be recognized as revenue in the next 12 months. This represents an approximately 42% year-over-year growth compared to our estimate in the same quarter last year.

在 90 億美元的 RPO 中，我們預期約 54% 將在未來 12 個月內認列為營收。相較於去年同季的估計值，這代表約 42% 的年增幅。

Our Q2 results reinforce our commitment to delivering both growth and margin expansion. In Q2, non-GAAP operating margin expanded over 400 basis points year over year to reach 15%. Our outperformance was driven by strong revenue growth and disciplined headcount management.

我們第二季的結果強化了我們同時實現成長與毛利率擴張的承諾。第二季非 GAAP 營業利益率年增超過 400 個基點，達到 15%。我們的優於預期表現來自強勁的營收成長與嚴謹的人力編制管理。

Year to date, we have added 334 employees, which includes 173 from our Observe acquisition. This compares to 935 added in the year-ago period.

年初至今，我們新增 334 名員工，其中包含因收購 Observe 而增加的 173 名。相較之下，去年同期新增 935 名。

We ended the quarter of $4.3 billion in cash, cash equivalents, short-term and long-term investments.

本季末我們持有 43 億美元的現金、約當現金、短期與長期投資。

Moving to our outlook, as always, our forecast is based on observed consumption patterns. There are no changes to our forecast methodology or our guidance philosophy.

接著談展望，一如既往，我們的預測是基於觀察到的使用量（consumption）模式。我們的預測方法或指引理念沒有任何改變。

Given the strength we have observed, both in our core data-platform business and AI business, we are raising our Product Revenue guidance for the year.

鑑於我們在核心資料平台業務與 AI 業務所觀察到的強勁表現，我們上調全年產品營收指引。

For FY27, we now expect Product Revenue of $6.07 billion, representing 36% year-over-year growth. This includes approximately 1 percentage point of growth from Observe, consistent with our previous outlook.

針對 FY27，我們目前預期產品營收為 60.7 億美元，年增 36%。其中包含來自 Observe 約 1 個百分點的成長，與我們先前展望一致。

In Q3, we expect Product Revenue between $1.588 billion and $1.593 billion, representing 37% to 38% year-over-year growth.

在第三季，我們預期產品營收介於 15.88 億至 15.93 億美元之間，年增約 37% 至 38%。

Turning to margins, for FY27, we now expect 74% non-GAAP Product gross margin. This revised outlook includes a higher revenue mix from fast-growing AI workloads, which carry a lower contribution margin today.

再來看毛利率，針對 FY27，我們目前預期非 GAAP 產品毛利率為 74%。這項修正後的展望反映出來自快速成長的 AI 工作負載的營收占比提高，而這些工作負載目前的貢獻毛利率較低。

We are delivering continued operating margin expansion, as we offset growing cloud costs with slowing headcount expense. We are increasing our FY27 non-GAAP operating-margin guidance from 13.5% to 14.5%. For Q3, we expect non-GAAP operating margin of 15.5%.

我們持續推動營業利益率擴張，透過放緩人力成本成長來抵消雲端成本上升。我們將 FY27 非 GAAP 營業利益率指引由 13.5% 上調至 14.5%。第三季我們預期非 GAAP 營業利益率為 15.5%。

We are reiterating our full-year non-GAAP adjusted free cash flow margin guide of 23%. I would like to close with my two key goals for the year:

我們重申全年非 GAAP 調整後自由現金流利潤率指引為 23%。最後，我想以今年兩個關鍵目標作結：

First, help the business to deliver growth and margin expansion. Second, support ongoing excellence in our go-to-market motion.

第一，協助業務實現成長與毛利率擴張。第二，支持我們市場推進動能的持續卓越表現。

AI is fundamental to our progress against both goals. As we help our customers modernize their data and business operations, AI is becoming a powerful growth driver.

AI 對於我們推進這兩項目標至關重要。當我們協助客戶現代化其資料與業務營運時，AI 正成為強而有力的成長驅動力。

Internally, AI is unlocking greater productivity. Across the organization, from sales to engineering to finance, our use of AI is transforming our daily work. AI is driving greater efficiency and reducing our reliance on headcount growth.

在內部，AI 正釋放更高的生產力。在整個組織中，從銷售、工程到財務，我們對 AI 的使用正在改變日常工作。AI 正帶來更高效率，並降低我們對人力編制成長的依賴。

Our progress against both priorities is evident in the strength of our Q2 results. With that, I will pass the call to the operator for Q&A.

我們在兩項優先事項上的進展，已清楚反映在第二季結果的強勁表現上。接下來，我會把電話會議交給接線員進行問答。

Thank you. (Operator Instructions)

謝謝。（接線員指示）

Sanjit Singh, Morgan Stanley.

Morgan Stanley 的 Sanjit Singh。

Yeah. Thank you for taking the question. And congrats on the second quarter of a pretty material acceleration.

是的。謝謝讓我提問。也恭喜你們第二季出現相當顯著的加速成長。

The spirit of my question is around the quality of the acceleration that you are seeing. And just as a backdrop, around the time the company went public, growth was being driven by a lot of investment in cloud-native companies that may have been unprofitable.

我問題的核心在於你們所看到的加速成長的「品質」。作為背景，在公司上市前後，成長在很大程度上是由對雲原生公司（其中一些可能尚未獲利）的大量投資所驅動。

So I wanted to ask the question on the quality of the acceleration on two levels:

因此我想從兩個層面詢問這次加速成長的品質：

First, on the right to win, in the script, you guys mentioned supply-chain use cases and finance use cases. The question here is: Why is CoCo, along with the platform, the right mousetrap for these use cases that extend beyond classic business-analytics use cases?

第一，關於「勝出權」（right to win），在講稿中你們提到供應鏈使用案例與財務使用案例。這裡的問題是：為什麼 CoCo 連同平台，會是這些超越傳統商業分析使用案例的正確解法？

And then, on the durability of the growth, are you seeing any irrational behavior or poor operational hygiene, when it comes to consuming both CoCo and CoWork? So, really, this is a question on the quality of the acceleration you are seeing.

第二，關於成長的持久性，你們是否看到在使用 CoCo 與 CoWork 時有任何不理性的行為或不佳的營運衛生（operational hygiene）？所以，這其實是在問你們所看到的加速成長的品質。

This is Sridhar. Let me take a first cut at this. Other folks can add on since it is a pretty broad question.

我是 Sridhar。我先初步回應一下。這是個相當廣泛的問題，其他人也可以補充。

First, I think we see the acceleration come from a very broad swath of customers. It is not concentrated, for example, with, let us say, AI-native companies. They continue to be a small part of our overall revenue stream.

首先，我認為我們看到的加速成長來自非常廣泛的客戶群。它並非集中在某一類客戶，例如 AI 原生公司。這些公司在我們整體營收來源中仍只占很小一部分。

I think the thing that is also materially different this time around with folks that are investing is that products like CoCo make optimization far, far easier than before. You can point CoCo at a query that is taking too long to run or you can basically have it debug the top 10 longest-running queries or the most idle warehouses.

