數據狗 (DDOG) 2025 Q4 法說會逐字稿

Good day and welcome to the Q4 2025 Data dog earnings conference call. (Operator Instructions) As a reminder, this call may be recorded.

大家好，歡迎參加Data Dog 2025年第四季財報電話會議。 （操作說明）提醒您，本次電話會議可能會被錄音。

I would like to turn the call over to Yuka Broderick, Senior Vice President of Investor Relations. Please go ahead.

現在我將把電話交給投資者關係高級副總裁尤卡·布羅德里克女士。請開始吧。

Thank you, Michelle. Good morning, and thank you for joining us to review Datadog's fourth quarter 2025 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.

謝謝米歇爾。早安，感謝您參加本次電話會議，共同回顧Datadog 2025年第四季的財務業績，該業績已於今天上午發布的新聞稿中公佈。今天與我一同參加電話會議的還有Datadog聯合創辦人兼執行長Olivier Pomel，以及Datadog財務長David Obstler。

During this call, we will make forward-looking statements, including statements related to our future financial performance, our outlook for the first quarter and fiscal year 2026 and related notes and assumptions, our product capabilities and our ability to capitalize on market opportunities.

在本次電話會議中，我們將發表前瞻性聲明，包括與我們未來財務業績、2026 年第一季度和財年展望及相關說明和假設、我們的產品能力以及我們把握市場機會的能力相關的聲明。

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 today and are subject to a variety of risks and uncertainties that could cause actual results to differ materially.

「預期」、「相信」、「繼續」、「估計」、「期望」、「打算」、「將」等詞語及類似表達旨在識別前瞻性陳述或對未來預期的類似表述。這些陳述反映了我們目前的觀點，並受多種風險和不確定因素的影響，實際結果可能與預期有重大差異。

For a discussion of the material risks and other important factors that could affect our actual results, please refer to our Form 10-Q for the quarter ended September 30, 2025. Additional information will be made available in our upcoming Form 10-K for the fiscal year ended December 31, 2025, and other filings with the SEC.

有關可能影響我們實際業績的重大風險和其他重要因素的討論，請參閱我們截至 2025 年 9 月 30 日的季度報告（10-Q 表格）。更多資訊將在我們即將發布的截至 2025 年 12 月 31 日的財政年度報告（10-K 表格）以及向美國證券交易委員會提交的其他文件中提供。

This information is also available on the Investor Relations section of our website, along with a replay of this call. We will 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.

這些資訊以及本次電話會議的錄音回放也可在公司網站的「投資者關係」欄位中找到。我們將討論非GAAP財務指標，這些指標已在盈利報告中的表格中與其最直接可比的GAAP財務指標進行了核對，該盈利報告可在 investors.datadoghq.com 上查閱。

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

接下來，我想把電話交給奧利維爾。

Thanks, Yuka, and thank you all for joining us this morning to go over with a very strong Q4 and overall, a really productive 2025. Let me begin with this quarter's business drivers. We continue to see broad-based positive trends in the demand environment. With the ongoing momentum of cloud migration, we experienced strength across our business, across our product lines and across our diverse customer base. We saw a continued acceleration of our revenue growth.

謝謝Yuka，也感謝各位今天早上與我們一起回顧了強勁的第四季度業績，以及2025年整體的豐碩成果。首先，我想談談本季的業務驅動因素。我們持續看到市場需求呈現全面正面的趨勢。隨著雲端遷移的持續推進，我們的業務、產品線以及多元化的客戶群都取得了顯著成長。我們的營收成長也持續加速。

This acceleration was driven in large part by the inflection of our broad-based business outside of the AI-native group of customers we discussed in the past. And we also continue to see very high growth within this AI-native customer group as they go into production and grow in users, tokens and new products.

這一加速成長很大程度上得益於我們業務的廣泛拓展，不再局限於先前討論過的AI原生客戶群。同時，隨著AI原生客戶群投入生產，用戶數量、代幣數量和新產品數量不斷增長，我們也持續看到他們自身的高速成長。

Our go-to-market teams executed to a record $1.63 billion in bookings, up 37% year-over-year. This included some of the largest deals we have ever made. We signed 18 deals over $10 million in TCV this quarter, of which two were over $100 million and one was in HCG land with a leading AI motor company.

