Snowflake Inc (SNOW) 2024 Q1 法說會逐字稿

Good afternoon. Thank you for attending today's Snowflake Q1 Fiscal Year '24 Earnings Conference Call. My name is Cole, and I will be your moderator for today's call. (Operator Instructions) I'd now like to pass the conference over to our host, Jimmy Sexton. Please go ahead.

下午好。感謝您參加今天的 Snowflake Q1 24 財年收益電話會議。我叫 Cole，我將擔任今天電話會議的主持人。 （操作員說明）我現在想將會議轉交給我們的主持人 Jimmy Sexton。請繼續。

Good afternoon, and thank you for joining us on Snowflake's Q1 Fiscal 2024 Earnings Call. With me in Bozeman, Montana are Frank Slootman, our Chairman and Chief Executive Officer; Mike Scarpelli, our Chief Financial Officer; and Christian Kleinerman, our Senior Vice President of Product, who will join us for the Q&A session.

下午好，感謝您加入我們的 Snowflake 2024 財年第一季度收益電話會議。和我一起在蒙大拿州博茲曼的是我們的董事長兼首席執行官 Frank Slootman；我們的首席財務官 Mike Scarpelli；以及我們的產品高級副總裁 Christian Kleinerman，他將加入我們的問答環節。

During today's call, we will review our financial results for the first quarter of fiscal 2024 and discuss our guidance for the second quarter and full year fiscal 2024.

在今天的電話會議上，我們將回顧 2024 財年第一季度的財務業績，並討論我們對 2024 財年第二季度和全年的指導意見。

During today's call, we will make forward-looking statements, including statements related to the expected performance of our business, future financial results, strategy, products and features, long-term growth, our stock repurchase program and overall future prospects. These statements are subject to risks and uncertainties, which could cause them to differ materially from actual results.

在今天的電話會議上，我們將做出前瞻性陳述，包括與我們的業務預期業績、未來財務業績、戰略、產品和特點、長期增長、我們的股票回購計劃和整體未來前景相關的陳述。這些陳述受風險和不確定因素的影響，可能導致它們與實際結果存在重大差異。

Information concerning those risks is available in our earnings press release distributed after market close today and in our SEC filings, including our most recently filed Form 10-K for the fiscal year ended January 31, 2023, and the Form 10-Q for the quarter ended April 30, 2023, that we will file with the SEC. We caution you to not place undue reliance on forward-looking statements and undertake no duty or obligation to update any forward-looking statements as a result of new information, future events or changes in our expectations.

有關這些風險的信息可在我們今天收市後發布的收益新聞稿和我們向美國證券交易委員會提交的文件中找到，包括我們最近提交的截至 2023 年 1 月 31 日的財政年度的 10-K 表格和本季度的 10-Q 表格截至 2023 年 4 月 30 日，我們將向 SEC 提交。我們提醒您不要過分依賴前瞻性陳述，並且不承擔因新信息、未來事件或我們預期的變化而更新任何前瞻性陳述的責任或義務。

We'd also like to point out that on today's call, we will report both GAAP and non-GAAP results. We use these non-GAAP financial measures internally for financial and operational decision-making purposes and as a means to evaluate period-to-period comparisons.

我們還想指出，在今天的電話會議上，我們將報告 GAAP 和非 GAAP 結果。我們在內部使用這些非 GAAP 財務措施來進行財務和運營決策，並作為評估期間比較的一種方式。

Non-GAAP financial measures are presented in addition to, and not as a substitute for, financial measures calculated in accordance with GAAP. To see the reconciliations of these non-GAAP financial measures, please refer to our earnings press release distributed after -- earlier today and our investor presentation, which are posted at investors.snowflake.com. A replay of today's call will also be posted on the website.

非 GAAP 財務措施是根據 GAAP 計算的財務措施的補充，而不是替代。要查看這些非 GAAP 財務措施的對賬，請參閱我們今天早些時候發布的收益新聞稿和我們在 investors.snowflake.com 上發布的投資者介紹。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 Frank.

有了這個，我現在想把電話轉給弗蘭克。

Thanks, Jimmy. Welcome, everybody, listening to today's earnings announcement. Snowflake's product revenue grew 50% in Q1 fiscal year 2024 totaling $590 million. Net revenue retention rate reached 151%, and remaining performance obligations came in at $3.4 billion, up 31% year-on-year. Non-GAAP adjusted free cash flow was $287 million, up 58% year-over-year.

謝謝，吉米。歡迎大家收聽今天的收益公告。 Snowflake 的產品收入在 2024 財年第一季度增長了 50%，總計 5.9 億美元。淨收入保留率達到 151%，剩餘履約義務收入為 34 億美元，同比增長 31%。非 GAAP 調整後自由現金流為 2.87 億美元，同比增長 58%。

We are, however, operating in an unsettled demand environment, and we see this reflected in consumption patterns across the board. While enthusiasm for Snowflake is high, enterprises are preoccupied with costs in response to their own uncertainties. We proactively work with customers to optimize their environments. This may well continue near term, but cycles like this eventually run their course. Our conviction in the long-term opportunity remains unchanged.

然而，我們在一個不穩定的需求環境中運營，我們看到這反映在全面的消費模式中。儘管對 Snowflake 的熱情很高，但企業為應對自身的不確定性而全神貫注於成本。我們積極與客戶合作以優化他們的環境。這很可能在短期內持續下去，但像這樣的周期最終會結束。我們對長期機會的信念保持不變。

Generative AI, with its text style of interaction, has captured the imagination of society at large. It will bring disruption to productivity as well as obsolescence to tasks and entire industries alike.

生成式人工智能以其文本交互方式吸引了整個社會的想像力。它將對生產力造成破壞，並使任務和整個行業都過時。

Generative AI is powered by data. That's how models trained and become progressively more interesting and relevant. Models have been primarily been trained with Internet and public data, and we believe enterprises will benefit from customizing this technology with their own data. As Snowflake manages a vast and growing universe of public and proprietary data, the data role in advancing this trend becomes pronounced. AI's focus on large language models and textual data, both structured and unstructured, will lead to a rapid proliferation of model types and specializations. Some models will be broadly capable with shallow in functions, others will be deep, specialized and impactful in their specific realm.

生成式 AI 由數據提供支持。這就是模型如何訓練並變得越來越有趣和相關的方式。模型主要使用互聯網和公共數據進行訓練，我們相信企業將受益於使用自己的數據定制該技術。隨著 Snowflake 管理著龐大且不斷增長的公共和專有數據，數據在推動這一趨勢方面的作用變得越來越明顯。 AI 專注於結構化和非結構化的大型語言模型和文本數據，這將導致模型類型和專業化的迅速擴散。一些模型將具有廣泛的能力，但功能較淺，而另一些模型將在其特定領域具有深度、專業性和影響力。

For years, we focused on the extensibility of our platform via Snowpark, making Snowflake ideally suited for a rapid adoption of new and interesting language models as they become available. AI is also not limited to textual data, equally far-reaching will be seen with audio, video and other modalities.

多年來，我們通過 Snowpark 專注於我們平台的可擴展性，使 Snowflake 非常適合在新的和有趣的語言模型可用時快速採用它們。 AI 也不局限於文本數據，音頻、視頻和其他形式也同樣具有深遠的影響。

The Snowflake mission is to steadily demolish any and all limits to data, users, workloads, applications and new forms of intelligence. You will, therefore, continue to see us add, evolve and expand our functions and feature sets. Our goal is for all the world's data to find its way to Snowflake and not encounter any limitations in terms of use and purpose.

Snowflake 的使命是穩步消除對數據、用戶、工作負載、應用程序和新形式智能的所有限制。因此，您將繼續看到我們添加、發展和擴展我們的功能和特性集。我們的目標是讓世界上所有的數據都能找到通往 Snowflake 的途徑，並且在使用和目的方面不會遇到任何限制。

From our perspective, machine learning, data science and AI are workloads that we enable with increased capability, continuous performance and efficiency improvements. Data has gravitational pull. And given the vast universe of data Snowflake already manages, it's no surprise that interest in these capabilities is escalating while its users are still evolving.

從我們的角度來看，機器學習、數據科學和人工智能是我們通過增強能力、持續性能和效率改進來實現的工作負載。數據具有引力。鑑於 Snowflake 已經管理的海量數據，當其用戶仍在不斷發展時，人們對這些功能的興趣正在升級也就不足為奇了。

Data science, machine learning and AI use cases on Snowflake are growing every day. In Q1, more than 1,500 customers leverage Snowflake for one of these workloads, up 91% year-over-year. A large U.S. financial institution uses Snowflake for model training. Facing memory constraints with their prior solution, they chose to move feature engineering workloads to Snowflake. With Snowflake, they can fully ingest all data, replacing a sampling approach, which left models less predictive and long running.

Snowflake 上的數據科學、機器學習和人工智能用例每天都在增長。在第一季度，超過 1,500 家客戶將 Snowflake 用於其中一個工作負載，同比增長 91%。美國一家大型金融機構使用 Snowflake 進行模型訓練。面對之前解決方案的內存限制，他們選擇將特徵工程工作負載轉移到 Snowflake。借助 Snowflake，他們可以完全攝取所有數據，取代抽樣方法，這使得模型的預測性較差且運行時間較長。

Snowflake enables machine learning for a broad spectrum of user types, not just programmers. For analysts, we have introduced in preview ML-powered SQL extensions, such as anomaly detection, top insights and time series forecasting.

Snowflake 使機器學習適用於廣泛的用戶類型，而不僅僅是程序員。對於分析師，我們推出了預覽版 ML 支持的 SQL 擴展，例如異常檢測、頂級洞察和時間序列預測。

SQL proficient users can now leverage powerful machine learning extensions without the need to master the underlying data science. For data scientists and engineers, Snowpark is our platform for programmability. New here is a PyTorch data loader and an MLflow plug-in, both in private preview. PyTorch is a popular framework for machine learning, and MLflow helps manage the life cycle and operations of machine learning.