我認為這次與過去相比，投資者所看到的另一個重大差異是，像 CoCo 這樣的產品讓最佳化變得比以前容易得多、得多。你可以讓 CoCo 針對執行時間過長的查詢進行分析，或基本上讓它除錯前 10 個執行最久的查詢，或找出最閒置的倉庫。

Things like that are a lot easier to do. In fact, our cost-management skill in CoCo is a top 10 skill.

像這樣的事情做起來容易得多。事實上，我們在 CoCo 的成本管理能力是前 10 名的核心能力之一。

And it is also the case that, as a company, we have learnt the lessons of the pandemic. And one thing that we stress with each and every one of our customers is the need to drive spend in an efficient way.

而且同樣地，作為一家公司，我們也從疫情中汲取了教訓。我們對每一位客戶都強調的一點是：必須以高效率的方式推動支出。

And this is also a mantra that our sales team, itself, adopts pretty aggressively because they know that every such case where they go to a customer and point out things that they could be doing better is a trust-building exercise that is going to more than pay for itself in new projects that customers will implement on Snowflake.

這也成了我們銷售團隊本身相當積極採用的口訣，因為他們知道，每一次去客戶那裡指出他們可以做得更好的地方，都是一種建立信任的過程，而這份信任在客戶接下來會在 Snowflake 上落地的新專案中，將帶來遠超投入的回報。

So, overall, I am pretty happy with the both -- the fact that our growth is coming from a very broad swath of our customers, without a whole lot of concentration in any one particular sector; and, also, about the fact that the very tools that make it possible to do things quickly also come with a set of functions that make it pretty easy to optimize.

所以整體而言，我對兩件事都相當滿意——第一，我們的成長來自非常廣泛的客戶群，並沒有在任何單一產業出現很高的集中度；第二，那些讓事情能快速完成的工具，同時也配備了一套功能，讓最佳化變得相當容易。

And the final point -- as I said, others will add onto it -- about our right-to-win for the business use cases that, perhaps, we previously were not there in the conversation for. AI, as you know, has massively shrunk the distance between data and value.

最後一點——如我所說，其他人也會補充——關於我們在某些商業使用案例上的「勝出權」（right-to-win），也就是過去我們可能還沒能進入對話的那些場景。如你所知，AI 大幅縮短了資料與價值之間的距離。

I am sure all of you live it in your day-to-day life. But, certainly, I, as a CEO, can get a whole lot of value out of data a lot faster because of tools like CoCo and CoWork.

我相信各位在日常生活中都能感受到。但確實地，身為 CEO，我可以因為像 CoCo 和 CoWork 這樣的工具，更快地從資料中取得大量價值。

And the agentic harness is, indeed, a very powerful weapon for solving many different kinds of problems. And it is our ability to take these powerful tools and drive our own transformation, whether it is in making SDRs more efficient or in making account planning work much more effectively at scale or in letting our sales leaders inspect and run their businesses a lot more effectively or our finance team, under Brian, to be a lot more effective with what they do.

而 agentic harness 的確是解決各式各樣問題的一項非常強大的武器。我們能把這些強大的工具用來推動自身轉型——無論是讓 SDR 更有效率、讓帳戶規劃在規模化下運作得更有效、讓銷售主管能更有效地檢視並經營他們的業務，或是讓由 Brian 帶領的財務團隊在工作上更有效率。

We are able to go to our customers and not just preach but also demonstrate what we have shown for ourselves internally. That just gives us a lot of credibility, going into these conversations about transformation.

我們能走向客戶，不只是宣講，還能示範我們在內部已經為自己做到的成果。這讓我們在談轉型時具備很高的可信度。

I will add just a little onto what Sridhar said: From a durability perspective, we give our guidance based observed behavior. So we have seen a couple quarters of this behavior.

我再補充 Sridhar 所說的一點：從可持續性（durability）的角度來看，我們的指引是基於已觀察到的行為。因此，我們已經看到這種行為持續了幾個季度。

Our sales team is doing a great job with proving the business value of the use cases. And we are continuing to see great new logo additions.

我們的銷售團隊在證明這些使用案例的商業價值方面做得非常好。而且我們也持續看到很棒的新客戶（new logo）增加。

CoCo, when we look at CoCo, the accounts that are using CoCo are consuming more of the core, as well. And so there is this flywheel effect that we talk about.

CoCo——當我們看 CoCo 時，使用 CoCo 的帳戶也同時在消耗更多核心產品。因此就出現了我們所說的飛輪效應。

We had 9,100 CoCo accounts this quarter. That is up significantly from last quarter. And the gross retention rate has been relatively flat across the last several quarters.

本季我們有 9,100 個 CoCo 帳戶。相較上季大幅增加。而總留存率（gross retention rate）在過去幾個季度相對持平。

And then, just want to emphasize what Sridhar said, as well -- is, we are actually selling into way more personas today. So in a given week, I have three to five conversations with CFOs of existing customers of ours or customers that want to be.

另外也想強調 Sridhar 所說的——我們今天實際上正在銷售給更多不同的角色（persona）。所以在任何一週，我會和我們既有客戶、或想成為我們客戶的公司之 CFO 進行三到五次對話。

And so the CFOs are now making the purchase decision -- the CRO, CMO, CEOs. And so so there is a lot more personas that we are selling into this broader portfolio of products.

因此現在由 CFO 來做採購決策——還有 CRO、CMO、CEO。所以我們在這個更廣的產品組合上，面向的角色類型多了很多。

Appreciate the thoughts. Thank you.

感謝分享想法。謝謝。

Kirk Materne, Evercore ISI.

Kirk Materne，Evercore ISI。

Yeah. Thanks very much for taking the question. Congrats on a great start to the year.

是的。非常感謝讓我提問。恭喜今年開局表現很棒。

I was wondering if you guys could try to separate out a little bit or give us a little bit of color on how we should think about what portion of the acceleration is coming from these newer products that are obviously getting really rapid adoption versus the flywheel of those newer products on the core.

我想請問你們是否能稍微拆分一下，或提供一些說明，讓我們理解：這次加速成長中，有多少是來自這些顯然正在快速被採用的新產品，相對於這些新產品帶動核心產品的飛輪效應，各自占比大概如何？

I assume, just given the size of the core, it's -- the core growing faster is probably the bigger factor. But I was wondering if there's any way for us to distill down what these newer products are having, maybe, on their own account. Thanks.

我猜，考量核心產品的規模，核心成長變快可能是更大的因素。但我想知道，有沒有任何方式能讓我們萃取出這些新產品本身（單獨）帶來的影響？謝謝。

I would roughly call it even. Our AI products, which is a pretty broad swath at this point -- absolutely, it's CoCo and CoWork, but it's also things like AI functions that make data operations proceed at an impressive scale or even newer products like the AI Gateway. They contributed approximately half of the acceleration that we are seeing.