我們的市場推廣團隊取得了創紀錄的16.3億美元訂單額，年增37%。其中包括一些我們有史以來達成的最大型交易。本季我們簽署了18筆總合約價值超過1000萬美元的交易，其中兩筆超過1億美元，還有一筆是與一家領先的人工智慧汽車公司在人機互動領域達成的。

Finally, churn has remained low, with gross revenue retention stable in the mid- to high 90s, highlighting the mission-critical nature of our platform for our customers. Regarding our Q4 financial performance and key metrics, revenue was $953 million, an increase of 29% year-over-year and above the high end of our guidance range. We ended Q4 with about 32,700 customers, up from about 30,000 a year ago.

最後，客戶流失率一直維持在較低水平，總收入留存率穩定在90%以上，凸顯了我們平台對客戶至關重要。關於我們第四季的財務表現和關鍵指標，營收為9.53億美元，年增29%，高於我們預期範圍的上限。第四季末，我們的客戶數量約為32,700家，高於去年同期的約30,000家。

We also added Q4 with about 4,310 customers with an ARR of $100,000 or more, up from about 3,610 a year ago. These customers generated about 90% of our ARR. And we generated free cash flow of $291 million with a free cash flow margin of 31%.

第四季度，我們新增了約4,310家年經常性收入（ARR）在10萬美元或以上的客戶，高於去年同期的約3,610家。這些客戶貢獻了我們約90%的ARR。此外，我們實現了2.91億美元的自由現金流，自由現金流利潤率為31%。

Turning to product adoption. Our platform strategy continues to resonate in the market. At the end of of Q4, 84% customers use two or more products, up from 83% a year ago, 55% of customers used four or more products, up from 50% a year ago, 33% of our customers used six or more products, up from 26% a year ago, 18% of our customers used eight or more products, up from 12% a year ago, and as a sign of continued penetration of our platform, 9% of our customers used 10 or more products, up from 6% a year ago.

再來看看產品採用情況。我們的平台策略在市場上持續獲得認可。截至第四季末，84% 的客戶使用兩種或兩種以上產品，高於去年同期的 83%；55% 的客戶使用四種或四種以上產品，高於去年同期的 50%；33% 的客戶使用六種或六種以上產品，高於去年同期的 26%；18% 的客戶使用八種或六種以上產品，高於去年同期的 26%；18% 的客戶使用八種或六種以上產品，高於去年同期的 26%；18% 的客戶使用八種或八種以上的產品，高於去年同期的 9% 高於十種。 6%，這顯示我們的平台滲透率持續提升。

During 2025, we continue to land and expand with larger customers. As of December 2025, 48% of the Fortune 500 are Datadog customers. We think many of the largest enterprises are still very early in their journey to the cloud. The median Datadog ARR for our Fortune 500 customers is still less than $0.5 million, which leaves a very large opportunity for us to grow with these customers.

2025年，我們將持續拓展並贏得更多大型客戶。截至2025年12月，財星500強企業中有48%是Datadog的客戶。我們認為，許多大型企業在雲端轉型方面仍處於起步階段。目前，Datadog在財富500強客戶中的年度經常性收入（ARR）中位數仍低於50萬美元，這意味著我們與這些客戶共同發展仍有很大的成長空間。

So we're lending more customers and delivering more value, and we also see that with the ARR milestones we're reaching with our products. We continue to see strong growth dynamics with our core three pillars of observability infrastructure monitoring, APM and log management as customers are adopting the cloud, AI and modern technologies.

因此，我們吸引了更多客戶，創造了更多價值，這也反映在我們產品達成的年度經常性收入 (ARR) 里程碑上。隨著客戶採用雲端運算、人工智慧和現代技術，我們核心的三大支柱業務——可觀測性基礎設施監控、應用效能管理 (APM) 和日誌管理——持續保持強勁成長動能。

Today, infrastructure monitoring contributes over $1.6 billion in ARR. This includes innovations deliver visibility and insights across our customers environments, whether they are on-prem, virtualized servers, containerized host, severe deployment or parallelize the GPU fleets.

如今，基礎設施監控為公司貢獻了超過16億美元的年度經常性收入。這其中包括多項創新，旨在為客戶的各種環境（無論是本地部署、虛擬化伺服器、容器化主機、嚴重依賴型部署或並行GPU叢集）提供可視性和洞察力。

Meanwhile, log management is now over $1 billion in ARR. And this includes continued rapid growth with Flex Logs, which is nearing $100 million in ARR. And our third pillar, the end-to-end suite of APM and DEM products also crossed $1 billion in ARR. This includes an acceleration of our core APM product into the mid-30% year-over-year and currently our fastest-growing core pillar.