精通 SQL 的用戶現在可以利用強大的機器學習擴展，而無需掌握底層數據科學。對於數據科學家和工程師來說，Snowpark 是我們的可編程性平台。這里新增了一個 PyTorch 數據加載器和一個 MLflow 插件，兩者都處於私有預覽狀態。 PyTorch 是一種流行的機器學習框架，MLflow 有助於管理機器學習的生命週期和操作。

Snowflake had an early start in support of language models through last year's acquisition of Applica, now in private preview. Applica's language model solves a real business challenge, understanding unstructured data. Users can turn documents such as invoices or legal contracts into structured properties. These documents are now referenceable for analytics, data science and AI, something that is quite challenging in today's environment.

通過去年收購 Applica，Snowflake 很早就開始支持語言模型，現在處於私人預覽階段。 Applica 的語言模型解決了真正的業務挑戰，理解非結構化數據。用戶可以將發票或法律合同等文檔轉換為結構化屬性。這些文檔現在可供分析、數據科學和人工智能參考，這在當今環境中極具挑戰性。

Streamlit is the framework of choice for data scientists to create applications and experiences for AI and ML. Over 1,500 LLM-powered Streamlit apps have already been built.

Streamlit 是數據科學家為 AI 和 ML 創建應用程序和體驗的首選框架。已經構建了超過 1,500 個 LLM 支持的 Streamlit 應用程序。

GPT Lab is one example. GPT Lab offers pretrained AI assistance that can be shared across users. We announced our intent to acquire Neeva, a next-generation search technology powered by language models. Engaging with data through natural language is becoming popular with advancements in AI. This will enable Snowflake users and application developers to build a rich search-enabled and conversational experiences. We believe Neeva will increase our opportunity to allow non-technical users to extract value from their data.

GPT 實驗室就是一個例子。 GPT Lab 提供可在用戶之間共享的預訓練 AI 幫助。我們宣布了收購 Neeva 的意向，Neeva 是一種由語言模型提供支持的下一代搜索技術。隨著人工智能的進步，通過自然語言處理數據正變得越來越流行。這將使 Snowflake 用戶和應用程序開發人員能夠構建豐富的支持搜索和對話的體驗。我們相信 Neeva 將增加我們允許非技術用戶從他們的數據中提取價值的機會。

More broadly, Snowflake continues to enable industries and workloads. In Q1, more than 800 customers engaged with Snowpark for the first time. Approximately 30% of all customers are now using Snowpark on at least a weekly basis, up from 20% at the end of last quarter. Snowpark consumption is up nearly 70% quarter-over-quarter.

更廣泛地說，Snowflake 繼續支持行業和工作負載。第一季度，超過 800 名客戶首次與 Snowpark 互動。大約 30% 的客戶現在至少每週使用一次 Snowpark，高於上季度末的 20%。 Snowpark 消費量環比增長近 70%。

The Snowflake connector for ServiceNow is in public preview. Customers can access ServiceNow data inside of the data cloud without needing to manually integrate APIs or third-party tools. ServiceNow data is significant because it holds a wealth of IT and security data.

ServiceNow 的 Snowflake 連接器處於公共預覽階段。客戶無需手動集成 API 或第三方工具，即可訪問數據云內部的 ServiceNow 數據。 ServiceNow 數據非常重要，因為它包含大量 IT 和安全數據。

The connector is the first so-called native app built by Snowflake. Native apps, which are in private preview, run inside the Snowflake governance perimeter and make use of common services.

該連接器是 Snowflake 構建的第一個所謂的本機應用程序。私人預覽版的本機應用程序在 Snowflake 治理範圍內運行並使用公共服務。

Today, developers waste time convincing customers to expose their data. With native apps, developers can focus on their core interest, application development. They offer security and deployment concerns to Snowflake.

如今，開發人員浪費時間說服客戶公開他們的數據。使用原生應用程序，開發人員可以專注於他們的核心興趣，即應用程序開發。他們為 Snowflake 提供安全和部署問題。

During the quarter, we also launched a manufacturing data cloud, which focuses on supply chain management as a data problem. Supply chain management is one of the few remaining realms in enterprise software that have struggled to platform itself. Supply chains are all somewhat unique and data siloing problem prevent supply chain visibility essential to managing it.

本季度，我們還推出了製造數據云，將供應鏈管理作為數據問題重點關注。供應鏈管理是企業軟件中為數不多的難以實現平台化的領域之一。供應鏈都有些獨特，數據孤島問題阻礙了對管理它至關重要的供應鏈可見性。

With the Manufacturing Cloud, Snowflake continues to evolve from being a data cloud to also being an operational hub for large enterprises and institutions. We also announced that Blue Yonder, one of the largest software companies in supply chain management, will fully replatform onto Snowflake. Blue Yonder is a key participant in both the manufacturing and the retail data clouds. They are the first major supply chain provider to make this commitment to creating the end-to-end supply chain platform on Snowflake.

借助製造雲，Snowflake 繼續從數據云發展成為大型企業和機構的運營中心。我們還宣布，供應鏈管理領域最大的軟件公司之一 Blue Yonder 將完全重新平台化到 Snowflake 上。 Blue Yonder 是製造和零售數據云的主要參與者。他們是第一家承諾在 Snowflake 上創建端到端供應鏈平台的主要供應鏈供應商。

Supply chain management is requiring network discipline as the change are typically comprised of numerous different entities. We, therefore, expect significant network effects from the strategic alliance with Blue Yonder.

供應鏈管理需要網絡紀律，因為變更通常由許多不同的實體組成。因此，我們預計與 Blue Yonder 的戰略聯盟會產生顯著的網絡效應。

Our Summit conference in June will feature more significant product announcements, and we look forward to seeing you there. With that, I'll turn the call over to Mike.

我們 6 月份的峰會將發布更多重要的產品，我們期待與您相見。有了這個，我會把電話轉給邁克。

Thank you, Frank. Q1 product revenues were $590 million, representing 50% year-over-year growth, and remaining performance obligations grew 31% year-over-year totaling $3.4 billion. Of the $3.4 billion in RPO, we expect approximately 57% to be recognized as revenue in the next 12 months. This represents a 40% increase compared to our estimate as of the same quarter last year. Our net revenue retention rate of 151% includes 5 new customers with $1 million in trailing 12-month product revenue.

謝謝你，弗蘭克。第一季度產品收入為 5.9 億美元，同比增長 50%，剩餘履約義務同比增長 31%，總計 34 億美元。在 34 億美元的 RPO 中，我們預計大約 57% 將在未來 12 個月內確認為收入。與我們對去年同期的估計相比，這意味著增長了 40%。我們 151% 的淨收入保留率包括 5 個新客戶，過去 12 個月的產品收入為 100 萬美元。

Q1 revenue reflects strong performance in a challenging environment. We continue to focus on growth and efficiency. We generated $287 million of non-GAAP adjusted free cash flow, outperforming our Q1 target.

第一季度的收入反映了在充滿挑戰的環境中的強勁表現。我們繼續關注增長和效率。我們產生了 2.87 億美元的非 GAAP 調整後自由現金流，超過了我們第一季度的目標。

In Q1, consumption varies from month to month. We benefited from strong consumption in February and March. Starting in April, consumption slowed after the Easter holidays through today. The strength in the quarter was driven by our health care and manufacturing customers. Financial services customers outperformed our expectations.

在第一季度，消費量因月而異。我們受益於 2 月和 3 月的強勁消費。從 4 月開始，復活節假期過後到今天消費放緩。本季度的實力是由我們的醫療保健和製造業客戶推動的。金融服務客戶的表現超出了我們的預期。

From a geographic standpoint, we saw in line performance globally with the exception of our SMB and APJ segments. It is challenging to identify a single cause of the consumption slowdown between Easter and today. A few of our largest customers have scrutinized Snowflake costs as they face headwinds in their own businesses. For example, some organizations have reevaluated their data retention policies to delete stale and less valuable data. This lowers their storage bill and reduces compute cost. We have worked with a few large customers more recently on these efforts and expect these trends to continue. History has shown that price performance benefits long-term consumption.

從地域的角度來看，我們在全球範圍內看到了在線表現，但我們的 SMB 和 APJ 部分除外。很難確定從復活節到今天消費放緩的單一原因。我們的一些最大客戶在他們自己的業務面臨逆風時仔細審查了 Snowflake 的成本。例如，一些組織重新評估了他們的數據保留政策，以刪除陳舊和價值較低的數據。這降低了他們的存儲費用並降低了計算成本。我們最近與一些大客戶就這些努力進行了合作，並預計這些趨勢將繼續下去。歷史表明，價格表現有利於長期消費。

From a booking standpoint, we saw headwinds globally with the exception of our North American large enterprise segment. This is not due to competitive pressures, but because customers remain hesitant to sign large multiyear deals. Productivity is not where we want it to be and our updated outlook reflects this. Q1 is always a challenging bookings quarter, and the current macro environment, magnifies that, but we are still not satisfied with our results. We will only invest in areas that yield returns. For that reason, we will prioritize existing sales resources to drive growth before we onboard new capacity.

從預訂的角度來看，我們在全球範圍內看到了逆風，但我們的北美大型企業部門除外。這不是由於競爭壓力，而是因為客戶仍然對簽署大型多年期交易猶豫不決。生產力不是我們想要的，我們更新的展望反映了這一點。第一季度始終是一個充滿挑戰的預訂季度，當前的宏觀環境放大了這一點，但我們仍然對我們的結果不滿意。我們只會投資於有回報的領域。出於這個原因，我們將優先考慮現有的銷售資源以推動增長，然後再增加新產能。

Q1 represented another quarter of continued progress on profitability. Our non-GAAP product gross margin was 77%, more favorable pricing with our cloud service providers, product improvements, scale in our public cloud data centers and continued growth in large customer accounts will contribute to year-over-year gross margin improvements. Non-GAAP operating margin was 5%, benefiting from revenue outperformance and savings on sales and marketing spend. Our non-GAAP adjusted free cash flow margin was 46%, positively impacted by strong linearity of collections and some early collections of May receivables. We continue to have a strong cash position with $5 billion in cash, cash equivalents and short-term and long-term investments. We used approximately $192 million of our cash to repurchase approximately 1.4 million shares to date at an average price of $136. We will continue to opportunistically repurchase shares using our free cash flow.