我大致會說兩者差不多各半。我們的 AI 產品——到目前為止涵蓋面相當廣——當然包括 CoCo 和 CoWork，但也包括像是能讓資料作業以驚人規模推進的 AI functions，甚至還有像 AI Gateway 這樣更新的產品。它們大約貢獻了我們所看到加速成長的一半。

But there are a lot of other products that are also demonstrating robust growth. And Brian touched on some of them, whether it's notebooks or applications written in Streamlit or React that are deployed into Snowflake and, of course, migrations themselves going faster.

但也有許多其他產品同樣展現強勁成長。Brian 也提到其中一些，無論是 notebooks、用 Streamlit 或 React 撰寫並部署到 Snowflake 的應用程式，當然還包括遷移本身也在加速。

I have talked pretty much in every single earnings call over the past six quarters about migrations. And that is an area where we continue to get faster and faster.

在過去六個季度的每一場財報電話會議上，我幾乎都談到遷移（migrations）。而這個領域我們持續變得越來越快。

And some of the recent advances, both in models and harnesses, are letting us run long-duration tasks of a scale and complexity that we haven't been able to do before.

而近期在模型與 harness 方面的一些進展，讓我們能執行長時間任務，其規模與複雜度是以前做不到的。

And the rate at which workloads are coming onto Snowflake is also an important factor. And one anecdotal example (technical difficulty) -- that a big network-equipment manufacturer is doing a Teradata migration in less than three quarters this year. And this is something that would have taken probably two to three years in any previous time.

另外，工作負載導入 Snowflake 的速度也是一個重要因素。舉一個軼事例子（technical difficulty）——某家大型網路設備製造商今年正在用不到三個季度完成一次 Teradata 遷移。而這在以往任何時候可能都需要兩到三年。

So these are some of the things that are contributing to our acceleration and beat.

所以，以上是一些促成我們加速成長並超出預期（beat）的因素。

Thanks so much, Sridhar.

非常感謝你，Sridhar。

Karl Keirstead, UBS.

Karl Keirstead，UBS。

Okay. Great. So maybe I'll direct this to Sridhar and Christian.

好的。很好。那我可能把這題指向 Sridhar 和 Christian。

I'd love to ask about model neutrality and model choice. I'm guessing the bulk of tasks completed by CoCo are being directed to [Frontier Labs]. But I'm just curious, during the quarter, did you detect any interesting behavioral shift? Let's say, a mix shift from open-class models to Sonnet-class models?

我想請教模型中立（model neutrality）與模型選擇（model choice）。我猜 CoCo 完成的大多數任務都被導向[Frontier Labs]。但我很好奇，在本季度你們是否觀察到任何有趣的行為轉變？例如，從 open-class 模型轉向 Sonnet-class 模型的組合變化（mix shift）？

And if that happens, Brian, is there any effect, potentially positive, on gross margins to Snowflake's financials?

如果真的發生這種情況，Brian，對 Snowflake 的財務來說，是否可能對毛利率帶來任何影響（可能是正面）？

And, Sridhar, is being model-neutral -- is that becoming a competitive advantage in cases where Snowflake competes directly with the prospect of a customer using one of the Frontier Labs' standalone? Thanks so much.

另外，Sridhar，維持模型中立——在 Snowflake 直接與「客戶改用 Frontier Labs 其中一家的獨立方案」這種可能性競爭時，這是否正在成為一項競爭優勢？非常感謝。

I'll start. Christian will add on.

我先開始。Christian 會再補充。

As models have gotten more powerful, cost has absolutely become a concern. And all of you know this, at least as far as the Frontier Labs go, there used to be somewhat of a dichotomy where Anthropic was available extensively on AWS, while the OpenAI models tended to be more on Azure.

隨著模型變得更強大，成本確實成為一個顧慮。而你們大家都知道，至少就前沿實驗室（Frontier Labs）而言，過去某種程度上存在一種二分：Anthropic 在 AWS 上廣泛可用，而 OpenAI 的模型則較多在 Azure 上。

The material change that's happened is that both the companies are deploying substantial capacity of their own, but it's also the case that they are available in other clouds than the ones that they started with.

發生的重大變化是，兩家公司都在部署相當可觀的自有算力，但同時它們也不再只侷限於最初起步的那一家雲，而是在其他雲上也可用。

And we are absolutely seeing a lot of interest in being able to switch between different models and, also, to optimize cost. And this is also where open-source models come in.

我們確實看到市場對於能在不同模型之間切換、以及最佳化成本有很高的興趣。這也是開源模型發揮作用的地方。

There's obviously been several generations of these open-source models. And we support many of them within Snowflake.

顯然，這些開源模型已經歷了好幾代。而我們在 Snowflake 內支援其中許多模型。

And, yes, we have pretty different economics, when it comes to open-source models, since we run the inference ourselves so that offers a lot of potential for future optimization.

是的，就開源模型而言，我們的經濟性相當不同，因為推論是由我們自己執行，因此在未來最佳化方面有很大的潛力。

And within our harnesses, many of the requests that we get from customers come in this mode that we call auto, where we can pair up the task with the model that is most appropriate for that particular task. And that gives us a lot of leeway in being able to optimize tasks for our customers.

在我們的框架中，客戶提出的許多請求會以我們稱為 auto 的模式進來，在這個模式下，我們可以把任務與最適合該任務的模型配對。這讓我們在為客戶最佳化任務方面有很大的操作空間。

Yeah. Karl, in addition to what Sridhar said, another interesting trend that -- I would call it early, but we're hearing from a number of customers, is the desire to post-train open models, which, the training itself is an opportunity for us, and we're starting to see a lot of interest.

是的。Karl，除了 Sridhar 所說的之外，另一個有趣的趨勢——我會說還在早期，但我們已從不少客戶那裡聽到——是希望對開放模型做後訓練（post-train）。而訓練本身對我們而言是一個機會，我們也開始看到很高的興趣。

And to your question on whether neutrality is a competitive advantage, absolutely, it is. We have heard from many, many customers that they made large commitments to one specific model company and later on, are saying, oh, I should have wanted to do a different model. Whereas the commitment to Snowflake gives them that flexibility, and as Sridhar said, automatic routing into what is the right model for the right task.

至於你問到中立性是否是競爭優勢，絕對是。我們聽到非常非常多客戶表示，他們曾對某一家特定模型公司做出很大的承諾，後來又覺得，噢，我其實應該選另一個模型。相較之下，選擇 Snowflake 的承諾能帶來那種彈性；而如 Sridhar 所說，還能自動路由到「對的任務用對的模型」。

So definitely a very strong advantage for us.

所以這對我們而言確實是非常強的優勢。

This is a theme that, clearly, Christian and early Snowflake pioneered in terms of being able to offer really great capability across the cloud-service providers.

