Snowflake Inc (SNOW) 2025 Q1 法說會逐字稿

Hello, everyone. Thank you for attending today's Q1 Fiscal Year 2025 Snowflake Earnings Call. My name is Sierra, and I will be your moderator today. (Operator Instructions) I would now like to pass the conference over to our host, Jimmy Sexton, Head of Investor Relations.

大家好。感謝您參加今天的 2025 財年第一季雪花收益電話會議。我叫 Sierra，今天我將擔任你們的主持人。 （操作員指示）我現在想將會議轉交給我們的東道主投資者關係主管吉米·塞克斯頓 (Jimmy Sexton)。

Good afternoon, and thank you for joining us on Snowflake's Q1 Fiscal 2025 Earnings Call. Joining me on the call today is 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 session. During today's call, we will review our financial results for the first quarter fiscal 2025 and discuss our guidance for the second quarter and full year fiscal 2025.

下午好，感謝您參加 Snowflake 2025 年第一季財報電話會議。今天和我一起參加電話會議的是我們的執行長 Sridhar Ramaswamy； Mike Scarpelli，我們的財務長；我們的產品執行副總裁 Christian Kleinerman 將參加問答環節。在今天的電話會議中，我們將回顧 2025 財年第一季的財務業績，並討論我們對 2025 財年第二季和全年的指導。

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 actual results. Information concerning these risks and uncertainties are available in the earnings press release, our most recent Form 10-K and 10-Q and our other SEC reports. All our statements are made as of today based on information currently available to us. Except as required by law, we assume no obligation to update any such statements.

在今天的電話會議中，我們將做出前瞻性聲明，包括與我們的業務營運和財務績效相關的聲明。這些陳述存在風險和不確定性，可能導致它們與實際結果有重大差異。有關這些風險和不確定性的資訊可在收益新聞稿、我們最新的 10-K 和 10-Q 表格以及我們的其他 SEC 報告中找到。截至今天，我們的所有聲明都是根據我們目前掌握的資訊做出的。除法律要求外，我們不承擔更新任何此類聲明的義務。

During today's call, we will also discuss certain non-GAAP financial measures. A reconciliation of GAAP to non-GAAP measures is included in today's earnings press release. The earnings press release and an accompanying investor presentation are available on our website at investors.snowflake.com. A replay of today's call will also be posted on the website.

在今天的電話會議中，我們還將討論某些非公認會計準則財務指標。今天的收益新聞稿中包含了 GAAP 與非 GAAP 指標的調整表。收益新聞稿和隨附的投資者簡報可在我們的網站 Investors.snowflake.com 上取得。今天電話會議的重播也將發佈在網站上。

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

現在，我想將電話轉給斯里達爾。

Thanks, Jimmy, and good afternoon, everyone. Before we get into it, many of you have given me a warm welcome to my new role over the past few months, and I just wanted to say thank you.

謝謝吉米，大家下午好。在我們開始之前，你們中的許多人在過去幾個月裡對我的新角色表示熱烈歡迎，我只想說謝謝。

I've been focused on 3 key priorities in my first quarter as CEO: listening to and learning from our customers, driving execution and alignment within our go-to-market teams, and fueling our innovation and product delivery. I have been really impressed by how the team has responded and by our overall pace of play. We have a lot of opportunity ahead of us, and there's a lot of excitement across our company to go and get it.

在擔任執行長的第一季度，我一直專注於三個關鍵優先事項：傾聽客戶的意見並向他們學習，推動我們的行銷團隊的執行和協調，以及推動我們的創新和產品交付。球隊的反應以及我們的整體比賽節奏給我留下了深刻的印象。我們面前有很多機會，我們公司上下都非常興奮去抓住它。

When I look at the Snowflake growth story, it was first driven by an amazing data product and then by the layers of collaboration and applications that we added on top to make Snowflake a true data cloud. What is exciting about AI is that it can turbocharge our capabilities and growth on all 3 layers. It also helps democratize access to all the amazing enterprise data in Snowflake, massively increasing our reach. The progress we have made in AI over the last year culminating in the past quarter is remarkable. We believe AI is going to continue to fuel our platform, helping our customers perform and deliver customer experiences better than ever.

當我回顧 Snowflake 的成長故事時，它首先是由令人驚嘆的數據產品驅動的，然後是由我們在其上添加的協作和應用程式層驅動的，以使 Snowflake 成為真正的數據雲。人工智慧的令人興奮之處在於它可以增強我們所有 3 個層面的能力和成長。它還有助於實現對 Snowflake 中所有令人驚嘆的企業數據的民主化訪問，從而極大地擴大了我們的覆蓋範圍。去年我們在人工智慧領域的進展在上個季度達到了頂峰，令人矚目。我們相信人工智慧將繼續為我們的平台提供動力，幫助我們的客戶表現並提供比以往更好的客戶體驗。

As evidenced by our Q1 results, our core business is very strong. We are still in the early innings of our plan to bring our world-class data platform to customers around the globe. And in the first quarter alone, we saw some of our largest customers meaningfully increase their usage of our core offering. The combination of our incredibly strong Data Cloud now powerfully boosted by AI is the strength and story of Snowflake.

正如我們第一季的業績所證明的那樣，我們的核心業務非常強勁。我們仍處於為全球客戶提供世界級數據平台的計劃的早期階段。僅在第一季度，我們就看到一些最大的客戶有意義地增加了對我們核心產品的使用。我們極其強大的數據雲的結合現在得到了人工智慧的有力推動，這就是 Snowflake 的優勢和故事。

I want to touch on our Q1 results, and Mike will get into the details with you. I'm really proud that our team delivered a very strong Q1. Product revenue for the quarter was $790 million up 34% year-over-year. Remaining performance obligations totaled $5 billion. Year-over-year growth accelerated to 46%, and non-GAAP adjusted free cash flow margin was 44%. Given the strong quarter, we are increasing our product revenue outlook for the year.

我想談談我們第一季的結果，麥克將與您詳細討論。我真的很自豪我們的團隊在第一季取得了非常強勁的成績。該季度產品營收為 7.9 億美元，年增 34%。剩餘履約義務總計 50 億美元。年成長加速至 46%，非 GAAP 調整後自由現金流利潤率為 44%。鑑於季度的強勁表現，我們提高了今年的產品收入預期。

Working through the second quarter and beyond, our priorities remain the same. I've had conversations with over 100 customers over the past several months, and I'm very optimistic. Snowflake is a beloved platform, and the value we bring come through in every customer conversation I have.

在第二季及以後，我們的優先事項保持不變。在過去的幾個月裡，我已經與 100 多個客戶進行了交談，我對此非常樂觀。 Snowflake 是一個深受喜愛的平台，我們帶來的價值體現在我與客戶的每一次對話中。

We are critical in helping our customers run their businesses. For example, one of the largest U.S. telcos relies on us to help them close their books every month. We also help a global financial service customer run their counterparty credit risk process. The art of the possible on Snowflake is really incredible.

我們在幫助客戶經營業務方面發揮著至關重要的作用。例如，美國最大的電信公司之一依賴我們幫助他們每月結帳。我們也幫助全球金融服務客戶運行其交易對手信用風險流程。 Snowflake 上的可能性藝術確實令人難以置信。

It's also probably no surprise that AI is top of mind for our customers as well. They want to make all business data in Snowflake available to everyone, not just the business analyst. They want us to help drive clarity, value creation and reliability as they enter this new frontier.

人工智慧也是我們客戶最關心的問題，這並不令人意外。他們希望讓 Snowflake 中的所有業務資料可供所有人使用，而不僅僅是業務分析師。他們希望我們在他們進入這一新領域時幫助推動清晰度、價值創造和可靠性。

Over the last quarter, my time spent with our go-to-market teams has been focused on driving execution and alignment. Internally, we emphasize consumption and new customer acquisition, and we're developing an end-to-end cadence for both priorities. This includes developing sales motions and specific workloads such as AI and data engineering. We have more to gain as we standardize our consumption mindset and effectively execute. We expect that this efficiency will contribute to further revenue growth.

在上個季度，我與市場推廣團隊一起度過的時間主要集中在推動執行和協調。在內部，我們強調消費和新客戶獲取，我們正在為這兩個優先事項制定端到端的節奏。這包括制定銷售動議和特定工作負載，例如人工智慧和數據工程。當我們規範消費思維並有效執行時，我們會收穫更多。我們預計這種效率將有助於收入的進一步成長。

Those who know me, know that I have a relentless focus on product innovation and delivery. Teams across the company are building and delivering at an incredible pace. Earlier this month, we announced that Cortex, our AI layer, is generally available. Iceberg, Snowpark Container Services and hybrid tables will all be generally available later this year.

認識我的人都知道，我不懈地專注於產品創新和交付。全公司的團隊正在以令人難以置信的速度進行建造和交付。本月早些時候，我們宣布我們的 AI 層 Cortex 全面可用。 Iceberg、Snowpark Container Services 和混合桌子都將於今年稍後全面上市。

We are investing in AI and machine learning, and our pace of progress in a short amount of time has been fantastic. What is resonating most with our customers is that we are bringing differentiation to the market. Snowflake delivers enterprise AI that is easy, efficient and trusted.

我們正在投資人工智慧和機器學習，我們在短時間內取得的進展速度非常驚人。最能引起客戶共鳴的是我們正在為市場帶來差異化。 Snowflake 提供簡單、有效率且值得信賴的企業人工智慧。

We have seen an impressive ramp in Cortex AI customer adoption since going generally available. As of last week, over 750 customers are using these capabilities. Cortex can increase productivity by reducing time-consuming tasks. For example, Sigma Computing uses Cortex language models to summarize and categorize customer communications from their CRM.

