數據狗 (DDOG) 2023 Q1 法說會逐字稿

Good morning, and thank you for standing by. Welcome to the First Quarter 2023 Datadog Earnings Conference Call. (Operator Instructions) Please be advised that today's conference is being recorded.

早上好，謝謝你的支持。歡迎來到 Datadog 2023 年第一季度收益電話會議。 （操作員說明）請注意，今天的會議正在錄製中。

I would now like to hand the conference over to your speaker today, Yuka Broderick, Vice President of Investor Relations. Please go ahead.

我現在想把會議交給今天的發言人，投資者關係副總裁 Yuka Broderick。請繼續。

Thank you, Michelle. Good morning, and thank you for joining us to review Datadog's first quarter 2023 financial results, which we announced in our press release issued this morning. Joining me on the call today are Olivier Pomel, Datadog's Co-Founder and CEO; and David Obstler, Datadog's CFO.

謝謝你，米歇爾。早上好，感謝您加入我們，回顧我們在今天上午發布的新聞稿中公佈的 Datadog 2023 年第一季度財務業績。今天和我一起參加電話會議的是 Datadog 的聯合創始人兼首席執行官 Olivier Pomel；和 Datadog 的首席財務官 David Obstler。

During this call, we will make forward-looking statements, including statements related to our future financial performance, our outlook for the second quarter and the fiscal year 2023 and related notes and assumptions, our gross margins and operating margins, our strategy, our product capabilities and our ability to capitalize on market opportunities. The words anticipate, believe, continue, estimate, expect, intend, will and similar expressions are intended to identify forward-looking statements or similar indications of future expectations. These statements reflect our views only as of today and are subject to a variety of risks and uncertainties that could cause actual results to differ materially.

在此次電話會議中，我們將做出前瞻性陳述，包括與我們未來財務業績、我們對第二季度和 2023 財年的展望以及相關註釋和假設、我們的毛利率和營業利潤率、我們的戰略、我們的產品相關的陳述能力和我們利用市場機會的能力。預期、相信、繼續、估計、預期、打算、將和類似表達等詞語旨在識別前瞻性陳述或未來預期的類似指示。這些陳述僅反映我們截至今日的觀點，並受各種風險和不確定因素的影響，這些風險和不確定因素可能導致實際結果存在重大差異。

For a discussion of the material risks and other important factors that could affect our actual results, please refer to our Form 10-K for the year-ended December 31, 2022. Additional information will be made available in our upcoming Form 10-Q for the fiscal quarter-ended March 31, 2023, and other filings with the SEC. This information is also available on the Investor Relations section of our website, along with a replay of this call. We will also discuss non-GAAP financial measures, which are reconciled to their most directly comparable GAAP financial measures in the tables in our earnings release, which is available at investors.datadoghq.com.

有關可能影響我們實際結果的重大風險和其他重要因素的討論，請參閱我們截至 2022 年 12 月 31 日止年度的 10-K 表格。更多信息將在我們即將發布的 10-Q 表格中提供截至 2023 年 3 月 31 日的財政季度，以及向美國證券交易委員會提交的其他文件。此信息也可在我們網站的投資者關係部分以及此電話會議的重播中找到。我們還將討論非 GAAP 財務指標，這些指標與我們收益發布表中最直接可比的 GAAP 財務指標相一致，可在 investors.datadoghq.com 獲取。

With that, I'd like to turn the call over to Olivier.

有了這個，我想把電話轉給奧利維爾。

Thanks, Yuka. Thank you all for joining us this morning. We are pleased with our execution in Q1 as we continued broadening our platform, delivering new use cases for our existing users as well as signing up more customers, all on the backdrop of continued macro uncertainty and optimization of cloud workloads.

謝謝，由香。感謝大家今天早上加入我們。我們對第一季度的執行情況感到滿意，因為我們繼續拓寬平台，為現有用戶提供新的用例以及簽約更多客戶，所有這些都是在持續的宏觀不確定性和雲工作負載優化的背景下進行的。

Let me start with a review of our Q1 financial performance. In Q1, revenue was $482 million, an increase of 33% year-over-year and above the high end of our guidance range. Note that this number factors in the impact of a service outage we experienced in March and which reduced our revenue for the quarter by about $5 million.

讓我首先回顧一下我們第一季度的財務業績。第一季度，收入為 4.82 億美元，同比增長 33%，高於我們指導範圍的上限。請注意，這個數字考慮了我們在 3 月份經歷的服務中斷的影響，這使我們本季度的收入減少了約 500 萬美元。

We ended with about 25,500 customers, up from about 19,800 last year. Also note that we are now including customers who joined following our acquisition of Cloudcraft, representing about 1,400 net new customers to Datadog this quarter.

我們最終擁有約 25,500 名客戶，高於去年的約 19,800 名。另請注意，我們現在包括在收購 Cloudcraft 後加入的客戶，代表本季度 Datadog 的約 1,400 名淨新客戶。

We ended the quarter with about 2,910 customers with ARR of $100,000 or more, up from about 2,250 last year. The customers generated about 85% of our ARR. And we generated free cash flow of $116 million, with a free cash flow margin of 24%.

本季度結束時，ARR 為 100,000 美元或以上的客戶約有 2,910 名，高於去年的約 2,250 名。客戶產生了我們大約 85% 的 ARR。我們產生了 1.16 億美元的自由現金流，自由現金流利潤率為 24%。

Our platform strategy continues to resonate in the market. As of the end of Q1, 81% of customers were using 2 or more products, in line with last year. 43% of customers were using 4 or more products, up from 35% a year ago. And 19% of our customers were using 6 or more products, up from 12% last year.

我們的平台戰略繼續在市場上引起共鳴。截至第一季度末，81% 的客戶使用 2 種或更多產品，與去年持平。 43% 的客戶使用 4 種或更多產品，高於一年前的 35%。 19% 的客戶使用 6 種或更多產品，高於去年的 12%。

Now let's discuss this quarter's business drivers. Overall, we experienced business conditions that were similar to the previous several quarters. In Q1, user's growth from existing customers came in roughly as expected. We saw existing customer users growth in Q1 improved from the levels we saw in Q4, but remain a bit lower than the levels we experienced in Q2 and Q3. And as in recent quarters, we continue to see customers optimize their cloud spend, particularly those further along in their cloud migration and hosting a larger portion of their infrastructure in the cloud.

現在讓我們討論本季度的業務驅動因素。總體而言，我們經歷的業務狀況與前幾個季度相似。第一季度，來自現有客戶的用戶增長大致符合預期。我們看到第一季度的現有客戶用戶增長比我們在第四季度看到的水平有所改善，但仍略低於我們在第二季度和第三季度經歷的水平。與最近幾個季度一樣，我們繼續看到客戶優化了他們的雲支出，尤其是那些進一步進行雲遷移並將大部分基礎設施託管在雲中的客戶。

Additionally, our new logo acquisition and bookings in Q1 were solid for what is a seasonally slower quarter. New local bookings reached a new record for Q1 and were up slightly from last year as we continued to add many promising new logos, which I'll discuss in a bit. With our land and expand model, we expect many of these new logos will turn into much larger customers as they add up more of our products over time.

此外，我們在第一季度的新徽標收購和預訂對於季節性較慢的季度來說是穩定的。新的本地預訂在第一季度創下新紀錄，並且比去年略有上升，因為我們繼續添加許多有前途的新徽標，我將在稍後討論。通過我們的土地和擴展模型，我們預計這些新徽標中的許多將隨著時間的推移增加我們的產品而變成更大的客戶。

Despite a more cost-conscious demand environment, we have continued to land new customers and expand existing ones, and we are very proud to achieve several key milestones in Q1. So first, our total ARR exceeded $2 billion for the first time, a true achievement for all of us at Datadog even though we all know we're only getting started.

儘管需求環境更加註重成本，但我們繼續吸引新客戶並擴大現有客戶，我們為在第一季度實現幾個關鍵里程碑而感到自豪。首先，我們的總 ARR 首次超過 20 億美元，這對我們 Datadog 的所有人來說都是一項真正的成就，儘管我們都知道我們才剛剛起步。

Second, our APM suite and log management products together exceeded $1 billion in ARR. This demonstrates the expansion of our business well beyond our (inaudible) monitoring product and our successful execution on the broad of the mobility platform.

其次，我們的 APM 套件和日誌管理產品的 ARR 合計超過 10 億美元。這表明我們的業務擴展遠遠超出了我們的（聽不清）監控產品以及我們在廣泛的移動平台上的成功執行。

Remember that our APM suite includes 4 Datadog products: core APM, Synthetics, Real User monitoring and Continuous profile.

請記住，我們的 APM 套件包括 4 個 Datadog 產品：核心 APM、Synthetics、真實用戶監控和連續配置文件。

Third, we continue to make steady progress with our cloud security products with continued growth in ARR and in customers. And I'm very pleased to announce that we now have more than 5,000 customers using our cloud security products.

第三，隨著 ARR 和客戶的持續增長，我們繼續在雲安全產品方面取得穩步進展。我很高興地宣布，我們現在有超過 5,000 名客戶在使用我們的雲安全產品。

Now let's move on to R&D. We introduced a number of new security capabilities last month. We announced the general availability of Application Vulnerability Management, which provides visibility into the attack surface of production environment by automatically surfacing vulnerabilities. And instead of submerging users with thousands upon thousands of vulnerabilities, this new functionality is observability data to prioritize risks based on the estimated impact to the business and closes the loop between security, operations and development teams.

現在讓我們繼續研發。上個月我們引入了一些新的安全功能。我們宣布了應用程序漏洞管理的普遍可用性，它通過自動顯示漏洞來提供對生產環境攻擊面的可見性。這項新功能不是讓用戶被成千上萬的漏洞淹沒，而是通過可觀察性數據來根據對業務的估計影響來確定風險的優先級，並關閉安全、運營和開發團隊之間的循環。

We also introduced a number of new capabilities to our Cloud Security Management product. Workload security profiles allow customers to flag anomalous activity and improve overall accuracy of threat detection directly within the workload. And we now offer vulnerability detection for containers, automatically scanning live container images for known vulnerabilities.

