Snowflake Inc (SNOW) 2026 Q1 法說會逐字稿

Good afternoon and thank you for attending the Snowflake Inc. Q1 fiscal year '26 earnings call. My name is Matt, and I'll be the moderator for today's call. (Operator Instructions) I'd now like to pass the conference over to our host, Jimmy Sexton, Head of Investor Relations. Jimmy, please go ahead.

下午好，感謝您參加 Snowflake Inc. 26 財年第一季財報電話會議。我叫馬特，我將擔任今天電話會議的主持人。（操作員指示）現在，我想將會議交給我們的主持人、投資者關係主管 Jimmy Sexton。吉米，請繼續。

Good afternoon and thank you for joining us on Snowflake's Q1 fiscal 2026 earnings call. Joining me on the call today are Sridhar Ramaswamy, our Chief Executive Officer; Mike Scarpelli, our Chief Financial Officer; and Christian Kleinerman, our Executive Vice President of Product, who will participate in the Q&A. During today's call, we will review our financial results for the first quarter of fiscal 2026 and discuss our guidance for the second quarter and full year fiscal 2026. 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.

下午好，感謝您參加 Snowflake 2026 財年第一季財報電話會議。今天與我一起參加電話會議的還有我們的執行長 Sridhar Ramaswamy；我們的財務長 Mike Scarpelli；以及我們的產品執行副總裁 Christian Kleinerman，他將參與問答環節。在今天的電話會議上，我們將回顧 2026 財年第一季的財務業績，並討論 2026 財年第二季和全年的指導。在今天的電話會議中，我們將做出前瞻性陳述，包括與我們的業務營運和財務績效相關的陳述。這些聲明受風險和不確定性的影響，可能導致它們與我們的實際結果有重大差異。

Information concerning these risks and uncertainties is available in our earnings press release, our most recent Forms 10-K and 10-Q and other SEC reports. All our statements are made as of today based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During today's call, we will also discuss certain non-GAAP financial measures. See our investor presentation for a reconciliation of GAAP to non-GAAP measures and business metric definitions, including adoption.

有關這些風險和不確定性的資訊可在我們的收益新聞稿、最新的 10-K 和 10-Q 表格以及其他 SEC 報告中找到。我們所有的聲明都是根據我們目前掌握的資訊截至今天所做的。除法律要求外，我們不承擔更新任何此類聲明的義務。在今天的電話會議中，我們還將討論某些非公認會計準則財務指標。請參閱我們的投資者介紹，以了解 GAAP 與非 GAAP 指標和業務指標定義（包括採用情況）的對照組。

The earnings press release and investor presentation are available on our website at investors.snowflake.com. A replay of today's call will also be posted on the website. With that, I would now like to turn the call over to Sridhar.

收益新聞稿和投資者介紹可在我們的網站 investors.snowflake.com 上查閱。今天的電話會議重播也將發佈在網站上。說完這些，我現在想把電話轉給 Sridhar。

Thanks, Jimmy, and hi, everyone. Thank you, all, for joining us today. We are off to a strong start to the year and I couldn't be more proud of our team. Our core business is very strong, our product delivery remains in overdrive, and our go-to-market engine continues to get stronger and stronger. We are in the zone and there's still an enormous opportunity ahead.

謝謝，吉米，大家好。謝謝大家今天加入我們。我們今年的開局很強勁，我為我們的團隊感到無比自豪。我們的核心業務非常強大，我們的產品交付仍然保持超速發展，我們的市場引擎也變得越來越強大。我們正處於這個階段，前方仍有巨大的機會。

At Snowflake, our mission is to empower every enterprise to achieve its full potential through data and AI. Our AI data cloud helps customers get more value out of their data, innovate faster and remove friction from their business operations. And as I have shared in the past few quarters, we are extending that value throughout the data life cycle. We remain disciplined in driving operational rigor across our business, gaining greater efficiency even as we continue to invest aggressively in growth. We are building our strength in executing with urgency and focus to capture the opportunities ahead and sustain durable momentum.

在 Snowflake，我們的使命是透過數據和人工智慧賦能每個企業充分發揮其潛力。我們的 AI 資料雲可協助客戶從資料中獲取更多價值、更快創新並消除業務營運中的摩擦。正如我在過去幾季所分享的，我們正在整個資料生命週期中延伸這一價值。我們始終嚴格執行業務營運的紀律，在持續大力投資成長的同時，也獲得了更高的效率。我們正在增強執行力，緊迫而專注，以抓住未來的機會並保持持久的動力。

Product revenue for Q1 was $997 million, up a strong 26% year-over-year. Excluding the impact of leap year, product revenue grew 28% year-over-year. Our growth rate was stable quarter-over-quarter, showing no deceleration. Remaining performance obligations totaled $6.7 billion with year-over-year growth of 34%. Our net revenue retention was a very healthy 124%.

第一季產品營收為 9.97 億美元，較去年同期強勁成長 26%。剔除閏年的影響，產品營收年增28%。我們的成長率環比保持穩定，沒有出現減速。剩餘履約義務總額為 67 億美元，年增 34%。我們的淨收入保留率達到了非常健康的124%。

As you can see, we have started the year with strong revenue growth, and overall, very healthy results. And we are increasing our growth expectations for the year. As I've shared in the past few quarters, Snowflake is obsessed with creating product cohesion to make it easier and easier for our customers to innovate faster and unlock more value from their data from ingestion to Enterprise leaders like Canva and JPMorgan Chase bet their business on Snowflake because our platform is easy to use, connected to enable fluid access to data wherever it sits, and trusted by companies of all sizes and industries.

如您所見，我們今年的營收成長強勁，整體而言，業績非常健康。我們正在提高今年的成長預期。正如我在過去幾個季度中分享的那樣，Snowflake 致力於創造產品凝聚力，使我們的客戶能夠越來越輕鬆地進行創新，並從數據中釋放更多價值，從攝入到交付，Canva 和摩根大通等企業領導者將他們的業務押注於 Snowflake，因為我們的平台易於使用、可連接，無論數據位於何處，都可以流暢地訪問數據，並且行業受到各種規模的信任。

And we are continuing to deliver on our vision of being the end-to-end technology provider for our customers' data journey. We've made important progress in delivering an accessible and flexible connectivity platform, both unstructured as well as structured data. Snowflake connectors, which leverages the technology from our acquisition of Datavolo enables customers with seamless connectivity and data integration with key platforms like Google Drive, Workday, Slack, SharePoint and more to tap into critical data across the business.

我們將繼續實現我們的願景，成為客戶資料旅程的端到端技術提供者。我們在提供可存取且靈活的連接平台（包括非結構化資料和結構化資料）方面取得了重要進展。Snowflake 連接器利用我們收購的 Datavolo 的技術，使客戶能夠與 Google Drive、Workday、Slack、SharePoint 等主要平台進行無縫連接和數據集成，從而利用整個企業的關鍵數據。

Global pharmaceutical leader, AstraZeneca, for example, can now analyze critical business data from systems like SAP and Workday with ease. And customers like CloudZero leverage hundreds of powerful, active data sharing connections to securely exchange data with their partners and customers, driving value across our ecosystem. As our data engineering business continues to show strength, we are helping our customers streamline and scale their data pipeline with less and less friction and realize meaningful cost savings.

例如，全球製藥巨頭阿斯特捷利康現在可以輕鬆分析來自 SAP 和 Workday 等系統的關鍵業務數據。而像 CloudZero 這樣的客戶利用數百個強大、活躍的數據共享連接來安全地與合作夥伴和客戶交換數據，從而推動我們整個生態系統的價值。隨著我們的數據工程業務繼續展現實力，我們正在幫助客戶以越來越少的摩擦簡化和擴展他們的數據管道，並實現有意義的成本節約。

By consolidating data in Snowflake, Dentsu, a global marketing agency managing data for numerous Fortune 500 clients, reduced costs by 30% from simplified data architecture and reduced dependence on third-party tools. They now use Snowflake Data Clean Room to help global brands securely combine customer data without compromising privacy, enabling more personalized marketing campaigns while reducing risk.

透過在 Snowflake 中整合數據，為眾多財富 500 強客戶管理數據的全球行銷機構電通 (Dentsu) 透過簡化數據架構和減少對第三方工具的依賴，將成本降低了 30%。他們現在使用 Snowflake Data Clean Room 來幫助全球品牌安全地合併客戶資料而不損害隱私，從而實現更個人化的行銷活動並降低風險。

On the analytics front, our world-class solutions continue to power mission-critical operations for our customers. Global technology leader, Siemens, is collaborating with Snowflake to help manufacturers unlock new levels of operational efficiency and scale. This enables customers to unify their information technology data such as supply chain management and financial data with operational technology data like data from systems and industrial equipment, leveraging Siemens' industrial edge and Snowflake AI data cloud to gain better insight, improve machine performance and optimize production processes across their business. And as AI reshapes the enterprise, Snowflake is helping our customers lead the way with AI-ready data. Take Samsung a leader in connected TV advertising.

在分析方面，我們世界一流的解決方案繼續為我們的客戶提供關鍵任務營運支援。全球技術領導者西門子正在與 Snowflake 合作，幫助製造商實現新的營運效率和規模水準。這使得客戶能夠將其資訊技術數據（例如供應鏈管理和財​​務數據）與營運技術數據（例如來自系統和工業設備的數據）統一起來，利用西門子的工業優勢和 Snowflake AI 數據雲來獲得更好的洞察力、提高機器性能並優化整個業務的生產流程。隨著人工智慧重塑企業，Snowflake 正在幫助我們的客戶利用人工智慧資料引領潮流。以連網電視廣告領域的領導者三星為例。

They leveraged Snowflake to connect advertisers with millions of Samsung consumers while upholding strict privacy standards. By unifying their data on Snowflake, Samsung drives innovation in personalized customer experiences and accelerates the development of new AI and ML-forward advertising features, enabling advertisers to deliver more relevant content and enhance the advertising We have an incredible product momentum, and we are continuing to innovate at lightning speed.