自全面推出以來，我們看到 Cortex AI 客戶採用率出現了令人印象深刻的成長。截至上週，超過 750 家客戶正在使用這些功能。皮層可以透過減少耗時的任務來提高生產力。例如，SigmaComputing 使用 Cortex 語言模型對 CRM 中的客戶通訊進行總結和分類。

In the quarter, we also announced Arctic, our own language model. Arctic outperformed leading open models such as Llama 2 70B and Mixtral 8x7B in various benchmarks. We developed Arctic in less than 3 months at 1/8 the training cost of peer models.

在本季度，我們也發布了我們自己的語言模型 Arctic。 Arctic 在各種基準測試中均優於 Llama 2 70B 和 Mixtral 8x7B 等領先的開放模式。我們在不到 3 個月的時間內發展出 Arctic，訓練成本僅為同儕模式的 1/8。

AI is a bridge between structured and unstructured data. We see this with Document AI. Customers find value in extracting features on the fly from piles of documents.

人工智慧是結構化資料和非結構化資料之間的橋樑。我們透過 Document AI 看到了這一點。客戶發現從成堆的文檔中動態提取特徵的價值。

We are making meaningful progress on Snowpark Container Services being generally available in the second half of the year and dozens of partners are already building solutions that will leverage container services to serve their end customers. We view Snowpark and other new features as our emerging businesses. These are in the early days of revenue contribution, but we are seeing very healthy demand. More than 50% of customers are using Snowpark as of Q1. Revenue from Snowpark is driven by Spark migrations. In Q1, we began the process of migrating several large Global 2000 customers to Snowpark.

我們在 Snowpark 容器服務方面取得了有意義的進展，將於今年下半年全面推出，數十個合作夥伴已經在建立利用容器服務為其最終客戶提供服務的解決方案。我們將 Snowpark 和其他新功能視為我們的新興業務。這些還處於收入貢獻的早期階段，但我們看到了非常健康的需求。截至第一季，超過 50% 的客戶正在使用 Snowpark。 Snowpark 的營收由 Spark 遷移驅動。在第一季度，我們開始將幾家大型 Global 2000 客戶遷移到 Snowpark。

Our collaboration capability is also a key competitive advantage for us. Nearly 1/3 of our customers are sharing data products as of Q1 2025, up from 24% 1 year ago. Collaboration already serves as a vehicle for new customer acquisition. Through a strategic collaboration with Fiserv, Snowflake was chosen by more than 20 Fiserv financial institutions and merchant clients to enable secure direct access to their financial data and insights.

我們的協作能力也是我們的關鍵競爭優勢。截至 2025 年第一季度，我們有近 1/3 的客戶正在共享資料產品，高於一年前的 24%。協作已成為獲取新客戶的工具。透過與 Fiserv 的策略合作，Snowflake 被 20 多個 Fiserv 金融機構和商業客戶選擇，以確保安全地直接存取他們的財務資料和見解。

We announced support for unstructured data over 2 years ago. Now about 40% of our customers are processing unstructured data on Snowflake. And we've added more than 1,000 customers in this category over the last 6 months.

我們在兩年多前就宣布支持非結構化資料。現在，我們大約 40% 的客戶正在 Snowflake 上處理非結構化資料。在過去 6 個月裡，我們已在此類別中新增了 1,000 多個客戶。

Iceberg is enabling us to play offense and address a larger data footprint. Many of our largest customers have indicated that they will now leverage Snowflake for more workloads as a result of this functionality. More than 300 customers are using Iceberg in public preview.

Iceberg 讓我們能夠進攻並解決更大的數據足跡。我們的許多最大客戶表示，由於此功能，他們現在將利用 Snowflake 來處理更多工作負載。超過 300 家客戶正在公開預覽版中使用 Iceberg。

Snowflake has a powerful and unique partner ecosystem. Part of our success is that we have many partners that amplify the power of our platform. They range from big organizations like EY and Deloitte but also firms like LTIMindtree and Next Pathway. S&P Global sees us as a strong collaborator in their cloud distribution model. And companies like Observe, Blue Yonder, RelationalAI, Fivetran, Hex and Domo have built their software on top of Snowflake. These partners bring on entirely new capabilities and unlock new use cases for us and our customers. They also often bring new customers to us, and they really care about how easy it is to build on Snowflake, how reliable Snowflake is and also about how we can go to customers jointly. Partners bring enormous power to our data cloud vision. Their success creates success for us and our customers.

Snowflake擁有強大而獨特的合作夥伴生態系統。我們成功的部分原因在於我們擁有許多合作夥伴來增強我們平台的力量。其中既有安永 (EY) 和德勤 (Deloitte) 等大型組織，也有 LTIMindtree 和 Next Pathway 等公司。 S&P Global 將我們視為其雲端分銷模式的強大合作者。 Observe、Blue Yonder、RelationalAI、Fivetran、Hex 和 Domo 等公司都在 Snowflake 之上建立了自己的軟體。這些合作夥伴為我們和我們的客戶帶來全新的功能並解鎖新的用例。他們也經常為我們帶來新客戶，他們非常關心在Snowflake上建造有多容易，Snowflake有多可靠，也關心我們如何共同走向客戶。合作夥伴為我們的數據雲願景帶來巨大的力量。他們的成功為我們和我們的客戶創造了成功。

To wrap it up, Snowflake is the world's best enterprise AI data platform. Combined with our collaboration capability and thriving application platform, we are driving powerful network effects that will fuel our growth. AI vastly amplifies this opportunity, both in the near and medium terms.

總而言之，Snowflake 是世界上最好的企業人工智慧資料平台。結合我們的協作能力和蓬勃發展的應用程式平台，我們正在推動強大的網路效應，從而推動我們的成長。無論是在短期還是中期，人工智慧都大大放大了這個機會。

Our product philosophy is simple: one platform with all features available. We are turning every analyst and data engineer into a sophisticated AI analyst. The magic of Snowflake is that we make difficult tasks easy.

我們的產品理念很簡單：一個平台提供所有功能。我們正在將每一位分析師和資料工程師轉變為經驗豐富的人工智慧分析師。 Snowflake 的神奇之處在於我們可以讓困難的任務變得簡單。

Stay tuned for more to come at Snowflake Data Cloud Summit coming up in San Francisco, June 3 through the 6th. I look forward to seeing you all there.

請繼續關注 6 月 3 日至 6 日在舊金山舉行的 Snowflake 資料雲高峰會的更多資訊。我期待在那裡見到你們大家。

Now I'll turn it over to Mike.

現在我將把它交給麥克。

Thank you, Sridhar. Q1 product revenue grew 34% year-over-year to $790 million. Our largest growth contributors included a media and entertainment Global 2000 and a large retail and consumer goods company. Smaller accounts outside of the Global 2000 were an important source of outperformance. Intra-quarter, we saw strong growth in February and March. Growth moderated in April. We view this variability as a normal component of the business.

謝謝你，斯里達爾。第一季產品營收年增 34% 至 7.9 億美元。我們最大的成長貢獻者包括一家媒體和娛樂全球 2000 強公司以及一家大型零售和消費品公司。全球 2000 強之外的較小帳戶是業績優異的重要來源。季度內，我們在二月和三月看到了強勁的成長。 4 月份成長放緩。我們將這種變化視為業務的正常組成部分。

Excluding the impact of leap year, product revenue grew approximately 32% year-over-year. We continue to see signs of a stable optimization environment. Seven of our top 10 customers grew quarter-over-quarter. Q1 marked the first quarter under our FY '25 sales compensation plan. Our sales reps are executing well against their plan.

剔除閏年影響，產品營收年增約32%。我們繼續看到穩定優化環境的跡象。我們的十大客戶中有七個實現了季度環比增長。第一季是我們 25 財年銷售補償計畫的第一季。我們的銷售代表按照他們的計劃執行得很好。

In Q1, we exceeded our new customer acquisition and consumption quotas. Non-GAAP product gross margin of 76.9% was down slightly year-over-year. As mentioned on our prior call, we have headwinds associated with GPU-related costs as we invest in new AI initiatives.

第一季度，我們超出了新客戶獲取和消費配額。非 GAAP 產品毛利率為 76.9%，較去年同期略有下降。正如我們在先前的電話會議中提到的，當我們投資新的人工智慧計畫時，我們遇到了與 GPU 相關成本相關的阻力。

Our non-GAAP operating margin of 4% benefited from revenue outperformance. Our non-GAAP adjusted free cash flow margin was 44%. As a reminder, Q1 and Q4 are our seasonally strong quarters for non-GAAP adjusted free cash flow. We ended the quarter with $4.5 billion in cash, cash equivalents, short-term and long-term investments. In Q1, we used $516 million to repurchase 3 million shares at an average price of $173.14. We have $892 million remaining under our original $2 billion authorization.

我們 4% 的非 GAAP 營業利潤率得益於收入的優異表現。我們的非 GAAP 調整後自由現金流利潤率為 44%。提醒一下，第一季和第四季是我們非公認會計準則調整後自由現金流的季節性強勁季度。截至本季末，我們的現金、現金等價物、短期和長期投資為 45 億美元。第一季度，我們用 5.16 億美元回購了 300 萬股股票，平均價格為 173.14 美元。我們最初的 20 億美元授權還剩 8.92 億美元。

Now let's turn to our outlook. As a reminder, we only forecast product revenue based on observed behavior. This means our FY '25 guidance includes contributions from Snowpark. FY '25 guidance doesn't include revenue from newer features such as Cortex until we see material consumption.