同時，日誌管理業務的年度經常性收入 (ARR) 已超過 10 億美元。這其中包括 Flex Logs 的持續快速成長，其 ARR 已接近 1 億美元。我們的第三大支柱—端對端應用效能管理 (APM) 和資料事件管理 (DEM) 產品套件的 ARR 也已突破 10 億美元。這其中，我們的核心 APM 產品實現了加速成長，年成長率達到 30% 以上，目前是我們成長最快的核心支柱。

We have now enabled our customers with the easiest onboarding and implementation in the market, while delivering unified deep end-to-end visibilities into their applications. Now remember that even with these three pillars, we are still just getting started as about half of our customers do not buy all three pillars from us, or at least still not yet.

我們現已為客戶提供市場上最便捷的入駐和實施流程，同時為其應用程式提供統一、深入的端到端可視性。但請記住，即便有了這三大支柱，我們也才剛起步，因為大約一半的客戶並未購買全部三大支柱，或至少目前還沒有。

Moving on to R&D and what we built in 2025. We believe over 400 new features and capabilities this year. That's too much for us to cover today, but let's go over some of our innovation. We are executing relentlessly on our very ambitious AI road map, and I will split our efforts into two buckets, AI for Datadog and Datadog for AI. So first, let's look at AI for Datadog.

接下來談談研發以及我們2025年的計畫。我們預計今年將推出超過400項新功能和功能。今天篇幅有限，無法一一詳述，但我們不妨回顧一下其中的一些創新。我們正在全力推進雄心勃勃的人工智慧路線圖，並將我們的工作分為兩部分：Datadog的人工智慧和Datadog的人工智慧。首先，我們來看看Datadog的人工智慧部分。

These are AI products and capabilities that make the Datadog platform better and more useful for customers. We launched Bit AI SRE agent for general availability in December to accelerate root cause analysis and internet response.

這些人工智慧產品和功能使 Datadog 平台更加完善，更能為客戶帶來價值。我們於 12 月正式發布了 Bit AI SRE 代理，旨在加速根本原因分析和網路回應。

Over 2,000 trial and paying customers have run investigations in the past month which indicates significant interest and showed great outcomes with Bits AI. And we're well on our way with Bits AI agent, which detects code level issues, generate fixed uses production contracts and can even help release the monitor a fix.

過去一個月，超過 2000 名試用和付費客戶進行了調查，這表明他們對 Bits AI 表現出了濃厚的興趣，並且取得了顯著成效。 Bits AI 代理程式也進展順利，它能夠偵測程式碼級問題，產生適用於生產環境的修復方案，甚至可以幫助監控系統發布修復程式。

And Bits AI security agent, which autonomously triage SIM signals, conduct investigations and delivers documentation. The Datadog MCP server is being used by thousands of customers in preview. Our MCP server responds to the AI agent and user promps and uses real-time production data and restated of context to drive travel shooting, root cause analysis and automation.

此外，Bits AI 安全代理程式能夠自主對 SIM 卡訊號進行分類、進行調查並產生文件。 Datadog MCP 伺服器目前已被數千家客戶預覽使用。我們的 MCP 伺服器能夠回應 AI 代理和使用者提示，並利用即時生產資料和上下文資訊來驅動故障排查、根本原因分析和自動化流程。

And we're seeing explosive growth in MCP usage with a number of tool calls growing elevenfold in Q4 compared to Q3. Second, let's talk about Datadog for AI. This includes capabilities that deliver end-to-end observability and security across the AI stack. We are seeing an acceleration in growth for LLM Observability.

我們看到 MCP 的使用量呈現爆炸性成長，第四季工具呼叫次數比第三季成長了 11 倍。其次，我們來談談 Datadog for AI。它包含能夠為整個 AI 技術堆疊提供端到端可觀測性和安全性的功能。我們看到 LLM Observability 的成長正在加速。

Over 1,000 customers are using the product and the number of 10% has increased 10 times over the last 6 months. In 2025, we broadened the product to better support application development and iteration and in capabilities such as LLM Experiments and LLM Playground and LLM components and custom LLM as a judge. And we will soon release our AI agent console to monitor usage and adoption of AI agents and cutting assistance.