第一季度是盈利能力持續進步的又一個季度。我們的非 GAAP 產品毛利率為 77%，與我們的雲服務提供商的更優惠定價、產品改進、我們公共雲數據中心的規模以及大客戶賬戶的持續增長將有助於毛利率同比提高。非 GAAP 營業利潤率為 5%，受益於收入表現出色以及銷售和營銷支出的節省。我們的非 GAAP 調整後自由現金流利潤率為 46%，受到收款線性度強和 5 月應收賬款提前收款的積極影響。我們繼續保持強勁的現金狀況，擁有 50 億美元的現金、現金等價物以及短期和長期投資。迄今為止，我們使用了大約 1.92 億美元的現金以平均 136 美元的價格回購了大約 140 萬股股票。我們將繼續利用我們的自由現金流機會主義地回購股票。

As Frank mentioned, we are acquiring Neeva. We are excited to welcome approximately 40 employees from Neeva to Snowflake, and the full impact is reflected in our outlook.

正如 Frank 提到的，我們正在收購 Neeva。我們很高興地歡迎來自 Neeva 的大約 40 名員工來到 Snowflake，我們的前景反映了全部影響。

Before turning to guidance, I would like to discuss the recent trends we've been observing. As I mentioned, we have seen slower-than-expected revenue growth since Easter. Contrary to last quarter, the majority of this underperformance is driven by older customers. Although we expect this to reverse, we are flowing these patterns through to the full year due to our lack of predictability and visibility of (inaudible). As a result, we're reining in costs until we see a consistent change in consumption.

在轉向指導之前，我想討論一下我們最近觀察到的趨勢。正如我所提到的，自複活節以來，我們的收入增長低於預期。與上一季度相反，這種表現不佳的主要原因是老客戶。儘管我們預計這種情況會逆轉，但由於我們缺乏（聽不清）的可預測性和可見性，我們正在將這些模式貫穿到全年。因此，我們會控製成本，直到我們看到消費量出現持續變化。

We are still focused on investing in efficient growth with a concentration on continuing to sign new customers, ensuring these customers are migrated quickly and successfully, leveraging our PS team and partner resources when selling our newer solutions such as Snowpark and Streamlit to win more personas in the enterprise. We are confident that this will ultimately lead to the data cloud network effects we have laid out over the past few years. We still believe we can achieve $10 billion of product revenue in fiscal 2029 with a better margin profile than we laid out last year.

我們仍然專注於投資於有效增長，專注於繼續與新客戶簽約，確保這些客戶快速成功地遷移，在銷售我們的新解決方案（如 Snowpark 和 Streamlit）時利用我們的 PS 團隊和合作夥伴資源贏得更多角色企業。我們有信心，這最終將導致我們在過去幾年中佈局的數據云網絡效應。我們仍然相信，我們可以在 2029 財年實現 100 億美元的產品收入，並且利潤率比我們去年計劃的要好。

Now let's turn to guidance. For the second quarter, we expect product revenues between $620 million and $625 million, representing year-over-year growth between 33% and 34%.

現在讓我們轉向指導。對於第二季度，我們預計產品收入在 6.2 億美元至 6.25 億美元之間，同比增長 33% 至 34%。

Turning to margins. We expect, on a non-GAAP basis, 2% operating margin, and we expect 361 million diluted weighted average shares outstanding. For the full year fiscal 2024, we expect product revenues of approximately $2.6 billion, representing year-over-year growth of approximately 34%.

轉向利潤率。我們預計，在非 GAAP 基礎上，營業利潤率為 2%，我們預計有 3.61 億攤薄加權平均流通股。對於 2024 財年全年，我們預計產品收入約為 26 億美元，同比增長約 34%。

Turning to profitability. For the full year fiscal 2024, we expect, on a non-GAAP basis, approximately 76% product gross margin, 5% operating margin and 26% adjusted free cash flow margin. And we expect 362 million diluted weighted average shares outstanding. We will continue to prioritize hiring in product and engineering. We have slowed our hiring plan for the year, and we expect to add approximately 1,000 employees in fiscal 2024, inclusive of M&A.

轉向盈利能力。對於 2024 財年全年，我們預計，在非 GAAP 基礎上，產品毛利率約為 76%，營業利潤率為 5%，調整後的自由現金流利潤率為 26%。我們預計有 3.62 億股攤薄加權平均流通股。我們將繼續優先招聘產品和工程人員。我們已經放慢了今年的招聘計劃，我們預計在 2024 財年將增加約 1,000 名員工，包括併購。

And lastly, we will host our Investor Day on June 27 in Las Vegas in conjunction with Snowflake 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 27 日在拉斯維加斯舉辦投資者日活動，同時舉辦我們的年度用戶大會 Snowflake Summit。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. I apologize for all the coughing.

有了這個，接線員，你現在可以打開問題熱線了。我為所有的咳嗽道歉。

(Operator Instructions) Our first question is from Mark Murphy with JPMorgan.

（操作員說明）我們的第一個問題來自摩根大通的馬克墨菲。

Frank, do you sense any connection to the cadence of hyperscaler cost optimization activity? In other words, if the AWS and Azure optimizations begin to normalize within a few quarters, do you think that Snowflake's consumption patterns and sequential growth rates would perk up around the same time? Or do you look at this as a more separate kind of phenomenon? And then I have a quick follow-up.

弗蘭克，您是否感覺到與超大規模成本優化活動的節奏有任何联系？換句話說，如果 AWS 和 Azure 的優化在幾個季度內開始正常化，您認為 Snowflake 的消費模式和連續增長率會在大約同一時間振作起來嗎？還是您將其視為一種更獨立的現象？然後我有一個快速跟進。

Yes. Well, we think that because Amazon is such a large percentage of our overall deployments that they are a good proxy. We just know from talking to them that what they experience, we experience as well. So there's definitely a ripple effect because we're in the stack. So the answer generally speaking is, yes, we will see that. Microsoft is smaller so they're not as predictive of our experience as AWS would be.

是的。好吧，我們認為，因為亞馬遜在我們的整體部署中所佔比例如此之大，所以他們是一個很好的代理。通過與他們交談，我們只知道他們所經歷的，我們也經歷過。所以肯定會產生連鎖反應，因為我們在堆棧中。所以一般來說答案是，是的，我們會看到的。微軟規模較小，因此他們無法像 AWS 那樣預測我們的體驗。

Okay. Then as a quick follow-up, Mike, I'm sorry to ask you a question. It sounds like you've got a bit of a cold. But is it safe to assume that you're completely through the revenue headwinds from Graviton adoption and the warehouse builder product? I think that's the case. But I'm also curious, are there any other analogous developments on the horizon that we could be thinking about that you might have baked into guidance in the next several quarters?

好的。然後作為快速跟進，邁克，我很抱歉問你一個問題。聽起來你有點感冒了。但是，可以安全地假設您已經完全擺脫了 Graviton 採用和倉庫構建器產品帶來的收入逆風嗎？我認為是這樣的。但我也很好奇，在接下來的幾個季度中，我們是否可以考慮您可能已經納入指導的任何其他類似的發展？

Yes. We've fully migrated all of our customers in AWS to Graviton, too. And that's the bulk of where our revenue is. And I want to remind you, there's really 3 types of optimization. There's the optimizations by the cloud vendors, and that's with better hardware, better performance. Then there's the optimizations that we do regularly in our software, which improve performance and hence cheaper for our customers. And generally, those 2 combined, we forecast that there's a 5% headwind every year to our revenue associated with those.

是的。我們也已將 AWS 中的所有客戶完全遷移到 Graviton。這就是我們收入的大部分。我想提醒您，實際上有 3 種優化類型。雲供應商進行了優化，硬件更好，性能更好。然後是我們在我們的軟件中定期進行的優化，它提高了性能，因此對我們的客戶來說更便宜。一般來說，將這兩者結合起來，我們預測與這些相關的收入每年都會有 5% 的逆風。

And the third optimization is the one that we really saw in a few of our largest customers with them just wanting to really change their storage retention policies. Like one customer went from 5 to 3 years, and it's a massive -- petabytes and petabytes of data. And so we lose that storage revenue. But on top of that, now your queries run quicker because you're querying less amounts of data. And we are seeing more customers wanting to do that. And I spoke to some of the hyperscalers. I won't say which one. And they confirm they're seeing retention policies change within their customers wanting to archive for older data.

第三個優化是我們在一些最大的客戶中真正看到的，他們只是想真正改變他們的存儲保留策略。就像一個客戶從 5 年到 3 年，這是一個巨大的——數 PB 和 PB 的數據。所以我們失去了存儲收入。但最重要的是，現在您的查詢運行得更快，因為您查詢的數據量更少了。我們看到越來越多的客戶想要這樣做。我還與一些超大規模用戶進行了交談。我不會說是哪一個。他們確認他們看到他們的客戶想要存檔舊數據的保留政策發生了變化。

Our next question is from Kash Rangan with Goldman Sachs.

我們的下一個問題來自高盛的 Kash Rangan。

If you could just offer, to the degree that you can, what are your customers that are going through consumption optimization telling you with respect to when that's likely to plateau and when they are likely to come back to "normal consumption," if you can?

如果你能提供，在你能做到的程度上，你的客戶正在經歷消費優化，告訴你什麼時候可能會停滯不前，什麼時候他們可能會回到“正常消費”，如果你能的話？

Yes. Kash, I would say, look, there's -- just to put a little bit more color on, there's optimization with is just how do we run what we're already running more efficiently and driving a level of savings that way. But there's sort of another layer on top of that. I would call it rationalization.