現在讓我們來談談我們的展望。提醒一下，我們僅根據觀察到的行為來預測產品收入。這意味著我們的 25 個財政年度指南包括 Snowpark 的貢獻。在我們看到材料消耗之前，25 財年的指導不包括來自 Cortex 等新功能的收入。

Iceberg will be GA later this year. We have invested in Iceberg because we expect it to increase our future revenue opportunity. However, for the purpose of guidance, we continue to model revenue headwinds associated with the movement of data out of Snowflake and into Iceberg storage. The negative impact is weighted to the back half of the year.

Iceberg 將於今年稍後正式上市。我們投資 Iceberg 是因為我們期望它能增加我們未來的收入機會。然而，出於指導目的，我們繼續對與資料從 Snowflake 移動到 Iceberg 儲存相關的收入逆風進行建模。負面影響將集中到今年下半年。

For Q2, we expect product revenue between $805 million and $810 million. We are increasing our FY '25 product revenue guidance. We now expect full year product revenue of approximately $3.3 billion, representing 24% year-over-year growth.

對於第二季度，我們預計產品收入在 8.05 億美元至 8.1 億美元之間。我們正在提高 25 財年產品收入指引。我們目前預計全年產品收入約 33 億美元，年增 24%。

Turning to margins. We are lowering our full year margin guidance in light of increased GPU-related costs related to our AI initiatives. We are operating in a rapidly evolving market, and we view these investments as key to unlocking additional revenue opportunities in the future. As a reminder, we have GPU-related costs in both cost of revenue and R&D.

轉向邊緣。鑑於與我們的人工智慧計畫相關的 GPU 相關成本增加，我們正在降低全年利潤指引。我們正在一個快速發展的市場中運營，我們認為這些投資是未來釋放更多收入機會的關鍵。提醒一下，我們的營收成本和研發成本都有與 GPU 相關的成本。

We announced our intent to acquire certain technology assets and hire key employees from TruEra. TruEra is an AI observability platform that provides capabilities to evaluate and monitor large language model apps and machine learning models in production. We are excited to welcome approximately 35 employees from TruEra to Snowflake. The impact of the transaction is reflected in our outlook.

我們宣布有意收購 TruEra 的某些技術資產並聘請關鍵員工。 TruEra 是一個 AI 可觀察平台，提供評估和監控生產中的大型語言模型應用程式和機器學習模型的功能。我們很高興歡迎來自 TruEra 的約 35 名員工加入 Snowflake。該交易的影響反映在我們的展望中。

For Q2, we expect 3% non-GAAP operating margin. For FY '25 we expect 75% non-GAAP product gross margin, 3% non-GAAP operating margin and 26% non-GAAP adjusted free cash flow margin.

對於第二季度，我們預計非 GAAP 營業利潤率為 3%。對於 25 財年，我們預期非 GAAP 產品毛利率為 75%，非 GAAP 營業利潤率為 3%，非 GAAP 調整後自由現金流利潤率為 26%。

Finally, we will host our Investor Day on June 4 in San Francisco in conjunction with the Snowflake Data Cloud Summit, our annual users conference. If you are interested in attending, please e-mail ir@snowflake.com.

最後，我們將於 6 月 4 日在舊金山舉辦投資者日活動，同時舉辦我們的年度用戶大會 Snowflake 資料雲高峰會。如果您有興趣參加，請發送電子郵件至 ir@snowflake.com。

With that, operator, you can now open up the line for questions.

接線員，現在可以撥打電話提問了。

(Operator Instructions) our first question today comes from Keith Weiss with Morgan Stanley.

（操作員說明）今天我們的第一個問題來自摩根士丹利的 Keith Weiss。

Excellent. Very nice quarter, guys. Looking at the front page of the investor relations page, 5 billion queries. Looks like your query volume is actually accelerating now again. Can you walk us through some of the drivers of that acceleration? Is it new products that are driving the acceleration? Or is it the relief of optimization or just like better data center? So just a little bit more clarity on what's driving that acceleration.

出色的。非常好的季度，夥計們。看看投資人關係頁面的首頁，有50億個查詢。看來您的查詢量現在實際上又加速了。您能否向我們介紹一下這種加速的一些驅動因素？是新產品推動了這項加速嗎？還是優化帶來的緩解還是就像更好的資料中心一樣？因此，我們可以更清楚地了解推動這種加速發展的因素。

And then on the other side, that equation, it looks like there's still pressures on like the price per query. Any indications on whether that like pressure on the price per query is coming more from the compute side of the equation or the storage side of the equation? Any color there would be super helpful.

另一方面，這個等式看起來仍然存在壓力，例如每次查詢的價格。有沒有跡象表明每次查詢價格的壓力更多來自等式的計算端還是存儲端？任何顏色都會非常有幫助。

Thank you. Overall, as both Mike and I said, our core business is very strong and growth is coming from both new customers as well as expansion from existing customers. And as we gain more and different kinds of workloads, for example, AI, data engineering are increasing quite nicely, they're all contributing to additional query growth. And the relationship between query growth and cost per query is not a simple straightforward one. And we look for broad growth across the different categories of workloads that we handle, and they've all been doing really well.

謝謝。總的來說，正如麥克和我所說，我們的核心業務非常強勁，成長來自新客戶以及現有客戶的擴張。隨著我們獲得越來越多不同類型的工作負載，例如人工智慧、資料工程，它們的成長速度非常快，它們都有助於查詢的額外成長。查詢成長和每次查詢成本之間的關係並不簡單。我們希望我們處理的不同類別的工作負載能夠實現廣泛成長，而且它們都表現得非常好。

Our next question today comes from Mark Murphy with JPMorgan.

今天我們的下一個問題來自摩根大通的馬克墨菲。

I'll add my congratulations. Sridhar, you trained Arctic LLM with pretty amazing efficiency. Could you walk us through the architectural difference in the product that might allow it to run more efficiently than other products out there in the market?

我會補充我的祝賀。 Sridhar，您以相當驚人的效率培訓了北極法學碩士。您能否向我們介紹一下該產品的架構差異，這些差異可能使其比市場上的其他產品運作得更有效率？

And Mike, is there any directional change to the $50 million target for GPU spend this year just considering the launch of Cortex and Arctic LLM and it sounds like some Snowpark traction? Should we think of that trending a little higher?

Mike，考慮到 Cortex 和 Arctic LLM 的推出，今年 5000 萬美元的 GPU 支出目標是否會發生任何方向性變化？我們是否應該認為這種趨勢會更高一點？

Thank you. So absolutely, we did train Arctic in a remarkably short period of time, a little over 3 months on a remarkably small amount of GPU compute. A lot of the training efficiency of these models do come from architectures. We had a rather unique mixture of expert architecture. These are increasingly the architectures that are driving impressive gains for all of the other leading AI companies.

謝謝。因此，我們確實在非常短的時間內（僅 3 個月多一點）使用非常少量的 GPU 運算訓練了 Arctic。這些模型的許多訓練效率確實來自於架構。我們有一個相當獨特的專家架構組合。這些架構越來越多地為所有其他領先的人工智慧公司帶來令人印象深刻的收益。

But what also went into it was just an amazing amount of a pre-experimentation in order to figure out things like what are the right data sets, what order should they be fed in and how do we make sure that they're actually optimizing for enterprise metrics, the kind of things our customers care about, which are things like are these models really good at creating SQL queries, for example, so that they can talk to data.

但其中還需要進行大量的預實驗，以便弄清楚什麼是正確的數據集、它們應該以什麼順序輸入以及我們如何確保它們實際上正在優化企業指標，我們的客戶關心的事情，例如這些模型是否真的擅長建立SQL 查詢，以便它們可以與資料對話。

And so we are taking very much the view of how do we make AI much better in an enterprise context because, naturally, that's the place where we have the most value to add. And our AI budgets are modest in the scheme of things, and so being creative in how we develop these models is something that the team comes to naturally expect. And I think that kind of discipline and scarcity, to be honest, produces a lot of innovation. And I think that's what you're seeing.

因此，我們非常關注如何在企業環境中使人工智慧變得更好，因為這自然是我們最能增加價值的地方。而且我們的人工智慧預算在計劃中並不多，因此團隊自然期望在開發這些模型時發揮創意。老實說，我認為這種紀律和稀缺性會產生大量創新。我想這就是你所看到的。

And then in terms of investments, I'll hand over to Mike in a second. But I'm comfortable with the amount of investments that we are making. Part of what we gain as Snowflake is the ability to fast follow on a number of fronts, is the ability to optimize against metrics that we care about, not producing like the latest, greatest, biggest model, let's say, for image generation. And so having that kind of focus lets us operate on a relatively modest budget pretty efficiently.

然後在投資方面，我稍後會交給麥克。但我對我們正在進行的投資金額感到滿意。作為 Snowflake，我們獲得的部分好處是能夠在許多方面快速跟進，能夠根據我們關心的指標進行最佳化，而不是像最新、最好、最大的模型那樣產生影像生成。因此，有了這種關注，我們就可以在相對適度的預算下非常有效地運作。

And so the focus very much now is on how do we take all of the products that we have released into production. We have over 750 customers that are busy developing against our AI platform. This is a fast-moving space, but we are very comfortable with both the pace, the investments and the choices that we are making to make AI effective for Snowflake.

因此，現在的重點是我們如何將我們已發布的所有產品投入生產。我們有超過 750 家客戶正忙於根據我們的 AI 平台進行開發。這是一個快速發展的領域，但我們對讓 AI 對 Snowflake 有效的步伐、投資和選擇感到非常滿意。

Mike?