目前已有超過 1,000 位客戶正在使用該產品，過去 6 個月中，10% 的用戶數量增加了 10 倍。 2025 年，我們將擴展產品功能，以更好地支援應用程式開發和迭代，並新增 LLM 實驗、LLM Playground、LLM 元件以及自訂 LLM 作為評判工具等功能。此外，我們也將很快發布 AI 代理控制台，用於監控 AI 代理的使用情況和採用率，並提供切割輔助功能。

We are working with design partners on GPU monitoring, and we are seeing GPUs increase in our customer base overall. And we are building into our products the ability to secure the AI stack against pop-injection attacks, model hijacking and data poisoning among many other risks.

我們正與設計合作夥伴共同開發GPU監控功能，我們看到GPU在客戶群中的整體使用率正在上升。此外，我們正在產品中建構保護AI堆疊免受pop-injection攻擊、模型劫持和資料投毒等諸多風險的能力。

Overall, we continue to see increased interest among our customers in next-gen AI. Today, about 5,500 customers use one or more Datadog AI integrations to send us data about their machine learning, AI and usage.

總體而言，我們看到客戶對下一代人工智慧的興趣持續增長。目前，約有 5,500 家客戶使用一項或多項 Datadog AI 整合方案，向我們發送有關其機器學習、人工智慧和使用情況的資料。

In 2025, our observability platform delivered deeper and broader capabilities for our customers. We reached a major milestone of more than 1,000 integrations, making it easy for our customers to bring in every type of data they need and engage with the latest technologies from cloud to AI. In log management, we're seeing success with our consolidation motion.

2025年，我們的可觀測性平台為客戶提供了更深入、更廣泛的功能。我們實現了超過1000個整合的重要里程碑，使客戶能夠輕鬆匯入所需的各種資料類型，並使用從雲端運算到人工智慧的最新技術。在日誌管理方面，我們的整合措施也取得了成功。

During 2025, we saw an increasing demand to replace a large legacy vendors, we take out in nearly 100 deals for tens of millions of dollars of new revenue. And we improved log management with notebooks, reference tables, look patterns, calculated fields and an improved life tale among many other innovations.

2025年，我們看到市場對替換大型傳統供應商的需求日益增長，我們成功完成了近百筆交易，帶來了數千萬美元的新收入。此外，我們也透過筆記本、參考表、外觀模式、計算欄位以及改進的生命週期故事等諸多創新，提升了日誌管理水準。

We launched data observability for general availability. Data is becoming even more critical in the AI era. With data observability, we are enabling end-to-end visibility across the entire data life cycle. We launched storage management last month, providing regular insights into cloud storage and recommendations to reduce spend.

我們正式發布了數據可觀測性功能。在人工智慧時代，數據變得愈發重要。借助數據可觀測性，我們實現了對整個數據生命週期的端到端可視性。上個月，我們推出了儲存管理功能，定期提供雲端儲存洞察和降低成本的建議。

We delivered communities auto scaling, so users can quickly identify which other provision clusters and deployments and right size their infrastructure. In the digital experience monitoring area, we launched product analytics to have product designers make better design decisions with clear data about user experience and behavior.

我們實現了社群自動擴展功能，使用戶能夠快速識別其他資源配置叢集和部署，並合理調整基礎架構規模。在數位體驗監控領域，我們推出了產品分析功能，幫助產品設計師利用清晰的使用者體驗和行為數據做出更明智的設計決策。

And we delivered run without limits, giving front-end teams full visibility into user traffic and performance and dynamically choosing the most useful sessions to retain. In security, we are seeing increasing traction and are actively displacing existing market-leading solutions with cloud SIEM in non-enterprise.

我們實現了無限運行，使前端團隊能夠全面了解用戶流量和效能，並動態選擇保留最有價值的會話。在安全領域，我們看到市場反應日益熱烈，並且積極地以雲端 SIEM 解決方案取代非企業級市場中現有的領先解決方案。

This year, our engineers shipped many new capabilities, including a tripling of the amount of content packs built into the product, and most importantly, the tight integration with Bits AI security agent, which has already shown promise as a strong differentiator in the market.