我們還為我們的雲安全管理產品引入了許多新功能。工作負載安全配置文件允許客戶標記異常活動並直接在工作負載內提高威脅檢測的整體準確性。我們現在為容器提供漏洞檢測，自動掃描實時容器鏡像以查找已知漏洞。

Now moving on from security to observability, we also announced the general availability of Data Streams Monitoring. These products specifically targets queuing, streaming and even driven pipelines, such as Kafka or RabbitMQ. These systems often span many different teams and technologies and are notoriously difficult to manage and troubleshoot. And for this, even standard APM and log management solutions are not specialized enough. Data Streams Monitoring automatically identifies the topology, interdependencies and key metrics of complex streaming data pipelines, allowing customers to maintain availability, correctness and latency for what is now a critical part of their business.

現在從安全性轉向可觀察性，我們還宣布了數據流監控的普遍可用性。這些產品專門針對隊列、流甚至驅動管道，例如 Kafka 或 RabbitMQ。這些系統通常跨越許多不同的團隊和技術，並且眾所周知難以管理和排除故障。為此，即使是標準的 APM 和日誌管理解決方案也不夠專業。 Data Streams Monitoring 自動識別複雜流數據管道的拓撲結構、相互依賴性和關鍵指標，使客戶能夠保持其業務關鍵部分的可用性、正確性和延遲。

Lastly, we were thrilled to unveil our newest data center in Japan last month. We see a large opportunity to serve our customers in the Asia Pacific region, which have seen significant growth over time and now represents high single digits as a percent of revenue.

最後，我們很高興上個月在日本推出了我們最新的數據中心。我們看到了為亞太地區客戶提供服務的巨大機會，這些客戶隨著時間的推移取得了顯著增長，現在佔收入的百分比很高。

I also want to take a moment and share our excitement for the latest wave of AI innovation. And I'm going to use AI in this community here to refer to the recent advances in deep learning, large language models and generative AI.

我還想花點時間分享一下我們對最新一波人工智能創新的興奮之情。我將在這裡使用這個社區中的 AI 來指代深度學習、大型語言模型和生成 AI 方面的最新進展。

First, from a market perspective, over the long term, we believe AI will significantly expand our opportunity in observability and beyond. We seek massive improvements in developer productivity will allow individuals to write more applications and to do so faster than ever before. And as with past productivity increases, we think this will further shift value from writing code to observing, managing, fixing and securing live applications.

首先，從市場的角度來看，從長遠來看，我們相信人工智能將顯著擴大我們在可觀察性及其他方面的機會。我們尋求大幅提高開發人員的工作效率，讓個人能夠編寫更多的應用程序，而且速度比以往任何時候都快。隨著過去生產力的提高，我們認為這將進一步將價值從編寫代碼轉移到觀察、管理、修復和保護實時應用程序。

In the short to medium term, we believe the rise of AI will increase the demand for compute and storage to train and run models, but it will also increase the value of proprietary data and further drive digital transformation and cloud migration as these are all prerequisites for adoption. We also do expect quite a bit of noise in the market as the technology stack is progressing and changing very quickly.

在中短期內，我們認為人工智能的興起將增加對計算和存儲以訓練和運行模型的需求，但它也將增加專有數據的價值並進一步推動數字化轉型和雲遷移，因為這些都是先決條件收養。隨著技術堆棧的進步和變化非常迅速，我們也確實預計市場上會有相當多的噪音。

Now from a product perspective, we believe that we at Datadog are uniquely positioned to deliver value to our customers in this new world. First, we built Datadog from day 1 as a pure SaaS business precisely to be able to put our data to work at full scale and to train models to solve our customers' problems.

現在從產品的角度來看，我們相信我們在 Datadog 處於獨特的位置，可以在這個新世界中為我們的客戶提供價值。首先，我們從第一天起就將 Datadog 構建為純 SaaS 業務，正是為了能夠將我們的數據全面投入使用，並訓練模型來解決客戶的問題。

Second, our large assets of contact with our customers gives us the insertion points to make AI relevant. This is where we see the value of having a variable customer base and being designed to be used every day by every single engineer.

其次，我們與客戶的大量聯繫資產為我們提供了使 AI 相關的插入點。在這裡，我們看到了擁有可變客戶群並設計為供每位工程師每天使用的價值。

And third, we serve today some of the largest builders and consumers of AI services and are quickly adapting to their needs in a rapidly changing field.

第三，我們今天為一些最大的 AI 服務建設者和消費者提供服務，並正在迅速適應他們在瞬息萬變的領域中的需求。

So in other words, we are really excited about the potential of AI for us and for the observability and security market, and I'm sure we'll discuss this topic further in the future.

所以換句話說，我們對 AI 對我們以及對可觀察性和安全市場的潛力感到非常興奮，我相信我們將來會進一步討論這個話題。

Okay. Let's move on to sales and marketing. As I said earlier, our go-to-market teams had another productive quarter. So let's discuss some of our wins. First, we signed an expansion into 8 figures ARR with a leading AI company. This customer saw an order of magnitude increase in user demand and a third in new customers following enormous innovation and interest in generative AI. As a result, this customer now uses 6 Datadog products and relies on our platform to track and correlate key business metrics, ranging from uptime data to newer subscriptions and revenue.

好的。讓我們繼續進行銷售和營銷。正如我之前所說，我們的上市團隊又迎來了一個富有成效的季度。因此，讓我們討論一下我們的一些勝利。首先，我們與一家領先的 AI 公司簽署了 8 位數 ARR 的擴展協議。隨著對生成人工智能的巨大創新和興趣，該客戶的用戶需求增加了一個數量級，新客戶增加了三分之一。因此，該客戶現在使用 6 種 Datadog 產品，並依靠我們的平台來跟踪和關聯關鍵業務指標，從正常運行時間數據到更新的訂閱和收入。

Next, we signed a high 7 figures expansion to another 8-figure ARR deal with one of the world's largest fintech companies. This customer has expanded meaningfully over time, and today see Datadog platform used by thousands of users across dozens of business units. With this expansion, this customer now uses 14 Datadog products and is consolidating multiple open source, homegrown and commercial tools across observability and security into the Datadog platform.

接下來，我們與世界上最大的金融科技公司之一簽署了一項高達 7 位數的擴展到另一項 8 位數的 ARR 交易。隨著時間的推移，該客戶的規模不斷擴大，如今，Datadog 平台已被數十個業務部門的數千名用戶使用。通過這次擴展，該客戶現在使用 14 種 Datadog 產品，並將跨可觀察性和安全性的多個開源、自主開發和商業工具整合到 Datadog 平台中。

Next, we signed a 7-figure expansion with a Fortune 500 health care company. Before using Datadog, major incidents would mobilize up to 150 employees for an average of 3 to 4 hours. With Datadog, they only need 20 employees for about 30 minutes with an opportunity to further reduce these numbers.

接下來，我們與一家財富 500 強醫療保健公司簽署了 7 位數的擴張協議。在使用 Datadog 之前，重大事件需要動員多達 150 名員工平均花費 3 到 4 個小時。有了 Datadog，他們只需要 20 名員工工作大約 30 分鐘，並有機會進一步減少這些數字。

I will note that we're also replacing a commercial observability competitor whose new pricing model was causing an increase in cost with lower value. This customer now expects to save more than $0.5 million every year by moving that to Datadog across several business units.

我會注意到，我們也在更換一個商業可觀察性競爭對手，其新定價模型導致成本增加而價值較低。該客戶現在希望通過跨多個業務部門將其轉移到 Datadog 每年節省超過 50 萬美元。

Next, we signed a 6-figure land with a multinational clothing company. This company was previously heavily siloed, with each team using different monitoring tools. And as is often the case, this caused issues impacting revenue and customer experience. This customer is starting with 5 Datadog products and expect to consolidate and replace a total of 13 commercial and open-source tools with Datadog.

接下來，我們與一家跨國服裝公司簽下了6位數的土地。這家公司以前是嚴重孤立的，每個團隊都使用不同的監控工具。通常情況下，這會導致影響收入和客戶體驗的問題。該客戶從 5 個 Datadog 產品開始，並期望用 Datadog 整合和替換總共 13 個商業和開源工具。

And last but not least, we signed a 7-figure multiyear land with a leading university in Australia. This customer has historically relied on open source solutions. They evaluated a few commercial competitors, and Datadog won as their requirements involve both cloud and on-premise across logs, user experience and network device monitoring. This customer plans over time to migrate from more than 10 tools to the Datadog platform.

最後但並非最不重要的一點是，我們與澳大利亞一所一流大學簽署了一份價值 7 位數的多年期土地。該客戶歷來依賴開源解決方案。他們評估了一些商業競爭對手，Datadog 獲勝，因為他們的要求涉及跨日誌、用戶體驗和網絡設備監控的雲和內部部署。隨著時間的推移，該客戶計劃從 10 多種工具遷移到 Datadog 平台。

And that's it for this quarter's highlights. I'd like to thank our go-to-market teams again for their continued execution in Q1.

這就是本季度的亮點。我要再次感謝我們的上市團隊在第一季度的持續執行。

Now switching gears. Let me speak to our longer-term outlook. Overall, we continue to see no change to the multiyear trend towards digital transformation and cloud migration. We do continue to see customers optimizing their cloud usage, and visibility remains limited as to when this optimization cycle will end, but we firmly believe it will.

現在換檔。讓我談談我們的長期前景。總體而言，我們繼續認為數字化轉型和雲遷移的多年趨勢沒有變化。我們確實繼續看到客戶優化他們的雲使用，並且這個優化週期何時結束的可見性仍然有限，但我們堅信它會。

As before, we remain confident that we will continue to deliver value to more customers in their digital transformation and cloud migration journeys. And it is increasingly clear with each wave of technical innovation that every company in every industry and every geographic region has to take advantage of the cloud, microservices, container and generative AI and more. By relentlessly broadening the Datadog platform, we will continue to help our customers save on costs, execute with better engineering efficiency, drive competitive differentiation and deliver value to their own customers.