他們利用 Snowflake 將廣告商與數百萬三星消費者聯繫起來，同時堅持嚴格的隱私標準。透過在 Snowflake 上統一數據，三星推動了個人化客戶體驗的創新，並加速了新的 AI 和 ML-forward 廣告功能的開發，使廣告主能夠提供更相關的內容並增強廣告效果。我們擁有令人難以置信的產品勢頭，並且我們正在以閃電般的速度繼續創新。

In fact, this quarter alone, we have brought over 125 product capabilities to market, a 100% increase over what we delivered in Q1 of last year. We continue to see strong adoption of open data formats, especially truly open modern table formats like Apache Iceberg. We recently announced that our customers can now leverage many of Snowflake's core capabilities including data sharing, security and performance optimization using Apache Iceberg, giving them even more flexibility to manage and query data at scale.

事實上，光是本季度，我們就向市場推出了超過 125 種產品功能，比去年第一季增加了 100%。我們繼續看到開放資料格式的廣泛採用，尤其是像 Apache Iceberg 這樣真正開放的現代表格格式。我們最近宣布，我們的客戶現在可以利用 Snowflake 的許多核心功能，包括使用 Apache Iceberg 的資料共享、安全性和效能最佳化，讓他們更靈活地大規模管理和查詢資料。

When it comes to AI, pretty amazing to see our progress. A year ago, we were just getting started. Now we have over 5,200 accounts using our AI and machine learning on a weekly basis. Cortex AI has gone from a nascent product area to a foundational pillar of enterprise AI strategies for customers around the world. It's accelerating clinical research for health care companies with unified access to information and turning automotive customer reviews into actionable insights to help them personalize their service.

說到人工智慧，我們所取得的進步真是令人驚嘆。一年前，我們才剛起步。現在每周有超過 5,200 個帳戶使用我們的人工智慧和機器學習。Cortex AI 從一個新興的產品領域發展成為全球客戶企業 AI 策略的基礎支柱。它透過統一的資訊存取加速醫療保健公司的臨床研究，並將汽車客戶評論轉化為可操作的見解，幫助他們個人化服務。

As one of the world's largest food and beverage company, Kraft Heinz, is leveraging Snowflake Cortex to empower its employees with innovative AI tools like or -- their new internal AIS system. This initiative is designed to revolutionize internal workflows, enhance efficiency and drive AI adoption across the organization, paving the way for future advancement in agentic AI.

作為全球最大的食品和飲料公司之一，卡夫亨氏正在利用 Snowflake Cortex 為其員工提供創新的 AI 工具，例如其新的內部 AIS 系統。該計劃旨在徹底改變內部工作流程、提高效率並推動整個組織採用人工智慧，為代理人工智慧的未來發展鋪平道路。

Earlier this year, we launched Cortex Agent, which is now helping customers like Luminate Data, a leading provider of entertainment insights, scale how they process and retrieve both unstructured and structured data. That foundation is critical for developing, deploying and orchestrating the data agents driving their AI applications. And we have further solidified our leadership by continuing to integrate cutting-edge models into Cortex, ensuring day one availability of Meta's Llama 4 model.

今年早些時候，我們推出了 Cortex Agent，它目前正在幫助領先的娛樂洞察提供商 Luminate Data 等客戶擴展其處理和檢索非結構化和結構化資料的方式。該基礎對於開發、部署和協調驅動其 AI 應用程式的資料代理至關重要。我們繼續將尖端模型整合到 Cortex 中，確保 Meta 的 Llama 4 模型第一天即可上市，從而進一步鞏固了我們的領導地位。

As I shared last quarter, we announced an expanded partnership with Microsoft to host OpenAI models on Microsoft Azure. We continue to provide our customers with choice and flexibility to leverage the world's leading models for their enterprise AI applications. We also launched the first of our AI-powered migration enhancement. Now our customers can use Cortex to test and review issues during their migration journey, making a time-intensive process much more efficient. And this is just the start of what AI can do to make migrations go fast.

正如我在上個季度所分享的，我們宣布擴大與微軟的合作關係，在 Microsoft Azure 上託管 OpenAI 模型。我們將繼續為客戶提供選擇和靈活性，以便他們可以利用世界領先的模型來實現企業 AI 應用。我們也推出了第一個由人工智慧驅動的遷移增強功能。現在，我們的客戶可以使用 Cortex 在遷移過程中測試和審查問題，從而使耗時的流程更有效率。這只是人工智慧加速遷移的開始。

All of these innovations are focused on driving real value for our customers. We are making it easy to tap into structured data. We are making it easy to tap into unstructured data as well. And we're helping our customers build a strong foundation to lead in the era of agentic AI. We're continuing on this momentum, and you'll see even more from us in just a few weeks.

所有這些創新都致力於為我們的客戶創造真正的價值。我們正在使利用結構化資料變得更容易。我們也正在使利用非結構化資料變得更加容易。我們正在幫助客戶建立堅實的基礎，以引領代理人工智慧時代。我們將繼續保持這股勢頭，短短幾週後您就會看到我們更多的成果。

During the first week of June, we'll be joined by tens of thousands of customers, partners and developers at Snowflake Summit, a four-day event, one of our biggest yet, where we will reveal some truly exciting new capabilities we are bringing to market to support our customers at every stage of their data journey. As we innovate, we remain committed to scaling efficiently. Under the leadership of our new Chief Revenue Officer, Mike Gannon, we have renewed focus and rigor across our go-to-market case. We're growing our go-to-market operations while maintaining our close collaboration across engineering, product marketing and sales to bring products to market effectively. This ensures that we are able to deliver greater value to our existing customers while continuing to win new ones.

六月的第一周，我們將與數萬名客戶、合作夥伴和開發人員一起參加為期四天的雪花峰會，這是我們迄今為止規模最大的活動之一，我們將在會上展示一些真正令人興奮的新功能，我們將推向市場，以支持我們的客戶在其數據旅程的每個階段。在創新的同時，我們始終致力於高效率擴充。在我們新任首席營收長 Mike Gannon 的領導下，我們重新關注並嚴格執行我們的行銷策略。我們正在擴大我們的市場推廣業務，同時保持與工程、產品行銷和銷售部門的密切合作，以有效地將產品推向市場。這確保我們能夠為現有客戶提供更大的價值，同時繼續贏得新客戶。

We're also expanding our addressable market. With the launch of Snowflake Public Sector Inc., and our recent Department of Defense Impact Level provisional authorization, we're now equipped to deliver mission-critical data and AI solutions to the national security community, including the United States Department of Defense, its military branches and industry partners. We also introduced new automotive solutions as part of our AI data cloud for manufacturing. These solutions empower companies like CarMax and Nissan with advanced data and AI solutions to drive innovation and efficiency. And they are using our own AI internally to boost productivity.

我們也在擴大我們的潛在市場。隨著 Snowflake Public Sector Inc. 的成立以及我們最近獲得的國防部影響級臨時授權，我們現在有能力向國家安全界（包括美國國防部、其軍事部門和產業合作夥伴）提供關鍵任務資料和人工智慧解決方案。我們也推出了新的汽車解決方案，作為製造業人工智慧資料雲的一部分。這些解決方案為 CarMax 和 Nissan 等公司提供先進的數據和人工智慧解決方案，以推動創新和提高效率。他們在內部使用我們自己的人工智慧來提高生產力。

Our go-to-market teams use our sales knowledge assistant powered by Cortex to access insights from our sales knowledge phase using natural language with fast apps like our Customer 360 app, they can tap into rich insight on customer consumption front. I'm proud of the discipline and efficiency we have built across the business. We've got a strong operational rhythm, we're investing strategically for growth, and we are laying the groundwork for scale. Mike, why don't you take us through more of the financial details?

我們的行銷團隊使用由 Cortex 支援的銷售知識助手，透過自然語言和快速應用程式（例如我們的客戶 360 應用程式）獲取來自銷售知識階段的洞察，他們可以從客戶消費方面獲得豐富的洞察。我為我們在整個企業中建立的紀律和效率感到自豪。我們擁有強大的營運節奏，我們正在為成長進行策略性投資，並為規模奠定基礎。麥克，你為什麼不向我們詳細介紹一下財務細節呢？

Thank you, Sridhar. In Q1, product revenues grew 26% year-over-year to reach $997 million. As Sridhar mentioned, we saw no deceleration in the business when adjusting for leap day. We continued to see meaningful growth from new product offerings. Both Snowpark and Dynamic Tables outperformed expectations in Q1.

謝謝你，Sridhar。第一季度，產品營收年增26%，達到9.97億美元。正如 Sridhar 所提到的，我們在調整閏日時並未發現業務減速。我們繼續看到新產品的顯著成長。Snowpark 和 Dynamic Tables 在第一季的表現都超乎預期。

Other areas of strength included technology and retail sectors. Q1 was a strong quarter for bookings. On our last call, I noted two large customers ran out of capacity in Q4 and elected to delay their larger renewals. As expected, both of these accounts signed $100 million-plus contracts in Q1. We view this variability in bookings as normal for our model.

其他優勢領域包括科技和零售業。第一季是預訂量強勁的一個季度。在我們上次通話中，我注意到兩個大客戶在第四季度用完了產能，並選擇推遲更大規模的續約。正如預期的那樣，這兩個帳戶在第一季都簽署了價值超過 1 億美元的合約。我們認為預訂量的這種變化對於我們的模型來說是正常的。

Our focus on new customer acquisitions is yielding positive results. We added 451 net new customers in Q1, growing 19% year-over-year. Turning to margins. In Q1, our non-GAAP product gross margin was 75.7%. Our non-GAAP operating margin was 9%, up 442 basis points year-over-year.