麥克風？

And I will add that, yes, we think we may be spending a little bit more on GPUs, but it's also people that we're hiring, specifically in AI. We talked about the acquisition of TruEra. Those people all fall into that organization. And so as I mentioned, the world of AI is rapidly evolving, and we're investing in that because we do think there's a massive opportunity for Snowflake to play there. And it will have a meaningful impact on future revenues.

我要補充一點，是的，我們認為我們可能會在 GPU 上花費更多一點，但我們也在招募人員，特別是在人工智慧領域。我們討論了收購 TruEra。那些人都屬於那個組織。正如我所提到的，人工智慧世界正在迅速發展，我們正在對此進行投資，因為我們確實認為 Snowflake 在那裡有巨大的發展機會。這將對未來的收入產生有意義的影響。

Our next question today comes from Kirk Materne with Evercore.

今天我們的下一個問題來自 Evercore 的 Kirk Materne。

Congrats on the quarter. Sridhar, can you just talk a little bit about how we should think about your customers' time to value with Cortex, meaning how long do you think it takes them to start using the technology before it can start to translate into a little bit faster consumption patterns?

恭喜本季。 Sridhar，您能否簡單談談我們應該如何考慮客戶使用 Cortex 實現價值的時間，即您認為他們需要多長時間才能開始使用該技術才能開始轉化為更快的消費模式？

And then just one for Mike. Mike, can you just talk a little bit about deferred. This quarter was down perhaps a little bit more sequentially than we've seen in prior years. I don't know if there's anything onetime in nature there, but if you could just touch upon that, that would be great.

然後只給麥克一份。麥克，你能談談延遲嗎？本季的下降幅度可能比我們前幾年看到的要大一些。我不知道大自然中是否有曾經存在過的東西，但如果你能觸及這一點，那就太好了。

Thank you. One of the cool things about Cortex AI and our AI products in general, in the context of the consumption model, is that our customers don't have to make big investments to see what value that they're going to get because they don't have to make commitments to how many GPUs that they are going to be renting, for example. They just use Cortex AI, for example, from SQL, which is very, very easy to do without a pre commit. And this means that they can focus very much on sort of value creation.

謝謝。在消費模式的背景下，Cortex AI 和我們的人工智慧產品的一個很酷的事情是，我們的客戶不必進行大量投資來看看他們將獲得什麼價值，因為他們不這樣做例如，不必承諾要租用多少GPU。他們只是使用 Cortex AI，例如來自 SQL，這非常非常容易，無需預先提交。這意味著他們可以非常專注於價值創造。

And the structure of Cortex AI is also so that anybody that can write SQL can now begin to do really interesting things, for example, look at how often, let's say, a particular product was mentioned in an earnings transcript or being able to go from other kinds of unstructured information like whether it is text or whether it is images to structured information, which Document AI, our AI product there, does.

Cortex AI 的結構也使得任何可以編寫 SQL 的人現在都可以開始做真正有趣的事情，例如，看看收益記錄中提到特定產品的頻率，或者能夠從其他類型的非結構化訊息，例如文字或圖像到結構化訊息，我們的人工智慧產品Document AI 就是這樣做的。

And so we very much want to structure all of these efforts as ones in which our customers are able to iterate very quickly, take things to production, get value out of it and then make bigger commitments on top. And that's one of the benefits that you get from making the technology super easy to adopt. There's not a massive learning curve, neither is there a GPU commitment or other kinds of software engineering that needs to happen in order to use AI with Snowflake.

因此，我們非常希望將所有這些工作建構成我們的客戶能夠非常快速地迭代、將產品投入生產、從中獲取價值，然後做出更大承諾的工作。這就是使技術變得超級易於採用所帶來的好處之一。不需要大量的學習曲線，也不需要 GPU 承諾或其他類型的軟體工程，以便將 AI 與 Snowflake 結合使用。

Yes. On your question on deferred, Kirk, if you're referring to January to today, the end of the year, Q4 is always a very, very big billing quarter. Q1 is not as big of a billing quarter, so you have that flowing through on the deferred revenue. However, RPO and you can see RPO, as Sridhar mentioned, is up 46% year-over-year. And we do have -- for instance, we signed a $100 million deal this quarter with a customer who pays us monthly in arrears, so it doesn't show up in deferred revenue. We've signed a number of deals with big companies that pay us monthly in arrears that don't show up in deferred revenue, but they're in RPO.

是的。關於您關於延期的問題，柯克，如果您指的是一月到今天，即年底，第四季度始終是一個非常非常大的計費季度。第一季的結算季度規模不大，因此您可以透過遞延收入來流動。然而，正如 Sridhar 所提到的，RPO，您可以看到 RPO 年比增長了 46%。我們確實有——例如，我們本季與一位客戶簽署了一項價值 1 億美元的協議，該客戶每月向我們拖欠付款，因此它不會出現在遞延收入中。我們與大公司簽署了許多協議，這些公司每月向我們支付欠款，這些欠款不會出現在遞延收入中，但會出現在 RPO 中。

Our next question today comes from Karl Keirstead with UBS.

今天我們的下一個問題來自瑞銀集團的 Karl Keirstead。

Mike, could you elaborate on the comment that usage growth moderated in April. Maybe you could unpack that and explain why it usually does. And then also when I look at your 2Q and fiscal '25 revenue guidance, it's actually pretty solid. So that would lead one to believe that whatever moderation there might be in April, it doesn't feel like it, according to your guidance, rolled into May. Just curious if that's the correct interpretation.

Mike，您能否詳細解釋一下四月份使用量成長放緩的評論。也許你可以解開它並解釋為什麼它通常會這樣。然後，當我查看你們的第二季和 25 財年收入指引時，它實際上非常可靠。因此，這會讓人們相信，無論四月可能出現什麼溫和的情況，根據您的指導，感覺不會持續到五月。只是好奇這是否是正確的解釋。

Well, what I would say is February and March were very strong. And I'm saying April was more muted. April, just as a reminder, and it really impacts Europe and some others, that is Ascension Day or Easter holiday. And in Europe, they take a long time off. That does have an impact on consumption. Remember, this is a daily consumption model, and the guidance we gave is based upon what we're seeing through our customers as of this week.

嗯，我想說的是二月和三月非常強勁。我是說四月更加沉默。四月，只是提醒一下，它確實影響了歐洲和其他一些國家，那就是耶穌升天節或復活節假期。在歐洲，他們會休息很久。這確實對消費產生影響。請記住，這是一種日常消費模式，我們給出的指導是基於截至本週我們透過客戶看到的情況。

Okay. And Mike, if I could ask a follow-up. You had mentioned previously, including, I think, at a conference in March that your efforts around that tiered storage side, whereby we could see some roll-off on the storage revenues could begin to impact the P&L in the April quarter. Was that the case? And would you be able to approximate what impact maybe the roll-off on the storage reps had?

好的。麥克，我是否可以詢問後續情況。您之前提到過，包括我認為在三月份的一次會議上，您圍繞分層存儲方面所做的努力，我們可以看到存儲收入的一些下滑可能會開始影響四月份季度的損益表。是這樣嗎？您能否估算一下此次縮減對儲存代表可能產生的影響？

Sure. We did roll out to all of our customers, and we started, by the way, doing it at the end of last year, whereby depending on the amount of commitment you're making on an annual basis, you get tiered storage pricing. So in essence, you get your storage discounted from the list price of $23 per terabyte.

當然。我們確實向所有客戶推出了這項服務，順便說一句，我們從去年年底開始這樣做，根據您每年做出的承諾量，您將獲得分級儲存定價。因此，從本質上講，您的儲存價格比每 TB 23 美元的標價有折扣。

We started rolling that out, and that actually in the quarter impacted us somewhere between $6 million and $8 million. I forget exactly what that is that is pure margin that, that impacted. That's not to say there are other customers, big customers where we've always discounted their storage given their size. That is just the pure because of the tiered storage that's rolled out to everyone, and that will continue to have an impact as people continue to renew their contracts. But storage mix as a percent of revenue has remained pretty much consistent at 11% of our revenue, is associated with storage. That did not change. We're actually seeing growth storage in Snowflake.

我們開始推出該功能，實際上在本季對我們產生了 600 萬至 800 萬美元的影響。我完全忘記了影響的純利潤率是什麼。這並不是說還有其他客戶、大客戶，我們總是根據他們的規模來折扣他們的儲存空間。這純粹是因為分層儲存已向所有人推出，並且隨著人們繼續續簽合同，這將繼續產生影響。但儲存組合佔收入的百分比幾乎保持在我們收入的 11%，與儲存相關。這並沒有改變。我們實際上看到了 Snowflake 儲存的成長。

Next question comes from Raimo Lenschow with Barclays.

下一個問題來自巴克萊銀行的 Raimo Lenschow。

Sridhar, thank you for all your comments around the AI evolution for you guys. Where -- is there a kind of a vision for you -- or where is the demarcation line in a way where you want to play versus where you don't want to play in this kind of new AI world? Obviously, like there's like how many LLMs do you need to own the acquisition today. The question is like do you need to do observability? Or is that more people hire with kind of knowledge? Can you just kind of -- how is your thinking there evolving?

Sridhar，感謝你們對人工智慧進化的所有評論。在這種新的人工智慧世界中，你有什麼願景嗎？顯然，就像你需要多少法學碩士才能擁有今天的收購一樣。問題是你需要進行可觀察性嗎？還是更多的人僱用了具有某種知識的人？能簡單說一下──你的想法是如何演變的嗎？

This is a fabulous question. Like first and foremost, I think it is important for all of us to acknowledge that AI language models are going to have an impact at multiple levels of what you can think of as a data stack. So for example, the way in which people are going to be migrating from an old system, an on-prem system, to something like Snowflake is going to be aided by the presence of a copilot that can do much of the translation. We already have such a translation product, and we think AI is going to make that go even faster.