這顯然是一個主題：Christian 以及早期的 Snowflake 在能夠跨雲端服務供應商提供非常出色的能力方面，算是先行者。

To quote Yogi Berra, it feels like deja vu all over again, when it comes to model neutrality.

引用 Yogi Berra 的話，談到模型中立性時，感覺就像「似曾相識，一再重演」。

Okay. Very helpful. Thank you.

好的。非常有幫助。謝謝。

Thank you.

謝謝。

Karl, just -- oops -- real quickly, I just wanted to hit on the margin aspect to your question.

Karl，另外——哎呀——很快補充一下，我想回應你問題裡關於利潤率的部分。

Yeah. Thank you, Brian.

好的。謝謝你，Brian。

Going back to when we develop products, the number 1 thing is we want to develop a great product. That is the key thing that we want to do.

回到我們開發產品時，第一要務是我們要做出一個很棒的產品。這是我們最關鍵想做到的事。

Secondly, we want to make sure that we have massive adoption through use cases and driving benefit to then, in turn, drive revenue. And then, we will work on the margin implication of that.

第二，我們希望透過各種使用情境（use cases）達到大規模採用，並帶動效益，進而推動營收。然後，我們才會去處理其對利潤率的影響。

Sridhar and I are very committed to driving overall operating margin leverage in the business. And so you saw our non-GAAP product gross margin go down to 74% because we have increased our guidance so much.

Sridhar 和我都非常致力於推動公司整體的營業利潤率槓桿（operating margin leverage）。因此你看到我們的非 GAAP 產品毛利率降到 74%，因為我們大幅上調了指引。

And so the mix between our AI products and the course changed a little, but we are still committed, as we guided to increasing our overall operating margin.

因此，我們 AI 產品與核心業務之間的組合稍微有些變化，但我們仍然致力於、也如我們所指引的那樣，提高整體營業利潤率。

And so, as we go through and do model choice and use different models, the best thing for us, right now, is to give our customers the best answer with the best business outcome.

所以，當我們在模型選擇上、使用不同模型時，對我們來說眼下最重要的是，為客戶提供最佳答案並帶來最佳的商業成果。

And then, we will continue to work on margins, as we go forward. But we are committed to driving operating leverage in the model.

接著，我們會在往前推進的同時持續改善利潤率。但我們承諾會在這個模式中推動營運槓桿。

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

Karl，關於這點我再補充一件事：即便是前沿模型（Frontier models），也一直在定期下調價格，並為其產品家族推出更多模型；相較於各組織的使用量，這在一定程度上讓成本維持在可控範圍內。

Thank you.

謝謝。

Raimo Lenschow, Barclays.

Barclays 的 Raimo Lenschow。

Thank you. Congrats from me, as well.

謝謝。我也要向你們表示祝賀。

If I look at the organization and if I look at where revenue's coming from at the moment, you're still relatively indexed towards US, North America.

如果我看你們的組織，以及目前營收的來源，你們仍然相對偏重美國、北美市場。

And can you talk a little bit about what you're seeing in other regions like Europe, Asia? Because it does seem there's a, like, big opportunity to expand the footprint there. Thank you.

你們能否談談在歐洲、亞洲等其他地區看到的情況？因為看起來在那裡擴大版圖有很大的機會。謝謝。

Yeah. Absolutely.

是的。當然。

I think this isn't region-specific. I sat in a sales [QBR] just a month ago and looked at the performance. And all regions are performing. And the outlook for our regions are factored into our guidance, but all regions are operating very well.

我認為這並不是特定地區的問題。我大約一個月前參加了一場銷售 [QBR]，看了績效表現。所有地區都表現良好。我們對各地區的展望也已納入指引之中，但所有地區的營運都非常順利。

Thank you.

謝謝。

Ryan MacWilliams, Wells Fargo.

Wells Fargo 的 Ryan MacWilliams。

Hey. Thanks for taking the question.

嗨。謝謝讓我提問。

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

這看起來真的是資料領域的 AI 時刻。你會說，為什麼 AI 現在正在加速 Snowflake 的營收，最大的改變是什麼？是 Cortex Code 幫助使用者更快啟用 AI 嗎？是你們其他產品改進，再加上現在更好的 AI 模型，讓 AI 使用情境更具吸引力嗎？還是客戶只是更準備好導入 AI 了？

What do you think has led to this AI moment for Snowflake? Thanks.

你認為是什麼促成了 Snowflake 的這個 AI 時刻？謝謝。

I spoke earlier about the flywheel. It's a lot of things coming together. What products like CoWork firmly demonstrated was the ability to get really flexible and quick value from data.

我先前談到過飛輪效應。這是很多事情同時匯聚在一起。像 CoWork 這類產品非常明確地展示了：可以從資料中取得非常彈性且快速的價值。

The demo that I have unfailingly showed every CEO that I've met is the one in which I look up their company as a customer on Snowflake. It really brings alive the power of data in ways that abstract expressions never can.

我幾乎每次見到 CEO 都一定會展示的一個 demo，是我在 Snowflake 上查他們公司作為客戶的那個示範。它讓資料的力量變得非常具體生動，是任何抽象的表述都做不到的。

And there's this growing realization that AI is a massive unlock for getting the data to the right person. And most data teams are embracing this moment because they see this as a way to get past the unending backlogs that they've had pretty much since time immemorial.

而且大家越來越意識到，AI 是一個巨大的解鎖器，能把資料交到對的人手上。多數資料團隊都在擁抱這個時刻，因為他們把這視為一種方式，能夠突破幾乎自古以來就存在、永無止境的待辦清單（backlogs）。

That's a little bit of effect number 1.

這算是第一個影響。

And what CoCo has done for us in a super native way is it's made the entirety of Snowflake absolutely -- our sales team -- AI native. They feel a lot more confident about being able to support any use case on Snowflake because the answer to most problems that a customer or you run into is to simply ask CoCo how you solve the problem and, in most cases, it can solve it by itself.

而 CoCo 以非常原生的方式為我們做到的是：它讓整個 Snowflake——以及我們的銷售團隊——徹底成為 AI 原生（AI native）。他們對於能在 Snowflake 上支援任何使用情境更有信心，因為客戶或你遇到的大多數問題，答案就是直接問 CoCo 要怎麼解決；而在多數情況下，它可以自己把問題解掉。

And so we see a lot of customers, a lot of partners take on migrations, get projects done that, honestly, we would not even have conceived of when we originally wrote Cortex Code. That's the magic of these coding agents.

因此我們看到很多客戶、很多合作夥伴承接遷移工作，把專案做完——老實說，這些專案在我們最初撰寫 Cortex Code 時，甚至都不曾想像得到。這就是這些編碼代理（coding agents）的魔力。

And in a funny way, CoCo also makes it far easier to create agents and get value from the data itself. And this is the combination that makes Snowflake so attractive.