今年，我們的工程師推出了許多新功能，包括將產品內建的內容包數量增加了兩倍，最重要的是，與 Bits AI 安全代理程式的緊密整合，這已經展現出成為市場上強大差異化優勢的潛力。

We launched code security, enabling customers to detect and remediate vulnerabilities in their code and open source libraries from development to production. And we continue to advance our cloud security offering, adding pasture as code or IAC security, with detects and resolve security issues with TerraForm. And we launched our security graph to identify and evaluate attacks fast.

我們推出了程式碼安全解決方案，使客戶能夠從開發到生產的各個階段檢測並修復程式碼和開源庫中的漏洞。我們也持續推進雲端安全產品，新增了「牧場即代碼」（IaC）安全功能，利用 Terraform 偵測並解決安全問題。此外，我們還推出了安全圖譜，用於快速識別和評估攻擊。

In software delivery, in January, we launched future plans. They combined with our real-time observability to enable canary rollouts, so teams can deploy new code with confidence. And we expect them to gain importance in the future. As they serve as a foundation for automating the validation and release of applications in an AI agentic development world.

在軟體交付方面，我們於一月份推出了未來計劃。這些計劃與我們的即時可觀測性相結合，實現了金絲雀發布，使團隊能夠充滿信心地部署新程式碼。我們預計這些計劃在未來將變得更加重要，因為它們將成為在人工智慧代理開發環境中自動化應用程式驗證和發布的基礎。

We are also building out our internal developer portal, which includes software catalog and score cards to help developers navigate infrastructure and application complexity, provide reach context to AI development agents and ultimately enable a faster release cadence.

我們也正在建立內部開發者門戶，其中包括軟體目錄和評分卡，以幫助開發者應對基礎設施和應用程式的複雜性，為 AI 開發代理提供更廣泛的上下文，並最終實現更快的發布節奏。

In Cloud Service Management, we launched Encore, and now support over 3,000 customers with their incident response processes. And I already mentioned Bits AI agent, which bears us on go to accelerate our customer an resolution.

在雲端服務管理領域，我們推出了 Encore，目前為超過 3000 家客戶提供事件回應流程支援。我還提到了 Bits AI 代理，它能幫助我們更快地為客戶解決問題。

As you can tell, we've been very busy, and I want to thank our engineers for a very productive 2025. And most importantly, I'm even more excited in the model plans for 2026. So let's move on to sales and marketing.

如您所見，我們一直非常忙碌，我要感謝我們的工程師們在2025年取得的豐碩成果。更重要的是，我對2026年的新車型計畫更加充滿期待。那麼，接下來我們來談談銷售和行銷。

I want to highlight some of the great deals we closed this quarter. First, we landed an 8-figure annualized deal and our biggest Datadog deal to date with one of the largest AI financial model companies. This customer had a fragmented availability stack and cumbersome monitoring workflows leading to poor productivity.

我想重點介紹一下我們本季達成的一些重要交易。首先，我們與一家大型人工智慧金融模型公司達成了一筆年化金額達八位數的交易，這也是Datadog迄今為止最大的一筆交易。這家客戶的可用性堆疊分散，監控工作流程繁瑣，導致生產力低。

This is a consolidation of more than five open source, commercial, hyper scaler and in-house observability tools into the unified Datadog platform that has returned meaningful time to developers and has enabled a more cohesive approach to observability. This customer is experiencing very rapid growth.

此次整合將五種以上的開源、商業、超大規模和企業內部可觀測性工具整合到統一的 Datadog 平台中，為開發人員節省了大量時間，並實現了更統一的可觀測性方法。該客戶正經歷著快速成長。

Datadog allows them to focus on product development and supporting their users, which is critical to their business success. Next, we will come back a customer that was a European data company in a nearly 7-figure annualized deal. These customers lock focus observability solution had poor user experience and integrations which led to limited user adoption and gap in coverage.

Datadog 使他們能夠專注於產品開發和用戶支持，這對他們的業務成功至關重要。接下來，我們將介紹一位歐洲數據公司客戶，他們與我們簽訂了一份年化金額接近七位數的合約。這些客戶鎖定的可觀測性解決方案使用者體驗和整合性較差，導致使用者採用率有限，覆蓋範圍也存在缺口。

By returning to Datadog and consolidating seven observability tools, they expect to reduce tooling overheads and improve engineering productivity with faster incident resolution. They will adopt 9 Datadog products at the stock, including some of our newer products such as Flex Log, observability pipeline, top cost management, data observability and on call.