和以前一樣，我們仍然有信心繼續為更多客戶在他們的數字化轉型和雲遷移之旅中創造價值。隨著每一波技術創新浪潮，越來越明顯的是，每個行業和每個地理區域的每個公司都必須利用雲、微服務、容器和生成 AI 等。通過不懈地擴展 Datadog 平台，我們將繼續幫助我們的客戶節省成本，以更高的工程效率執行，推動競爭差異化並為他們自己的客戶創造價值。

So our long-term plans have not changed. We are continuing to invest to capture our long-term opportunities. And as David will discuss in a moment, the strength of our business model allows us to balance that with delivering financial performance.

所以我們的長期計劃沒有改變。我們將繼續投資以抓住我們的長期機會。正如 David 稍後將討論的那樣，我們業務模式的優勢使我們能夠在實現財務績效之間取得平衡。

With that, I will turn it over to our CFO. David?

有了這個，我會把它交給我們的首席財務官。大衛？

Thanks, Olivier. In Q1, we continued to execute well and deliver value to our customers. Revenue was $482 million, up 33% year-over-year and up 3% quarter-to-quarter.

謝謝，奧利維爾。在第一季度，我們繼續良好執行並為我們的客戶創造價值。收入為 4.82 億美元，同比增長 33%，環比增長 3%。

To dive into some of the drivers of the Q1 performance. First, we had an unusual outage in March, and we estimate that the impact to our revenues from that outage to be about $5 million. As we mentioned last quarter, we saw subdued usage growth in the month of December which created a lower growth trajectory to start the first quarter and drove seasonally weaker sequential growth in the first quarter.

深入探討第一季度業績的一些驅動因素。首先，我們在 3 月份發生了一次不尋常的停電，我們估計這次停電對我們收入的影響約為 500 萬美元。正如我們在上個季度提到的那樣，我們看到 12 月份的使用量增長疲軟，這導致第一季度開始時增長軌跡較低，並推動第一季度季節性較弱的連續增長。

During Q1, we experienced a linearity pattern that is typical for us, which included usage growth in March that was higher than that in January and February. Overall, we saw existing customer usage growth in Q1 improved from the levels we saw in Q4, though it was slightly lower than the levels we experienced in Q2 and Q3 last year.

在第一季度，我們經歷了一種典型的線性模式，其中包括 3 月份的使用量增長高於 1 月和 2 月。總體而言，我們看到第一季度的現有客戶使用量增長比我們在第四季度看到的水平有所改善，儘管它略低於我們去年第二季度和第三季度的水平。

Next, we continue to see larger spending customers grow smaller -- grow slower than smaller-spending customers. From an industry perspective, we continue to see the slowest growth in the consumer discretionary vertical, particularly in e-commerce and food delivery. Geographically, we saw faster year-over-year growth in international than in North America.

接下來，我們繼續看到大消費客戶變小——比小消費客戶增長更慢。從行業角度來看，我們繼續看到非必需消費品垂直領域增長最慢，尤其是在電子商務和食品配送領域。從地域上看，我們看到國際市場的同比增長快於北美。

Our trailing 12-month dollar-based net retention rate, or NRR, continued to be over 130% as customers increase their usage and adopted more products. Based on our current growth trajectory, however, we expect our trailing 12-month NRR to be below 130% in Q2. While our net retention rate is expected to go below 130%, we continue to execute strongly on our platform innovation and our land and expand business model, as evidenced by our latest product announcements, our expanding cross-sell products and the examples of the strong Q1 renewals that Olivier discussed. Our dollar-based gross retention rate remained stable in the mid- to high 90s, an indication of the mission-critical nature of the Datadog platform for our customers.

隨著客戶增加使用量並採用更多產品，我們過去 12 個月的美元淨保留率 (NRR) 繼續超過 130%。然而，根據我們目前的增長軌跡，我們預計我們過去 12 個月的 NRR 在第二季度將低於 130%。雖然我們的淨保留率預計將低於 130%，但我們將繼續大力執行我們的平台創新和我們的土地並擴展業務模式，正如我們最新的產品公告、我們不斷擴大的交叉銷售產品和強大的例子所證明的那樣Olivier 討論的 Q1 續約。我們以美元為基礎的總保留率在 90 年代中後期保持穩定，這表明 Datadog 平台對我們客戶的任務關鍵性質。

Now moving on to our financial results. Billings were $511 million, up 15% year-over-year. We had a large upfront bill for a client in Q1 '22 that did not recur at the same level or timing in Q1 '23. Pro forma for this client, billings growth was in the low 30s percent year-over-year. Remaining Performance Obligations, or RPO, was $1.14 billion, up 33% year-over-year. Current RPO growth was in the high 20s percent year-over-year. We continue to believe revenue is a better indication of our business trends than billings and RPO as those can fluctuate relative to revenue based on the timing of invoicing and the duration of customer contracts.

現在繼續我們的財務業績。帳單為 5.11 億美元，同比增長 15%。我們在 22 年第一季度為客戶支付了一筆大額預付款，但在 23 年第一季度沒有以相同的水平或時間再次發生。對於該客戶的備考，賬單同比增長率低於 30%。剩餘履約義務 (RPO) 為 11.4 億美元，同比增長 33%。目前的 RPO 同比增長率高達 20%。我們仍然認為，與賬單和 RPO 相比，收入更能反映我們的業務趨勢，因為它們可能會根據開票時間和客戶合同期限而相對於收入波動。

Now let's review some key income statement results. Unless otherwise noted, all metrics are non-GAAP. We have provided a reconciliation of GAAP to non-GAAP financials in our earnings release. Gross profit in the quarter was $388 million, representing a gross margin of 80.5%. This compares to a gross margin of 80.6% last quarter and 80.4% in the year ago quarter. We continued to experience efficiencies in cloud costs, reflected in our cost of goods sold this quarter.

現在讓我們回顧一些關鍵的損益表結果。除非另有說明，否則所有指標均為非 GAAP。我們在收益發布中提供了 GAAP 與非 GAAP 財務的對賬。本季度毛利為 3.88 億美元，毛利率為 80.5%。相比之下，上一季度的毛利率為 80.6%，去年同期為 80.4%。我們繼續體驗云成本的效率，這反映在我們本季度的商品銷售成本上。

In the mid- to long term, we continue to expect gross margin to be in the high 70s percent range. Our Q1 non-GAAP OpEx grew 45% year-over-year. This is a decline from 54% year-over-year growth in the previous quarter. We continue to grow our headcount in R&D and go-to-market, but at a more moderate pace than last year.

從中長期來看，我們繼續預計毛利率將處於 70% 的高位。我們第一季度的非 GAAP 運營支出同比增長 45%。與上一季度 54% 的同比增長率相比有所下降。我們繼續增加研發和進入市場的員工人數，但增幅低於去年。

Q1 operating income was $86 million or an 18% operating margin, flat sequentially to Q4 2022 margin also of 18%.

第一季度營業收入為 8600 萬美元，營業利潤率為 18%，與 2022 年第四季度的利潤率也為 18% 持平。

In the year ago quarter, operating margins was 23%, and it benefited from lack of in-person office, travel and event costs due to our COVID policies during the pandemic.

在去年同期，營業利潤率為 23%，這得益於我們在大流行期間的 COVID 政策導致的現場辦公、差旅和活動成本的減少。

Turning to the balance sheet and cash flow statements. We ended the quarter with $2 billion in cash, cash equivalents, restricted cash and marketable securities. Cash flow from operations was $134 million in the quarter. After taking into consideration capital expenditures and capitalized software, free cash flow was $116 million for a free cash flow margin of 24%.

轉向資產負債表和現金流量表。本季度結束時，我們擁有 20 億美元的現金、現金等價物、受限制的現金和有價證券。本季度運營現金流為 1.34 億美元。考慮資本支出和資本化軟件後，自由現金流為 1.16 億美元，自由現金流利潤率為 24%。

Now for our outlook for the second quarter and the rest of fiscal year 2023. In forming our guidance, we continued to use conservative assumptions as to the organic growth of our customers compared to historical periods. And as usual, we are basing our near-term guidance on recent activity we see with our customers. While existing customers are still expanding with us, we continue to assume in our guidance that cloud optimization is affecting their expansion rate for the rest of 2023.

現在是我們對第二季度和 2023 財年剩餘時間的展望。在形成我們的指導意見時，我們繼續對我們客戶的有機增長與歷史時期相比使用保守假設。和往常一樣，我們的近期指導基於我們與客戶一起看到的近期活動。雖然現有客戶仍在與我們一起擴展，但我們在指南中繼續假設雲優化正在影響他們在 2023 年剩餘時間內的擴展速度。

For the second quarter, as a result, we expect revenue to be in the range of $498 million to $502 million, which represents 23% to 24% year-over-year growth. Non-GAAP operating income is expected to be in the range of $82 million to $86 million. And non-GAAP net income per share is expected to be in the $0.27 to $0.29 per share range based on approximately 349 million weighted average diluted shares outstanding.

因此，我們預計第二季度收入將在 4.98 億美元至 5.02 億美元之間，同比增長 23% 至 24%。非 GAAP 營業收入預計在 8200 萬美元至 8600 萬美元之間。根據約 3.49 億股加權平均攤薄流通股，預計每股非 GAAP 淨收入將在 0.27 美元至 0.29 美元之間。

For the fiscal year 2023, we expect revenue to be in the range of $2.08 billion to $2.10 billion, which represents 24% to 25% year-over-year growth. Non-GAAP operating income is expected to be in the range of $340 million to $360 million. And non-GAAP net income per share is expected to be in the range of $1.13 to $1.20 per share based on approximately 351 million weighted average diluted shares outstanding.