而且有趣的是，CoCo 也讓建立代理並從資料本身取得價值變得容易得多。而正是這種組合讓 Snowflake 如此具吸引力。

And it is not just acquiring customers. We track this metric called time to 80% of purchased consumption for new logos that we acquire. And we measure it cohort by cohort.

而且這不只是獲取客戶。我們追蹤一個指標，叫做新客戶（new logos）達到已購買用量的 80% 所需時間。我們按批次（cohort）逐一衡量。

Basically, of the customers that you acquired, let us say, in January, what fraction of them are consuming more than 80% of their purchase capacity, call it, three months after their purchase month?

基本上，對於你在一月取得的客戶來說，假設在購買月份之後三個月，有多少比例的客戶使用量已超過其購買容量的 80%？

And this metric has very, very visibly improved for the newest cohorts of customers that we are acquiring. That is the power of AI. It is faster to get projects done. It is faster to get value from data. And that is the flywheel that we think is really driving the acceleration in our overall business.

而這個指標在我們最新取得的客戶批次上，已經非常、非常明顯地改善。這就是 AI 的力量。專案完成得更快。從資料取得價值也更快。而這就是我們認為真正推動整體業務加速的飛輪效應。

And as models continue to get smarter, as our ability to run more long-duration things, agents in the cloud continue to mature, we expect this flywheel to accelerate even more.

隨著模型持續變得更聰明，隨著我們在雲端執行更長時間任務的能力提升、雲端代理持續成熟，我們預期這個飛輪會加速得更快。

Appreciate the color. Thank you.

感謝補充說明。謝謝。

Matt Hedberg, RBC Capital Markets.

RBC Capital Markets 的 Matt Hedberg。

Great. Thanks for taking my question. Congrats from me, as well.

很好。謝謝回答我的問題。我也要恭喜你們。

I wanted to piggyback on the CoCo, CoWork line of questioning. It just seems, increasingly, that both products are really well-positioned to agentify the modern enterprise.

我想延續剛才關於 CoCo、CoWork 的提問脈絡。看起來，這兩個產品愈來愈像是非常適合用來把現代企業「代理化」（agentify）。

Sridhar, you mentioned you use it every day. Your sales team is using it every day.

Sridhar，你提到你每天都在用。你們的銷售團隊也每天都在用。

I am just curious, how deep within your knowledge worker base is CoCo being used? Like, things like procurement, as an example?

我只是好奇，在你們的知識工作者族群中，CoCo 的使用滲透到底有多深？例如採購（procurement）這類部門也會用嗎？

And is the right way to think about CoCo being more of a sandbox, as some of these use cases become more repeatable, that these can be brought over to CoWork as more turnkey use cases of agents?

另外，是否可以把 CoCo 視為更像一個沙盒（sandbox）：當某些使用案例變得更可重複時，就能把它們移到 CoWork，作為更即插即用（turnkey）的代理使用案例？

This is Cristian's favorite question so I will let him answer it.

這是 Cristian 最喜歡的問題，所以我讓他來回答。

Absolutely. Like, the pattern that we are seeing is we are leveraging CoCo and CoWork throughout pretty much every function and every key business process throughout Snowflake.

當然。我們看到的模式是：在 Snowflake 幾乎每個職能、每個關鍵業務流程中，我們都在運用 CoCo 和 CoWork。

And we are leveraging that not only to inform the quality and completeness of our products, but also go in and engage with our customer, tell them, this is how you become AI native. This is how you go and drive efficiencies.

而我們運用它不僅是為了回饋產品的品質與完整性，也會進一步與客戶互動，告訴他們：這就是你如何成為 AI 原生（AI native）。這就是你如何推動效率提升。

And that continues to accelerate and inform one another.

而這些也持續加速，並彼此相互促進。

And you are talking about how deep it is used by knowledge workers. Like, just in my organization, we are using it in deal desk, in tax, in accounting, in internal audit, FP&A, treasury.

你問到知識工作者使用的深度。以我自己的組織來說，我們在 deal desk、稅務、會計、內部稽核、FP&A、財務（treasury）都在使用。

So we have over 150 (inaudible) -- [Snowflakes] on [Snow] within the organization, where people are using CoCo to fundamentally change the way that they do work. And so the adoption within the finance organization is almost at 100%.

所以在公司內部，我們有超過 150 位（聽不清）——[Snowflakes] 在 [Snow] 上——大家都在使用 CoCo，從根本上改變他們的工作方式。因此在財務組織內的採用率幾乎達到 100%。

And it is true across functions.

而且各個職能部門都是如此。

Mm-hmm.

嗯哼。

Absolutely.

完全同意。

Koji Ikeda, Bank of America.

美國銀行的 Koji Ikeda。

Hey, guys. Thanks so much for taking the question.

嗨，各位。非常感謝讓我提問。

So you described AI as a structural multiplier because customers using AI consume more across the broader Snowflake platform.

你們把 AI 描述為一種結構性乘數，因為使用 AI 的客戶會在更廣泛的 Snowflake 平台上消耗更多用量。

And so what is the consumption uplift for AI adopters, relative to comparable non-adopters? How has that developed across the earliest cohorts? And what evidence are you seeing? Or, maybe, what is giving you the confidence that all of this reflects higher lifetime consumption, rather than projects just being pulled forward?

那麼，相較於可比的非採用者，AI 採用者的用量提升幅度是多少？在最早期的各批次客戶中，這個情況如何演變？你們看到了哪些證據？或者說，是什麼讓你們有信心：這反映的是更高的終身用量（lifetime consumption），而不只是把專案提前（pulled forward）？

Thank you.

謝謝。

Yeah. I will take a first cut. And Brian will add on.

是的。我先回答第一部分。Brian 會再補充。

At this time, we are not ready to share the exact uplift numbers, but we do measure cohort behavior. And, as CoCo adoption gets deeper, more users within an account adopting and more accounts and more customers themselves adopting, the effect is pretty noticeable for all the different cohorts that we have worked with.

目前我們還沒準備好分享確切的提升數字，但我們確實會衡量各批次（cohort）的行為。而且，隨著 CoCo 採用更深入——同一帳戶內有更多使用者採用、更多帳戶採用、以及更多客戶本身採用——我們在所有已合作過的不同批次上，都能很明顯看到效果。

And what gives us confidence that this is not merely projects being pulled forward is both the breadth and depth of use cases that are coming our way in terms of what people are doing with CoCo and CoWork, it is allowing people to do fairly sophisticated actions that previously would have required things like applications.

至於是什麼讓我們有信心這不只是把專案提前：一方面是使用案例的廣度與深度——人們透過 CoCo 和 CoWork 在做的事情——它讓大家能執行相當複雜的動作，而這些過去往往需要像應用程式那樣的東西。

Our own sales-leadership teams, for example, have been experimenting a lot with their inspection process, how they can drive their business forward.