這是一個很棒的問題。首先，我認為我們所有人都必須承認人工智慧語言模型將對資料堆疊的多個層面產生影響。舉例來說，人們從舊系統（本地系統）遷移到 Snowflake 之類的系統時，將需要能夠完成大部分翻譯工作的副駕駛的幫助。我們已經有了這樣的翻譯產品，我們認為人工智慧將使這一過程變得更快。

But in other areas like data cleansing, data engineering that are perhaps not as sexy but nevertheless required a huge amount of investment in order to make sure that the data is enterprise grade, we think AI is going to play a big role both in the creation of those pipelines but also in things like how does one make sure that the data is clean. For example, if PII accidentally flips into a table or a distribution goes very wonky, language models can help detect deviations from patterns.

但在資料清理、資料工程等其他領域，這些領域可能不那麼吸引人，但仍需要大量投資才能確保資料是企業級的，我們認為人工智慧將在創造和創造方面發揮重要作用這些管道，還包括如何確保數據乾淨等問題。例如，如果 PII 意外地翻轉到表格中或分佈變得非常不穩定，語言模型可以幫助偵測與模式的偏差。

And then going up the stack, we have a very acclaimed product for writing SQL, our Copilot within our user interface that can significantly accelerate in analysts' ability to get to know a data set and be productive with it; and then, of course, to something like a data API, which now begins to put enterprise data into the hands of a business user but with a very high degree of reliability.

然後，在堆疊中，我們有一個非常受好評的用於編寫 SQL 的產品，即我們用戶介面中的 Copilot，它可以顯著提高分析師了解資料集並提高使用資料集的能力；當然，還有像資料 API 之類的東西，它現在開始將企業資料交到業務用戶手中，但可靠性非常高。

And so my point is there is broad impact. And I think things like automating some of the work that an analyst has to do, for example, to troubleshoot problems, will be things that a language model can do. Having said that, for a variety of problems, small models, which we are perfectly capable of developing from scratch like we have done for Document AI or more a midsized model like what we did with Arctic, actually suffices for the vast majority of the applications that I'm talking about.

所以我的觀點是它有廣泛的影響。我認為，諸如自動化分析師必須做的一些工作（例如解決問題）之類的事情將是語言模型可以做的事情。話雖如此，對於各種問題，我們完全有能力從頭開始開發小型模型，就像我們為Document AI 所做的那樣，或者像我們為Arctic 所做的那樣的中型模型，實際上足以滿足絕大多數應用程式我正在談論的。

And so there are academic benchmarks. Like there's one called MMLU. It's a notoriously difficult benchmark and depends very much on model size and how many dollars people are throwing at training those models. We can get a huge amount done with a small team under modest investment without needing to play at that level where you're talking -- companies are talking about spending billions of dollars. I don't think we need to be there. I think being very focused on what we need to deliver for our customers will take us a long way with the amount of investments that we are making.

所以有學術基準。就像有一個叫做 MMLU 的人。這是一個眾所周知的困難基準，很大程度上取決於模型大小以及人們在訓練這些模型上投入的資金。我們可以用一個小團隊在適度的投資下完成大量工作，而不需要達到你所說的水平——公司正在談論花費數十億美元。我認為我們不需要在那裡。我認為，非常專注於我們需要為客戶提供的服務將使我們在投資方面取得長足的進步。

And finally, I will add that we have amazing partnerships with a ton of people. Even today, I wrote about how we're collaborating with landing.ai, Andrew Ng's company, but we have partnerships with Mistral, with Reka, with a ton of other companies. The deal of AI is so large that I don't think there's going to be one company that is going to make every model that every person is going to use.

最後，我要補充一點，我們與許多人建立了令人驚嘆的合作關係。即使在今天，我也寫過我們如何與吳恩達 (Andrew Ng) 的公司 Landing.ai 合作，但我們與 Mistral、Reka 以及許多其他公司都有合作夥伴關係。人工智慧的規模如此之大，我認為不會有一家公司能夠製造出每個人都會使用的每種模型。

We are very good at developing the models that we need in our core, and we actively collaborate with a large set of players for other kinds of models. And obviously, they see value in the 10,000 customers we have and being able to go to market together. And so I think this is likely to continue for the indefinite future in terms of what we need to do.

我們非常擅長開發我們核心需求的模型，並且我們積極與大量參與者合作開發其他類型的模型。顯然，他們看到了我們擁有的 10,000 名客戶以及能夠一起進入市場的價值。因此，我認為就我們需要做的事情而言，這種情況可能會在無限期的未來持續下去。

Our next question today comes from Brent Thill with Jefferies.

今天我們的下一個問題來自 Jefferies 的布倫特希爾 (Brent Thill)。

Mike, on the acceleration of RPO up 46%, I know you mentioned the $100 million deal. But was there anything else that was surprising to you in the quarter that helped in this reacceleration? Any other notable trends that maybe you haven't seen or you're starting to see now?

麥克，關於 RPO 加速成長 46%，我知道您提到了 1 億美元的交易。但是，本季還有其他令您感到驚訝的事情有助於這種重新加速嗎？您可能還沒有看到或現在開始看到的任何其他值得注意的趨勢？

Yes. Remember that 46% is up year-over-year, so the year ago comparison didn't have the $250 million deal we signed in Q4 that went into there. There was another $100 million deal that was signed subsequent to that, too. So -- but what I will say is -- and as I mentioned, we are very pleased with the number of cap ones in our bookings in Q1, and there are -- as I mentioned, we did a $100 million deal in Q1, and we will do another $100 million deal this quarter potentially, too. So we're very pleased with our business and more of the commitment that our customers are making in Snowflake long term.

是的。請記住，同比增長了 46%，因此去年的比較中沒有我們在第四季度簽署的 2.5 億美元的交易。隨後也簽署了另一筆價值 1 億美元的協議。所以- 但我要說的是- 正如我所提到的，我們對第一季預訂中的上限數量非常滿意，並且- 正如我所提到的，我們在第一季度達成了1 億美元的交易，本季我們也有可能再進行 1 億美元的交易。因此，我們對我們的業務以及我們的客戶對 Snowflake 所做的長期承諾感到非常滿意。

And quickly for Sridhar, I know you mentioned the priorities are the same, but you are the new CEO. I guess from your perspective, where are your top priorities for the rest of '24?

對於 Sridhar 來說，我知道您提到優先事項是相同的，但您是新任執行長。我想從你的角度來看，24 年剩餘時間裡你的首要任務是什麼？

I touched on them. Driving product innovation faster is definitely way up there in the list, and you see this coming to fruition with things like how fast our AI platform, Cortex AI, came to market or what we did with Arctic. But I want to stress again that we see incredible potential across our AI data cloud. The AI layer is one part, but support for Iceberg is actually an exciting new chapter for all players in data. We had an announcement yesterday and today at the Build Conference.

我觸及了它們。更快地推動產品創新無疑是其中的重中之重，你會看到這一點正在透過我們的人工智慧平台 Cortex AI 上市速度或我們在 Arctic 上所做的事情來實現。但我想再次強調，我們看到了人工智慧資料雲的巨大潛力。 AI 層只是一部分，但對 Iceberg 的支持實際上對所有數據參與者來說是令人興奮的新篇章。我們在昨天和今天的構建大會上宣布了這一消息。

But the general theme is we are able to bring Snowflake to bear on more of the data that is sitting in data lake. And then beyond that, we have things like hybrid tables that are kind of coming out; container services, which massively expand the kind of applications that can run on top of Snowflake. So product innovation is one focus.

但總的主題是我們能夠讓 Snowflake 處理資料湖中的更多資料。除此之外，我們還推出了諸如混合錶之類的東西；容器服務，大大擴展了可以在 Snowflake 上運行的應用程式類型。因此產品創新是重點之一。

Just as equally importantly, helping our go-to-market teams take these products to market, having the specialization to be able to zone in on the applications that deliver the most value for our customers, upping the game on just enablement within Snowflake and also doing a great job of enablement with the many partners that we work with. That broad suite of taking products to market, I would say, is my other like priority inside.

同樣重要的是，幫助我們的上市團隊將這些產品推向市場，擁有專業知識，能夠專注於為我們的客戶提供最大價值的應用程序，提高 Snowflake 內的支持能力，並且與我們合作的許多合作夥伴一起做了出色的支援工作。我想說，將產品推向市場的廣泛工作是我的另一個優先事項。

I also spend a substantial amount of time on the road talking to customers. I would say, on average, I'm out traveling every other week. That's kind of how you get to meet over 100 customers in, what, 70-odd days. But that's a rough breakdown of my priorities, make sure that I'm in front of customers and with folks in the field, focus on product execution and also on just go-to-market efficiency.

我還花大量時間在路上與客戶交談。我想說，平均而言，我每隔一周就會出去旅行一次。這就是你在 70 多天內會見 100 多個客戶的方式。但這是我優先事項的粗略細分，確保我在客戶和現場人員面前，專注於產品執行以及進入市場的效率。

Our next question today comes from Matt Hedberg with RBC.

今天我們的下一個問題來自加拿大皇家銀行的馬特·赫德伯格。

Sridhar, we spend a lot of time focused on the investments you're making in R&D and GPUs. But I'm wondering about your sales and marketing forecast and maybe what you've learned from your time there, especially when you noted expanding your reach. And I guess, specifically, does your sales motion need to change or evolve when talking to, say data science team for instance?