是的。 Kash，我會說，看，有——只是為了增加一點色彩，優化就是我們如何更有效地運行我們已經運行的東西，並以這種方式推動一定程度的節省。但在那之上還有另一層。我稱之為合理化。

One of the things that we've seen happen over the last couple of quarters is that the CFO is in the business, and this is sort of an expression that we use in enterprise. What we're selling is that there is a level of oversight and scrutiny that's normally not there. And this is not a frequent occurrence. You only see this happening in fairly severe episodes. And in the beginning it's like, hey, we do smaller contracts or term contracts. But then it's like, hey, you're going to live within your means. Here's the amount of money you're going to spend, and you're going to make it work. And you can figure out where you're going to cut to fit into our box. So that's really dynamics that we've seen playing out there.

我們在過去幾個季度看到的一件事是首席財務官在業務中，這是我們在企業中使用的一種表達方式。我們出售的是通常不存在的一定程度的監督和審查。而且這種情況並不經常發生。您只會在相當嚴重的事件中看到這種情況。一開始就像，嘿，我們簽訂較小的合同或定期合同。但是就像，嘿，你會量入為出。這是你要花的錢，你要讓它發揮作用。你可以弄清楚你要在哪里切割以適應我們的盒子。所以這就是我們在那裡看到的真正的動態。

Now in terms of your question, when is it all going to be over? These things do run their course because in the end, we're settling in. I said in my prepared remarks, things are unsettled. But eventually, they will settle. We will settle into new patterns, and then we sort of resume from there. But I think as of right now, I think things are still unsettled, and people are adjusting. And we don't have real strong visibility in terms of, okay, when is it all going to be different.

現在就你的問題而言，這一切什麼時候結束？這些事情確實會順其自然，因為最終，我們正在安頓下來。我在準備好的發言中說過，事情還沒有解決。但最終，他們會安定下來。我們將適應新的模式，然後我們從那裡恢復。但我認為截至目前，我認為事情仍未解決，人們正在調整。而且我們沒有真正強大的可見性，好吧，什麼時候一切都會不同。

Our next question is from Keith Weiss with Morgan Stanley.

我們的下一個問題來自摩根士丹利的 Keith Weiss。

Excellent. Mike, this one's for you. It might be a little unfair, but it's the one that I'm getting most from investors, and it's about kind of guidance methodology and if anything has changed in that. We've seen the forward forecast have to come down a couple of times over the past couple of quarters. And there's a lot of moving pieces in both the macro environment and kind of how your customers are acting. How can we give investors confidence that this is the last cut, that we're not going to be running into new types of optimization on a go-forward basis and further taking down our forecast for the fiscal year?

出色的。邁克，這是給你的。這可能有點不公平，但這是我從投資者那裡得到最多的，它是關於一種指導方法，如果有什麼改變的話。在過去的幾個季度中，我們已經看到前瞻性預測不得不下調幾次。在宏觀環境和客戶行為方面都有很多變化。我們如何才能讓投資者相信這是最後一次削減，我們不會在前進的基礎上進行新型優化，並進一步降低我們對本財年的預測？

Well, the way we -- there is no change in our forecast methodology, and we forecast looking literally at consumption trends on a daily basis, literally 4 weeks prior to the earnings through yesterday. And what I would say was wholly unique.

好吧，我們的方式 - 我們的預測方法沒有變化，我們預測從字面上看每天的消費趨勢，實際上是在收益前 4 周到昨天。我要說的是完全獨一無二的。

This, in the past, is we literally saw 4 weeks in April where there was no week-over-week growth per se or not material. And we do think that was driven a lot by some of these customers. That's when it happened, some of these big optimizations on storage retention policies. But in a consumption model, customers have the ability to dial it back, and they can increase it as well, too, when they get more confidence in their business. And I can only guide based upon the data we have available to us.

在過去，我們確實在 4 月份看到了 4 週，其中本身沒有周環比增長或沒有實質性增長。我們確實認為這是由其中一些客戶推動的。就在那時，對存儲保留策略進行了一些重大優化。但在消費模式中，客戶有能力調回它，當他們對自己的業務更有信心時，他們也可以增加它。我只能根據我們可用的數據進行指導。

Our next question is from Alex Zukin with Wolfe Research.

我們的下一個問題來自 Wolfe Research 的 Alex Zukin。

So maybe one financial one. If we -- and then a technical one. If we look at the balance of the growth headwinds from optimization versus rationalization, or meaning how much people are doing less of versus still tight with the purse strings to do more with, kind of how does that balance look? How has it changed over the course of the last 6 to 9 months?

所以也許是一個金融的。如果我們——然後是技術問題。如果我們看看優化與合理化帶來的增長逆風之間的平衡，或者意味著有多少人做的更少與錢包仍然緊張以做更多的事情，那麼這種平衡看起來如何？在過去的 6 到 9 個月中它發生了怎樣的變化？

And then maybe just from a technical perspective, what do you get with Neeva? Why is it important? What does it unlock for your customer base relative to generative AI?

然後也許只是從技術角度來看，您從 Neeva 得到了什麼？它為什麼如此重要？相對於生成 AI，它為您的客戶群解鎖了什麼？

So in terms of what customers are doing, actually, the number of jobs, the number of queries actually grew 57% year-over-year in the quarter. It's outpacing our revenue. The queries are just running more efficiently. And that is because of some of the optimizations, both if you reduce the amount of storage you're running queries on, they run faster. It's also the impact you're getting right now of the full Graviton to this year versus the -- compared from last year. So the number of jobs is actually -- growth is actually outpacing revenue and just we're becoming so much more efficient for our customers. And on Neeva, we'll go to Christian who's here.

因此，就客戶在做什麼而言，實際上是工作數量，查詢數量在本季度實際上同比增長了 57%。它超過了我們的收入。查詢只是更有效地運行。這是因為一些優化，如果你減少運行查詢的存儲量，它們運行得更快。這也是你現在對今年的完整 Graviton 與去年相比的影響。因此，工作崗位的數量實際上是——增長實際上超過了收入，而且我們對客戶的效率越來越高。在 Neeva，我們將去找在場的 Christian。

Yes. Christian here. The broad vision that we communicated to all of you over the last several years is Snowflake is on a mission to extend its capabilities so we can bring computation to happen close to the data. It has evolved us into an application platform. And a core use case for applications is not only search and search-enabled experiences, but with the advent of generative AI is the notion of conversational experiences.

是的。基督徒在這裡。在過去的幾年裡，我們向大家傳達了一個廣闊的願景，即 Snowflake 的使命是擴展其功能，以便我們可以使計算發生在靠近數據的地方。它使我們發展成為一個應用程序平台。應用程序的核心用例不僅是搜索和支持搜索的體驗，而且隨著生成人工智能的出現，對話體驗的概念也隨之出現。

And the folks from Neeva are the ones who's going to power, help us accelerate the efforts around Snowflake as a platform for search and conversational experiences, but most important, within the security perimeter of Snowflake with the customers' data so that they can leverage all these new innovation and technology, but with the safety on the privacy and security of the data.

來自 Neeva 的人們將提供動力，幫助我們加快圍繞 Snowflake 作為搜索和對話體驗平台的努力，但最重要的是，在 Snowflake 的安全範圍內與客戶數據一起使用，以便他們可以利用所有這些新的創新和技術，但在隱私和數據安全上具有安全性。

Our next question is from Raimo Lenschow with Barclays.

我們的下一個問題來自巴克萊銀行的 Raimo Lenschow。

Yes. Mike, I hope you feel better soon. The quick question, last quarter, we talked about like the newer cohorts kind of expanding slightly at the lower pace compared to the more established ones -- the more older ones. Have you seen any change in momentum there? Or is it like if you think about it, like we had the last quarter slower expansion from the newer ones. And now this quarter, we have like more optimization from the older ones, is that like the 2 things? Or are there other kind of factors at work there?

是的。邁克，我希望你快點好起來。快速的問題，上個季度，我們談到了與更成熟的人群相比，較新的人群以較低的速度略有擴張——更老的人群。你看到那裡的勢頭有什麼變化嗎？或者，如果您考慮一下，就像我們上個季度的擴張速度較新季度放緩一樣。而現在這個季度，我們希望從舊版本中獲得更多優化，就像這兩件事一樣嗎？還是有其他因素在起作用？

No, good question. The newer ones are growing faster. The older, obviously, are the larger dollars. So when they do optimizations, that has a bigger impact. And it's interesting too, the net revenue retention for growth within AWS, those customers are materially above where our overall company is, and that's because we're relatively new to that. So the Azure cloud is really starting to take off for us as well.

不，好問題。新的增長速度更快。顯然，越老的美元越多。因此，當他們進行優化時，會產生更大的影響。有趣的是，AWS 內部增長的淨收入保留，這些客戶大大高於我們整個公司的水平，這是因為我們相對較新。因此，Azure 雲也真正開始為我們騰飛。

Okay. And then one maybe, to help you with your voice, for Frank. But Frank, if you think about the changes in policy in terms of storage, retention and stuff like that, I mean there was a reason why people store their data, like for a certain number of years, et cetera. Do you think this -- what you're seeing now is kind of more a temporary thing? Or -- and as we're coming out, people just kind of do it -- have a different approach? Kind of do you think that's kind of a permanent move that's happening here?

好的。然後也許，為了幫助你的聲音，弗蘭克。但是弗蘭克，如果你考慮存儲、保留等方面的政策變化，我的意思是人們存儲數據是有原因的，比如一定年限，等等。你認為這 - 你現在看到的是一種暫時的東西嗎？或者——當我們出來的時候，人們只是在做這件事——有不同的方法？您是否認為這是這裡發生的永久性舉措？

I don't think it's permanent. Look, like I said, the CFOs in the business, they're giving very direct guidance in terms of here's where you need to be. Then the operating teams are starting to look at, okay, how do we implement this? Sometimes the low-hanging fruit is we'll just cut the data back. The processes might actually not be running as well, okay? So there's actually a cost. But you know what? The cost concern is prevailing at the moment because of the general sentiment that we are.