對於 2023 財年，我們預計收入將在 20.8 億美元至 21 億美元之間，同比增長 24% 至 25%。非 GAAP 營業收入預計在 3.4 億美元至 3.6 億美元之間。根據約 3.51 億股稀釋後的加權平均流通股，非 GAAP 每股淨收入預計在每股 1.13 美元至 1.20 美元之間。

Some additional notes in our guidance. First, we have continued to balance near-term financial strength with investment in our large, long-term opportunities, and we are executing well on our plans to invest efficiently. We expect the continued moderation of headcount growth, and the lapping of the COVID-affected historical expenses to result in continued slowing of OpEx growth during the remainder of 2023.

我們指南中的一些附加說明。首先，我們繼續在短期財務實力與對大型長期機會的投資之間取得平衡，並且我們正在很好地執行有效投資計劃。我們預計員工人數增長持續放緩，以及受 COVID 影響的歷史費用的重疊將導致 2023 年剩餘時間內運營支出增長持續放緩。

We now plan to grow our non-GAAP operating expenses, excluding COGS, in fiscal year 2023 by approximately 30% year-over-year with an exit rate in Q4 in the low 20s percent year-over-year. We continue to expect net interest and other income for fiscal year 2023 to be approximately $75 million. And we expect our tax expense in fiscal year 2023 to be in the range of $14 million to $16 million. Finally, we continue to expect capital expenditures and capitalized software together to be in the range of 4% to 5% of revenues in fiscal year 2023.

我們現在計劃在 2023 財年將我們的非 GAAP 運營費用（不包括 COGS）同比增長約 30%，第四季度的退出率同比下降 20%。我們繼續預計 2023 財年的淨利息和其他收入約為 7500 萬美元。我們預計 2023 財年的稅收支出將在 1400 萬至 1600 萬美元之間。最後，我們繼續預計 2023 財年的資本支出和資本化軟件將佔收入的 4% 至 5%。

Now finally, to reiterate Olivier's comments, we remain excited about our long-term opportunities as our customers embark and expand on their cloud migration and digital transformation plans. We are continuing to invest to further expand the ways we reach our customers and help them along these journeys. I want to thank our Datadogs worldwide for their efforts in this quarter.

最後，重申 Olivier 的評論，隨著我們的客戶開始並擴展他們的雲遷移和數字化轉型計劃，我們對我們的長期機會感到興奮。我們將繼續投資，以進一步擴大我們接觸客戶的方式，並在這些旅程中幫助他們。我要感謝我們全球的 Datadogs 在本季度所做的努力。

With that, we will open the call for questions. Operator, let's begin the Q&A. Thanks.

有了這個，我們將打開問題的電話。接線員，讓我們開始問答。謝謝。

(Operator Instructions) Our first question comes from Raimo Lenschow with Barclays.

（操作員說明）我們的第一個問題來自巴克萊銀行的 Raimo Lenschow。

Could I speak -- could you speak to the optimization, Olivier, a little bit more in detail? Obviously, it's a journey for customers and there is like the initial steps. And then there's a follow-on. So if you think about the customer behavior in terms of what they do on optimization, is that still -- are you still seeing the same steps that are getting taken? Or are we going to kind of round 2, round 3, et cetera? So I'm just trying to understand a little bit where we are on that optimization journey if you have more color. And then I have one follow-up, please.

我可以談談——Olivier，你能否更詳細地談談優化？顯然，這是客戶的旅程，就像最初的步驟一樣。然後是後續。因此，如果您根據客戶在優化方面所做的事情來考慮他們的行為，那仍然是——您是否仍然看到正在採取的相同步驟？還是我們要進行第 2 輪、第 3 輪等等？因此，如果您有更多顏色，我只是想稍微了解一下我們在優化過程中所處的位置。然後我有一個跟進，請。

Yes. I mean, the short of it is we don't know exactly yet. So we -- and I'm going to give an answer that's going to be very similar to the answer I gave last quarter, but we see customers taking another bite at their own workforce, like we saw it in a number of companies this morning actually even. So I don't think customers themselves know where they are done. So we're very prudent in terms of assuming an end to it in the near future.

是的。我的意思是，簡而言之，我們還不確切知道。所以我們 - 我將給出一個與我上個季度給出的答案非常相似的答案，但我們看到客戶對他們自己的員工又咬了一口，就像我們今天早上在許多公司看到的那樣實際上甚至。所以我不認為客戶自己知道他們在哪裡完成。因此，我們非常謹慎地假設它會在不久的將來結束。

So when we look at our data, when we look at the -- what we hear from the hyperscalers also, we also listen carefully to their commentary on what they foresee in the near future. We don't see anything that gives us confidence that we can call an end to optimization next quarter or the quarter after that. So as far as our guidance goes and our plans for the year, we assume that this is going to continue at a similar level for the rest of the year.

因此，當我們查看我們的數據時，當我們查看 - 我們也從超大規模用戶那裡聽到的內容時，我們也會仔細聽取他們對他們在不久的將來預見的事情的評論。我們沒有看到任何讓我們有信心可以在下個季度或之後的季度結束優化的東西。因此，就我們的指導方針和今年的計劃而言，我們假設這將在今年餘下的時間裡繼續保持類似水平。

Now obviously, when we look at our customer base, we see some customers that obviously seem to be done with it and others that haven't done anything. So we try to keep an eye on that and model it, but we're not including any of that in our guidance.

現在很明顯，當我們查看我們的客戶群時，我們看到一些客戶顯然已經完成了它，而其他客戶則沒有做任何事情。所以我們試著關注它並對其進行建模，但我們沒有在我們的指南中包含任何內容。

Yes. Okay. Perfect. And then, David, on the cost side, so thanks for giving us the OpEx rate for the year and the exit rate. If you think about the -- there's always that situation of investing for the future and at some point, you did come out versus like surviving or like living in the current environment. How do you think about that investment philosophy of balancing like today versus getting ready for tomorrow?

是的。好的。完美的。然後，大衛，在成本方面，感謝你給我們提供了當年的運營支出率和退出率。如果你考慮 - 總是存在為未來投資的情況，並且在某個時候，你確實出來了，而不是喜歡生存或喜歡生活在當前環境中。您如何看待平衡今天與為明天做好準備的投資理念？

Yes, I would say that we've made substantial and consistent investments both in our go-to-market and our R&D over the last few years. And this year, we're balancing -- continuing to do that with 2 things, with prioritization as well as injecting some optimization or efficiency into the investments we had made. We're seeing the opportunity to get returns from the previous investments and to think through a little more the prioritization of those.

是的，我要說的是，在過去幾年中，我們在上市和研發方面進行了大量且持續的投資。今年，我們正在平衡 - 繼續通過兩件事來做到這一點，確定優先級以及為我們所做的投資注入一些優化或效率。我們看到了從以前的投資中獲得回報的機會，並仔細考慮了這些投資的優先順序。

Please stand by for our next question. Our next question comes from Sanjit Singh with Morgan Stanley.

請等待我們的下一個問題。我們的下一個問題來自摩根士丹利的 Sanjit Singh。

I had a question, Olivier, on AI. It seems like we're on the cusp of another sort of compute cycle driven by AI. The last compute cycle, you guys were all over in terms of being ahead of the curve, in terms of the shift from monolith to micro services. And so with this new sort of compute cycle that we're about to embark on, what do you see -- how are sort of applications and the sort of the application stack, how is that going to change? And what are the implications for Datadog as a monitoring vendor? What are potentially the puts and takes with this in terms of how applications can be built going forward?

Olivier，我有一個關於 AI 的問題。我們似乎正處於另一種由 AI 驅動的計算週期的風口浪尖。在上一個計算週期中，就從整體式服務到微服務的轉變而言，你們都處於領先地位。因此，隨著我們即將開始的這種新型計算週期，您看到了什麼——應用程序和應用程序堆棧的種類如何，這將如何改變？作為監控供應商，Datadog 有何影響？就未來如何構建應用程序而言，這可能會帶來哪些影響？

Yes. So first, I'd say it's a -- (inaudible), it's fascinating time to be alive, to see all these, rapid innovation in the world of AI. The first thing I'd say is that it's still fairly early in terms of what the market going to look like in the AI world. Right now, there's one particular thing that's been -- that used to be very hard, which was building conventional models and chat bots and things like that, which almost overnight became almost a commodity basically. Anybody can incorporate it in any application. It's an API call away, and there is even a number of different options, commercial open source you can use today.

是的。所以首先，我想說這是一個 - （聽不清），這是一個令人著迷的活著的時刻，看到人工智能世界中所有這些快速創新。我要說的第一件事是，就人工智能世界的市場前景而言，現在還為時過早。現在，有一件特別的事情——過去非常困難，那就是構建傳統模型和聊天機器人之類的東西，幾乎在一夜之間基本上變成了一種商品。任何人都可以將它合併到任何應用程序中。這是一個 API 調用，甚至還有許多不同的選項，您今天可以使用商業開源。

So that just happened. That one has massive traction. You see it everywhere. But it also is opening the gate to many, many more, I would say, more customized, deeper applications on AI that may be built by a few vendors or may be built by a large number of companies instead, it's not quite clear yet.

所以這就發生了。那個有巨大的牽引力。你到處都能看到它。但它也為許多更多的人工智能打開了大門，我想說的是，更多定制化、更深入的人工智能應用程序可能由少數供應商構建，也可能由大量公司構建，目前還不太清楚。

What we see on our end is that it's going to drive more compute, it's going to drive more value in the data that is being gathered by companies. It's going to drive digital transformation. It's going to drive cloud migration because, again, you can't actually adopt AI unless you have the data. You can't actually adopt it without having a modern architecture and an application you can scale up and down and infrastructure you can quickly provision and de-provision. You need to capture all of your -- to capture your data, you need to be digitally transformed. So you have data of all your customer interactions and everything that is proper to your business.