我們對新客戶獲取的關注正在產生積極的成果。我們第一季新增了 451 位淨客戶，較去年同期成長 19%。轉向利潤。第一季度，我們的非公認會計準則產品毛利率為 75.7%。我們的非公認會計準則營業利益率為 9%，較去年同期成長 442 個基點。

We continue to focus on driving greater efficiency across the entire company while investing for growth. Non-GAAP adjusted free cash flow margin was 20%. As discussed on our last earnings call, we had several large customers purchase as they consumed in Q4. This booking behavior impacts the seasonality of our free cash flow. We expect this year to be more second half weighted.

我們將繼續致力於提高整個公司的效率，同時投資於成長。非公認會計準則調整後的自由現金流利潤率為20%。正如我們上次財報電話會議上所討論的那樣，我們有幾個大客戶在第四季度購買了產品。這種預訂行為會影響我們的自由現金流的季節性。我們預計今年下半年的權重將更大。

In Q1, we used $491 million to repurchase 3.2 million shares at an average weighted price per share of $152.63. We still have $1.5 billion remaining on our authorization through March 2027. We ended the quarter with $4.9 billion in cash, cash equivalents, short-term and long-term investments. Now moving to our outlook. We expect Q2 product revenue between $1.035 billion and $1.04 billion, representing 25% year-over-year growth.

第一季度，我們使用 4.91 億美元回購了 320 萬股，平均加權價格為每股 152.63 美元。截至 2027 年 3 月，我們的授權金額仍為 15 億美元。截至本季末，我們擁有 49 億美元的現金、現金等價物、短期和長期投資。現在轉向我們的展望。我們預計第二季產品營收在 10.35 億美元至 10.4 億美元之間，年增 25%。

We expect Q2 non-GAAP operating margin of 8%. For FY26, we are increasing our revenue guidance to $4.325 billion, representing 25% year-over-year growth. As always, we forecast based on observed customer behavior. We expect non-GAAP gross -- product gross margin of approximately 75%, non-GAAP operating margin of 8%, non-GAAP adjusted free cash flow margin of 25%. Finally, we will host our Investor Day on June 3 in San Francisco in conjunction with Snowflake Summit.

我們預計第二季非公認會計準則營業利益率為 8%。對於 2026 財年，我們將營收預期上調至 43.25 億美元，年增 25%。像往常一樣，我們根據觀察到的客戶行為進行預測。我們預期非 GAAP 毛產品毛利率約為 75%，非 GAAP 營業利潤率為 8%，非 GAAP 調整後自由現金流利潤率為 25%。最後，我們將於 6 月 3 日在舊金山與 Snowflake 高峰會聯合舉辦投資者日。

If you are interested in attending, please e-mail ir@snowflake.com. Before opening up the line for questions, I just want to update you on my transition as mentioned on the Q4 conference call last quarter. We are in the process of interviewing many great candidates, and we will make an announcement in the future when we have more firm details to share. With that, operator, you can now open up the line for questions.

如果您有興趣參加，請發送電子郵件至ir@snowflake.com。在開始提問之前，我想先向大家介紹一下我在上個季度第四季電話會議上提到的我的過渡情況。我們正在面試許多優秀的候選人，當我們有更多確切細節可以分享時，我們將在未來發佈公告。接線員，現在您可以打開熱線來回答問題了。

(Operator Instructions) Keith Weiss, Morgan Stanley.

（操作員指示） 摩根士丹利的 Keith Weiss。

This is Sanjit Singh for Keith Weiss. Congrats on an outstanding Q1. Sridhar, I want to talk about some of the trend lines of the business, particularly around consumption as it progressed through the quarter. How was consumption sort of exiting the quarter and through the month of May? That's my first question, then I have a follow-up.

我是 Keith Weiss 的 Sanjit Singh。恭喜您第一季取得優異成績。Sridhar，我想談談業務的一些趨勢，特別是本季的消費趨勢。本季和五月的消費情況如何？這是我的第一個問題，然後我還有一個後續問題。

As you know, we don't comment on consumption within a quarter. Overall, Q1 consumption was very strong coming out of the holiday period, and you see that in our results. Of course, Q1 had one less day compared to Q1 last year. But overall, we feel very good about our consumption.

如你所知，我們不對一個季度內的消費情況發表評論。總體而言，假期過後第一季的消費非常強勁，您可以從我們的結果中看到這一點。當然，與去年第一季相比，第一季少了一天。但整體來說，我們對自己的消費感覺很好。

And I would say, Sanjay, we just gave guidance for the quarter and that's based upon the customer behaviors we're seeing through today.

我想說，桑傑，我們剛剛給出了本季度的指導，這是基於我們今天看到的客戶行為。

Yes, and that Q2 guide was very strong so I think there's a lot to take away from there. And then, Sridhar, on the product front, you mentioned about the adoption of Cortex continuing to build. I was wondering to see what are sort of the monetization trends associated with Cortex. And to what extent are customers making commitments associated with Cortex in mind and driving that into their sort of overall consumption of Snowflake?

是的，第二季的指南非常有力，所以我認為有很多東西可以從中學習。然後，Sridhar，在產品方面，您提到了 Cortex 的採用正在繼續建造。我很想知道與 Cortex 相關的貨幣化趨勢是什麼樣的。客戶在多大程度上考慮了與 Cortex 相關的承諾並將其融入他們對 Snowflake 的整體消費中？

I would split that into a few parts, Sanjay. One is that it is very, very clear that people invest in Snowflake. People invest in data systems not just for what they used to be able to do before, which is things like analytics and machine learning, but increasingly for what they will be able to do today and in the future. And part of what I tell our customers is that by working with us, by bringing data into Snowflake, they are making their data, they are making their processes AI-ready as a firm. And we have taken a very measured approach to how we have had our customers use AI.

我會把它分成幾個部分，桑傑。一是，人們投資 Snowflake 的情況非常明顯。人們投資數據系統不僅僅是為了以前能夠做的事情，例如分析和機器學習，而是為了現在和將來能夠做的事情。我告訴客戶，透過與我們合作，將資料引入 Snowflake，他們就可以將資料、將流程變成人工智慧公司。我們對客戶如何使用人工智慧採取了非常謹慎的態度。

As you know, we don't sell AI separately. It's not a SKU. Customers are not signing up for contracts on AI so it's on their existing spend. We have focused a lot on use cases that deliver value today. I've talked about some of these examples.

如您所知，我們不單獨出售人工智慧。這不是一個 SKU。客戶沒有簽署人工智慧合同，因此這是他們現有的支出。我們非常關注當今能夠帶來價值的用例。我已經談論過其中一些例子。

It's everything from being able to create chatbots on documents like our own internal enablement or Siemens has created for all of the PDF manuals of their 150,000 devices. Two, putting business data directly into the hands of end users without needing analyst or BI tools in the mix. We are beginning to see compound systems get adopted, where you bring in more than one data source that can integrate between the kind of questions that the user has, or multi-step flows where you take data from one source and use that to answer questions or do follow-ups from others.

它包括能夠在文件上建立聊天機器人，例如我們自己的內部支援文件或西門子為其 150,000 台設備的所有 PDF 手冊創建的聊天機器人。二、將業務資料直接交到最終使用者手中，無需分析師或 BI 工具。我們開始看到複合系統被採用，在這種系統中，您可以引入多個資料來源，這些資料來源可以整合使用者提出的問題類型，或者多步驟流程，您可以從一個來源獲取資料並使用它來回答問題或跟進其他來源的問題。

So it's very graduated from that perspective. But the overall point that I want to make is that every user of data, every CTO, including our own, now realizes that their data strategy, especially one with Snowflake, is a direct unlock for whatever they're going to do with AI, both today and several years down the road. So in that sense, I think the road maps are emerging. It's not do AI separately on the side. It is more of invest in Snowflake to get your data house in gear and realize value from AI as we go along.

所以從這個角度來看，它是非常漸進的。但我想表達的總體觀點是，每個數據用戶、每個 CTO（包括我們自己的 CTO）現在都意識到，他們的數據策略，尤其是 Snowflake 的數據策略，將直接決定他們現在和幾年後如何使用 AI。因此從這個意義上來說，我認為路線圖正在浮現。這不是單獨做AI。這更多的是對 Snowflake 進行投資，讓您的資料庫運作起來，並在前進的過程中實現 AI 的價值。

Kirk Materne, Evercore ISI.

柯克·馬特恩（Kirk Materne），Evercore ISI。

Congrats on a nice start to the year. Sridhar, I was wondering if you could just dive in a little bit on the comment around Snowpark and Dynamic Tables outperforming. I was just curious, and I'm sure it's a bit of both, how much is just the product maturation and sort of the readiness for customers to take those on versus some of the things you guys have got done on the go-to-market side over the last year in terms of enablement and sales enablement with those products?

恭喜您新的一年有一個好的開始。Sridhar，我想知道您是否可以稍微深入談談有關 Snowpark 和 Dynamic Tables 表現優異的評論。我只是很好奇，而且我確信兩者都有一點，產品成熟度和客戶接受度的準備程度與你們去年在產品支援和銷售支援方面在市場推廣方面所做的一些事情相比如何？

Good question. It's clearly going -- clearly, it's going to be -- it's both. You need great products that drive utilities. And in addition to those features that you mentioned, Snowpark and Dynamic Tables, I would say that our investments in things like also vastly increased the scope of the kinds of things that our customers can do with the data. And similarly, Snowflake connectors is then going to make more and more data available for these data engineering tools as well as.