透過回歸 Datadog 並整合七款可觀測性工具，他們期望降低工具開銷，並透過更快的事件解決速度提高工程效率。他們將在此次交易中採用 9 款 Datadog 產品，包括一些較新的產品，例如 Flex Log、可觀測性管道、成本管理、數據可觀測性和 On Call。

Next, we signed an 8-figure annualized expansion with a leading e-commerce and digital payments platform. These customers products have an enormous reach its commercial APM solution had scaling issues, lack correlation across silos and had a pricing model that was difficult to understand or predict.

接下來，我們與一家領先的電子商務和數位支付平台簽署了一份年化金額達數千萬美元的擴張協議。該客戶的產品覆蓋範圍極為廣泛，但其商業應用績效管理 (APM) 解決方案存在擴展性問題，缺乏跨系統關聯性，且定價模式難以理解和預測。

With this expansion, they are standardizing on Datadog APM using open telemetry so their teams can correlate metric, traction logs to detect and resolve issues faster. And they have already seen meaningful impact with a 40% reduction in resolution times by their own estimates. This customer has adopted 17 products across the Datadog platform.

透過此次擴展，他們採用基於開放遙測技術的 Datadog APM 進行標準化部署，以便團隊能夠關聯指標和流量日誌，更快地發現並解決問題。據他們估計，問題解決時間已縮短 40%，效果顯著。該客戶已在 Datadog 平台上部署了 17 款產品。

Next, we signed a 7-figure annualized expansion for an 8-figure annualized deal with a Fortune 500 food and beverage retailer. This long-time customer to the Datadog platform across many products, but still has over 30 other observability tools and embarked on consolidating for cost savings and better outcomes.

接下來，我們與一家財富500強食品飲料零售商簽署了一份年化金額達七位數的擴容協議，最終達成了一份年化金額達八位數的合作協議。這家零售商長期以來一直是Datadog平台眾多產品的忠實用戶，但目前仍擁有30多種其他可觀測性工具，此次合作旨在整合這些工具以降低成本並提升效果。

With this expansion, Datadog log management and Flex Logs will replace the legacy logging product for all ops use cases with expected annual savings in the millions of dollars. This customer is expanding to 17 Datadog products.

透過此擴展，Datadog 日誌管理和 Flex Logs 將取代原有的日誌產品，滿足所有維運用例的需求，預計每年可節省數百萬美元。該客戶此次將使用的 Datadog 產品數量擴展至 17 種。

Next, we signed a 7-figure annualized expansion with a leading health care technology company. This company was facing reliability issues, impacting clinicians during critical workflows and putting customer trust at risk. The customer will consolidate six tools and adopt seven Datadog products, including LLM Observability to support their AI initiatives as well as Bits AI agents to further accelerate net response.

接下來，我們與一家領先的醫療保健技術公司簽署了一份價值數百萬美元的年度擴展協議。該公司正面臨可靠性問題，這不僅影響了臨床醫生在關鍵工作流程中的工作，也危及了客戶的信任。客戶將整合六種工具，並採用七種 Datadog 產品，包括 LLM Observability（用於支援其人工智慧計畫）以及 Bits AI 代理程式（用於進一步加快網路回應速度）。

Next, we signed an 8-figure annualized expansion, more than quadrupling their annualized commitment with a major Latin American financial services company. Given its successful tool consolidation projects and rapid adoption of Datadog products across all of its teams, this customer renewed early with us while expanding to additional products, including data observability, CI visibility, database monitoring and observability pipelines.

接下來，我們與一家拉丁美洲大型金融服務公司簽署了一份價值數千萬美元的年度擴張協議，使其年度投資翻了四倍多。鑑於該公司在工具整合專案方面取得的成功，以及其所有團隊對 Datadog 產品的快速採用，該客戶提前續簽了合同，並擴展了產品範圍，包括數據可觀測性、持續集成 (CI) 可視化、數據庫監控和可觀測性管道。

With Datadog, this customer showed measurable improvements in cost, efficiency, customer experience and conversion rates across multiple lines of business. That proof of value led them to broaden their commitment with us, and have firmly established Datadog as their mission-critical observability partner.