例如，我們自己的銷售領導團隊一直在大量嘗試他們的檢視（inspection）流程，看看如何推動業務向前。

And something like that would have required a specialized piece of software, a multi-quarter implementation cycle, and then, a staged roll-out. Things like that are literally now a matter of a pretty smart sales leader saying things in English and having CoWork translate that into what looks like a product.

而像那樣的事情，過去會需要一套專門軟體、跨好幾季的導入週期，然後再分階段上線。但現在，真的只要一位相當聰明的銷售主管用英文把需求說出來，CoWork 就能把它翻譯成看起來像一個產品的東西。

This, combined with the fact that we are now having conversations with our customers about a set of use cases that honestly would not have been considered before -- this is everything from supply-chain optimization or much better support systems, in the case of Sanofi, or much better fraud and risk-detection systems -- this is what gives us confidence that there is both breadth and depth in what AI is able to do for Snowflake.

再加上我們現在與客戶討論的一系列使用案例——老實說，過去根本不會被納入考量——從供應鏈最佳化、更好的支援系統（例如 Sanofi 的案例），到更好的詐欺與風險偵測系統——這些都讓我們有信心，AI 能為 Snowflake 帶來的價值同時具備廣度與深度。

Thank you.

謝謝。

Brent Thill, Jefferies.

Jefferies 的 Brent Thill。

Thanks, Sridhar. On CoCo, good to see 2,000 accounts added.

謝謝你，Sridhar。關於 CoCo，很高興看到新增了 2,000 個帳戶。

When you start to see now -- quarter over quarter, is there a difference you are seeing in adoption? Are you getting bigger lands, more users, bigger consumption right out of the gate? Anything that you are seeing that is a trend line since the [product] has shipped?

當你們現在觀察到——按季（quarter over quarter）來看——在採用上有看到差異嗎？你們是否一開始就拿下更大的落地（lands）、更多使用者、更高的用量？自從[產品]出貨以來，有沒有看到任何趨勢線？

Yeah. I work with the team that basically does go-to-market. This is the sales team, especially on the solution-engineering side, our specialist team, but also the product team.

有。我與基本上負責 go-to-market 的團隊一起工作。這包括銷售團隊，特別是解決方案工程（solution-engineering）端、我們的專家團隊，也包括產品團隊。

And we have a pretty sophisticated methodology for measuring CoCo penetration from, we need to get through legal terms, all the way to, there are a set of daily users of the product that are living inside CoCo.

我們有一套相當成熟的方法論來衡量 CoCo 的滲透率：從需要完成法務條款，到產品有一群每日活躍使用者在 CoCo 裡面使用。

We have our own pipeline for the different stages of this penetration. But, more importantly, we also now have a suite of tools, ranging from in-product guidance within Snowsight to hands-on labs that we run for three hours with our customers.

我們針對滲透的不同階段有自己的管線（pipeline）。但更重要的是，我們現在也有一整套工具，從 Snowsight 產品內的引導，到我們為客戶舉辦、長達三小時的實作實驗室（hands-on labs）。

And, obviously, we have a lot of customers. We can't do hands-on labs with each and every one of them. But we are getting much better at matching our actions to the things that are going to drive outcomes.

而且，很明顯地，我們有很多客戶。我們不可能對每一位客戶都進行手把手的實作實驗室教學。但我們在把行動與那些能推動成果的事情對齊方面，正變得越來越好。

We are also doing a good job of sharing best practices across the different theaters in the globe. All of this is driving, just, really positive momentum.

我們也很擅長在全球不同區域之間分享最佳實務。這一切正帶來非常正向的動能。

And, more importantly, this feels like a problem that is ours to solve and drive at scale for the simple reason that CoCo makes every single thing that a customer does with Snowflake go faster and better.

而且更重要的是，這看起來是我們可以在規模化層面去解決並推動的問題，原因很簡單：CoCo 讓客戶在 Snowflake 上做的每一件事都更快、更好。

So it's among the easiest sales that we have done to our customers. But I'm also pretty happy with how methodical and thorough we are being in driving CoCo adoption.

所以這是我們對客戶做過最容易成交的銷售之一。但我也對我們在推動 CoCo 採用上所展現的有條不紊與周延程度感到相當滿意。

Thank you.

謝謝。

Brad Zelnick, Deutsche Bank.

Brad Zelnick，德意志銀行。

Hey. Thanks. This is Dan, on for Brad. Congrats on a great quarter.

嗨。謝謝。我是 Dan，代替 Brad 提問。恭喜你們交出一個很棒的季度。

I wanted to maybe go back to an earlier question on model neutrality or optionality. With open and Frontier models now being offered, maybe there's a third leg around models of your own, like Arctic, that might be specifically tuned for the Snowflake platform.

我想回到先前關於模型中立或可選性的問題。現在既有開源模型也有 Frontier 模型可供選擇，也許還有第三條路：你們自有的模型，例如 Arctic，可能會針對 Snowflake 平台做特別調校。

I'd just be curious what the latest is in terms of your ambitions here, and how that all might fold into the overarching model strategy for CoCo and CoWork.

我想了解你們在這方面最新的企圖心是什麼，以及這將如何融入 CoCo 與 CoWork 的整體模型策略。

Yeah. So Christian here, Brad.

是的。我是 Christian，Brad。

We have not changed the direction we've been on, which is, we're not training models to go get into a Frontier type of model. But we have continued developing models in the Arctic family for tasks that are more specific, more constrained, that we can provide higher accuracy and more efficiency.

我們沒有改變既定方向，也就是我們不會去訓練模型、進入 Frontier 類型的模型競賽。但我們持續在 Arctic 系列中開發更具特定性、更受約束的任務型模型，以提供更高的準確度與更高的效率。

We do that in some of the AI functions. We do that for some of the document processing. We do that for embedding, et cetera. So we will continue doing that type of activity.

我們在一些 AI 功能上這麼做。我們在部分文件處理上這麼做。我們在向量嵌入（embedding）等方面也這麼做。所以我們會持續進行這類型的工作。

And, as you know, the mixing and matching of Frontier-closed models, open-weight models, and our own models with fine-tuned models will continue to be part of how we help customers, at the end of the day, to deliver or achieve what they want, which is: What is the right model for the right task that gives the correct results at the best efficiency?

而且如你所知，把 Frontier 的封閉模型、開放權重模型，以及我們自有模型與微調模型進行混搭，將持續是我們協助客戶的方式之一；歸根結底，是幫客戶達成他們想要的：針對正確的任務選擇正確的模型，在最佳效率下得到正確的結果。

Alex Zukin, Wolfe Research.

Alex Zukin，Wolfe Research。

Hey, guys. Thanks for taking the question. And congrats on an exceptional quarter.

嗨，各位。謝謝讓我提問。也恭喜你們交出非常出色的一季。

Maybe, Sridhar, it feels like we're still very early in the agentic-enterprise experience. And yet, you guys are already seeing pretty meaningful inflection. I appreciate that it's too, maybe, early to share the ARPU expansion at some of these early adopters.