好問題。顯然，兩者都會發生。您需要能夠推動公用事業發展的優質產品。除了您提到的 Snowpark 和 Dynamic Tables 等功能之外，我想說，我們在這些方面的投資也大大增加了客戶可以使用資料執行的操作範圍。同樣，Snowflake 連接器也將為這些資料工程工具提供越來越多的資料。

And this is why Christian and I stress the end-to-end data life cycle a lot. And so our motto often is we want to be there from injection to insight when it comes to data. Having said that, we have hired, in addition to Mike, amazing leaders of sales that are in charge of driving these more specialized motions. Yes, not everybody in the Snowflake sales team is going to become an expert on our AI product or the latest advancements in Snowflake connectors out of the box.

這就是為什麼克里斯蒂安和我非常強調端到端資料生命週期。因此，我們的座右銘通常是，當涉及到數據時，我們希望從注入到洞察都參與其中。話雖如此，除了麥克之外，我們還聘請了出色的銷售領導來負責推動這些更專業化的動作。是的，Snowflake 銷售團隊中並不是每個人都會成為我們 AI 產品或 Snowflake 連接器最新進展的專家。

So we have a specialist motion that is very targeted, that identifies the highest value use cases that our customers have, pioneers' implementations for them so that they can be used as a template to be repeated in other places and increasingly with our GSI partners. And so you need to be able to do both. You need to create products that create value but then a go-to-market team that can initiate the value and do the hard work of both establishing the flagship customers and then driving sales across the sales teams.

因此，我們有一個非常有針對性的專家動議，它可以識別客戶擁有的最高價值用例，為他們開創實施方案，以便它們可以用作模板，在其他地方重複使用，並越來越多地與我們的 GSI 合作夥伴重複使用。所以你需要能夠同時做到這兩點。您需要創造能夠創造價值的產品，然後組建一個能夠創造價值的行銷團隊，並努力建立旗艦客戶，然後推動整個銷售團隊的銷售。

See you in a couple of weeks.

幾週後見。

Raimo Lenschow, Barclays.

巴克萊銀行的 Raimo Lenschow。

Perfect. Sridhar, if you -- looking at Snowpark and adoption there, how do you see this playing out at the moment and maybe more in the future in terms of like going wall to wall with one vendor versus kind of having different pockets of data that are sitting in your system versus kind of other systems? How do you -- what are you seeing there at the moment and how do you think that will evolve?

完美的。Sridhar，如果您 - 看看 Snowpark 和那裡的採用情況，您認為目前以及未來這種情況會如何發展，例如與一個供應商全面合作，還是在您的系統中存儲不同的數據，還是在其他系統中存儲不同的數據？現在看到什麼了？您認為情況會如何發展？

First of all, I think one of the things that is making us successful, Snowflake as a company is our acknowledgment and willingness to work with customers that have complex data ecosystems. It's always going to be just actually true that there are on-prem legacy systems in most large customers, or that there are large data estates that are sitting in cloud storage. But I think what is unique about this moment is that customers are a little unhappy about needing to stitch together many different tools in order to achieve even relatively simple things.

首先，我認為 Snowflake 公司成功的原因之一是我們承認並願意與擁有複雜資料生態系統的客戶合作。事實上，大多數大客戶都擁有本地遺留系統，或者有大量資料資產儲存在雲端儲存中。但我認為此時的獨特之處在於，客戶對於需要將許多不同的工具拼湊在一起才能完成即使是相對簡單的事情感到有點不滿。

If you think about it, if you wanted -- if you and your company wanted to build a chatbot on a corporate sitting in SharePoint, it's sort of painful if you have to use four different tools in order to produce that. And at the end of it, you won't even have your governance right because you have to go Part of what we want to be able to do for cases like that is have Snowflake connectors point to the SharePoint repository.

如果你仔細想想，如果你想要——如果你和你的公司想在 SharePoint 上的企業版上建立一個聊天機器人，那麼如果你必須使用四種不同的工具來實現這一點，那就太痛苦了。最後，您甚至不會擁有正確的治理，因為您必須去做我們希望針對此類情況所做的事情之一就是讓 Snowflake 連接器指向 SharePoint 儲存庫。

And if there is any augmentation or transformation of the data that is needed, you get that done, for example, with Snowpark, and then you create an index with Cortex Search and hook it up to a chatbot. I think we will continue to see specialist players that exist. And we partner with them, and we value our partnerships with them. But there are also a number of use cases that are ripe for effectively like ease of use and consolidation. And that's the thing that we are leaning in. Anything to add, Christian?

如果需要對資料進行任何擴充或轉換，您可以使用 Snowpark 等工具來完成，然後使用 Cortex Search 建立索引並將其連接到聊天機器人。我認為我們將繼續看到現有的職業球員。我們與他們合作，我們重視與他們的合作關係。但也有一些用例已經成熟，可以有效地實現易用性和整合。這正是我們所傾向的。還有什麼要補充的嗎，克里斯蒂安？

Maybe super quickly, the other investment that we made is around Iceberg, which also creates customers who have an open architecture and be able to mix and match technologies as they see fit.

也許很快，我們進行的另一項投資就是圍繞 Iceberg，這也為客戶創造了一個開放的架構，並能夠根據自己的需求混合和匹配技術。

Yes. Okay, perfect. And then one quick one for Mike. Obviously, it was very good opportunistic on the share buyback side this quarter. How do you think about that traction there for the rest of the year now that shares are starting to look better?

是的。好的，完美。然後對 Mike 來說還有一個簡短的問題。顯然，本季在股票回購方面是一個非常好的機會。既然股價現在開始好轉，您如何看待今年剩餘時間的走勢？

We will continue to evaluate share buyback on a quarterly basis, and we have no plans right now. We've been more opportunistic in terms of the buyback, but we do fully anticipate between now and 2027, we will utilize that.

我們將繼續按季度評估股票回購，目前還沒有計劃。我們在回購方面更加投機，但我們確實完全預計從現在到 2027 年，我們將利用這一點。

Karl Keirstead, UBS.

瑞銀的卡爾·基爾斯特德（Karl Keirstead）。

Okay, great. Mike, in the comments from you and Sridhar, there was really no mention of macro per se and no evidence in the numbers that you guys really saw much pressure. You certainly did back in 2022, 2023. And I'm just curious, Mike, how you would draw a contrast. Is it that a lot of the post-COVID optimization efforts are now largely behind you, Snowflake just in a better place in terms of the product portfolio? Or maybe just the degree of macro pressure, the wobbliness we've all seen in the last couple of months is just not as severe as you had to deal with in that post-COVID downturn? Some contrast might be helpful.

好的，太好了。麥克，在你和斯里達爾的評論中，確實沒有提到宏觀本身，也沒有數字證據表明你們確實看到了很大的壓力。你在 2022 年、2023 年確實這麼做了。我只是好奇，麥克，你會如何進行對比。是不是說，疫情過後，你們所做的許多優化工作現在基本上都已經完成了，而 Snowflake 在產品組合方面處於更好的位置？或者也許只是宏觀壓力的程度，我們在過去幾個月中看到的動盪並不像你們在新冠疫情后的低迷時期所面臨的那麼嚴重？一些對比可能會有幫助。

I would say coming out of COVID, I think it was very different. In that environment, we had a lot of digitally native well-funded start-ups that were spending crazy and weren't really focused on costs as much. Our customer base has really evolved into the -- some of the largest companies in the world that are much more mature, that are much more cost-focused. And I am not seeing any big optimizations plan within our customers like what we saw coming out of COVID with those. But I will remind you, our customers are constantly optimizing.

我想說，走出新冠疫情後，情況就變得非常不同了。在那種環境下，我們有很多資金充足的數位原生新創企業，它們瘋狂花錢，並不太關注成本。我們的客戶群已經真正發展成為世界上一些最大的公司，它們更加成熟，並且更加重視成本。而且我並沒有看到我們的客戶制定任何像 COVID 期間那樣的重大優化計劃。但我要提醒你，我們的客戶正在不斷優化。

That may be a little bit, but they're always looking to do things more efficiently and that will continue. I would say in terms of the macro right now, we really have not seen the impact of anything with the current -- the news on tariffs and other things today. I think if we would have seen that, we would have saw in the new -- number of new customers. We have a great new customer add and we had great additions to RPO with this confidence, and that shows the confidence our customers have on making big bets with Snowflake.

這可能只是一點點，但他們總是希望更有效地做事，而且這種情況會持續下去。我想說，就目前的宏觀而言，我們確實還沒有看到任何當前事件的影響——今天有關關稅和其他事情的新聞。我想如果我們能看到這一點，我們就會看到新客戶的數量。憑藉這種信心，我們新增了一位出色的新客戶，並且 RPO 也得到了顯著增強，這表明我們的客戶對與 Snowflake 進行重大合作充滿信心。

The only small comment I might add is that this is something that our sales team practices as well, which is to make sure that whenever a use case gets implemented, that they actually take the trouble to tidy things up and make sure that things are optimized because our sales team has learned, thanks to 2022 and 2023, that inefficient spend from customers inevitably leads to a contraction later anyway. And we are better off making sure that it is always efficient spend.

我唯一想補充的一點是，這也是我們銷售團隊的做法，即確保每當一個用例被實施時，他們都會不厭其煩地整理事情，並確保事情得到優化，因為我們的銷售團隊已經從 2022 年和 2023 年的經歷中吸取了教訓，客戶的低效支出不可避免地會導致以後的萎縮。我們最好確保這筆錢的支出始終是高效率的。

Okay. And maybe as a follow-up to Mike. Mike, did Snowflake have any exposure to any of the larger AI natives that on the margin might have given you a little extra oomph this quarter?

好的。或許可以作為 Mike 的後續行動。麥克，Snowflake 是否與任何較大的 AI 原生公司有過接觸，這可能會給本季帶來一些額外的動力？

Nothing extraordinary. We do have a number of the AI companies, our customers, but none of them that are all less than 1% of our revenue.

沒什麼特別的。我們確實有許多人工智慧公司，它們是我們的客戶，但沒有一家公司的營收低於我們營收的 1%。

Mark Murphy, JPMorgan.