借助 Datadog，該客戶在多個業務領域都實現了成本、效率、客戶體驗和轉換率的顯著提升。這一價值證明促使他們擴大了與我們的合作，並最終將 Datadog 確立為他們關鍵任務可觀測性合作夥伴的地位。

Last and not least, we signed a 7-figure annualized expansion for an 8-figures annualized like deal with a leading fintech company. With this expansion, the customer is moving their log data on to our unified platform. So teams can correlate telemetry in one place and save between hours and weeks in time to resolution for incidents. This customer has opted 19 Datadog products across the platform, including all three pillars as well as digital experience, security, software delivery and service management. And that is for our wins.

最後，也是非常重要的一點，我們與一家領先的金融科技公司簽署了一份價值七位數的年度擴展協議，使其年度擴展達到八位數。透過此次擴展，該客戶將其日誌資料遷移到我們的統一平台上。這樣，團隊就可以在一個地方關聯遙測數據，從而節省數小時甚至數週的事件解決時間。該客戶選擇了平台上的 19 款 Datadog 產品，涵蓋三大支柱以及數位體驗、安全、軟體交付和服務管理。以上就是我們所取得的成果。

Congratulations to our entire go-to-market organization for great 2025 and a record Q4. It was inspiring to see the whole team at our [Estiko] last month and really exciting to embark on a very ambitious 2026. Before I turn it over to David for a financial review, I want to say a few words on our longer-term outlook.

恭喜我們整個市場推廣團隊在2025年取得的輝煌成就，以及第四季創紀錄的表現。上個月在[Estiko]見到整個團隊，令人深受鼓舞，也讓我們對雄心勃勃的2026年充滿期待。在將財務回顧交給David之前，我想就我們的長期展望談幾點看法。

There is no change to our overall view that digital transformation and cloud migration are long-term secular growth drivers for our business. So we continue to extend our platform to solve our customers' problems from end to end across their soft development, production, data stack, user experience and security needs. Meanwhile, we're moving fast in AI, by integrating into the Datadog platform to improve customer value and outcome and by building products to observe, secure and act across our customers' AI stack.

我們始終堅持認為，數位轉型和雲端遷移是推動業務長期成長的根本動力。因此，我們將持續擴展平台，從軟體開發、生產、資料堆疊、使用者體驗和安全等各個方面，全面解決客戶的問題。同時，我們在人工智慧領域也快速發展，透過與Datadog平台集成，提升客戶價值和成果；並建構產品，用於觀察、保護和處理客戶的人工智慧堆疊。

In 2025, we executed very well to deliver for our customers against their most complex mission critical problems. Our strong financial performance is an output of that effort. And we're even more excited about 2026 as we are starting to see an inflection in AI usage by our customers into their applications and as our customers begin to adopt real innovations, such as Bits AI agent.

2025年，我們出色地完成了任務，幫助客戶解決了他們最複雜的關鍵任務難題。我們強勁的財務表現正是這項努力的成果。我們對2026年更加充滿信心，因為我們開始看到客戶在其應用程式中對人工智慧的應用出現轉機，並且客戶開始採用真正的創新技術，例如Bits AI代理。

To hear about all that in detail and much more I welcome you all to join us at our next Investor Day this Thursday in New York between 1 PM and 5 PM. I'll be joined by our product and go-to-market leaders to share how we are serving our customers, who we innovate the broader platform and how we are delivering greater value with AI. For more details, please refer to the press release announcing the event or head to investors.datadoghq.com.

想了解更多詳情，歡迎各位參加本週四下午1點至5點在紐約舉行的下一場投資者日活動。屆時，我們的產品和市場推廣負責人將與我一起分享我們如何服務客戶、如何創新平台以及如何利用人工智慧創造更大價值。更多詳情，請參閱活動新聞稿或造訪 investors.datadoghq.com。

And with that, I will turn it over to our CFO, David.

接下來，我將把發言權交給我們的財務長大衛。

Thanks, Olivier. Our Q4 revenue was $953 million, up 29% year-over-year and up 8% quarter over-quarter. Now to dive into some of the drivers of our Q4 revenue growth, first, overall, we saw robust sequential usage growth from existing customers in Q4. Revenue growth accelerated with our broad base of customers, excluding the AI natives to 23% year-over-year, up from 20% in Q3.