Sridhar，也許我們在「代理式（agentic）企業」的體驗上仍處於非常早期。但你們似乎已經看到相當有意義的拐點。我理解現在可能還太早，無法分享這些早期採用者的 ARPU 擴張情況。

But you talked about accessing larger strategic priorities, maybe larger budgets. Maybe, can you just talk about -- what is the ambit of opportunity that you are now able to access and see in terms of budget dollars?

但你提到能觸及更大的策略優先事項，可能也能觸及更大的預算。你能否談談——就預算金額而言，你們現在能觸及並看到的機會版圖有多大？

And maybe weave in -- we've heard some really exciting tales of your FDE program and some of the exceptional traction that's getting out there in the marketplace, particularly on the outcome-based selling. So maybe just give us a sneak preview of that, as well.

另外也請帶到——我們聽到一些關於你們 FDE 計畫的精彩案例，以及在市場上取得的顯著進展，特別是在以成果為導向的銷售方面。所以也請先給我們一點搶先看。

Yeah. As I was remarking earlier, AI has dramatically lowered the distance between business value that somebody sees -- that a company sees, and the data estate that's next to it. And, often, it's not as complicated as it sounds.

好的。如我先前提到的，AI 大幅縮短了企業所看到的商業價值與其旁邊的資料資產之間的距離。而且很多時候，事情並沒有聽起來那麼複雜。

Recently, I was talking to an asset manager that manages tens of billions of dollars of assets. And they have this problem where they get a very large number of data sets delivered to them every single day.

最近，我和一家管理數百億美元資產的資產管理公司在交流。他們面臨的問題是：每天都會收到非常大量的資料集。

They have a large portfolio of assets that they have, and a set of decisions that they are in the process of making about new moves that they could be taking. Obviously, this is distributed across hundreds, if not thousands, of people.

他們有一個龐大的資產組合，以及一系列正在評估中的決策，關於接下來可能採取的新動作。很明顯地，這些工作分散在數百人、甚至上千人之間。

That act of distributing information effectively is basically manual at this place. It's spreadsheets being passed around. Someone has to download a spreadsheet and update a model that's probably sitting on their local PC.

在那裡，有效分發資訊這件事基本上仍是手動進行。就是到處傳遞試算表。有人必須下載試算表，並更新一個可能放在自己本機電腦上的模型。

And we are talking to them about how do we construct, effectively, like, a multiplexe, demultiplexer for the most important information that is coming, and that can meaningfully lower both their return and reduce their exposure because models just do a much better job of doing this work.

我們正在和他們討論，如何有效地建構一個類似多工器／解多工器（multiplexer/demultiplexer）的機制，把最重要的資訊進行分流與匯整；這能實質降低他們的成本並減少風險曝險，因為模型在做這類工作時表現要好得多。

And that's just one among many, many, many conversations that I end up having, which is pretty remarkable for a person effectively heading a data-infrastructure company.

而這只是我最終會進行的眾多、眾多、眾多對話中的一個；對一個本質上領導資料基礎設施公司的角色而言，這相當令人驚訝。

We've also hired a set of exceptional folks that have industry expertise that can answer simple questions around what are the top 6 things that are going to make the biggest difference to a company's top line and bottom line? And is there a new perspective that we can offer to these?

我們也延攬了一批非常優秀、具備產業專長的人才，他們能回答一些簡單問題：哪些前 6 件事最能影響一家公司的營收（top line）與獲利（bottom line）？以及我們是否能為這些議題提供新的觀點？

And this is what the Frontier-engineering team is doing. It is combining a knowledge of what is possible with the data platform with the harnesses like CoCo and CoWork, with the industry-specific knowledge needed to drive meaningful outcomes to our customers.

而這正是 Frontier 工程團隊在做的事。他們把對資料平台可行性的理解，與像 CoCo、CoWork 這樣的工具（harness）結合，再加上推動客戶取得有意義成果所需的產業特定知識。

We have talked publicly about working with folks like Sanofi in our Frontier-engineering program. But this is an area where there is breadth and depth of adoption.

我們曾公開談到在 Frontier 工程計畫中與賽諾菲（Sanofi）等夥伴合作。但這個領域的採用既廣且深。

We are, for example, helping a big financial institution effectively overhaul their digital and data strategy, and bring it to the modern world in a way that is very, very sustainable for them.

例如，我們正在協助一家大型金融機構，實質上全面翻新其數位與資料策略，並以對他們而言非常、非常可持續的方式，把它帶到現代化的世界。

And the confidence that we have going into these kinds of engagements is not just that we commit to delivering the outcome. Obviously, we get paid only when we deliver outcomes in situations like this. But it's also in the fact that Snowflake is an open, well-understood platform.

我們在投入這類合作案時的信心，不僅在於我們承諾交付成果。很明顯地，在這類情境下，我們只有在交付成果時才會收費。同時也在於 Snowflake 是一個開放且廣為理解的平台。

And compared to some pretty proprietary folks out there, where you have to go back to them after you get the first outcome, we can confidently tell them that their data team is very, very capable of driving further engagement with the projects that they have done and building on top of it.

相較於市面上一些相當封閉的專有方案——你在拿到第一個成果後還得回頭依賴他們——我們可以有信心地告訴客戶：他們的資料團隊非常、非常有能力在既有專案成果上持續推進後續工作，並在其上進一步建構。

It's the combination of these things -- our ability to truly talk about business outcomes, commit to delivering them, but deliver it on a clean, open, well-understood architecture -- that makes the customer looks good and stay good that I'm most excited by.

正是這些因素的結合——我們能真正以商業成果來對話、承諾交付，並在乾淨、開放、且廣為理解的架構上交付——讓客戶看起來很成功、也能持續成功；這是我最興奮的地方。

Excellent. Thank you.

非常好。謝謝。

Tyler Radke, Citi.

Tyler Radke，花旗（Citi）。

Hey. Thank you.

嗨。謝謝。

Sridhar, I wanted to ask your take on some of the moves we've seen from traditional SaaS companies partnering with LLMs and sbecoming more of a database themselves, as the LLMs take the UI layer.

Sridhar，我想請教你對我們看到的一些動向的看法：傳統 SaaS 公司與 LLM 合作，並且在 LLM 接管 UI 層之際，自己更像是資料庫。

How do you see this playing out? Does it make sense for Snowflake to take on more of this system-of-record data? And how do you anticipate what that competitive overlap looks over time?

你認為這會如何發展？Snowflake 是否有必要承擔更多這類系統紀錄（system-of-record）的資料？你又如何預期這種競爭重疊會隨時間演變？

The way I think about this is that, as software gets easier and easier to create, it is the data and semantics that acquire more and more importance.