摩根大通的馬克墨菲。

Congrats on the great execution, Mike. Even if we were expecting a strong start to the year in terms of the hiring in sales and marketing, I don't think we would have pictured these many hires. It's a very big number. Can you speak to that dynamic and just whether -- are you hiring into a pipeline that is strengthening around Cortex or Snowpark or some other opportunity that you see opening up?

恭喜你出色的表現，麥克。即使我們預計今年在銷售和行銷招募方面會有一個強勁的開端，我也不認為我們會想到會有這麼多的招募。這是一個非常大的數字。您能否談談這種動態，以及您是否正在為 Cortex 或 Snowpark 周圍正在加強的管道或您看到的其他機會招募人才？

What I would say, Mark, is just our we have in the business, and it's not just AI, it's everything that we're seeing in Snowflake. And as you know, Q1 is always our biggest hiring from a sales and marketing perspective because we try to get those people on board at the beginning of the year to deal -- so they can be part of our sales kickoff and all of our sales and enablement that we do with the employees in the beginning of the year call the new features and stuff. So I wouldn't read that much other than the confidence we see in our business. But as you know and we talked about, we're still really looking for operational excellence, and we are constantly looking at productivity of people and we will add people, and we will stop adding if we don't see productivity pay off with those people.

馬克，我想說的是，我們在這個行業中擁有的不僅僅是人工智慧，而是我們在 Snowflake 中看到的一切。如你所知，從銷售和行銷角度來看，第一季始終是我們招募規模最大的季度，因為我們試圖在年初讓這些人加入進來，這樣他們就可以成為我們銷售啟動的一部分，而我們在年初與員工一起進行的所有銷售和支援工作都稱為新功能和其他東西。因此，除了我們對我們的業務所看到的信心之外，我不會讀太多。但正如您所知，我們也談到了，我們仍然在真正追求卓越的運營，我們不斷關注人員的生產力，我們會增加人員，如果我們沒有看到這些人員的生產力帶來回報，我們就會停止增加人員。

Okay, understood. And then Sridhar, can you speak to the federal government opportunity because you had touched on it in your comments? Do you think that DOGE is going to run through this initial process of eliminating wasteful spend and then maybe pivot back towards issuing some new RFPs? And then you've got the right certifications. I'm just curious if you think some agencies might be moving off-legacy on-prem data warehouses and maybe moving some of that on the Snowflake a little later in the year.

好的，明白了。然後 Sridhar，您能談談聯邦政府的機會嗎，因為您在評論中提到過這個機會？您是否認為 DOGE 會先完成這項消除浪費支出的初步流程，然後再轉向發布一些新的 RFP？然後你就獲得了正確的認證。我只是好奇，您是否認為一些機構可能會在今年稍後將部分資料倉儲從傳統的本地資料倉儲中轉移出來，並可能將其中的部分資料倉儲轉移到 Snowflake 上。

This is an active topic of conversation with many departments in the government. I think -- I was in DC a few weeks ago, met with a number of folks, and there is both an increasing awareness of what Snowflake can do. The fact that we have very low operational overhead figures prominently. And there's also a little bit of an astute change where they're very much focused on how do we make sure that our data infrastructure is run efficiently. There's also a desire for things like cross-department sharing of data because that just leads to more efficiency.

這是政府許多部門正在積極討論的議題。我想——幾週前我在華盛頓特區，與一些人會面，他們越來越意識到 Snowflake 的功能。事實上，我們的營運開銷非常低。他們也做出了一些精明的改變，重點是如何確保我們的數據基礎設施高效運作。人們也希望實現跨部門資料共享，因為這會提高效率。

We'll have more to say on this topic. And Mike, our new CFO, is certainly actively looking at this area as well. We are optimistic and hopefully, we'll have more to say about this in the coming quarters.

關於這個話題我們還會多討論。我們的新任財務長麥克當然也在積極關注這一領域。我們對此持樂觀態度，並希望在接下來的幾個季度中對此有更多了解。

Kash Rangan, Goldman Sachs.

高盛的卡什·蘭根（Kash Rangan）。

It's really heartening to see the positive shift in the narrative strategic positioning and the execution and handling of everything, so good to see that. Two things still, regardless of, although you're approaching $4 billion run rate and growing 26%, 27%, which is remarkable, if I could take the liberty of poking at the NER, so 124% is good. Not many companies even reach that number at your scale if they reach your scale. But you have had [137] new products launched in the most recent quarter, and Scarpelli will be quick to point out that the NER metric is more of a 24-month trailing metric, which I completely appreciate. But why wouldn't and why shouldn't NER be better, given the tail effect of new product introduction, you're landing customers at a record pace, and to Mike's point, the yield of enterprise customers is higher quality?

看到敘事策略定位以及一切事情的執行和處理都發生了積極的轉變，真是令人欣慰，很高興看到這一點。無論如何，還有兩件事，儘管你的運行率接近 40 億美元，增長率達到 26%、27%，這是了不起的，但如果我可以冒昧地談談 NER，那麼 124% 就很好了。即使能達到您的規模，也沒有多少公司能達到這個數字。但你們在最近一個季度推出了 [137] 種新產品，Scarpelli 很快就會指出，NER 指標更像是一個 24 個月的追蹤指標，我非常欣賞這一點。但是，考慮到新產品推出的尾部效應，你以創紀錄的速度吸引客戶，並且按照 Mike 的觀點，企業客戶的收益質量更高，為什麼 NER 不會更好，為什麼不應該更好？

You've got AI which didn't exist in 2022. Volatility, which should play to your advantage. So anything on that front? And anything that your new CRO could do to land that number, as good as it is, even higher? And I have a very quick follow-up question.

你擁有的人工智慧在 2022 年還不存在。波動性應該對你有利。那麼這方面有進展嗎？那麼，您的新 CRO 可以做些什麼來讓這個數字達到甚至更高？我有一個非常快速的後續問題。

What I would say on that is a number of our newer customers that are not in that cohort are contributing to our growth beyond what the NRR is. And there was one of our large customers that grew so much last year. In this year, they're still doing very well but they didn't grow as much this year. And that's really the dynamics. It's those newer customers coupled with that. But once again, over time, NER and revenue growth rate will converge as we become a more mature company.

我想說的是，我們的許多新客戶不屬於該群體，但他們對我們的成長的貢獻超過了 NRR。我們的一個大客戶去年成長非常快。今年他們仍然表現很好，但沒有成長那麼多。這才是真正的動力。這是那些新客戶帶來的結果。但隨著時間的推移，隨著我們公司變得更加成熟，NER 和收入成長率將會趨於一致。

Got it. And maybe one for you -- Thank you, Mike. Good to hear your voice. One for you, Sridhar. When you look at what's happening with the hyperscalers, Microsoft certainly talked about Fabric yesterday, on and build, certainly seem to be making a lot of progress in the idea of data fabric at scale, truly open to enable AI agentic architecture.

知道了。也許還有一個適合你——謝謝你，麥克。很高興聽到你的聲音。給你一個，Sridhar。當你觀察超大規模企業的情況時，微軟昨天肯定談到了 Fabric，並且正在構建，似乎在大規模資料結構的想法上取得了很大進展，真正開放以支援 AI 代理架構。

It's a thing that's not lost on the big guys. Where does that lead Snowflake? And what is going to be the one or two or three things that the Snowflake's platform would do better so you can build your vision towards being a $10-plus billion revenue company?

大人物們都不會忘記這一點。這將會把 Snowflake 引向何方？那麼，Snowflake 平台可以在哪一、兩或三件事上做得更好，以便您能夠實現成為一家收入超過 100 億美元的公司的願景？

Just like the hyperscalers are formidable, they are amazing both from an engineering execution and business perspective, but they also work with Anthropic and OpenAI because they are the best -- among the best model makers in the world. Similarly, we are very uniquely positioned in terms of being the excellent data platform that is. And we've also learned how cooperating really leads to a better outcome, whether it is with AWS, which is our biggest partner, or more and more with Azure. There are many customers that Azure not play is just a better outcome for everybody that is involved. And we have deep partnerships between the team.

就像超大規模企業非常強大一樣，它們從工程執行和​​商業角度來看都令人驚嘆，但它們也與 Anthropic 和 OpenAI 合作，因為它們是最好的——世界上最好的模型製造商之一。同樣，作為優秀的數據平台，我們有非常獨特的優勢。我們也了解到合作如何真正帶來更好的結果，無論是與我們最大的合作夥伴 AWS 合作，還是與 Azure 合作。有許多客戶認為 Azure 不會參與其中，但這對所有相關人員來說都是更好的結果。我們團隊之間有著深厚的合作關係。

I think it was six, seven months ago that we announced, for example, that from Snowflake, you could read tables that are in one lake. And we are also actively talking to them about one lake being the data layer for Snowflake that's at the bottom. We also collaborate with them at the top, where we have things like Cortex analysts and Cortex agents be available as components in Office Copilot, for example.

例如，我想大概在六、七個月前，我們宣布可以透過 Snowflake 讀取同一個湖中的表格。我們也正在積極與他們討論將一個湖作為位於底部的 Snowflake 的資料層。我們也在頂層與他們合作，例如，我們將 Cortex 分析師和 Cortex 代理等作為 Office Copilot 中的元件提供。

So we very much take this approach of finding customers, for example, who are on a modernization routine or who want to get value, AI value from data and figure out how we can work together. Yes, there is competition, but I think there are more cases than not where we are very, very effectively working together, and it's on the uptick, especially with Azure.

因此，我們非常樂意採用這種方法來尋找客戶，例如，那些正在進行現代化改造或想要從數據中獲取價值、人工智慧價值的客戶，並找出我們如何合作的方法。是的，存在競爭，但我認為在大多數情況下，我們都在非常有效地合作，而且這種合作呈上升趨勢，尤其是與 Azure 的合作。

Matthew Hedberg, RBC.