我不認為它是永久性的。看，就像我說的，企業的首席財務官，他們就這裡是你需要去的地方提供非常直接的指導。然後運營團隊開始考慮，好吧，我們如何實施呢？有時，唾手可得的成果是我們只是削減數據。這些進程實際上可能也沒有運行，好嗎？所以實際上是有成本的。但你知道嗎？由於我們的普遍情緒，成本問題目前很普遍。

In 2020 and 2021, it was growth at all costs and the mentality was let it rip. Now we're in the complete inverse of that situation where we have strong certainly predictability on cost and so on. I don't think it will last. We're just on the other side of the spectrum right now, and we will reconverge to the mean at some point here.

在 2020 年和 2021 年，它是不惜一切代價實現增長的，心態被撕裂了。現在我們處於完全相反的情況，我們對成本等有很強的可預測性。我不認為它會持續下去。我們現在只是在頻譜的另一端，我們將在此處的某個時刻重新收斂到均值。

Our next question is from Brad Zelnick with Deutsche Bank.

我們的下一個問題來自德意志銀行的 Brad Zelnick。

Great. Mike, I know in a consumption model, obviously, it's difficult to predict the number of new workloads and transaction volumes. A lot of that we know is tied to macro. I just wanted to come back to the optimization topic. You talked about the 3 different types of optimization. Is there any way you can compare your total customer portfolio to the most optimized customer that you have just to get a sense of maybe what the downside is if everyone were optimized as your most optimized customer?

偉大的。邁克，我知道在消費模型中，顯然很難預測新工作負載和交易量的數量。我們所知道的很多都與宏觀有關。我只想回到優化主題。您談到了 3 種不同類型的優化。有什麼方法可以將您的總客戶組合與您擁有的最優化客戶進行比較，以了解如果每個人都被優化為最優化客戶，可能會有什麼不利影響？

That would be so hard to do. I don't have that data. Each customer is different.

那將很難做到。我沒有那個數據。每個客戶都是不同的。

Can I ask you a question? Right...

我可以問你一個問題嗎？正確的...

Yes?

是的？

Can I ask you a question where you do have the data -- oh, please go ahead, sorry.

我可以問你一個問題，你在哪裡有數據——哦，請繼續，抱歉。

No, I was going to add that in certain instances, some of these optimizations in the third category that Mike described and what Frank was alluding to, it's changing how the business thinks about their needs. So when you make the decision to reevaluate our storage policy, there's a business impact that only customers can do. So it's difficult for us to estimate that type of decision.

不，我要補充一點，在某些情況下，Mike 描述的第三類中的一些優化以及 Frank 所暗示的，它正在改變企業對他們需求的看法。因此，當您決定重新評估我們的存儲策略時，就會產生只有客戶才能做出的業務影響。所以我們很難估計這種類型的決定。

That's helpful. Mike, a question I know you do have the answer to. Since you forecast the trends every week, any commentary on how May looks relative to April?

這很有幫助。邁克，一個我知道你有答案的問題。既然你每週都會預測趨勢，那麼對 5 月相對於 4 月的情況有何評論？

That's reflected in the guide that I gave you, the $2.6 billion for the year. I would say that there were a couple periods in May, where it was strong, but it's kind of -- it's okay, but it's not where we want it to be, but that's reflected in the guide now.

這反映在我給你的指南中，即當年的 26 億美元。我會說 5 月有幾個時期，它很強勁，但它有點——沒關係，但它不是我們想要的，但這反映在現在的指南中。

Our next question is from Karl Keirstead with UBS.

我們的下一個問題來自瑞銀的 Karl Keirstead。

Okay. Mike, if I could just build on Brad's line of questioning. The spirit of it is what assumptions you're embedding in your second half guidance. Are you essentially reflecting the April, May environment you saw and straightlining it? Or are you taking a little bit more of a conservative approach and sort of haircutting that? It seems that it or maybe the FINS vertical gets a little bit weaker? That's question number one.

好的。邁克，如果我可以繼續 Brad 的提問的話。它的精神是您在下半年指南中嵌入的假設。您是否在本質上反映了您所看到的四月、五月的環境並將其直線化？還是您採取了更保守的方法並進行了理髮？似乎它或者 FINS 垂直變弱了一點？這是第一個問題。

Question number two, maybe this is best suited for Frank. Frank, Mike mentioned in his comments that sales productivity is not where Snowflake wanted it to be. Could you elaborate on that? Because that sounds like some of the pressure may not be entirely macro, but might be sales execution. So I'd love to hear a little bit if I interpreted that correctly. And the steps you're taking maybe to turn it around.

第二個問題，也許這最適合弗蘭克。弗蘭克，邁克在他的評論中提到，銷售效率並不是 Snowflake 想要的。你能詳細說明一下嗎？因為這聽起來有些壓力可能不完全是宏觀的，但可能是銷售執行。所以如果我的解釋正確的話，我很想听聽一點。你正在採取的步驟可能會扭轉局面。

Sorry, Karl. Yes, we are expecting that there will be week-over-week growth on average with our customers that will compound, but it's at a much lower pace than it was prior, and it's more of what we've been seeing in the last 4 weeks is what we're expecting inside there. I'm not expecting a straight line from where we are today to the end of the year.

對不起，卡爾。是的，我們預計我們的客戶平均每週都會有復合增長，但速度比以前低得多，而且我們在過去 4 年看到的更多weeks 是我們在裡面所期待的。我不希望從我們今天的位置到今年年底是一條直線。

Yes. On the sales productivity side, I do think that's very much a macro thing. There comes a point where you can't push any harder. And we have applied the resources, but we're not converting on the resources in a way that we think is optimal. So is there an execution aspect? There always is, right? I mean that's just day-to-day sales management. But in all the years of doing this kind of work, I felt like I've always sort of under-applied the resource. In hindsight, I thought that -- I always thought I could have done more.

是的。在銷售效率方面，我確實認為這是一個非常宏觀的事情。有一點你不能再努力了。我們已經應用了資源，但我們並沒有以我們認為最佳的方式轉換資源。那麼有執行方面嗎？總是有的，對吧？我的意思是這只是日常的銷售管理。但在從事此類工作的這些年裡，我覺得我總是沒有充分利用這些資源。事後看來，我認為——我一直認為我可以做得更多。

This is definitely a situation where I feel like we have applied tremendous amounts of resources. We've been very, very successful at it. But it comes to a point where, okay, we need to become more selective, more prioritized on driving the performance. So I definitely think it's a macro thing. I mean the sentiment out there is of a sort that you just can't push it any harder than up to a certain point.

這絕對是一種我覺得我們已經應用了大量資源的情況。我們在這方面非常非常成功。但到了這樣一個地步，好吧，我們需要變得更有選擇性，更優先考慮推動績效。所以我絕對認為這是一個宏觀的事情。我的意思是，外面的情緒是，你不能再把它推到某個點。

Our next question is from Patrick Walravens with JMP.

我們的下一個問題來自 JMP 的 Patrick Walravens。

We can't hear you, Pat.

我們聽不到你的聲音，帕特。

If I remember right, Blue Yonder is JDA as i2 and Manugistics. So anything about why that's so interesting would be great.

如果我沒記錯的話，Blue Yonder 就是 JDA 作為 i2 和 Manugistics。所以關於為什麼如此有趣的任何事情都會很棒。

Look, I had a long-term fascination with supply chain management because supply chain management has never been really platformed in terms of software. It's an e-mail spreadsheet operation. It's incredibly inefficient, and it's an incredibly high volume opportunity. And the reason that it couldn't be platformed is, first of all, each supply chain is different. So it's very hard to have a standard solution for something that is so variable. But secondly, is the data problem.

看，我對供應鏈管理長期著迷，因為供應鏈管理從未真正在軟件方面實現平台化。這是一個電子郵件電子表格操作。這是非常低效的，而且是一個非常大的機會。之所以不能平台化，首先是每個供應鏈都不一樣。因此，很難為變化如此之大的事物製定標準解決方案。但其次，是數據問題。

If you can't establish visibility across all the entities that make up the supply chain, you stand no chance of solving that problem. So the reason that I find it so interesting for Snowflake is that, look, all the entities in the supply chain will become Snowflake accounts, right, because that's the way everybody will have visibility to everybody else. And we have a real fighting chance of solving it.

如果您不能在構成供應鏈的所有實體中建立可見性，您就沒有機會解決該問題。所以我覺得 Snowflake 如此有趣的原因是，看，供應鏈中的所有實體都將成為 Snowflake 帳戶，對吧，因為這是每個人都可以看到其他人的方式。我們有解決它的真正戰鬥機會。

Secondly, the processes that run in supply chain management are extremely computationally intensive, and they run in very, very high volume. And of course, Snowflake is ideally suited for taking on those kinds of workloads. So I really think that supply chain management will be the most network segment of all industries that we're operating in. And today, the most network segment that we're running in is financial services by far. But I think it will be overtaken by manufacturing and retail in the fullness of time because there's absolutely no penetration right there. These are unsolved problems, very much almost in the history of computing. That's how serious that is. So fantastic historical opportunity for the technology to address.

其次，供應鏈管理中運行的流程計算量非常大，而且運行量非常非常大。當然，Snowflake 非常適合承擔這些類型的工作負載。所以我真的認為供應鏈管理將是我們經營的所有行業中網絡最多的部分。今天，我們經營的網絡最多的部分是金融服務。但我認為它會在適當的時候被製造業和零售業超越，因為那裡絕對沒有滲透。這些是未解決的問題，幾乎在計算的歷史上。那是多麼嚴重。技術解決的絕佳歷史機會。

Our next question is from Kirk Materne with ISI.

我們的下一個問題來自 ISI 的 Kirk Materne。

Frank, with sort of the explosion in questions around AI over the last 6 months, do you think that buyers or executives are tying the opportunities with AI to the data yet? Meaning, I know conceptually, they might get that, but are any of your conversations with customers sort of starting to percolate because of AI and the need to get your data sorted out to take advantage of that? Or is -- are most people still sort of in the discovery phase on that front?