我們最終看到的是，它將推動更多的計算，它將推動公司收集的數據產生更多價值。它將推動數字化轉型。它將推動雲遷移，因為除非你擁有數據，否則你無法真正採用人工智能。如果沒有現代架構和可以放大和縮小的應用程序以及可以快速配置和取消配置的基礎設施，您就無法真正採用它。你需要捕獲你的所有——為了捕獲你的數據，你需要進行數字化轉換。因此，您擁有所有客戶交互的數據以及適合您業務的所有內容。

So in the mid-term, we see that as a very clear accelerant to our business. Maybe with a little bit of noise, as I mentioned on the script earlier, in terms of what technologies end up being the winning ones and what technologies end up being sizzling after a few years, because it's still -- it's so early and there's so much innovation that out of 200 new things, there's probably only 10 or 20 that will matter to you somehow, but it's hard to know which ones are today.

因此，從中期來看，我們認為這是我們業務的一個非常明顯的促進劑。也許有一點噪音，正如我之前在劇本中提到的，就哪些技術最終成為獲勝者以及哪些技術在幾年後最終成為炙手可熱的技術而言，因為它仍然 - 它太早了，所以很多創新，在 200 種新事物中，可能只有 10 或 20 種以某種方式對你很重要，但今天很難知道哪些是。

So short answer is, mid-term, a lot more of the current workloads, the types of workloads we see maybe with different sets of technologies. Longer term, I think we can all glimpse at the future where productivity for everyone, including software engineers, increases dramatically. And the way we see that as a business is, our job is to help our customers absorb the complexity of the applications they've built so they can understand and modify them, run them, secure them. And we think that the more productivity there is, the more people can write in the amount of time. The less they understand the software they produce and the more they need us, the more value it sends our way. So that's what makes us very confident in the long term here.

所以簡短的回答是，中期，更多的當前工作負載，我們看到的工作負載類型可能與不同的技術集有關。從長遠來看，我認為我們都可以看到未來每個人（包括軟件工程師）的生產力都會大幅提高。我們認為，作為一家企業，我們的工作是幫助我們的客戶理解他們構建的應用程序的複雜性，以便他們能夠理解和修改它們，運行它們，保護它們。而且我們認為，生產力越高，人們可以投入的時間就越多。他們對自己生產的軟件了解得越少，對我們的需求就越大，它給我們帶來的價值就越大。所以這就是讓我們對這裡的長期充滿信心的原因。

I appreciate the thoughts, Olivier. As a follow-up and getting back to sort of the topic of cloud optimization. What have you -- what did you sort of see in April versus March and typically, maybe sort of baseline what April typically looks like for the company? And are there any customers that began optimization that maybe didn't start in 2022? Are you seeing incremental new customer cohorts get onboard the cloud optimization train?

我很欣賞這些想法，奧利維爾。作為後續行動，回到雲優化的話題。你有什麼 - 你在 4 月和 3 月看到了什麼，通常，也許是公司 4 月通常是什麼樣子的基線？是否有任何客戶開始了可能不是在 2022 年才開始的優化？您是否看到越來越多的新客戶加入雲優化列車？

So I would say April is broadly consistent with what we've seen in Q4 and Q1. I think it's -- there's no major difference to call out there. And it's too early also for us to call the quarter, obviously. So there's nothing very sharp to point out about April.

所以我想說 4 月份與我們在第四季度和第一季度看到的情況大致一致。我認為這是 - 那裡沒有什麼大的區別。顯然，現在我們稱這個季度還為時過早。因此，關於 4 月沒有什麼特別尖銳的地方需要指出。

On the customer optimization, Look, we have a large number of customers that are early in their cloud migration, early in the Datadog adoption and that are growing very fast. We haven't seen any optimization from those customers. It's possible we see optimization from some of those, which is why we remain very careful in our in our guidance. We don't assume basically that the optimization will stop at the customers that already have done it, and that the rest of the customer base is going to be fine after that.

在客戶優化方面，看，我們有大量客戶處於雲遷移的早期，採用 Datadog 的早期並且增長非常快。我們還沒有看到這些客戶的任何優化。我們有可能從其中一些中看到優化，這就是為什麼我們在我們的指導中仍然非常謹慎。我們基本上不會假設優化會在已經完成優化的客戶處停止，並且其他客戶群在那之後會好起來的。

Please stand by for our next question. The next question comes from Mark Murphy with JPMorgan.

請等待我們的下一個問題。下一個問題來自摩根大通的馬克墨菲。

David, looking at the math on this large upfront bill that did not recur, it seems to be about $65 million, if I'm running that correctly. Can you possibly shed a little more light? For instance, will you recapture that or some of that in Q2? And what type of customer and customer dynamic is operating at that level? And then I have a quick follow-up.

大衛，看看這個沒有重複發生的大額預付款賬單的數學，如果我沒記錯的話，它似乎大約是 6500 萬美元。你能再多說一點嗎？例如，您會在第二季度重新獲得那個或其中的一部分嗎？在該級別運行的是什麼類型的客戶和客戶動態？然後我有一個快速跟進。

Yes. We -- that is a customer of ours. What we said was the billing frequency changed and the size. So that customer's bill will, one, be spread out more over time. That company -- that was a crypto company and continues to be a customer of ours, but that was an early optimizer. We had always talked that some of the industries that were most affected optimized. And that is -- so we will get that -- we will get that bill at a smaller size than was billed last year in a more of a chunked up billing way.

是的。我們——那是我們的客戶。我們所說的是計費頻率和大小發生了變化。因此，隨著時間的推移，客戶的賬單會被分攤得更多。那家公司——那是一家加密貨幣公司，並且一直是我們的客戶，但那是一家早期的優化公司。我們一直在談論一些受影響最大的行業進行了優化。那就是 - 所以我們會得到 - 我們將以比去年更多的分塊計費方式獲得比去年更小的賬單。

And look, this is one of the situations where this customer was in an industry that -- vertical that got pretty much decimated over the past year. And their own business was cut in 3 or 4 in terms of their revenue. And when that's the case, that we really work with customers to restructure their contract with us. We want to be part of the solution for them, not part of the problem. And that's what we did here, we restructured that contract. So we kept them as a happy customer for many more years and do deal that works for everyone with their business profile.

看，這是該客戶所處行業的情況之一 - 垂直行業在過去一年中幾乎被摧毀。就收入而言，他們自己的業務減少了 3 或 4 倍。在這種情況下，我們真正與客戶合作，以重組他們與我們的合同。我們希望成為他們解決方案的一部分，而不是問題的一部分。這就是我們在這裡所做的，我們重組了合同。因此，我們讓他們成為滿意的客戶多年，並根據他們的業務資料進行適合每個人的交易。

In our company - since we've been public, we've pointed out when we have an unusual bill. So we don't have multiple of these types of situations. And what we've done, if you look back to our commentary, is when we've had one of those, a change of timing or a change of the duration of a bill or a size of a bill. We've tried to pro forma it in order to give everyone a sense of what the rest of the business is doing.

在我們公司 - 自從我們公開以來，我們會在收到不尋常的賬單時指出。所以我們沒有多種此類情況。如果你回顧一下我們的評論，我們所做的就是當我們有其中之一時，改變時間或改變法案的持續時間或法案的規模。我們試圖對其進行形式化，以便讓每個人都了解其他業務正在做什麼。

That's much appreciated on our part. And as a follow-up, Olivier, congrats on passing $2 billion in ARR, one of the fastest software companies ever to do that, pretty amazing. I did want to ask you as well on the optimizations. At the moment, Microsoft seems more optimistic that the optimization activity would start to normalize in the next couple or few quarters than Amazon does. And when we tear it apart, Azure has less exposure to tech companies which are doing layoffs. They have more exposure to generative AI, which is booming relative to AWS. So I'm wondering if that part lines up with your telemetry and your forecasting that perhaps your Azure monitoring business is going to start to turn the corner sooner, and then perhaps AWS would follow after that.

我們對此深表感謝。作為後續行動，奧利維爾 (Olivier) 祝賀 ARR 超過 20 億美元，這是有史以來最快的軟件公司之一，非常了不起。我也想問你關於優化的問題。目前，微軟似乎比亞馬遜更樂觀地認為優化活動將在未來幾個或幾個季度開始正常化。當我們把它拆開時，Azure 對正在裁員的科技公司的影響較小。他們更多地接觸到相對於 AWS 正在蓬勃發展的生成式人工智能。所以我想知道這部分是否與您的遙測和您的預測一致，即您的 Azure 監控業務可能會更快開始轉機，然後 AWS 可能會緊隨其後。

It's too early to tell. And by the way, it's a bit hard to just project our numbers from the -- this is just a reminder, it's hard to project our numbers from the numbers of the cloud providers because it's not a one-to-one in terms of what they report and what concerns the infrastructure and the applications directly. Different cloud providers have different things in there. And also, these vendors have -- in addition to the volumes that drive consumption on our end, they also have their own pricing dynamics. And typically, in those situations, the vendors with the largest bills are the ones that get pushed the most. So in this case, that would be AWS.

現在下結論還為時過早。順便說一句，僅從中預測我們的數字有點困難——這只是一個提醒，很難從雲提供商的數量中預測我們的數字，因為它不是一對一的他們直接報告與基礎設施和應用程序有關的內容。不同的雲提供商有不同的東西。而且，這些供應商 - 除了推動我們消費的數量外，他們還有自己的定價動態。通常，在這些情況下，賬單最多的供應商是受到最多推動的供應商。所以在這種情況下，那就是 AWS。

But in terms of what we see in our data, there's no -- nothing to suggest that any particular cloud is recovering from optimization just yet. We also tried to read the hyperscaler comments. And depending on what you've read before and after, they look more or less positive, so I'm not quite sure I would project as much enthusiasm in the Microsoft comment. But what we know is that, at the end of the day, it doesn't really matter for us. We're equally well positioned to capture workloads on Azure as we are on AWS and GCP.