馬修·赫德伯格（Matthew Hedberg），RBC。

This is Mike Richards on for Matt. Congrats on the results here. You've clearly made great progress on the products front here, but I'm just curious how you feel about the maturity of the go-to-market motion to support your AI developments. That's it for me.

我是麥克理查茲，代替馬特發言。恭喜你所取得的成果。您顯然在產品方面取得了巨大進步，但我只是好奇您對支援人工智慧發展的上市計劃的成熟度有何看法。對我來說就是這樣。

I'm actually even more pleased with how we've been able to seize the AI opportunity. I've spoken to you, folks, previously about how we created what we then called the AI ninjas, which were a group of solution engineers that were deeply versed with our AI products, that could be very close to our sales teams around the globe and just the excitement that our sales team feels about AI, but more importantly, the ability to drive AI use cases at scale to both pitch the vision, but also run POCs for them, win them and get them into production.

事實上，我更高興的是，我們能夠抓住人工智慧的機會。各位，我之前曾與你們討論過我們如何創建當時被稱為“AI 忍者”的團隊，他們是一群對我們的 AI 產品非常熟悉的解決方案工程師，他們與我們全球的銷售團隊非常接近，並且能夠感受到我們的銷售團隊對 AI 的興奮，但更重要的是，他們有能力大規模地推動 AI 用例，既可以宣傳它們，也可以為他們的青睞

That's been a pretty remarkable transformation for us. And we are now in the process of making this kind of specialized knowledge available to more and more of the sales team. I think it is this combination of specialist teams that know more about a sophisticated area like AI, again doing the initial work, but having more of the team participate in it.

對我們來說，這是一個相當了不起的轉變。我們現在正在努力讓越來越多的銷售團隊成員掌握這種專業知識。我認為正是這些專家團隊的組合更了解像人工智慧這樣的複雜領域，再次進行初步工作，但讓更多的團隊成員參與其中。

That's been hugely positive for us. And we apply similar techniques in data engineering, though with data engineering, I would say it's much closer to the knowledge and skill set of more of our sellers. In some sense, it's more natural to them. But AI is doing exceptionally well as well. And we have assembled a team of both specialist sellers and AI but also specialist technical experts that are driving change across the whole sales organization.

這對我們來說具有巨大的正面意義。我們在數據工程中應用了類似的技術，但我認為數據工程更接近我們更多賣家的知識和技能。從某種意義上說，這對他們來說更自然。但人工智慧也表現得非常出色。我們組建了一支由專業銷售人員和人工智慧專家組成的團隊，同時也包括專業技術專家，致力於推動整個銷售組織的變革。

That combined with an increasing understanding of what it takes to drive great use cases in general, not just in AI and data engineering but also across other areas like analytics, really heralds a new era of data-driven go-to-market, which I'm very, very happy about.

再加上對如何推動偉大用例的理解不斷加深，不僅在人工智慧和數據工程領域，而且在分析等其他領域，這確實預示著數據驅動的行銷新時代的到來，對此我感到非常非常高興。

Brad Zelnick, Deutsche Bank.

德意志銀行的布拉德‧澤爾尼克 (Brad Zelnick)。

Congrats on a good start to the year. Sridhar, as we think -- I just want to follow up on Kirk's question. As we think about Snowpark adoption from here, beyond capturing maybe the Spark jobs where data was moving off platform, can you talk about success that you're seeing in penetrating, where are the media or data science use cases and any anecdotal evidence that you're winning over the data science crowd and maybe the impact that notebooks are having would be great?

恭喜您今年有一個好的開始。Sridhar，正如我們所想——我只是想跟進 Kirk 的問題。當我們從這裡考慮 Snowpark 的採用時，除了捕捉資料從平台移出的 Spark 作業之外，您能否談談您在滲透方面所看到的成功，媒體或資料科學用例在哪裡，以及任何軼事證據表明您正在贏得資料科學人群的支持，也許筆記本會產生很大的影響？

Yes. I'll start the answer. Christian can add on. Our notebooks are doing very well. Several thousand customers are actively using them.

是的。我先開始回答。克里斯蒂安可以補充。我們的筆記本表現非常好。數千名客戶正在積極使用它們。

And there is increasing ability, for example, to train larger and larger machine learning mark. As you folks know, like the world has made enormous amount of progress on the basis of machine learning, even though AI is all the hotness these days. But when it comes to many, many interesting use cases, for example, next best action prediction, which the likes of Hilton do, are how to route guests to the next ride, which customers like Disney do. These are all things that we have gained increasing market share around. Notebooks continue to expand.

例如，訓練越來越大的機器學習標記的能力也越來越強。眾所周知，儘管人工智慧是當今的熱門話題，但世界在機器學習的基礎上已經取得了巨大的進步。但是當涉及到許多有趣的用例時，例如，下一個最佳行動預測（希爾頓等酒店所做的）是如何引導客人到達下一個遊樂設施，而迪士尼等客戶則這樣做。這些都是我們不斷獲得市場佔有率成長的領域。筆記型電腦的容量不斷擴大。

We continue to add product capabilities for training bigger, better, faster models on machines running and container services. These tend to be more technical in terms of the kind of people that are involved, the implementations that happen but definitely appealing to the developers, the data scientists, that sort of product-led motion is something that is going on pretty well. Christian?

我們繼續增加產品功能，以便在運行的機器和容器服務上訓練更大、更好、更快的模型。就涉及的人員類型和實施情況而言，這些往往更具技術性，但絕對對開發人員和資料科學家有吸引力，而這種以產品為主導的運動正在順利進行中。基督教？

Yes. [One book] addition is Snowpark is a collection of libraries and capabilities that help customers do a variety of activities. We see lots of people leveraging it for unstructured data processing, which is a core part of what we're doing. As Sridhar said, we're making more unstructured data available to customers. So Snowpark for extracting structure and signal and doing traditional ML on structured common use case we're seeing.

是的。 [一本書] 另外，Snowpark 是圖書館和功能的集合，可以幫助顧客進行各種活動。我們看到很多人利用它進行非結構化資料處理，這是我們工作的核心部分。正如 Sridhar 所說，我們正在向客戶提供更多非結構化資料。因此，我們看到 Snowpark 用於提取結構和訊號，並在結構化的常見用例上執行傳統 ML。

Maybe just a quick follow-up, Mike. Guidance implies a robust ramp through the remainder of the year. And I think we all see the pace of innovation. Excited for what's to come at Summit. But what, if anything, would you call out that underpins your confidence things we might not be thinking about or any key assumptions worth calling out?

也許只是一個快速的跟進，麥克。指導意見暗示今年剩餘時間內經濟將強勁成長。我認為我們都看到了創新的步伐。對峰會即將發生的事情感到興奮。但是，如果有的話，您會說出哪些我們可能沒有想到的支撐您信心的事情或任何值得指出的關鍵假設呢？

I would just say, as I said, our guidance is based upon the observed behavior we see within our customers, coupled with we spend a lot of time than we have for the last five quarters now, in really identifying new workloads going to production. We have a pretty good visibility of those, and we're very close with our customers, and we know what we're doing. Migrations are moving nicely. We announced where we've made Snow Convert available to all of our customers and partners, and we're seeing an uptick in the amount of usage around that, and that's what gives us the confidence in the guidance that we gave.

我只想說，正如我所說，我們的指導是基於我們在客戶中觀察到的行為，再加上我們花費了比過去五個季度更多的時間來真正確定投入生產的新工作負載。我們對這些有很好的了解，我們與客戶的關係非常密切，我們知道我們在做什麼。遷徙進展順利。我們宣布已向所有客戶和合作夥伴提供 Snow Convert，我們看到其使用量上升，這讓我們對我們給予的指導充滿信心。

Brent Thill, Jefferies.

布倫特·蒂爾（Brent Thill），傑富瑞集團。

This is Bo Yin on for Brent Thill. With features like Cortex AI analyst and Cortex agents that can help users to write more efficient queries, like are you seeing more query optimization as Cortex AI adoption picks up? And what's been a net impact on usage so far in terms of net new queries and query optimization?

這是 Bo Yin 為 Brent Thill 上場。Cortex AI 分析師和 Cortex 代理等功能可以幫助用戶編寫更有效率的查詢，隨著 Cortex AI 採用率的提高，您是否看到更多的查詢最佳化？到目前為止，就淨新查詢和查詢最佳化而言，對使用情況的淨影響是什麼？

With things like Cortex analyst, if anything, the end users are a step removed from writing the SQL query. The user semantic model to aid in figuring out the intent of the user query and then auto-generate the SQL from it. Certainly, we've made available Copilot-like, both for what we call worksheets, which is where people write SQL or Python, but also inside notebook.

有了 Cortex 分析師之類的工具，最終使用者就離編寫 SQL 查詢只有一步之遙了。使用者語意模型有助於弄清楚使用者查詢的意圖，然後從中自動產生 SQL。當然，我們已經提供了類似 Copilot 的功能，既可用於我們所說的工作表（人們可以在其中編寫 SQL 或 Python），也可用於筆記本內部。

To be honest with you, I think there is a huge amount of innovation that is coming there, some of which we will show at Summit. The bar for people being able to write code now are modern dollar per platforms like Cursor, which -- and get a Copilot and others, which can massively increase productivity in terms of the volume of queries that can be written as well as the amount of work that can be done. While we don't have concrete measurements of this leads to X percent more query, we are very happy about being able to help our customers write queries faster or write code faster and be able to debug as faster as well.