弗蘭克，在過去 6 個月裡，圍繞人工智能的問題呈爆炸式增長，你認為買家或高管是否正在將人工智能的機會與數據聯繫起來？意思是，我從概念上知道，他們可能會明白這一點，但是你與客戶的任何對話是否因為人工智能而開始滲透，並且需要整理你的數據以利用它？或者是——大多數人是否仍處於這方面的發現階段？

And then Mike, can you just talk if Neeva impacts the op margin guidance for the full year at all? I was just kind of curious, you've mentioned savings, but margins are sort of flattish year-over-year. I was just kind of curious if that had any impact.

然後邁克，你能不能談談 Neeva 是否會影響全年的運營利潤率指導？我只是有點好奇，你提到了儲蓄，但利潤率同比持平。我只是有點好奇這是否有任何影響。

It's Frank. Obviously, customers make the connection between data and the ability to take advantage of the large language models and the natural language interface and then all that kind of stuff, and that's already happening. And the services, they are today available on Snowflake, and they're also available in the AI space, you can already rig things together and make some interesting progress. But the thing is you need to have highly curated, highly optimized data. And then that is what we do at Snowflake to really power these models. You cannot just indiscriminately let these things loose on data that is -- that people don't understand in terms of its quality and its definition, its lineage and all these kinds of things.

是弗蘭克。顯然，客戶將數據與利用大型語言模型和自然語言界面的能力以及所有類似的東西聯繫起來，這已經在發生了。還有這些服務，它們現在可以在 Snowflake 上使用，它們也可以在 AI 領域使用，你已經可以將它們組合在一起並取得一些有趣的進展。但問題是您需要擁有高度精選、高度優化的數據。這就是我們在 Snowflake 所做的真正為這些模型提供動力的工作。你不能不分青紅皂白地讓這些東西散佈在數據上——人們不了解它的質量、它的定義、它的血統和所有這些類型的東西。

So I think we are in a really great place. And I said in the prepared remarks, data has a gravitational pull. So we will attract tremendous demand for these type of workloads. And our strategy is to enable that to the maximum towards the extent possible.

所以我認為我們處在一個非常好的地方。我在準備好的發言中說過，數據具有引力。因此，我們將吸引對此類工作負載的巨大需求。我們的策略是盡可能地實現這一點。

And then with regards to Neeva, Kirk, that's fully baked into the guidance. They have a number of -- well, actually, all of their engineers are very senior engineers, and they're all based in the U.S. They're very expensive people, these people.

然後關於 Neeva，Kirk，這完全融入了指南。他們有很多——嗯，實際上，他們所有的工程師都是非常高級的工程師，他們都在美國工作。他們是非常昂貴的人，這些人。

Our next question is from Brent Thill with Jefferies.

我們的下一個問題來自 Jefferies 的 Brent Thill。

Frank, this concept of Snow for everyone and having a simple chat like GPT UI in front of the Snowflake data, bring it to the mass market, I mean how long do you think this takes to -- where you start to see that where it's -- we have you deployed internally, but I have to go to 1 person that's the power user. When do you think that, ultimately, we can start seeing that in everyone's desktop?

弗蘭克，這個 Snow 的概念適用於每個人，並在 Snowflake 數據前進行像 GPT UI 這樣的簡單聊天，將其推向大眾市場，我的意思是你認為這需要多長時間——你從哪裡開始看到它在哪裡-- 我們已經在內部部署了您，但我必須去找 1 個超級用戶。您認為我們最終什麼時候可以開始在每個人的桌面上看到它？

Well, I think that the more -- I don't want to say simplistic, it might not be the right characterization. But for example, running these things on top of, for example, Salesforce data in Snowflake, which is a very common thing, something that we're already doing internally is -- that's going to be available in the second half all over the place.

好吧，我認為更多 - 我不想說簡單化，這可能不是正確的描述。但是，例如，在 Snowflake 中的 Salesforce 數據之上運行這些東西，這是很常見的事情，我們已經在內部做的事情是——下半年將在所有地方提供.

And people will like it. I like it. I mean I prefer it much over using dashboards and things like that because it just lets me ask questions. But they're also relatively simplistic questions. And where it gets harder, when you start asking much, much harder questions, that's when you start finding the limits of these kind of technologies. So I think we're still sort of in the front-end games state of the development of this technology.

人們會喜歡它。我喜歡。我的意思是我更喜歡它而不是使用儀表板和類似的東西，因為它只是讓我提出問題。但它們也是相對簡單的問題。當你開始提出更多、更難的問題時，就會變得更難，那就是你開始發現這類技術的局限性。所以我認為我們仍然處於這項技術發展的前端遊戲狀態。

And with the content generation side of this, this technology is fascinating and captivating for people. But asking really hard analytical questions that take people weeks and weeks or even months to figure out, that will take some workforce software to do that in a matter of seconds to be productive that way. So we're sort of at the top of the hype cycle. The real work really starts now.

從內容生成的角度來看，這項技術對人們來說是迷人而迷人的。但是，提出非常困難的分析問題需要人們花費數週、數週甚至數月才能弄清楚，這將需要一些勞動力軟件在幾秒鐘內完成，從而以這種方式提高工作效率。所以我們有點處於炒作週期的頂端。真正的工作現在才真正開始。

And then, Mike, you mentioned you're not effectively -- it doesn't sound like bringing on a lot of new capacity. There's still 183 job openings on your website. So I guess what you're saying is you're freezing quota-carrying rep onboarding in the interim until you see that capacity? Or are you still bringing people on? How are you thinking about this trend?

然後，邁克，你提到你效率不高——這聽起來不像是帶來了很多新的能力。您的網站上仍有 183 個職位空缺。所以我猜你的意思是你在臨時凍結配額攜帶代表入職，直到你看到那個容量？還是你還在招人？您如何看待這一趨勢？

In the sales organization -- in the sales organization, we're only doing backfills right now, and we will look at performance management and upgrading people. And we could reallocate heads from one region that's underperforming to another region, but no net new hires. Sorry.

在銷售組織中——在銷售組織中，我們現在只做回填，我們將著眼於績效管理和人員升級。我們可以將表現不佳的一個地區的負責人重新分配到另一個地區，但不會淨增聘新員工。對不起。

Our next question is from Gregg Moskowitz with Mizuho.

我們的下一個問題來自 Mizuho 的 Gregg Moskowitz。

All right. You mentioned the change in data retention as a more prevalent form of optimization recently. What about the refresh rate? Are you seeing customers pull back on the frequency with which the data are updating?

好的。您最近提到數據保留的變化是一種更普遍的優化形式。刷新率怎麼樣？您是否看到客戶降低了數據更新的頻率？

No. Christian here. We have not seen changes there. If anything, because of our cost model, the economics are fairly similar if people are updating more versus less frequently or reasonably similar, and we don't see changes in the patterns.

不，這裡是基督徒。我們沒有看到那裡的變化。如果有的話，由於我們的成本模型，如果人們更頻繁地更新而不是更不頻繁地更新或相當相似，那麼經濟學是相當相似的，而且我們沒有看到模式的變化。

All right. That's helpful. And then just a follow-up on Neeva, I guess, either for you or for Frank. So I think of the technology as fairly horizontal in terms of the potential appeal. I'm just wondering if you think this can be an avenue to help land new enterprise customers going forward? And then secondly, how much of a value add do you think that this can truly provide to the installed base?

好的。這很有幫助。然後只是對 Neeva 的跟進，我想，要么是為了你，要么是為了弗蘭克。因此，就潛在吸引力而言，我認為該技術相當水平。我只是想知道您是否認為這可以成為幫助未來獲得新企業客戶的途徑？其次，您認為這可以真正為已安裝的基礎提供多少附加值？

This is Frank. I'll go first. We view search and chat as really complete evolution under the influence of AI, of our relationship with data and how we interact with it. I think most of us remember, when search first became available, how that's just dramatically changed our relationship with data. I'm personally a search junkie. I can't leave it alone. I find it incredibly empowering. But the problem with search has been, it matches on strings, it has 0 context. It's not stateful. And now we have the technology to make search incredibly powerful, also to the point that when it can't find it, it can actually generate the code to answer the questions that are posted in search.

這是弗蘭克。我先走了我們將搜索和聊天視為在人工智能、我們與數據的關係以及我們如何與之交互的影響下真正完成的進化。我想我們大多數人都記得，當搜索首次可用時，它是如何極大地改變了我們與數據的關係。我個人是一個搜索迷。我不能不管它。我發現它令人難以置信的授權。但是搜索的問題是，它匹配字符串，它有 0 個上下文。它不是有狀態的。現在我們擁有的技術可以讓搜索功能變得異常強大，當它找不到時，它實際上可以生成代碼來回答搜索中發布的問題。

So this is incredibly important to basically what we said from the beginning, Snowflake is about mobilizing the world's data, and this is how we're going to do it. I mean search and chat are sort of morphing into a single natural language interface, but the other thing I would caution you, this is not at all about natural language interfaces. A lot of the intelligence that we're talking about is going to be manifested through the interfaces, not just through natural language.

所以這對於我們從一開始就說的基本上是非常重要的，Snowflake 是關於調動世界數據，這就是我們要做的事情。我的意思是搜索和聊天有點變成單一的自然語言界面，但我要提醒你的另一件事是，這根本不是關於自然語言界面。我們正在談論的很多智能將通過界面表現出來，而不僅僅是通過自然語言。

Our next question is from Brad Reback with Stifel.

我們的下一個問題來自 Stifel 的 Brad Reback。

Great. Mike, I hate to pose this to you, but you're probably the best to answer it. Beyond the week-to-week usage patterns in the installed base, are there any other operational data metrics that you're looking at to give you confidence on when NRR will bottom?