我的看法是，隨著軟體愈來愈容易開發，資料與語意的重要性就愈來愈高。

It is not lost on any of us that our ability to talk about new value with our customers is driven both by the breadth of the data estates that many, many of our customers have on Snowflake, combined with the power of the harness, obviously, using the best models.

我們都很清楚：我們能與客戶談論新價值的能力，既來自於許多、許多客戶在 Snowflake 上擁有的廣泛資料資產（data estates），也來自於我們的工具鏈（harness）的力量——當然，是搭配最好的模型。

So I have been very, very consistent for, now, two-plus years in my conviction, in our conviction, that owning the user experience is critical.

因此，過去兩年多以來，我一直非常、非常一致地堅信——也是我們的共同信念——掌握使用者體驗至關重要。

And we see CoCo and CoWork as fundamental to our future because they demonstrate to us and to our customers what is possible.

我們把 CoCo 和 CoWork 視為我們未來的基石，因為它們向我們以及客戶展示了什麼是可能的。

But, on the other hand, we understand that we live in a world where we have to play nice. Snowflake is only a part of the overall software estate that our customers have.

但另一方面，我們也理解我們身處一個必須好好合作的世界。Snowflake 只是客戶整體軟體資產的一部分。

We offer interoperability at multiple levels, but we think our flagship products are very important to our future.

我們在多個層面提供互通性（interoperability），但我們認為我們的旗艦產品對我們的未來非常重要。

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

是的。我補充一下：某些應用供應商成為資料庫玩家的概念並不是新趨勢。而我們從 CIO 與 CDO 那裡一再聽到的是：如果我使用三個應用，我不會把資料複製到三個不同的平台。把資料整合到像 Snowflake 這樣的單一中央平台更容易，這也是為什麼我們與其中許多夥伴建立了雙向、零拷貝（zero-copy）的合作。我們也看到很多客戶正把他們的資料資產與 Snowflake 對齊。

Yeah. Our investments in -- which Christian has pioneered and spearheaded with the team for a very long time -- around being able to host applications in Snowflake, small and big, also positions us exceptionally well for many applications, not just analytic ones but also systems of record, operational ones, that can be built right on top of Snowflake.

是的。我們在——Christian 長期以來與團隊開創並主導——能夠在 Snowflake 上託管應用（不論小型或大型）的投資，也讓我們在許多應用場景上具備極佳的定位；不僅是分析型應用，也包括系統紀錄（systems of record）、營運型（operational）應用，都可以直接建構在 Snowflake 之上。

And so, internally, we have many projects, some of which Christian and I, like, do not even know -- of people that are building interesting applications on top of the analytic data and operational stores that they are setting up within Snowflake.

因此在內部，我們有許多專案，其中一些甚至 Christian 和我可能都不太清楚——有人正在 Snowflake 內部所建立的分析資料與營運型儲存之上，打造很有意思的應用。

You can definitely expect to hear a lot more about things like Hybrid tables and Postgres because they are the foundation, we think, for a new generation of agentic applications, some of which will have UI and some of which will not, on top of Snowflake.

你絕對可以期待聽到更多關於 Hybrid tables 和 Postgres 之類的內容，因為我們認為它們是新一代代理式（agentic）應用的基礎；其中一些會有 UI，而另一些則不會——都會建構在 Snowflake 之上。

Thank you.

謝謝。

[Samik Chatterjee], JP Morgan.

[Samik Chatterjee]，摩根大通（JP Morgan）。

Hi. Thanks for taking my question. And congrats from my end on the strong results here.

嗨。謝謝讓我提問。也在此恭喜你們交出強勁的成績。

Maybe if I can ask on the full-year guide and trying to parse out the increase in the full-year guide between core increases on the core versus AI.

我想問一下全年指引，並嘗試拆解全年指引上調的來源：核心業務的提升與 AI 的貢獻各占多少。

I think the last quarter, you had mentioned, most of the full-year guide increase was on account of CoCo. This quarter, it sounds a lot more balanced between core and AI. And your confidence in forecasting acceleration and product revenue growth also seems to be much higher.

我記得上季你們提到，全年指引上調大多是因為 CoCo。這一季聽起來核心與 AI 之間更為均衡。而你們對加速成長與產品營收成長的預測信心似乎也高了很多。

So just wondering if there's something fundamentally that changed during the quarter in terms of consumption of the core from your customers that's driving that higher visibility and a raise to the full year? Or is it more just on account of visibility, after having got through half of the year at this point?

所以想請教：本季是否有什麼根本性的變化——例如客戶對核心產品的用量（consumption）——帶來更高的能見度並推升全年指引？還是主要只是因為目前已經走過半個會計年度，因此能見度自然提高？

Yeah. This is Brian. Thanks for the question.

是的。我是 Brian。謝謝你的問題。

We base our guidance based on observed behavior up until the call that we have. And what we saw is that we talked about CoCo, CoWork, and all the AI functions driving additional business but, as well as the people who adopt them, they're also increasing business within the core.

我們的指引是根據截至我們這通電話為止所觀察到的行為來制定的。我們看到，我們談到的 CoCo、CoWork 以及所有 AI 功能在帶來額外業務的同時，採用它們的客戶也在核心產品上增加了業務量。

So it's a reflection of the strength that we're seeing in our AI products, as well as the underlying strength that we're seeing in the core.

因此，這反映了我們在 AI 產品上看到的強勁動能，也反映了我們在核心業務上看到的基本面強勁。

Thank you.

謝謝。

Thank you. This concludes today's question-and-answer session.

謝謝。今天的問答環節到此結束。

I will now pass the call back to Snowflake for closing remarks.

我現在把電話交回 Snowflake 進行結語。

Thank you, everyone.

謝謝各位。

The Agentic Enterprise runs on Snowflake. We have just achieved 37% year-over-year Product Revenue growth, marking our third straight quarter of acceleration, while expanding our non-GAAP operating margin 400 basis points year over year to 15%.

代理式企業（Agentic Enterprise）運行於 Snowflake 之上。我們剛剛達成產品營收年增 37%，這是我們連續第三季加速成長，同時非 GAAP 營業利益率年增 400 個基點至 15%。

AI has created a powerful flywheel effect across our business, strengthening platform demand, driving adoption of our native AI products, and, in turn, fueling greater consumption across the business. And this flywheel is accelerating.

AI 在我們的業務中創造了強大的飛輪效應，強化平台需求、推動原生 AI 產品的採用，並進一步帶動全業務更高的用量（consumption）。而這個飛輪正在加速。

Based on this strength, we have increased our fiscal year '27 Product Revenue guidance by over 500 basis points to 36% year-over-year growth.

基於這股強勁動能，我們將 2027 會計年度產品營收指引上調超過 500 個基點，至年增 36%。

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

我們以紀律與專注執行，並看到前方有巨大的機會。謝謝。

Thank you. This does conclude today's call.

謝謝。今天的電話會議到此結束。

Thank you for your participation. You may now disconnect.

感謝各位參與。您現在可以掛線。