老實說，我認為將會有大量的創新出現，其中一些我們將在峰會上展示。現在人們能夠編寫程式碼的標準是每個平台的現代美元，例如 Cursor，以及 Copilot 和其他平台，它們可以大大提高可編寫的查詢量以及可完成的工作量的生產力。雖然我們沒有具體的測量方法來證明這是否會導致查詢量增加 X%，但我們很高興能夠幫助我們的客戶更快地編寫查詢或更快地編寫程式碼，並且能夠更快地進行偵錯。

Yes. We're saying in these calls and other forums that our preference, our goal is to make sure that customers are optimized all the time. I think none of us like to go and spend money and then optimize and go up and down. So we put a lot of effort in technology, some of the core examples that you have are part of that, but query insights, cost insights, governance insights all over this product, how we help customers be in a better optimized state all along.

是的。我們在這些電話會議和其他論壇上表示，我們的偏好和目標是確保客戶始終得到最佳化。我想我們都不喜歡去花錢然後優化和上下波動。因此，我們在技術上投入了大量的精力，您擁有的一些核心示例是其中的一部分，但查詢洞察、成本洞察、治理洞察貫穿整個產品，我們如何幫助客戶始終處於更好的優化狀態。

Patrick Colville, Scotiabank.

加拿大豐業銀行的派崔克‧科爾維爾。

I guess my one is for Sridhar. Last year, the Arctic LLM was launched. My question is how important are first-party foundation models to Snowflake's strategy as of today? Or is there like a slight pivot more to kind of partnering with third-party foundation models?

我想我的一個是給 Sridhar 的。去年，北極法學碩士課程啟動。我的問題是，截至目前，第一方基礎模型對 Snowflake 的策略有多重要？或是否有與第三方基礎模型合作的輕微轉變？

I said this before, I think the business of training a truly large foundation model has gotten to be a very expensive proposition. We have an amazing team of AI researchers, but they tend to focus more on things like training. We have always blogged, for example, about how we can be much more efficient, like much more correct at generating SQL queries by using post training techniques. This makes Cortex analyst better. I think at least for now, the era of us training, let's call it, frontier foundation models is not something that we are actively looking at.

我之前說過，我認為訓練一個真正大型的基礎模型的業務已經變得非常昂貴。我們擁有一支出色的人工智慧研究團隊，但他們更傾向於專注於培訓等方面。例如，我們一直在部落格中介紹如何透過使用後期訓練技術來提高效率，例如更正確地產生 SQL 查詢。這使得 Cortex 分析師變得更好。我認為至少就目前而言，我們訓練的時代，我們稱之為前沿基礎模型，並不是我們正在積極關注的東西。

But the research team continues to do amazing work, as I said, in post training but also in areas like inference optimization, which has a huge impact on latency, it has a huge impact on margins in AI. So we continue to have a robust presence in the area but we work with partners. Meta is a big partner. We were a day one launch partner for the Llama 4 model that came on. We actively collaborate with Anthropic, with OpenAI.

但正如我所說，研究團隊在後期訓練以及推理優化等領域繼續做出令人驚嘆的工作，這對延遲產生了巨大的影響，對人工智慧的利潤率產生了巨大的影響。因此，我們將繼續在該地區保持強大的影響力，但我們也與合作夥伴合作。Meta 是重要的合作夥伴。我們是 Llama 4 車型上市第一天的發布合作夥伴。我們積極與 Anthropic 和 OpenAI 合作。

Mistral lots of model providers. The one fun thing I'll add is that in the area of embedding models, these are small unsung heroes, but they are the models that essentially produce fingerprints of documents that you want to index for a chatbot, for example. We have robust embedding models that we have open source. We have to be opportunistic about where we can create value because can't afford to spend the billions of dollars that it takes to be a part of research today.

Mistral 有很多模型提供者。我要補充的一件有趣的事情是，在嵌入模型領域，這些都是無名英雄，但它們本質上是產生您想要為聊天機器人索引的文件指紋的模型。我們擁有開源的強大嵌入模型。我們必須抓住機會，尋找可以創造價值的地方，因為我們無法承擔今天參與研究所需的數十億美元。

Crystal clear. And can I just squeeze in a follow-on for Mike? I mean, the bottom line is clearly less of a focus when you're growing the top line 26% with the possibility to reaccelerate in the back half of the fiscal year. But one key operating margin was strong. Nonetheless, the fiscal year op margin was left unchanged, as was the fiscal year free cash flow margin target. So I guess, what were the puts and takes there as to why leave those targets unchanged?

晶瑩剔透。我可以為 Mike 擠出一點時間跟進嗎？我的意思是，當你的營業收入成長 26% 並且有可能在財政年度下半年再次加速成長時，底線顯然不再是關注的重點。但有一個關鍵的營業利潤率很高。儘管如此，本財年營業利潤率維持不變，本財年自由現金流利潤率目標也維持不變。所以我想，為什麼要維持這些目標不變呢？

Well, what I would say is in Q2 is when we have a big user event, and that's a very expensive event, Summit that we operate, and that typically has an impact on our operating margin in Q2. And that's factored in. And we'll just continue to revise our forecast for the year on a quarterly basis going forward.

嗯，我想說的是，在第二季度，我們會舉辦大型用戶活動，這是一個非常昂貴的活動，我們經營的峰會，這通常會對我們第二季度的營業利潤率產生影響。這已被考慮在內。今後，我們將繼續按季度修改今年的預測。

I actually think that we are being pretty thoughtful when it comes to expanding our operating margin. It was 4% in Q1 last year. It's 9% Q1 this year. And this is part of the benefit of practicing what we preach around AI. We spend a lot of time figuring out how engineers can be more productive with AI, how we can get small work done.

我實際上認為，在擴大營業利潤率方面，我們已經考慮得非常周到了。去年第一季這一比例為4%。今年第一季為 9%。這是我們實踐人工智慧理念的部分益處。我們花費大量時間研究如何利用人工智慧來提高工程師的工作效率，如何完成小型工作。

Similarly in the sales team, we want to automate many of the tasks that our sales team doesn't like to do anyway so that they can be more productive in front of sellers. We feel that we are in quite a bit of a Goldilocks moment where we can continue to grow revenue very strongly while continuing to be very efficient when it comes to operating margin and free cash flow.

同樣，在銷售團隊中，我們希望自動化許多銷售團隊不喜歡做的任務，以便他們在賣家面前更有效率。我們感覺我們正處於一個相當理想的時刻，我們可以繼續強勁地增加收入，同時在營業利潤率和自由現金流方面繼續保持高效。

Alex Zukin, Wolfe Research.

沃爾夫研究公司的亞歷克斯·祖金（Alex Zukin）。

Maybe just a high-level one first for you, Sridhar or Mike. I guess again, it seems like what we're seeing -- what we're hearing from you is the demand environment is really unchanged, untouched by all the macro headlines. You're seeing new product adoption ahead of expectations. So maybe just the first one, are you seeing a change? Like is this being driven by any kind of identifiable AI tailwinds?

對 Sridhar 或 Mike 來說，也許只是高層次的。我再次猜測，我們看到的——我們從您那裡聽到的是，需求環境確實沒有改變，沒有受到所有宏觀新聞的影響。您會看到新產品的採用超出了預期。所以也許只是第一個，你看到改變了嗎？這是否是由任何可識別的人工智慧順風推動的？

You're seeing a change with how customers are either investing in their AI stack with Snowflake or building agents? Specifically, they're building more on data rather than just focusing on the models. Like maybe just help us understand and conceptualize the AI tailwind or you're placed within these AI budgets that a lot of your large customers clearly are making those bets.

您是否看到客戶在使用 Snowflake 投資 AI 堆疊或建立代理商的方式發生了變化？具體來說，他們更多地基於數據進行構建，而不是僅僅關注模型。例如，也許只是幫助我們理解和概念化人工智慧的順風，或者你被置於這些人工智慧預算之內，你的許多大客戶顯然都在進行這些押注。

As I was saying earlier, I think more and more people have internalized that to be good at AI, your data needs to be in here. And what we have done on our side is to create a product, whether it's a semantic model that is very close to the data, usable by anyone, mind you, not just by Snowflake, but also products like Cortex Analyst that can actually unlock the value of that data, both by immediate use, like a chatbot on a specific data set but much more importantly for use in an agentic workflow.

正如我之前所說，我認為越來越多的人已經意識到，要想擅長人工智慧，就需要有數據。我們所做的就是創建一種產品，無論它是一個非常接近數據的語義模型，任何人都可以使用，請注意，不僅僅是 Snowflake，還有像 Cortex Analyst 這樣的產品，它實際上可以釋放數據的價值，既可以通過立即使用，就像特定數據集上的聊天機器人一樣，但更重要的是在代理工作流程中使用。

So more and more of our conversations can now focus on what creates value from a business perspective. So AI for Snowflake, rather than being this additional thing that we do, in some ways, becomes the natural end state for what investing well in data means. And of course, we are using AI ourselves both within the company but also in different aspects of the product.

因此，我們現在越來越多的對話可以集中在從商業角度創造價值的因素。因此，對於 Snowflake 來說，人工智慧並不是我們額外做的事情，從某種程度上來說，它成為了資料投資的自然最終狀態。當然，我們不僅在公司內部使用人工智慧，而且在產品的不同方面也使用人工智慧。

We talked about code generation and notebooks being accelerated by Cursor-like experiences. On the other hand, we make Snow Convert, our conversion tool free, so that anyone could use them. And we're bringing agentic workflows into Snow Convert so that people can do things like testing with synthetic data far sooner than what they would have done in a traditional waterfall-style migration. I'd say it's a combination of all of these trends that are driving Snowflake forward.

我們討論了透過類似 Cursor 的體驗來加速程式碼產生和筆記本。另一方面，我們免費提供轉換工具 Snow Convert，以便任何人都可以使用它們。我們將代理工作流程引入 Snow Convert，以便人們可以比傳統瀑布式遷移更快地完成使用合成資料進行測試等工作。我想說，所有這些趨勢的結合推動著 Snowflake 向前發展。

Excellent. And maybe just as a follow-up, maybe Sridhar for you or for Christian. There's been a lot of excitement that we've sensed around [Gen 2] and particularly the performance improvements that your customers are seeing. I guess maybe just touch on, is this potentially leading to unlocking new use cases around the capabilities introduced? Or how should we think about the potential for some of these new functions as they percolate in the platform?