但就我們在數據中看到的內容而言，沒有——沒有任何跡象表明任何特定的雲正在從優化中恢復。我們還嘗試閱讀超大規模用戶的評論。根據你之前和之後閱讀的內容，它們看起來或多或少是積極的，所以我不太確定我會在微軟的評論中表現出同樣的熱情。但我們知道的是，歸根結底，這對我們來說並不重要。我們同樣有能力在 Azure 上捕獲工作負載，就像我們在 AWS 和 GCP 上一樣。

And the only part I would say of the Microsoft stack that we don't cover as well is everything that is a lift and shift of purely Microsoft Technology Office or proprietary because that can typically be done very well with the built-in Microsoft tooling. But when you think of any market share gain that might happen now from now on from Microsoft and the others, if you can imagine that, that would be a more cloud workloads, next-gen workloads, workloads that have to gather data and go into AI models, and these are things that we are very well positioned to capture.

關於 Microsoft 堆棧，我要說的唯一我們沒有涵蓋的部分是純粹的 Microsoft Technology Office 或專有的提升和轉變，因為通常可以使用內置的 Microsoft 工具很好地完成。但是，當你想到微軟和其他公司從現在開始可能獲得的任何市場份額增長時，如果你能想像的話，那將是更多的雲工作負載，下一代工作負載，必須收集數據並進入的工作負載。人工智能模型，這些都是我們非常有能力捕捉的東西。

Please stand by for our next question. The next question comes from Kash Rangan with Goldman Sachs.

請等待我們的下一個問題。下一個問題來自高盛的 Kash Rangan。

I was curious, Olivier, that the generative AI, is it merely a fact of just waiting for these workloads to come on? And since you have such a strong presence in infrastructure monitoring, these workloads will just run on these big clouds and you will optimize them? Or do you have to do something specific on the product side to tool Datadog to better handle these generative AI workload? And I have a follow-up question.

Olivier，我很好奇，生成式 AI 是否僅僅是等待這些工作負載出現的事實？並且由於您在基礎設施監控方面擁有如此強大的影響力，這些工作負載將僅在這些大型雲上運行並且您將優化它們？或者您是否必須在產品方面做一些特定的事情來使用 Datadog 來更好地處理這些生成的 AI 工作負載？我有一個後續問題。

Well, so I think the -- clearly, there's going to be more productivity. And we -- the way this has played out in the past typically is you just end up generating more stuff and more mess. So basically, if one person can produce 10x more, you end up with 10x more stuff and that person will still not understand everything they've produced.

好吧，所以我認為 - 顯然，將會有更高的生產力。而我們 - 這在過去發揮作用的方式通常是你最終會產生更多的東西和更多的混亂。所以基本上，如果一個人可以多生產 10 倍，你最終會多生產 10 倍的東西，而那個人仍然不會理解他們生產的所有東西。

So the way we imagine the future is companies are going to deliver a lot more functionality to their users a lot faster. They're going to solve a lot more problems in software. But the they won't be as tight and understanding from their engineering team as to what it is they've built and how they built it and what might break and what might be the corner cases that don't work and things like that. And that's consistent with what we can see people building with a copilot today and things like that. So they're very, very good for solving a small problem, but they don't help you build consistent code babies or didn't help you build software platforms like that, that is still out of reach. So the -- again, the way we see it in the future is we'll see our customers do a lot more, and they will still need help to catch up with everything they are doing. And we'll be the ones to do that for them.

因此，我們想像未來的方式是，公司將更快地向用戶提供更多功能。他們將解決更多的軟件問題。但是他們不會像他們的工程團隊那樣嚴格和理解他們構建了什麼，他們是如何構建它的，什麼可能會破壞，什麼可能是不起作用的極端情況等等。這與我們今天可以看到人們與副駕駛一起建造以及類似的東西是一致的。所以它們非常非常適合解決一個小問題，但是它們不能幫助你構建一致的代碼嬰兒或者沒有幫助你構建像那樣的軟件平台，那仍然是遙不可及的。所以——再一次，我們在未來看到它的方式是我們會看到我們的客戶做更多的事情，他們仍然需要幫助來趕上他們正在做的一切。我們將成為為他們做這件事的人。

Got it. And also Microsoft talked about the anniversary effect of optimization that the headwinds would be less going forward, whereas AWS called out deceleration in the month of April. But it looks like your business is still steady in the month of April. So it's fair to say that you're sort of decoupling away from the AWS deceleration and maintaining its steady pace as far as consumption trends are concerned.

知道了。微軟還談到了優化的周年紀念效應，即未來的逆風將減少，而 AWS 在 4 月份呼籲減速。但看起來您的業務在 4 月份仍然穩定。因此，可以公平地說，就消費趨勢而言，您正在與 AWS 減速脫鉤並保持其穩定的步伐。

Well, I mean, look, we're -- for the rest of the year, we're not assuming any change in trajectory. So now in terms of where we are compared to the other cloud providers, I mean one thing you can do is you can look at the sequential growth numbers quarter-to-quarter. And you'll see that if you look at the 3 cloud manager providers, they've decelerated to about 1% quarter-to-quarter growth for the last quarter. We're still significantly higher than that.

好吧，我的意思是，看，我們 - 在今年餘下的時間裡，我們不會假設軌蹟有任何變化。所以現在就我們與其他雲提供商的比較而言，我的意思是你可以做的一件事是你可以按季度查看連續增長數字。你會發現，如果你看一下這 3 家云管理器提供商，他們在上個季度的季度環比增長率已經放緩至 1% 左右。我們仍然比那高得多。

And when you look at our ARR, considering the fact that we ended -- we exited slower in '22 and we exited higher in end of Q1 because the quarter is actually differently around the holiday, we actually maintained an ARR that was already higher also than the -- on ARR growth that was higher than the -- sequentially than the cloud providers. So we already decoupled from the growth of the hyperscalers to a certain extent.

當您查看我們的 ARR 時，考慮到我們結束的事實 - 我們在 22 年退出速度較慢，而我們在第一季度末退出較高，因為假期前後該季度實際上有所不同，我們實際上保持了已經較高的 ARR比 - ARR 增長高於 - 順序高於雲提供商。因此，我們已經在一定程度上與超大規模用戶的增長脫鉤。

Please stand by for the next question. The next question comes from Fatima Boolani with Citi.

請等待下一個問題。下一個問題來自花旗的 Fatima Boolani。

This is [Joel] on for Fatima. So just to check in another vertical here. Given the continued uncertainty in the financial services vertical, just wondering if you could speak to your exposure here and perhaps any related behavior you're seeing with your customers, if at all? And then I just have a quick follow-up.

這是 Fatima 的 [Joel]。所以只是在這裡檢查另一個垂直方向。鑑於金融服務垂直領域的持續不確定性，只是想知道您是否可以在這裡談談您的風險敞口，以及您在客戶身上看到的任何相關行為，如果有的話？然後我只是快速跟進。

So we don't-- I don't think we'll have numbers to share on the exact exposure to financial services. Obviously, it's been a growing vertical for us as the financial services are early adopters of software. And I would say it's not necessarily earliest adopters of the cloud, but definitely adopter at scale today of cloud technology.

所以我們不——我認為我們不會有關於金融服務確切敞口的數字可以分享。顯然，這對我們來說是一個不斷增長的垂直領域，因為金融服務是軟件的早期採用者。我會說它不一定是雲的最早採用者，但今天肯定是雲技術的大規模採用者。

We haven't seen any changes in customer behavior on that side. And that includes, when there were all this trouble -- we saw this trouble with SVB and other banks failing, we were still seeing great upticks from our products from their financial services, new logos as well as expansion deals. So nothing to report.

我們還沒有看到這方面的客戶行為有任何變化。這包括，當出現所有這些麻煩時——我們看到 SVB 和其他銀行倒閉的麻煩，我們仍然看到我們的產品從他們的金融服務、新標識和擴張交易中得到了很大的提升。所以沒有什麼可報告的。

Okay. And then just -- you mentioned the expansion deal with a large fintech, which displaced open source software in addition to other tools. So I just wondered if you could speak to the competitive dynamics versus open source, especially in this cost-sensitive environment and why Datadog still wins and perhaps works as a consolidation destination.

好的。然後——你提到了與一家大型金融科技公司的擴張協議，除了其他工具外，它還取代了開源軟件。所以我只是想知道你是否可以談談競爭動態與開源，特別是在這個對成本敏感的環境中，以及為什麼 Datadog 仍然獲勝並且可能作為整合目的地。

Yes. I mean the situation is very similar to what it's always been. It's -- we win because we deliver more value. It's -- at the end of the day, it works better and cheaper with what we do and what we provide than trying to build it yourself and mobilizing your own team and trying to stitch together different parts of open source.

是的。我的意思是情況與以往非常相似。這是 - 我們贏了，因為我們提供了更多價值。歸根結底，與嘗試自己構建和動員自己的團隊並嘗試將開源的不同部分拼接在一起相比，它與我們所做的和我們提供的東西一起工作得更好、更便宜。

For some customers, they will still want part of that. They will still want to build somebody, that's more a cultural thing. But for the vast majority of customers, it's not a rational thing to do. And that's why we win in the end. The dynamic there is remarkably unchanged from -- I could have said the exact same thing 10 years ago. Obviously, the open source projects, we're different. Our footprint as the community was different, but the dynamics when we sold the customer was very, very similar.

對於一些客戶來說，他們仍然想要其中的一部分。他們仍然會想要建立一個人，這更像是一種文化。但對於絕大多數客戶來說，這不是一件理性的事情。這就是我們最終獲勝的原因。與 10 年前我本可以說完全相同的話，那裡的動態明顯沒有變化。顯然，開源項目，我們是不同的。我們作為社區的足跡不同，但我們銷售客戶時的動態非常非常相似。

Please stand by for our next question. The next question comes from Brad Reback with Stifel.