Sridhar，我們花了很多時間專注於您在研發和 GPU 方面的投資。但我想知道您的銷售和行銷預測，以及您從那裡學到的東西，尤其是當您注意到擴大業務範圍時。我想，具體來說，在與資料科學團隊等交談時，您的銷售動作是否需要改變或發展？

This is a great question, and I touched on this in the answer to my previous question. Absolutely. I think the kind of product offerings that are needed to be able to effectively have a conversation with a data science team is a little bit different from, say, the team that's running warehouses. What is exciting, and I can tell you that today from many conversations that I've had with customers, is that applications written on top of Snowflake, something we call managed applications where our customers write applications on top and then using things like our collaboration to actively share data with their customers.

這是一個很好的問題，我在上一個問題的回答中談到了這一點。絕對地。我認為能夠與資料科學團隊進行有效對話所需的產品類型與運行倉庫的團隊有點不同。令人興奮的是，我今天可以告訴你，從我與客戶進行的許多對話來看，在Snowflake 之上編寫的應用程序，我們稱之為託管應用程序，我們的客戶在上面編寫應用程序，然後使用我們的協作之類的東西積極與客戶共享數據。

That is actually -- puts us in conversation directly with business leaders in these companies because we now become a part of their top line of actually helping them generate revenue. And yes, so there are different product motions that are needed for different products and the different people that are going to benefit from these. We created a specialized partner organization, for example, that is focused explicitly on data providers on who can bring additional data to Snowflake and then how do we drive revenue opportunity for them.

這實際上是讓我們直接與這些公司的業務領導者進行對話，因為我們現在成為他們實際幫助他們創造收入的頂線的一部分。是的，不同的產品需要不同的產品動議，不同的人將從中受益。例如，我們創建了一個專門的合作夥伴組織，該組織明確關注數據提供商，誰可以為 Snowflake 帶來更多數據，然後我們如何為他們帶來收入機會。

And similarly with AI, for example, we need people feel much more comfortable in the world of language models. Our magic is also that we make AI available to all analysts, and that's a big boost that they are going to get from how they use Snowflake. Absolutely, there is change going into our go-to-market motion, but as you know, it is a gradual change. We are constantly looking for what's the best way to take a particular product to market or how to solve a specific customer problem. And you see that reflected in how our field organizations are organized and managed.

與人工智慧類似，例如，我們需要人們在語言模型的世界中感到更舒適。我們的魔力還在於，我們為所有分析師提供人工智慧，這將是他們從使用 Snowflake 的方式中獲得的巨大推動力。當然，我們的上市行動正在發生變化，但如您所知，這是一個漸進的變化。我們不斷尋找將特定產品推向市場或解決特定客戶問題的最佳方法。您會看到這反映在我們的現場組織的組織和管理方式上。

That's great. That's great. And maybe just a quick one for Mike. Appreciate the color on consumption trends. That's super helpful. I know you said you based your guidance on what you've seen this week. I guess maybe just a question on May. Have you seen May then bounce back a bit versus what sounds like a seasonally slow April traditionally?

那太棒了。那太棒了。也許對麥克來說只是一個快速的。欣賞消費趨勢的色彩。這非常有幫助。我知道你說過你的指導是基於你本週所看到的。我想也許只是五月的一個問題。您是否看到五月有所反彈，而傳統上四月聽起來似乎是季節性緩慢的？

As I said, our guidance is based upon consumption patterns we're seeing in the quarter, and that's reflected inside there.

正如我所說，我們的指導是基於我們在本季看到的消費模式，這也反映在其中。

Our next question comes from Brent Bracelin with Piper Sandler.

我們的下一個問題來自 Brent Bracelin 和 Piper Sandler。

Sridhar, in your opening remarks, you flagged Iceberg as a potential unlock that could accelerate growth. Maybe that's a longer-term view. But can you just walk through how or why spending could actually go up for Snowflake in an environment where customer moves to Iceberg?

Sridhar，在您的開場白中，您將 Iceberg 標記為可以加速增長的潛在解鎖點。也許這是一個更長遠的觀點。但是，您能否簡單說明一下，在客戶轉向 Iceberg 的環境中，Snowflake 的支出實際上如何或為何會增加？

So first of all, Iceberg is a capability, and it is a capability to be able to read and to write file in a structured interoperable format. And yes, there will be some customers that will move a portion of their data from Snowflake into an Iceberg format because they have an application that they want to run on top of the data. But the fact of the matter is that data lakes or cloud storage in general for most customers has data that is often 100 or 200x amount of data that is sitting inside Snowflake.

首先，Iceberg 是一種能力，它是一種能夠以結構化的可互通格式讀寫檔案的能力。是的，有些客戶會將部分資料從 Snowflake 轉移到 Iceberg 格式，因為他們有一個應用程式想要在資料之上運行。但事實是，對於大多數客戶來說，資料湖或雲端儲存的資料通常是 Snowflake 中資料量的 100 或 200 倍。

And now with Iceberg as a format under our support for it, all of a sudden, you can run workloads with Snowflake directly on top of this data. And we don't have to wait for some future time in order to be able to pitch and win these use cases. Whether it's data engineering or whether it is AI, Iceberg becomes a seamless pipe into all of this information that existing customers already have, and that's the unlock that I'm talking about. I'll also have Christian say a word. He's been at this for a very long time and has a lot of insight on this.

現在，有了 Iceberg 作為我們支援的格式，突然之間，您可以直接在這些資料之上使用 Snowflake 運行工作負載。我們不必等待未來的某個時間就能推銷並贏得這些用例。無論是資料工程還是人工智慧，Iceberg 都成為了現有客戶已經擁有的所有這些資訊的無縫管道，這就是我所說的解鎖。我還要請克里斯蒂安說一句話。他在這方面已經從事了很長時間，並且對此有很多見解。

Yes. I would just add to what Sridhar said. We have many of our existing customers. Echoing what Sridhar said, they have lots of data, tens of petabytes of data ready to be analyzed. They don't think that it makes sense for -- that they have to be copied or ingested into Snowflake, but they have use cases where they want to combine data in Snowflake with that existing data. So the opportunity is very real. And what Sridhar also alluded to, the announcement we made with Microsoft in the last 2 days is entirely about that. How do we take the data that is available in Microsoft Fabric and through Iceberg, make it available to Snowflake. So the opportunity is not a long-term one. It's not framed as something that we'll have to wait a lot for.

是的。我只想補充一下斯里達爾所說的內容。我們有許多現有客戶。正如 Sridhar 所說，他們有大量數據，數十 PB 的數據可供分析。他們認為必須將它們複製或攝取到 Snowflake 中是沒有意義的，但他們有一些用例希望將 Snowflake 中的資料與現有資料結合。所以這個機會是非常真實的。 Sridhar 也提到，我們在過去 2 天與微軟發布的公告完全就是關於這一點的。我們如何取得 Microsoft Fabric 中可用的資料並透過 Iceberg 提供給 Snowflake。所以這個機會不是一個長期的機會。它並沒有被認為是我們必須等待很長時間的事情。

Quick clarification for Mike here. Knocking down some big deals, another $100 million deal in Q1. It sounds like another one in Q2. Last I checked, the macro is pretty tough. What's driving that? Is the AI road map helping?

在這裡向麥克快速澄清一下。敲定了一些大交易，第一季又達成了 1 億美元的交易。這聽起來像是第二季的另一場比賽。最後我檢查了一下，宏非常難。是什麼推動了這一點？人工智慧路線圖有幫助嗎？

These are all existing customers and large customers. And it still is core data warehousing, but they're all interested and want to have a discussion around what we're doing in AI. But many of these, both the one in Q1, we are core to their business and the one that's going to do in Q -- the current quarter now, we are core to how they run their business. And that is what's really driving these customers to make these big long-term commitments with us.

這些都是現有客戶和大客戶。它仍然是核心資料倉儲，但他們都很感興趣，並希望圍繞我們在人工智慧領域所做的事情進行討論。但其中許多，無論是在第一季度，我們都是他們業務的核心，也是在第二季度要做的事——現在這個季度，我們是他們如何經營業務的核心。這才是真正促使這些客戶與我們做出這些重大長期承諾的原因。

And then several of these deals, not the one that Mike mentioned, but in several other very large ones, collaborations are actually having snowflake be the conduit by which these large customers monetize their data by having their customers access this data, serves as a very powerful catalyst. And absolutely, AI is a help in all of these, and these are the folks that are leaning into and creating AI applications on top of Snowflake. But at its core, you should see these very large investments as a bet on Snowflake as the AI data platform.

然後，其中的幾筆交易，不是麥克提到的交易，而是在其他幾筆非常大的交易中，合作實際上是讓雪花成為這些大客戶透過讓他們的客戶存取這些數據來貨幣化他們的數據的管道，作為一種非常好的方式。毫無疑問，人工智慧在所有這些方面都提供了幫助，而這些人正在研究並在 Snowflake 之上創建人工智慧應用程式。但從本質上講，你應該將這些非常大的投資視為對 Snowflake 作為人工智慧數據平台的賭注。

Our next question today comes from Patrick Colville.

今天我們的下一個問題來自派崔克·科爾維爾。

This is Joe Vandrick on for Patrick Colville. Sridhar, I know you joined Snowflake about a year ago, but you've now been CEO for about 3 months. So just wondering if there's anything that surprised you or that's worth calling out that you've learned since stepping into the CEO role? And then also curious of your view on a few other products, Streamlit and Unistore, if you could talk a bit about customer engagement you're seeing there.