偉大的。邁克，我不想向你提出這個問題，但你可能是最好的回答者。除了安裝基礎中的每週使用模式之外，您是否正在查看任何其他運營數據指標來讓您對 NRR 何時觸底充滿信心？

We're -- obviously, that's not the only thing I'd look at. I look at pipeline generation, weighted pipeline. I'm typically looking out 3 to 4 quarters. I'm looking at -- I sit in on the sales call every Monday. We're spending a lot of time with reps these days on what is going on within their accounts. And so -- but the most important thing is consumption patterns today are the biggest indicator of the future. And also looking at new products that may come out. It's hard to forecast anything for them, but that gives us somewhat of confidence. We have some big announcements that are going GA towards -- Streamlit is one of them. We talked about Applica's in private preview. But Streamlit, we think, will be meaningful. And we're really pleased with what we're seeing in the Snowpark daily credit consumption right now.

我們 - 顯然，這不是我唯一要看的東西。我看管道生成，加權管道。我通常會關注 3 到 4 個季度。我正在看——我每週一都會參加銷售電話會議。這些天我們花了很多時間與銷售代表討論他們賬戶中發生的事情。所以——但最重要的是，今天的消費模式是未來的最大指標。並且也在尋找可能出現的新產品。很難為他們預測任何事情，但這給了我們一些信心。我們發布了一些重大公告，這些公告正朝著 GA 的方向發展——Streamlit 就是其中之一。我們在私人預覽中討論了 Applica 的。但我們認為 Streamlit 將是有意義的。我們對目前在 Snowpark 每日信用消費中看到的情況感到非常滿意。

Our next question is from Tyler Radke with Citi.

我們的下一個問題來自花旗銀行的 Tyler Radke。

I'll pose this to Frank to give Mike a break there. But just on Microsoft, so obviously, they're hosting their Build conference this week and a ton of new product announcements, including in data and analytics. But I wanted to ask you more on the partnership front. I think you commented on just seeing some better traction there. I think they've evolved their partner program, including adding you as a Tier 1 partner. So could you just talk about kind of the status of that relationship, how you're fitting in, given some of these announcements, like fabric, which kind of unifying Microsoft's own products, but just the status quo on that relationship and the opportunity with this new partnership.

我會把這個交給弗蘭克，讓邁克在那裡休息一下。但就微軟而言，很明顯，他們本週將舉辦他們的 Build 大會，並發布大量新產品，包括數據和分析方面的產品。但我想問你更多關於夥伴關係方面的問題。我想你評論說只是看到那裡有更好的牽引力。我認為他們已經發展了他們的合作夥伴計劃，包括將您添加為一級合作夥伴。那麼你能不能談談這種關係的現狀，你是如何適應的，考慮到其中一些公告，比如 fabric，它在某種程度上統一了微軟自己的產品，但只是這種關係的現狀以及與這種新的伙伴關係。

Yes. Just Microsoft relationship has been growing faster than the other 2 cloud platforms that we support. It's been very clear from the top Microsoft that they're viewing Azure as a platform, not as a sort of a single integrated proprietary Microsoft stack. And they've said over and over that we're about choice, we're about innovation. And yes, we will compete. We've been competing with Microsoft from day 1, and that will -- and we've been very successful in that regard for a whole bunch of different reasons.

是的。與我們支持的其他 2 個雲平台相比，與 Microsoft 的關係增長得更快。微軟高層非常清楚，他們將 Azure 視為一個平台，而不是一種單一的集成專有微軟堆棧。他們一遍又一遍地說我們是關於選擇，我們是關於創新。是的，我們會競爭。從第一天起，我們就一直在與微軟競爭，而且這將是——出於各種不同的原因，我們在這方面取得了非常成功。

But people keep on coming, and that's -- and we expect that. And I think that's sort of a net benefit for the world at large. As they get better and better products, then they get more choice.

但是人們不斷湧入，這就是 - 我們期望如此。我認為這對整個世界來說都是一種淨收益。隨著他們獲得越來越好的產品，他們就會有更多的選擇。

The good news is that I think the relationship is relatively mature, meaning that when there is friction or people are not following the rules, we have good established processes for addressing and resolving that. And that's incredibly important, right, as we sort of get out of that juvenile state, where things are dysfunctional at a field level. So I have no reason to believe that, that will not continue in that manner. So I think Azure will continue to grow and grow faster than the other platforms.

好消息是，我認為這種關係相對成熟，這意味著當出現摩擦或人們不遵守規則時，我們有很好的既定流程來處理和解決這些問題。這非常重要，對吧，因為我們有點擺脫那種在現場水平上功能失調的少年狀態。所以我沒有理由相信，這種情況不會繼續下去。所以我認為 Azure 將繼續增長，並且比其他平台增長得更快。

Great. And on Snowpark, it sounded like that you're pleased with the consumption this quarter. Could you just give us a sense for expectations on the revenue ramp there? And what are the big use cases you're seeing today? Is it Hadoop migrations, data engineering? Just give us a sense on kind of how you're expecting that ramp up and what are the main use cases driving that?

偉大的。在 Snowpark，聽起來您對本季度的消費感到滿意。你能告訴我們對那裡收入增長的預期嗎？您今天看到的大用例是什麼？是 Hadoop 遷移、數據工程嗎？只是讓我們了解一下您期望這種增長的方式以及推動這種增長的主要用例是什麼？

Yes. So here's the important thing to understand about Snowpark. Snowpark is the programmability platform for Snowflake. Now originally, I know Snowflake was conceived with SQL interfaces, and that was the mode through which you would address the platform. So this has really sort of opened up a whole host of modalities, if you will, onto the platform.

是的。所以這是了解 Snowpark 的重要事項。 Snowpark 是 Snowflake 的可編程平台。最初，我知道 Snowflake 是用 SQL 接口構思的，這是您處理平台的模式。因此，如果您願意的話，這確實在平台上開闢了一大堆模式。

Basically, our posture is, look, if it reached our rights to Snowflake, we want to own these processes. And Snowpark is the platform to achieve that. Now the supply chain, if you will, how the data comes into Snowflake is through data engineering processes, often these are Spark workloads and processes. We think they ought to run on Snowpark. The reason is they're going to be cheaper, they're going to be faster, they're going to be operationally simpler, and they're going to be fully governed, right? So we think if you are a Snowflake customer and you're not running these processes on Snowpark, you're just missing out in all those 4 dimensions that I just listed.

基本上，我們的態度是，看，如果它達到了我們對 Snowflake 的權利，我們希望擁有這些流程。 Snowpark 是實現這一目標的平台。現在供應鏈，如果你願意，數據如何進入雪花是通過數據工程流程，通常這些是 Spark 工作負載和流程。我們認為他們應該在 Snowpark 上運行。原因是它們會更便宜，它們會更快，它們的操作會更簡單，而且它們會受到全面監管，對吧？因此，我們認為，如果您是 Snowflake 客戶並且您沒有在 Snowpark 上運行這些流程，那麼您只是錯過了我剛剛列出的所有這 4 個維度。

On the consumption end, it's the same thing. If you're doing analytics, if you're doing data science, if you're doing machine learning, if you're doing AI, if it reads from and writes back to Snowflake, we think that's Snowpark. And we have taken a very emphatic posture to this. We're campaigning Snowpark very, very hard around the world. The interest is tremendously high. As I said in the prepared remarks, we went from 20% in 1 quarter to 30% of our customers using it on at least a weekly basis. We think that's going to go to 100%. I think Snowpark will become extremely prevalent around the use of Snowflake.

在消費端，也是一樣。如果你在做分析，如果你在做數據科學，如果你在做機器學習，如果你在做人工智能，如果它從 Snowflake 讀取並寫回，我們認為那就是 Snowpark。我們對此採取了非常強調的態度。我們在世界範圍內非常非常努力地宣傳 Snowpark。興趣非常高。正如我在準備好的評論中所說，我們的客戶從 1 個季度的 20% 增加到至少每週使用它的 30%。我們認為這將達到 100%。我認為隨著 Snowflake 的使用，Snowpark 將變得非常普遍。

Now beyond that, there's a whole wide world that we're obviously also very interested in, and we're going to start at home and own everything that is -- that we can own over there.

現在除此之外，還有一個我們顯然也非常感興趣的廣闊世界，我們將從家裡開始，擁有我們可以在那裡擁有的一切。

Our next question is from Brent Bracelin with Piper Sandler.

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

Frank, maybe for you, I totally get the current cost concerns and optimization efforts underway. I'd be more curious to hear what you think could get us out of the current slowdown? Are there products or workload that you would flag as the key ones to watch that drives the reacceleration of the business? Just thinking through what's in your control? Or do you think we have to wait until -- for the macro to improve?

弗蘭克，也許對你來說，我完全了解當前的成本問題和正在進行的優化工作。我更想知道您認為什麼可以讓我們擺脫當前的經濟放緩？您是否將某些產品或工作負載標記為可推動業務重新加速的關鍵觀察產品或工作負載？只是想一想你能控制什麼？或者你認為我們必須等到 - 宏觀改善？

Definitely, the #1 issue is sentiment out there, just the lack of visibility, the anxiety. Watching CNBC all day doesn't give you any hope. That's absolutely #1. Because what we're seeing is that when we're dealing with CTOs and chief data officers, these people are chomping at the bit, but they are now literally getting stopped, as I said earlier, by the CFO being in the business saying, "Well, I get that's all good and well, but here's how much you're going to spend. You know you're going to get a new contract, you're going to live within the confines of the contract that you have. So really artificially constraining the demand because of the general anxiety that exists in the economy." So that really needs to start lifting. And that will happen. These things run their course. We've been through these episodes before. So I think that's really the requirement. There's plenty of demand out there, absolutely. And with AI right now, I mean, it's going to drive a whole other vector in terms of workload development. It's going to be hard to stop, CFOs or no CFO.