請等待我們的下一個問題。下一個問題來自 Stifel 的 Brad Reback。

Olivier, Earlier, you mentioned sort of the opportunity or your sort of focus on the Azure ecosystem. What types of things on a go-to-market perspective can you do to increase your penetration there as you've sort of historically been much larger inside of AWS?

Olivier，早些時候，你提到了某種機會或你對 Azure 生態系統的關注。從進入市場的角度來看，您可以做哪些類型的事情來增加您在那裡的滲透率，因為您在 AWS 內部的歷史規模要大得多？

So there's a lot of things we're doing, whether that's working directly with other cloud providers in addition to our very strong relationship with AWS, whether that's building more integration specifically to the ecosystem, technology integration. And if you look at our announcements, product announcements, you will see that we've done quite a bit in partnership with Azure and Microsoft, for example.

因此，我們正在做很多事情，除了我們與 AWS 的牢固關係之外，是否直接與其他雲提供商合作，是否正在構建更多專門針對生態系統、技術集成的集成。如果您查看我們的公告、產品公告，您會發現我們已經與 Azure 和 Microsoft 等合作開展了大量工作。

And we -- part of it is also expanding our sales force in territories that tend to be -- to lean more towards Microsoft in terms of their tech stack. So when you look at the early customers we had, which tended to be a lot of software companies that used to be based on the West Coast in the U.S., these typically didn't lean hard Microsoft. But when we look at the more recent enterprise teams we started in the past few years, for example, in U.S. Central areas, like these tend to be way heavier on the Microsoft stack.

而且我們——其中一部分也在擴大我們在往往是的地區的銷售隊伍——在他們的技術堆棧方面更傾向於微軟。因此，當您查看我們擁有的早期客戶時，這些客戶往往是許多過去位於美國西海岸的軟件公司，這些公司通常不會向微軟傾斜。但是，當我們查看過去幾年我們成立的較新的企業團隊時，例如在美國中部地區，這些團隊在 Microsoft 堆棧中的分量往往要重得多。

So it's a combination of all of the above, basically. Like it's not just one single thing we do, it's a lot of different things at many levels to make sure that we have the right product to show in front of the -- it can be from the right customer and have the right sales force to renew that.

所以它基本上是以上所有內容的組合。就像我們做的不僅僅是一件事情，而是在許多層面上做很多不同的事情，以確保我們有合適的產品展示在他們面前——它可以來自合適的客戶，並有合適的銷售人員來更新那個。

Please stand by for our next question. The next question comes from Matt Hedberg with RBC.

請等待我們的下一個問題。下一個問題來自 RBC 的 Matt Hedberg。

Thanks for the APM and log management data point, over $1 billion in ARR. I'm wondering, can you provide a frame of reference for maybe the growth of these 2 businesses versus core infrastructure? And maybe between the 2 APM logging, are they about equal in size? Or is one sort of relatively larger than the other?

感謝 APM 和日誌管理數據點，ARR 超過 10 億美元。我想知道，您能否為這兩項業務與核心基礎設施的增長提供一個參考框架？也許在 2 個 APM 日誌記錄之間，它們的大小是否相等？或者是一種比另一種相對更大？

Although it might look the same, I mean we're not going to give specific numbers there. But -- and the growth rates, of course, like the -- generally speaking, the smaller products grow faster than the products that are bigger than them. That's pretty much true over the whole set of products. The growth of all of the products have come down a little bit, especially the ones that have a lot of volume component, like, for example, logs, where if you -- when you think of optimization, like there is optimization that happens at the cloud provider level and there some that can happen at the observability level, too. Overwhelmingly, at the observability level, that's logged and everything that's for which customers have different knob to turn. For everything else, it's the utilization to where the cloud provide (inaudible).

雖然它可能看起來一樣，但我的意思是我們不會在那裡給出具體數字。但是——當然還有增長率——一般來說，較小的產品比比它們大的產品增長得更快。整套產品都是如此。所有產品的增長都略有下降，尤其是那些具有大量體積成分的產品，例如日誌，如果你 - 當你想到優化時，就像優化發生在雲提供商級別，還有一些也可能發生在可觀察性級別。絕大多數情況下，在可觀察性級別，這已被記錄下來，並且客戶有不同的選擇。對於其他一切，它是對雲提供的地方的利用（聽不清）。

Got it. And then on the outage that you referenced, the $5 million. I'm curious, what did you guys learn from that to prevent maybe this in the future? And outside of the $5 million hit that David talked about, are there any other repercussions from a customer perspective?

知道了。然後是您提到的中斷，即 500 萬美元。我很好奇，你們從中學到了什麼來防止將來發生這種情況？除了大衛談到的 500 萬美元的打擊之外，從客戶的角度來看，還有其他影響嗎？

So well, we've made a lifetime of learning in a day. So that's the positive part of having an incident like that. I want to say I was personally very impressed by the response from our team. We shared some information on postmortem on what happened. I encourage everyone to read it because it's a fascinating document. But because of the very wide nature of this issue, we ended up having 3 shifts of 500 to 600 engineers working on this outage. And that worked -- that part worked beautifully.

好吧，我們已經在一天內完成了一生的學習。所以這是發生這樣的事件的積極部分。我想說我個人對我們團隊的回應印象深刻。我們分享了一些關於所發生事情的事後分析信息。我鼓勵大家閱讀它，因為它是一份引人入勝的文件。但是由於這個問題的範圍非常廣泛，我們最終安排了 500 到 600 名工程師分 3 班來處理這次中斷。那行得通——那部分工作得很好。

Obviously, we've learned about a number of things beyond the root cause of the outage that is almost anecdotal, like it's one of those small things that can have a big impact, but I don't worry so much about it happening again. What we've learned is more about the various things we can do better to recover faster for our systems and to provide a better way for our customers to mitigate an issue when that happens. So these are the most of the learnings and most of the new sources of work that we have internally to follow up on that to make sure that we reduce the chance of it happening again. And when it does, we will recover faster and better for our customers.

顯然，我們已經了解了一些超出幾乎是軼事的中斷根本原因的事情，就像它是可以產生重大影響的小事情之一，但我不太擔心它再次發生。我們學到的更多是關於我們可以做得更好的各種事情，以更快地恢復我們的系統，並為我們的客戶提供更好的方法來緩解發生問題時的問題。因此，這些是我們必須在內部跟進的大部分知識和大部分新工作來源，以確保我們減少再次發生的機會。當它發生時，我們將為我們的客戶更快更好地恢復。

Overall, a very humbling experience. And I want to make sure that we drive our customers in the future there. We don't see any long-term impact. The way we've handled it is by being very transparent with our customers about what was happening and addressing any possible consequences with them after that. And we think in those situations, when you do things right and when you don't repeat these mistakes too often, the effect is actually to strengthen the relationship with the customer. And I hope we succeed in doing that.

總的來說，這是一次非常令人羞愧的經歷。我想確保我們將來會把我們的客戶帶到那裡。我們看不到任何長期影響。我們處理它的方式是對我們的客戶非常透明地了解正在發生的事情，並在此之後與他們一起解決任何可能的後果。我們認為，在這些情況下，當你做對事情並且不經常重複這些錯誤時，效果實際上是加強與客戶的關係。我希望我們能成功做到這一點。

Please stand by for our next question. The next question comes from Brent Thill with Jefferies.

請等待我們的下一個問題。下一個問題來自 Jefferies 的 Brent Thill。

David, just on the large customer adds, you were at 130 versus an average of 170 to 240 over the last 4 quarters. Any color there? Can you just talk to, is this just more customer selling because of the economy? Or is this execution-related? How would you the characterize the large enterprise traction?

大衛，就大客戶而言，過去 4 個季度，您的得分為 130，而平均水平為 170 至 240。那裡有什麼顏色？你能不能談談，這只是因為經濟的緣故，更多的客戶在銷售嗎？或者這與執行有關？您如何描述大型企業的牽引力？

Yes. A couple of factors. One, first quarter is our seasonally lowest amount of new logo ARR. The first quarter this year was very similar. I think we said slightly higher than the year before and similar to our other quarters. That's one factor.

是的。幾個因素。第一，第一季度是我們新標識 ARR 的季節性最低數量。今年第一季度非常相似。我認為我們說的比前一年略高，與我們的其他季度相似。這是一個因素。

Secondly, as we've talked about, most of our customers who are in that larger customer group aren't born there but expand into that. And when the organic growth rate goes down, you will naturally have a slower evolution or graduation of customers into that larger customer classification. So both of those factors caused that slowdown in accumulation of customers over $100,000.

其次，正如我們所談到的，我們在那個更大的客戶群中的大多數客戶並不是出生在那裡，而是擴展到那裡。當有機增長率下降時，您自然會減慢客戶的演變或升級到更大的客戶類別。因此，這兩個因素都導致了超過 100,000 美元的客戶積累速度放緩。

And Olivier, just a quick follow-up on the cloud authorization solution. Are you -- can you give us a sense of the traction of what you're seeing in terms of adoption there?

Olivier，只是對雲授權解決方案的快速跟進。你是——你能告訴我們你在採用方面所看到的吸引力嗎？

Adjustment for what? Sorry, I missed the beginning of your question.

調整什麼？對不起，我錯過了你問題的開頭。

Even build a cloud optimization solution to help your customers identify where there's potential opportunities, are you seeing an uptake in that product?

甚至構建雲優化解決方案來幫助您的客戶確定哪裡有潛在機會，您是否看到該產品的使用情況？

Yes, we're seeing extremely strong demand for that. And this is -- so this is definitely a product that customers want. And we -- and our immediate road map for it is very clear. So that's -- there's no doubt about it.

是的，我們看到了非常強烈的需求。這是 - 所以這絕對是客戶想要的產品。我們 - 以及我們的近期路線圖非常明確。這就是 - 毫無疑問。

Please stand by for our next question. The next question comes from Koji Ikeda with Bank of America Securities.