出色的。也許只是作為後續，也許您是 Sridhar 或 Christian。我們對 [Gen 2] 感到非常興奮，尤其是客戶所看到的效能改進。我想也許只是觸及一下，這是否有可能解鎖圍繞所引入功能的新用例？或者我們應該如何看待這些新功能在平台中滲透的潛力？

Yes, Christian here. The best way to think about Gen2 is our latest and greatest compute environment. What we've done is we've combined the latest hardware instances that we can get from the cloud providers, which are often faster but also more expensive with a good number of software and improvements that we have, and at the end of the day is part of our eternal ongoing promise to customers to always deliver the best price performance of the market. Some of the benchmarks that we have on Gen2 are completely phenomenal relative to both to Snowflake, say, a year ago, but also to many of the competitive patterns there. So think of it as price performance, which continues to correlate with time to insight and time to value, and it's a material step forward.

是的，我是克里斯蒂安。思考 Gen2 的最佳方式是我們最新、最出色的運算環境。我們所做的是，將我們從雲端供應商獲得的最新硬體實例（通常速度更快，但也更昂貴）與我們擁有的大量軟體和改進相結合，最終，這是我們對客戶永恆承諾的一部分，即始終提供市場上最佳的性價比。我們在 Gen2 上進行的一些基準測試相對於一年前的 Snowflake 以及那裡的許多競爭模式而言都是非常驚人的。因此，可以將其視為價格表現，它與洞察時間和價值實現時間持續相關，並且是向前邁出的實質一步。

Joel Fishbein, Truist.

喬爾‧菲什拜因 (Joel Fishbein)，Truist。

Mike, you mentioned earlier on the call you had the strongest new logo quarter, which was fantastic. Just a question around that. Are you seeing -- is this a result of better, stronger execution and strategic focus? Are you seeing a more favorable win rate in competitive environment? And just as a follow-up, too, to that is of those two $100 million deals, can you just tell us which verticals they were in?

麥克，你之前在電話中提到，你們擁有最強勁的新標誌季度，這太棒了。這只是關於此的一個問題。您是否看到了—這是更好、更強大的執行力和策略重點的結果？您是否看到了競爭環境中更有利的勝率？作為後續問題，關於這兩筆 1 億美元的交易，您能告訴我們它們涉及哪些垂直產業嗎？

So on the $100 million deals, they were both in the financial services vertical. And what I would say on the number of new customers, this is not a result of something we put in place this quarter. We started last year with really breaking out an acquisition team that is just focused on new logos, and we're seeing the benefits of the groundwork that we put in place last year and we're pleased with the results. I think we have a very good leader there, and we're replicating what we're doing in North America and in EMEA as well, too. So we're pleased with the number of new logos that we've added and it's a big focus of ours.

因此，在 1 億美元的交易中，他們都屬於金融服務垂直領域。關於新客戶的數量，我想說，這並不是我們本季實施的措施的結果。我們從去年開始真正組建一個專注於新標誌的收購團隊，我們看到了去年奠定的基礎所帶來的好處，我們對結果感到滿意。我認為我們在那裡有一位非常優秀的領導者，我們也在北美和歐洲、中東和非洲地區複製我們所做的事情。因此，我們對添加的新徽標的數量感到非常滿意，這也是我們關注的重點。

Brad Reback, Stifel.

布拉德·雷巴克（Brad Reback），Stifel。

Last quarter, Mike, you talked about some changes to the sales force comp plan as related to bookings and commits, not just consumption. Maybe an update on how that's tracking and if that had any impact on the strong bookings in the quarter.

麥克，上個季度，您談到了銷售人員補償計劃的一些變化，這些變化與預訂和承諾有關，而不僅僅是消費。也許是有關追蹤情況的更新，以及這是否對本季的強勁預訂量產生了影響。

Yes. Obviously, I think it helped, but the real strong bookings for those twp big deals that we knew were going to come in, I would say, I think in general, salespeople are happy with having a bookings component but still the principal driver is consumption revenue. And as a reminder, we paid on bookings (inaudible) last year.

是的。顯然，我認為這是有幫助的，但對於我們知道即將達成的兩筆大交易的真正強勁預訂量，我想說，我認為總的來說，銷售人員對預訂部分感到滿意，但主要驅動力仍然是消費收入。提醒一下，我們去年已經支付了預訂費（聽不清楚）。

So it's not like we weren't doing it last year. We just are giving them a quote for bookings as well, too. And I think it's going to take some time to see the real roots of that change, whether that worked or not, but I'm pleased for Q1. I think they had a very solid Q1 and it definitely helped.

所以這並不是說我們去年沒有這樣做。我們也只是向他們提供預訂的報價。我認為需要一些時間才能看到這種變化的真正根源，無論它是否有效，但我對第一季感到滿意。我認為他們的第一季表現非常穩健，這確實有所幫助。

That's great. And then just a quick one, getting into the weeds a little bit. CapEx was up a bunch to a fairly high number. Is there onetime items there, or is this the new level?

那太棒了。然後簡單說一下，稍微深入一點。資本支出大幅增加，達到相當高的數字。那裡有一次性物品嗎，或者這是新的等級？

No. The CapEx was really associated with our new headquarters in San Mateo. As I spoke about previously, we signed a new lease in a Menlo Park office and there's a fair bit of CapEx that went into that as well as in Bellevue. I talked about that before, too. That really -- that just opened this week, that office, and there was a fair bit of CapEx that went into that as well, too. I'm not expecting any major office buildouts the next couple of years actually now.

不。資本支出確實與我們位於聖馬刁的新總部有關。正如我之前提到的，我們在門洛帕克辦公室簽署了一份新的租約，並且在那裡以及在貝爾維尤都投入了相當多的資本支出。我之前也談過這個。那間辦公室本週剛開業，也投入了相當多的資本支出。實際上，我並不期望未來幾年會有任何大型辦公大樓擴建。

Tyler Radke, Citi.

花旗銀行的泰勒拉德克（Tyler Radke）。

Mike, you talked about some strength in technology customers in the quarter. I was wondering if you could double-click on that. And what are you seeing specifically among kind of larger AI native customers in terms of their consumption?

麥克，您談到了本季技術客戶的一些優勢。我想知道您是否可以雙擊它。就消費而言，您在大型 AI 原生客戶中具體看到了什麼？

It's good. But as I called it before, we have a number of AI companies and they're still less than 1% of our

很好。但正如我之前所說，我們有很多人工智慧公司，但它們仍然不到我們

Okay, great. And the follow-up question I had was for Sridhar. We recently saw Databricks acquire Neon, which was a company that Snowflake Ventures had invested in. And I'm just curious if we can get an update on your strategy around Unistore. And just sort of your view on the positioning or some of these serverless databases that are out there in the market.

好的，太好了。我的後續問題是問 Sridhar 的。我們最近看到 Databricks 收購了 Neon，這是 Snowflake Ventures 投資的一家公司。我只是好奇我們是否可以了解您圍繞 Unistore 的策略的最新進展。您對市場上一些無伺服器資料庫的定位有何看法？

We believed in transactional systems for this is why we got to work on Unistore [about five] years ago. Unistore, the product, is doing very well. as a standard is nothing to be scoffed at and it's adopted widely. But we are very happy with what we have invested in terms of transactional stores so far. And we will continue to invest in the area because it's a very natural addition to what we do.

我們相信交易系統，這就是我們五年前開始研究 Unistore 的原因。Unistore 這款產品表現得非常好。作為一個標準是不容小覷的，並且被廣泛採用。但我們對迄今為止在交易商店方面的投資感到非常滿意。我們將繼續在該領域投資，因為這是我們業務的自然補充。

Thank you for your question. There are no additional questions waiting at this time, so I'll pass the call back to Sridhar for any closing remarks.

感謝您的提問。目前沒有其他問題，因此我將把電話轉回給 Sridhar 以作結束語。

Thank you. In closing, Snowflake is at the center of today's enterprise AI evolution. Our focus on making Snowflake easy to use, connected to enable fluid access to data wherever it sits and trusted for enterprise-grade performance is what makes us differentiated and beloved by our customers. And we are committed to supporting them through their end-to-end data journey from inception to insights. Our product revenue growth and strong outlook for FY26 demonstrates our continued ability to execute at scale.

謝謝。最後，Snowflake 是當今企業 AI 發展的中心。我們專注於讓 Snowflake 易於使用、可連接以便無論位於何處都能流暢地存取數據，並提供值得信賴的企業級效能，這使我們與眾不同並深受客戶的喜愛。我們致力於為他們從開始到洞察的整個端到端資料旅程提供支援。我們的產品收入成長和 2026 財年的強勁前景證明了我們持續的大規模執行能力。

Our pace of innovation coupled with our ability to bring products to market quickly is driving high growth, and we are committed to maintaining that momentum. We believe Snowflake's long-term profile is one that showcases durable, high growth and continued margin expansion. It's an exciting time for our company. I look forward to sharing more of our progress in the quarters ahead. Thank you all for joining us.

我們的創新步伐加上我們快速將產品推向市場的能力正在推動高速成長，我們致力於保持這種勢頭。我們相信 Snowflake 的長期表現將展現出持久、高成長和持續的利潤率擴張。對我們公司來說這是一個令人興奮的時刻。我期待在未來幾季分享更多我們的進展。感謝大家的參與。

That concludes the call. Thank you for joining. You may now disconnect your lines.

通話到此結束。感謝您的加入。現在您可以斷開線路了。