我是帕特里克·科爾維爾的喬·範德里克。 Sridhar，我知道您大約一年前加入 Snowflake，但您現在已經擔任執行長大約 3 個月了。那麼，我想知道自從擔任執行長以來，是否有什麼事情讓您感到驚訝或值得一提的是您學到了什麼？然後也很好奇您對其他一些產品（St​​reamlit 和 Unistore）的看法，如果您能談談您在那裡看到的客戶參與度。

Yes. I've been here at Snowflake close to a year, and as I said, I've had a lot and I have a lot of customer conversations. The amount of love and respect that our customers have for the core product, how easy it is to use, how efficient it is and how maintenance free, dramatically lowering total cost of ownership, it is the thing that continues to pleasantly surprise me, is also obviously an important quality for us to preserve while we are releasing new products.

是的。我在 Snowflake 工作已經快一年了，正如我所說，我與客戶進行了很多交談。我們的客戶對核心產品的熱愛和尊重，它的易用性、效率和免維護性，大大降低了總體擁有成本，這些一直讓我感到驚喜。的重要品質。

And we take the trouble to do that. Uniformly, the feedback that we get about Cortex, which is our AI layer, from pretty tough tech reviewers is that, yes, we truly make the hard easy because anybody that can write SQL now is able to do some pretty nifty things with AI. I think that combination of simplicity and ease of use is an incredibly powerful quality for Snowflake. And while I knew it, I think it is still a surprise, a pleasant surprise every time customers bring it up.

我們不厭其煩地這樣做。一致的是，我們從非常嚴格的技術評論家那裡得到的關於Cortex（我們的AI 層）的反饋是，是的，我們確實讓困難變得容易，因為現在任何可以編寫SQL 的人都可以使用AI做一些非常漂亮的事情。我認為簡單性和易用性的結合是 Snowflake 令人難以置信的強大品質。雖然我知道這一點，但我認為這仍然是一個驚喜，每次顧客提起它時都是一個驚喜。

And then in terms of Streamlit, Streamlit is -- for those that don't know, is a rapid prototyping environment. It's a little bit like being able to write an application and have it be hosted on Snowflake without having to do any other work. You don't have to bring up a Kubernetes cluster or you don't have to deploy a binary, none of that stuff. You write a little application, and it just runs.

然後就 Streamlit 而言，對於那些不了解的人來說，Streamlit 是一個快速原型設計環境。這有點像是能夠編寫應用程式並將其託管在 Snowflake 上，而無需執行任何其他工作。您不必啟動 Kubernetes 集群，也不必部署二進位文件，這些都不需要。您編寫一個小應用程序，它就會運行。

There are a ton of applications inside Snowflake, for example, whether it's our compensation information or whether it is finance information, our forecast or even chatbots that I personally have created, these all run on Streamlit but with just incredible operational efficiency because they just run as part of our Snowflake instance that is already running in the customer deployment. There are folks that have adopted it very, very broadly.

Snowflake內部有大量的應用程序，例如，無論是我們的薪酬資訊還是財務資訊、我們的預測甚至是我個人創建的聊天機器人，這些都在Streamlit上運行，但具有令人難以置信的營運效率，因為它們只是運行作為已在客戶部署中運行的 Snowflake 實例的一部分。有些人非常非常廣泛地採用了它。

And we think of this as really like highlighting, showcasing Snowflake functionality, making it super easy to distribute these things to Snowflake users. And in that perspective, it's been a hugely, hugely positive application. And the team has also been the one, for example, that's been working on notebooks, which is going to be an important priority going forward, so lots of positive things on that side.

我們認為這實際上就像突出顯示、展示 Snowflake 功能一樣，使得將這些東西分發給 Snowflake 用戶變得非常容易。從這個角度來看，這是一個非常非常正面的應用。例如，該團隊也一直致力於筆記型電腦的開發，這將是未來的重要優先事項，因此這方面有很多積極的事情。

And then on Unistore or, as we call them, hybrid tables, these are really meant to address a different kind of workload that is more transactional in nature than the analytic workload that often runs on top of Snowflake. It is in public preview. It will be in GA later this year. I think it opens up several new classes of applications that can run very effectively on top of Snowflake. It's the same Snowflake sort of magic, which is you don't need to stand up servers. You don't need to go do a whole lot of work on top of them or deal with Kubernetes clusters. And we see, I think, it's close to 300 customers that are actively using hybrid tables. We can absolutely expect that number to go up by a lot.

然後在 Unistore 或我們所說的混合表上，這些實際上是為了解決一種不同類型的工作負載，這種工作負載本質上比通常在 Snowflake 上運行的分析工作負載更具事務性。它處於公共預覽狀態。它將在今年晚些時候發布。我認為它開闢了幾個新的應用程式類別，可以在 Snowflake 之上非常有效地運作。這與雪花魔法一樣，即您不需要建立伺服器。您無需在它們之上做大量工作或處理 Kubernetes 叢集。我認為我們看到有近 300 名客戶正在積極使用混合表。我們絕對可以預期這個數字會大幅上升。

Christian, any other thoughts on these 2?

克里斯蒂安，對這兩個還有其他想法嗎？

No. Streamlit is now generally available on all 3 clouds. That has driven a lot of interest and adoption. And the -- for the hybrid tables, many of our customers have liked the evaluation and they are actually waiting for the general availability later this year.

不會。這引起了極大的興趣和採用。對於混合表，我們的許多客戶都喜歡這個評估，他們實際上正在等待今年稍後的全面上市。

Our next question today comes from Brad Reback with Stifel.

今天我們的下一個問題來自 Stifel 的 Brad Reback。

This is Rob on for Brad. For Christian or Sridhar, over the past few months, including yesterday, Snowflake Ventures is investing in a few observability, [logging and SIEM] companies and I'm wondering what the underlying strategy is with these observability type investments, that maybe there is some bigger opportunity that you're trying to address.

這是羅布替補布萊德。對於Christian 或Sridhar 來說，在過去的幾個月裡，包括昨天，Snowflake Ventures 正在投資一些可觀察性、[日誌和SIEM] 公司，我想知道這些可觀察性類型投資的基本策略是什麼，也許有一些您想要抓住的更大機會。

Christian here. Observability is very important for our customers in 2 fronts. One is data observability and be able to understand things like data quality and variations on data itself; but also as we have evolved Snowflake into be able to host business logic and be an application platform, there's also observability for code. How do I know what my Snowpark Container Service is doing? Or how do I troubleshoot and monitor the alerts on Snowpark? That is the context for observability. It's an important priority for us, both data as well code, and we'll continue to partner with all the rich ecosystem that will help us go and more [really] understand what's happening data and code.

基督徒在這裡。可觀察性對於我們的客戶來說在兩個方面非常重要。一是數據可觀察性，能夠理解數據品質與數據本身的變化等；而且，隨著我們將 Snowflake 發展為能夠託管業務邏輯並成為應用程式平台，程式碼也具有可觀察性。我如何知道我的 Snowpark 容器服務正在做什麼？或如何排除故障並監控 Snowpark 上的警報？這就是可觀察性的背景。這對我們來說是一個重要的優先事項，無論是資料還是程式碼，我們將繼續與所有豐富的生態系統合作，這將幫助我們更[真正]了解資料和程式碼正在發生的事情。

And the general comment that I will make is that Snowflake is a great platform to develop applications on top of. And we end up collaborating, sometimes investing in a lot of companies that build interesting applications on top of Snowflake. Observability is one area. But just to give another example, we have close partnerships with several customer data platforms, and that list sort of keeps going on and on because we want there to be a vibrant ecosystem on top of Snowflake.

我的總體評論是，Snowflake 是一個在其上開發應用程式的絕佳平台。我們最終進行了合作，有時投資了很多在 Snowflake 上建立有趣應用程式的公司。可觀察性是一個領域。但再舉一個例子，我們與多個客戶資料平台有著密切的合作夥伴關係，而且這個清單一直在不斷增加，因為我們希望在 Snowflake 之上有一個充滿活力的生態系統。

Our next question today comes from Tyler Radke with Citi.

今天我們的下一個問題來自花旗銀行的泰勒拉德克 (Tyler Radke)。

Mike, you talked about some upside from smaller customers during the quarter. Could you just talk about the nature of those small customers, the start-ups, maybe GenAI companies? And was this more of a one-off? Or do you expect this strength to persist throughout the rest of the year?

麥克，您談到了本季小客戶的一些好處。您能談談那些小型客戶、新創企業，也許是 GenAI 公司的性質嗎？這更像是一次性的嗎？或者您預計這種勢頭將持續到今年剩餘時間？

It was very much broad-based, and it's across all industries. It's the non-G2K I'm talking about, and some of these are very large companies, a lot of private companies in there, too, and it's across the board.

它的基礎非常廣泛，遍及所有行業。我說的是非 G2K，其中一些是非常大的公司，也有很多私人公司，而且是全面的。

Got it. And then a quick follow-up on the sales and marketing side. So both the expenses and headcount increased quite a bit sequentially. Is that primarily quota-carrying hires? Is it marketing folks? Just give us a sense on exactly what's driving that higher investment?

知道了。然後是銷售和行銷方面的快速跟進。因此，費用和員工人數都連續增加了很多。這主要是配額招募嗎？是行銷人員嗎？請讓我們了解到底是什麼推動了更高的投資？

Well, first of all, on the expense side, we mentioned at the end of last quarter, because of our change in comp plan, you were going to see more commission expense being expensed immediately versus deferred and amortized. As I said, it doesn't really change the cash flow, but it did add to the expense. And we are adding a number of reps, principally a lot in the acquisition team in the commercial space as well as on the business development, the SDR side as well, too, within the company. But we are adding people throughout the sales organization, including SEs this year, you will see us.