請等待我們的下一個問題。下一個問題來自美國銀行證券的 Koji Ikeda。

Just one for me here in the interest of time. I wanted to ask you a question on the cloud security management platform and specifically the sales motion for it. So how is that shaping up this year? And how should we be thinking about the investments and strategy for that segment this year? And as you attack that opportunity, how has the competitive environment been shaping up as you head into deal bake-offs? Is it coming in as expected?

為了節省時間，這裡只給我一個。我想問你一個關於雲安全管理平台的問題，特別是它的銷售動議。那麼今年情況如何？我們應該如何考慮今年該細分市場的投資和戰略？當你抓住這個機會時，競爭環境在你進入交易競爭時是如何形成的？它是否按預期進入？

Yes. There's no dramatic change from what we've seen before. Our focus is really on getting the product in as many hands as possible. And as we mentioned on the call, it's working. We have more than 5,000 customers on those products. And this is really what gives us the -- basically, closes the loop so that we can keep building those products and bring them all to maturity.

是的。與我們之前看到的相比沒有顯著變化。我們的重點實際上是將產品交到盡可能多的人手中。正如我們在電話中提到的那樣，它正在發揮作用。我們有 5,000 多個客戶使用這些產品。這真的給了我們 - 基本上，關閉循環，這樣我們就可以繼續構建這些產品並使它們全部成熟。

The one thing I'll say is our strategy or approach for security is ambitious. We don't have -- we specifically are not trying to build a point solution, we're building -- which means we're moving in many different directions at the same time for this. And everything we do in terms of go-to-market and driving customer adoption for that is in service of this product development so we can build out that platform.

我要說的一件事是我們的安全戰略或方法是雄心勃勃的。我們沒有——我們特別沒有嘗試構建一個點解決方案，我們正在構建——這意味著我們正在為此同時朝著許多不同的方向前進。我們在進入市場和推動客戶採用方面所做的一切都是為了為該產品開發服務，因此我們可以構建該平台。

So with that in mind, the go-to-market hasn't changed from what we've done before. And we tend to land heavily on crossing over from DevOps team into security, and that's working well to get a lot more customer adoption.

因此，考慮到這一點，進入市場並沒有改變我們之前所做的。而且我們傾向於將重點放在從 DevOps 團隊過渡到安全方面，這對於獲得更多客戶採用來說效果很好。

On the community side, there's no big changes there. We see the same user suspects as before. The dynamics are a little bit different because they typically have narrower solution and more defined cell motions around those. And -- but we're very pleased with the customer adoption so far.

在社區方面，那裡沒有大的變化。我們看到與以前相同的用戶嫌疑人。動力學略有不同，因為它們通常具有更窄的解決方案和更明確的圍繞這些解決方案的細胞運動。而且 - 但到目前為止，我們對客戶的採用感到非常滿意。

Please stand by for our next question. The next question comes from Gregg Moskowitz with Mizuho.

請等待我們的下一個問題。下一個問題來自 Mizuho 的 Gregg Moskowitz。

Okay. Olivier, you laid out a pretty compelling case for why generative AI will have a positive impact on Datadog and presumably the observability market at large, both over the near to medium term as well as long term. But do you see any offsets, any partial offsets from organizations having greater intelligence and automation at their fingertips? Are there certain workloads that may no longer need to be monitored by an observability platform?

好的。 Olivier，你提出了一個非常有說服力的案例，說明為什么生成式人工智能將對 Datadog 產生積極影響，並可能對整個可觀察性市場產生積極影響，無論是在中短期還是長期。但是你是否看到任何抵消，任何來自組織的部分抵消，這些組織擁有更強大的智能和自動化？是否有某些工作負載可能不再需要由可觀察性平台監控？

Look, in the long-term future, everything is possible, but I don't think -- today, I don't think that's in the -- that's not what we hear or see. I think the -- again, the workloads are getting more complex as the integration management is getting better, and we see basically a continuation of that.

看，在長期的未來，一切皆有可能，但我不認為——今天，我不認為——這不是我們聽到或看到的。我認為 - 同樣，隨著集成管理越來越好，工作負載變得越來越複雜，我們基本上看到了這種情況的延續。

So in terms of what customers do today, like it's hard to project the current adoption of AI into what it might look like into the future because right now, AI is mostly used as an API call for most companies. But we don't think it's necessarily going to be the case 1, 2 or 5 years now.

因此，就客戶今天的行為而言，很難將當前對 AI 的採用預測到未來的樣子，因為目前，AI 主要用作大多數公司的 API 調用。但我們認為現在不一定是 1、2 或 5 年的情況。

Okay. Got it. And then just for David, on the outage, are the credits, et cetera, is that now fully behind you? Or is there any lingering financial impact in the Q2 that you're embedding in the guide?

好的。知道了。然後就大衛而言，在停電時，信用等等，現在完全落後於你了嗎？或者您在指南中嵌入的 Q2 是否有任何揮之不去的財務影響？

Yes. We provide for what we know in our -- at time in our accruals. So we believe that we've provided for that through the revenue impact in Q1.

是的。我們提供我們所知道的——在我們的應計項目中。因此，我們相信我們已經通過第一季度的收入影響提供了這一點。

Please standby for our last question. The last question comes from Mike Cikos with Needham.

請等待我們的最後一個問題。最後一個問題來自 Mike Cikos 和 Needham。

I guess a question for Oli. The first question is really around the ARR discussion. I appreciate the color you guys provided around that APM and log management. Can you highlight -- is Datadog winning new customers on pure APM and logging at this point? Or is infrastructure still the heavy lean as far as where those new logos are being predominantly won?

我想問奧利一個問題。第一個問題實際上是圍繞 ARR 討論的。我很欣賞你們圍繞 APM 和日誌管理提供的顏色。您能否強調——此時 Datadog 是否通過純 APM 和日誌記錄贏得了新客戶？或者就主要贏得這些新徽標的地方而言，基礎設施仍然很薄弱嗎？

And then secondarily, can you talk to maybe some of the newer products in the portfolio which you're seeing the strongest uptake today, just given we have, I think, it's 17-plus modules in the portfolio more broadly? Just where would -- where should we be focusing when thinking about growth going forward from here?

其次，您能否談談您今天看到的產品組合中的一些較新產品，我認為，鑑於我們在更廣泛的產品組合中有 17 個以上的模塊？在考慮未來的增長時，我們應該關注哪裡？

Yes. So look, motion is still to land with infrastructure, as you know, PMO and our logs when we start. I would say it's getting increasingly difficult to separate all of those because those market in general is converging and to -- I think, all 3 under 1 roof. So it's -- whether customers start the conversation about APM or logs, whether they start over from infrastructure, it doesn't matter a ton when we land new customers.

是的。所以看，運動仍然是基礎設施，如您所知，PMO 和我們開始時的日誌。我會說，將所有這些分開變得越來越困難，因為這些市場總體上正在融合——我認為，所有 3 個都在 1 個屋簷下。所以它是——無論客戶是開始談論 APM 還是日誌，無論他們是從基礎設施開始，當我們吸引新客戶時都無關緊要。

I would say though that we definitely have best-of-breed products also in APM and logs, knowing those products win on their own. And sometimes when we enter a new customer that we already have solutions for other things, we can start by displacing a vendor on the APM side or the log side and expect from there. That's definitely something that we do on a regular basis.

我要說的是，我們肯定在 APM 和日誌中也有同類最佳的產品，知道這些產品靠自己取勝。有時，當我們進入一個我們已經有其他解決方案的新客戶時，我們可以從替換 APM 端或日誌端的供應商開始，並從那裡開始。這絕對是我們定期做的事情。

On the question of the products that are growing the fastest, like the -- so all the newer products are actually getting pretty good uptake. So it's hard to pick one. I mentioned earlier, so cloud cost optimization is actually a very popular product right now. Still very early (inaudible), but it's a very popular product.

關於增長最快的產品的問題，比如——所以所有的新產品實際上都得到了很好的採用。所以很難選擇一個。我之前提到過，云成本優化實際上是目前非常流行的產品。還很早（聽不清），但它是一個非常受歡迎的產品。

And we also see a lot of interest actually for some of the new products we announced at the ASH last year, and some of which are still not generally available. So there's a number of things we're doing in terms of estimating response to issues and workflows and sorting through issues and managing issues for our life cycle that are getting a lot of attention from customers to that, too. So we're excited about what's coming next.

我們也看到很多人對我們去年在 ASH 上宣布的一些新產品感興趣，其中一些產品仍未普遍上市。因此，我們在評估對問題和工作流程的響應以及對問題進行分類和管理我們生命週期的問題方面正在做很多事情，這些事情也引起了客戶的廣泛關注。所以我們對接下來會發生什麼感到興奮。

Obviously, the expectation for some of those products are changing over time, too. Not everyone can see what can be done with AI. Everybody expects to see a lot more of that. So I guess we'll share more on that in the near future.

顯然，對其中一些產品的期望也在隨著時間的推移而變化。不是每個人都能看到人工智能可以做什麼。每個人都希望看到更多這樣的事情。所以我想我們會在不久的將來分享更多。

I would now like to turn the call over to Olivier for closing remarks.

我現在想把電話轉給 Olivier 作結束語。

Thank you. I want to thank again all -- everyone at Datadog for crossing these really important milestones. Obviously, we're already focused on the next ones. But I want to thank everyone for their hard work, shipping product, getting in front of customers. I also want to thank our team for responding to that system outage we had and our customers for very kindly working with us through that. And on this, I will speak to you again after Q2.

謝謝。我想再次感謝所有人 - Datadog 的每個人都跨越了這些非常重要的里程碑。顯然，我們已經專注於下一個。但我要感謝大家的辛勤工作，運送產品，出現在客戶面前。我還要感謝我們的團隊對我們遇到的系統中斷做出響應，並感謝我們的客戶非常友好地與我們合作。關於這一點，我將在第二季度後再次與您交談。

This concludes today's conference call. Thank you for participating. You may now disconnect.

今天的電話會議到此結束。感謝您的參與。您現在可以斷開連接。