毫無疑問，排名第一的問題是市場情緒，只是缺乏知名度和焦慮。整天看 CNBC 不會給你任何希望。那絕對是第一名。因為我們看到的是，當我們與 CTO 和首席數據官打交道時，這些人口齒不清，但正如我之前所說，CFO 在業務中說，他們現在確實被阻止了， “好吧，我知道這一切都很好，但這是你要花多少錢。你知道你將獲得一份新合同，你將生活在你所擁有的合同範圍內。由於經濟中存在普遍的焦慮，所以真的人為地限制了需求。”所以這真的需要開始解除。那將會發生。這些事情順其自然。我們以前經歷過這些事件。所以我認為這確實是要求。絕對有很多需求。現在有了人工智能，我的意思是，它將在工作負載開發方面推動一個全新的方向。無論是否有首席財務官，都很難停止。

Very helpful there. And then Christian, I wanted to follow up on Neeva. Streamlit, totally get that acquisition. Neeva, a little harder for me to fully understand. So as you look at Neeva and the tech stack, what was most interesting? Was it the team? Is there some sort of differentiated search engine under the hood? Is it their large language model expertise? What -- why Neeva?

在那裡很有幫助。然後克里斯蒂安，我想跟進 Neeva。 Streamlit，完全獲得收購。 Neeva，我有點難以完全理解。因此，當您查看 Neeva 和技術堆棧時，最有趣的是什麼？是團隊嗎？引擎蓋下是否有某種差異化的搜索引擎？是他們的大型語言模型專業知識嗎？什麼——為什麼是 Neeva？

Yes, it's a great question. I think it's the combination of traditional search technology with LLM technology. I think most of us have seen numerous demos of people that take an LLM in a couple of days or hours, produce something that looks good, but then there are problems on how precise that search is and how reliable those results are. While the Neeva team did extremely well, it was able to combine LLM and generative AI-type technology with traditional technology to be able to do attributional results. And it's very interesting in an enterprise setting where you want more precise answers. That combination was very appealing. And then, of course, it is a world-class team. And the combination of those 2 were appealing to us.

是的，這是一個很好的問題。我認為是傳統搜索技術與LLM技術的結合。我想我們大多數人都見過很多人在幾天或幾小時內獲得 LLM 學位的演示，產生了一些看起來不錯的東西，但是搜索的精確度和這些結果的可靠性存在問題。雖然 Neeva 團隊做得非常好，但它能夠將 LLM 和生成式 AI 類技術與傳統技術相結合，從而能夠得出歸因結果。在您需要更精確答案的企業環境中，這非常有趣。這種組合非常吸引人。然後，當然，這是一支世界級的球隊。這兩者的結合對我們很有吸引力。

Our next question is from Derrick Wood with TD Cowen.

我們的下一個問題來自 Derrick Wood 和 TD Cowen。

Great. I wanted to ask about the competitive and the pricing environment out there. I guess on the competitive side, have you guys seen any change in win rates or workload shifts to different platforms? And when it comes to pricing, you talked about customers focusing a lot on cost savings. How is this translating into your ability to hold kind of unit pricing, especially on renewals?

偉大的。我想詢問那裡的競爭和定價環境。我想在競爭方面，你們有沒有看到獲勝率或工作負載轉移到不同平台的任何變化？在定價方面，您談到客戶非常關注成本節約。這如何轉化為您保持某種單位定價的能力，尤其是在續訂方面？

It's Frank. I'll let Mike weigh in once he stops coughing. But the thing about pricing is, look, physics are physics, a read is a read, a write is a write. And there's economics. It costs a certain amount of money, right? And there's just not that much room other than playing games or temporarily sponsoring or subsidizing different parts of the business to really get a sustained pricing edge on one player or another. We're all converging to very, very similar economics. Where you see huge differences is in the total cost of ownership, and that is not the cost of computing the stories. And that is like what is the cost to run that technology? And this is where Snowflake has a huge advantage. And our customers know that. It's just -- it's reduced skill sets, far fewer people, not having to touch the complexity of the underlying platforms, I mean on and on and on. I mean we're more the sense of Apple and Tesla than being in the sense of Hadoop, like some people are in the marketplace, right? So we have really abstracted the complexity. And that's what generates this TCO advantage. But the raw cost of computing and storage, there's not that much opportunity to be had.

是弗蘭克。一旦他停止咳嗽，我會讓邁克稱重。但是關於定價的事情是，看，物理學就是物理學，讀就是讀，寫就是寫。還有經濟學。它需要一定的金錢，對吧？除了玩遊戲或臨時贊助或補貼業務的不同部分以真正獲得一個或另一個玩家的持續定價優勢之外，沒有太多空間。我們都在趨同於非常非常相似的經濟學。您看到巨大差異的地方是總擁有成本，而不是計算故事的成本。這就像運行該技術的成本是多少？而這正是 Snowflake 具有巨大優勢的地方。我們的客戶知道這一點。它只是 - 它減少了技能組合，更少的人，不必觸及底層平台的複雜性，我的意思是不斷。我的意思是我們更像是蘋果和特斯拉的感覺，而不是 Hadoop 的感覺，就像市場上的一些人一樣，對吧？所以我們真的抽象了複雜性。這就是產生這種 TCO 優勢的原因。但是計算和存儲的原始成本，並沒有那麼多機會。

I want to add something to highlight what Frank mentioned in his Snowpark answer, which is what we're seeing relative to competitive platforms, Spark and by Spark, we're seeing Snowpark being not only better performance, but better price performance. So interestingly enough, we see customers giving us technical wins and wanting to migrate because of the better economics, the competitive dynamics.

我想添加一些內容來強調 Frank 在他的 Snowpark 回答中提到的內容，這就是我們看到的與競爭平台 Spark 相關的內容，並且通過 Spark，我們看到 Snowpark 不僅性能更好，而且性價比更高。非常有趣的是，我們看到客戶給了我們技術上的勝利，並且因為更好的經濟性和競爭動態而希望遷移。

Great. And if I could squeeze one more in. Just in terms of LLMs, you guys are obviously sitting on a lot of data to be able to be mined and training models. Do you guys envision kind of building up GPU clusters and offering training and inference on your platform? Or do you think that's really the place for hyperscalers to be doing that?

偉大的。如果我能再擠一個進去。就 LLM 而言，你們顯然坐擁大量數據，可以挖掘和訓練模型。你們是否設想在你們的平台上建立 GPU 集群並提供訓練和推理？還是您認為這真的是超大規模企業這樣做的地方？

We're doing all of it. We alluded in the prepared remarks to Applica, which it is multi-model -- collection of models being built at Snowflake that require GPUs. So we're doing our part, but we're also working and we'll show more at our conference on how we surface GPU (inaudible). So all of the above, it's an important component of this gen AI wave of innovation.

我們正在做這一切。我們在準備好的對 Applica 的評論中提到，它是多模型的——在 Snowflake 構建的需要 GPU 的模型集合。所以我們正在儘自己的一份力量，但我們也在努力，我們將在我們的會議上展示更多關於我們如何展示 GPU（聽不清）的信息。因此，以上所有內容都是這一代 AI 創新浪潮的重要組成部分。

Our next question is from Patrick Colville with Scotiabank. Our next question is from Sterling Auty with MoffettNathanson.

我們的下一個問題來自豐業銀行的 Patrick Colville。我們的下一個問題來自 Sterling Auty 和 MoffettNathanson。

We're having problems.

我們有問題。

I'm just wondering -- yes. Sorry about that, Mike. It's hard to hear the operator. So just wondering, you've called out financial services as your largest vertical. Wondering how much of an impact that vertical had in the consumption patterns that you pointed out post the Easter holiday?

我只是想知道——是的。對不起，邁克。很難聽到接線員的聲音。所以只是想知道，你把金融服務稱為你最大的垂直行業。想知道您在復活節假期後指出的垂直消費模式有多大影響？

The financial services vertical is doing fine. It was very strong for us. It's still 23% of our revenue and growing quite fast. It was in some of the other areas with some of our bigger customers outside of financial services.

垂直金融服務表現良好。這對我們來說非常強大。它仍然占我們收入的 23%，並且增長非常快。在其他一些領域，我們有一些金融服務以外的大客戶。

Our next question is Michael Turrin with Wells Fargo.

我們的下一個問題是來自富國銀行的 Michael Turrin。

Operator, we're having a hard time hearing you. Now we hear you. Okay.

接線員，我們很難聽到你的聲音。現在我們聽到你了。好的。

No. The operator is fading. I would agree. I appreciate you sneaking me in. Just going back the revised guidance suggests growth falls below 30%, but you did mention confidence still in the longer-term $10 billion target. So if we could just spend some time on what you're hearing from customers that drives confidence around what you're seeing as temporary which suggests growth bounces back.

不，運營商正在衰落。我同意。我很感激你偷偷帶我進去。只是回顧修訂後的指導表明增長低於 30%，但你確實提到了對 100 億美元的長期目標仍有信心。因此，如果我們能花一些時間了解您從客戶那裡聽到的信息，這些信息會激發您對您所看到的暫時性事物的信心，這表明增長會反彈。

And as the second part on the bookings commentary, it sounded like North America large enterprise is the area that's spinning out favorably. I just want to make sure we have the right context there and if there's anything else you can add around what's driving that, it's appreciated.

作為預訂評論的第二部分，聽起來北美大型企業是發展勢頭良好的地區。我只是想確保我們有正確的上下文，如果您還有其他任何可以添加的驅動因素，我們將不勝感激。

What I would say is we have a lot of customers who we have only moved a fraction of their data that we know they have multiyear plans to go on Snowflake. And that's what gives us the confidence as well as the pipeline of deals. And I'm not just talking pipeline now. There's deals for next year that I know they're long sales cycles, these big customers. That's what gives us the pipeline on top of a lot of the new products we have coming out over the next couple of years.

我要說的是，我們有很多客戶，我們只移動了他們數據的一小部分，我們知道他們有多年計劃繼續使用 Snowflake。這就是給我們信心和交易渠道的原因。我現在不只是在談論管道。明年有一些交易，我知道它們的銷售週期很長，這些大客戶。這就是我們在未來幾年推出的許多新產品之上的管道。

That will be the last question. Thank you for your time and your participation. That concludes the conference call. You may now disconnect your line. That concludes the conference call.

那將是最後一個問題。感謝您的時間和參與。電話會議到此結束。您現在可以斷開線路。電話會議到此結束。