好吧，首先，在費用方面，我們在上個季度末提到，由於我們補償計劃的變化，您將看到更多的佣金費用立即支出，而不是遞延和攤銷。正如我所說，它並沒有真正改變現金流，但確實增加了開支。我們正在增加一些代表，主要是公司內部商業領域的收購團隊以及業務開發、特別提款權的代表。但今年我們將在整個銷售組織中增加人員，包括 SE，你會看到我們。

And I think we feel pretty good about our business. We've hit our numbers in the first quarter, and we're constantly looking at headcount. And we will continue to invest in the sales organization as we see that we can ramp them.

我認為我們對我們的業務感覺很好。我們在第一季就達到了目標，並且我們一直在關注員工人數。我們將繼續對銷售組織進行投資，因為我們認為我們可以擴大銷售組織的規模。

Our final question today comes from Alex Zukin with Wolfe Research.

我們今天的最後一個問題來自 Wolfe Research 的 Alex Zukin。

Apologize for the background noise. Congrats on a great quarter. Maybe just first for Sridhar. You mentioned some really interesting Cortex use cases from Sigma, I think, on the prepared remarks. Can you maybe dig in a bit more, share some of the vision of how some of your larger customers are thinking and deploying Cortex and maybe Arctic? And how can it impact daily consumption trends for these companies when they start deploying it in more production, more use cases?

對背景噪音表示歉意。恭喜您度過了一個出色的季度。也許對斯里達爾來說只是第一個。我認為，您在準備好的評論中提到了 Sigma 的一些非常有趣的 Cortex 用例。您能否深入挖掘一下，分享一下您的一些大客戶如何思考和部署 Cortex 以及 Arctic 的一些願景？當這些公司開始將其部署到更多生產、更多用例中時，它將如何影響這些公司的日常消費趨勢？

I think I got the gist of your question. I'll definitely address it. What Snowflake makes easy is the ability to analyze, for example, unstructured text information for things like sentiment or even like categories of feedback or by using things like vector embedding and soon the Cortex index, be able to do -- be able to figure out what are the most related support cases, let's say, for a new question that came in and auto generate a response.

我想我明白你問題的要點了。我一定會解決這個問題。 Snowflake 讓分析變得容易的是能夠分析非結構化文字訊息，例如情緒甚至回饋類別，或透過使用向量嵌入和很快的 Cortex 索引等東西，能夠做到——能夠弄清楚比方說，對於出現的新問題並自動產生回應，最相關的支援案例是什麼。

Increasingly, I think of this as the AI stack, where there is a central repository, let's say, a bunch of previously answered questions; and then a new question comes in, you are able to generate an answer for the new customer problem simply based on your history. This is a little bit like what companies do imperfectly today, where they will let you search over, let's say, a forum, Snowflake as a forum, for you to figure out, well, has this question already been answered. The magic of language models is that they can automate this process, so the truly new questions can get dispatched to a customer service rep to answer from scratch because the company does not know about it.

我逐漸將其視為人工智慧堆疊，其中有一個中央儲存庫，比如說，一堆以前回答過的問題；然後出現一個新問題，您可以根據您的歷史記錄為新客戶問題產生答案。這有點像今天公司做的不完美的事情，他們會讓你搜尋一個論壇，例如雪花論壇，讓你弄清楚這個問題是否已經得到解答。語言模型的神奇之處在於它們可以自動化這個過程，因此真正的新問題可以被分派給客戶服務代表從頭開始回答，因為公司對此一無所知。

But to me, that is a prototype, which is there is a central repository that's sitting in Snowflake. There's a language model that is basically getting requests from outside routed in and control logic that decides what to do with this. And obviously, something like just a pure chatbot, where you can just interact, we have one deployed on all of our IT questions internally at Snowflake, for example, is just so you can have like a quick conversation about a problem that somebody has already solved. We make things like this trivial.

但對我來說，這是一個原型，它有一個位於 Snowflake 中的中央儲存庫。有一個語言模型，基本上就是從外部路由進來的請求，並控制邏輯來決定如何處理這個請求。顯然，就像一個純粹的聊天機器人，你可以在其中進行交互，例如，我們在 Snowflake 內部部署了一個解決所有 IT 問題的機器人，這樣你就可以就某人已經解決的問題進行快速對話解決了。我們讓這樣的事情變得微不足道。

But perhaps what is really interesting about Cortex is basically language transformation. I talked about sentiment detection, but there's also other stuff like summarization or extracting like data from JSON or more complicated, extracting information from, let's say, images. We automate all of those things.

但也許 Cortex 真正有趣的地方基本上是語言轉換。我談到了情緒檢測，但還有其他內容，例如摘要或從 JSON 中提取類似資料或更複雜的內容，例如從圖像中提取資訊。我們將所有這些事情自動化。

And the beauty of our model is all of this is driven by consumption. There is no pre commit to spend. These applications get deployed. If they get a lot of usage, that generates consumption. And so it's almost Darwinian in how like great applications come up and drive usage. And obviously, making it this simple also means that complex tasks that required software engineering before just become a little pipeline that runs in Snowflake every hour, every 2 hours. That's acting on all of the data that is coming into Snowflake anyway.

我們模型的美妙之處在於這一切都是由消費驅動的。沒有預先承諾支出。這些應用程式得到部署。如果它們被大量使用，就會產生消費。因此，偉大的應用程式如何出現並推動使用，這幾乎是達爾文式的。顯然，讓它變得如此簡單也意味著以前需要軟體工程的複雜任務變成了一個小管道，在 Snowflake 中每小時、每 2 小時運行一次。無論如何，這都會對進入 Snowflake 的所有數據起作用。

So I would say the use cases that I'm talking about, these are just like things that you could do with Snowflake that are massively accelerated by the presence of language models. This is one category.

所以我想說的是我正在談論的用例，這些就像你可以使用 Snowflake 做的事情一樣，透過語言模型的存在大大加速了這些事情。這是一類。

The second one really is in how do language models make it much easier to access data that is structured data that is in Snowflake. You've heard me refer to it as like a data API. The idea basically is that it's currently quite hard. You have to go through an analyst, perhaps a BI tool, to get any new pieces of information. What we are working on, this is not yet in public preview, it will be soon, is a product by which by giving semantic information about a Snowflake schema, you essentially make it possible for people to have a conversation with it. We aren't quite here yet, but I'd like to give Mike Scarpelli [an ask] that knows about finance information that he's able to query but actually trust the information that is coming out of it.

第二個問題實際上是語言模型如何使存取 Snowflake 中的結構化資料變得更加容易。您已經聽過我將其稱為數據 API。這個想法基本上是目前相當困難。您必須通過分析師（也許是 BI 工具）才能獲得任何新資訊。我們正在開發的產品尚未公開預覽，但很快就會推出，透過提供有關雪花模式的語義訊息，您基本上可以讓人們與其進行對話。我們還沒有完全做到這一點，但我想向邁克·斯卡佩利（Mike Scarpelli）提出一個問題，他了解他能夠查詢的財務信息，但實際上信任從中得出的信息。

Obviously, the big unlock there is that any business user now has access to data within Snowflake, authorized and governed, of course, but it's a much larger user base that can directly interact with Snowflake. And that's the complement where there is a direct access to data to a much larger user base.

顯然，最大的解鎖是任何商業用戶現在都可以存取 Snowflake 中的數據，當然，經過授權和管理，但可以直接與 Snowflake 互動的用戶群要大得多。這是對更大用戶群直接存取數據的補充。

There's lots more. This is a topic that I'm super passionate about. I can keep going on and on. But hopefully, you get a feel for the kinds of application. The first class is unstructured data. The second class is structured data. Our vision is to bring all of these together into like a single box for the enterprise where you can ask any question and be able to get an answer to it.

還有很多。這是我非常熱衷的話題。我可以繼續下去。但希望您能對各種應用程式有所了解。第一類是非結構化資料。第二類是結構化資料。我們的願景是將所有這些整合到一個企業盒子中，您可以在其中提出任何問題並能夠得到答案。

Makes sense. And then, Mike, you talked about consumption exceeding expectations, exceeding quotas. I guess I just wanted to maybe dig into -- you talked about a broad-based driver. It wasn't like specific to any maybe customer size. But is there anything around any verticals or any geos that were specifically strong? Or did Snowpark momentum contribute to that strength? Is there anything more you can give us there?

說得通。然後，麥克，你談到消費超出預期，超出配額。我想我只是想深入研究一下——你談到了一個廣泛基礎的驅動程式。它並非針對任何可能的客戶規模。但是，在任何垂直領域或任何地理區域周圍是否有什麼特別強大的東西？還是 Snowpark 的勢頭促成了這種力量？您還有什麼可以給我們的嗎？

No, it's really the strength in our core business, and it was across all verticals. Financial services continues to be our biggest. With that said, though, we did see some pretty good uptick in the technology and health care space. Their growth outperformed a number of the other groups in the company, but it's broad-based.

不，這確實是我們核心業務的優勢，而且遍及所有垂直領域。金融服務仍然是我們最大的業務。儘管如此，我們確實看到科技和醫療保健領域出現了一些相當好的成長。他們的成長超過了公司中的許多其他部門，但基礎廣泛。

That will conclude today's conference call. Thank you all for your participation. You may now disconnect your lines.

今天的電話會議到此結束。感謝大家的參與。現在您可以斷開線路。