謝謝奧利維爾。我們第四季營收為9.53億美元，年增29%，季增8%。接下來深入探討第四季營收成長的驅動因素。首先，總體而言，第四季度現有客戶的使用量實現了強勁的環比成長。剔除人工智慧原生企業後，我們廣泛的客戶群推動營收成長加速，較去年同期成長23%，高於第三季的20%。

We saw strong growth across our customer base with broad-based strength across customer size, spending bands and industries. And we have seen this trend of accelerated revenue growth continue in January. Meanwhile, we are seeing continued strong adoption amongst AI-native customers with growth that significantly outpaces the rest of the business.

我們的客戶群實現了強勁成長，無論客戶規模、消費水準或產業，都呈現出廣泛的成長勢頭。而且，這種加速成長的趨勢在1月仍在持續。同時，人工智慧原生客戶群也持續保持強勁的成長勢頭，其成長速度顯著超過了其他業務領域。

We see more AI native customers using Datadog with about 650 customers in this group. And we are seeing these customers grow with us, including 19 customers spending $1 million or more annually with Datadog. Among our AI customers are the largest companies in this space as today, 14 of the top 20 AI native companies are Datadog customers.

我們看到越來越多的原生人工智慧客戶使用Datadog，目前已有約650家客戶。這些客戶與我們共同成長，其中19家客戶每年在Datadog上的支出達到或超過100萬美元。我們的人工智慧客戶中不乏業內頂尖企業，目前排名前20的原生人工智慧企業中，有14家都是Datadog的客戶。

Next, we also saw continued strength from new customer contribution. Our new logo bookings were very strong again this quarter, and our go-to-market teams converted a record number of new logos and average new logo land sizes continues to grow strongly.

其次，新客戶的貢獻也持續強勁。本季我們的新logo訂單量再次表現強勁，市場推廣團隊成功轉化了創紀錄的新logo數量，平均新logo投放面積也持續快速成長。

Regarding retention metrics, our trailing 12-month month net retention -- revenue retention percentage was about 120%, similar to last quarter, and our trailing 12-month gross revenue retention percentage remains in the mid- to high-90s.

關於客戶留存指標，我們過去 12 個月的淨留存率（收入留存率）約為 120%，與上個季度類似，而我們過去 12 個月的總收入留存率仍維持在 90% 以上。

And now moving on to our financial results. First, billings were $1.21 billion, up 34% year-over-year. Remaining performance obligations, or RPO, was $3.46 billion, up 52% year-over-year. And current RPO growth was about 40% year-over-year. RPO duration increased year-over-year as the mix of multiyear deals increased in Q4.

接下來是我們的財務表現。首先，帳單金額為12.1億美元，年增34%。剩餘履約義務（RPO）為34.6億美元，年增52%。目前RPO年增約40%。由於第四季多年合約佔比增加，RPO期限也隨之延長。

We continue to believe revenue is a better indication of our business trends than billing and RPO. Now let's review some of the 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.

我們仍然認為，與帳單金額和RPO相比，收入更能反映我們的業務趨勢。現在讓我們回顧一些關鍵的損益表結果。除非另有說明，所有指標均為非GAAP指標。我們在獲利報告中提供了GAAP與非GAAP財務資料的調節表。

First, our Q4 gross profit was $776 million with a gross margin percentage of 81.4%. This compares to a gross margin of 81.2% last quarter and 81.7% in the year ago quarter. Q4 OpEx grew 29% year-over-year versus 32% last quarter and 30% in the year ago quarter.

首先，我們第四季的毛利為7.76億美元，毛利率為81.4%。相較之下，上季的毛利率為81.2%，去年同期為81.7%。第四季的營運支出較去年同期成長29%，而上季和去年同期分別為32%和30%。

And we continue to grow our investments to pursue our long-term growth opportunities, and this OpEx growth is an indication of our successful execution on our hiring plans. Our Q4 operating income was $230 million for a 24% operating margin compared to 23% last quarter and 24% in the year ago quarter.

我們持續加大投資，以掌握長期成長機遇，而營運支出成長也顯示我們的招募計畫執行得非常成功。第四季營業收入為2.3億美元，營業利益率為24%，高於上一季的23%和去年同期的24%。

Now turning to the balance sheet and cash flow statements. We ended the quarter with $4.47 billion in cash, cash equivalents and marketable securities. Cash flow from operations was $327 million in the quarter. After taking into consideration capital expenditures and capitalized software, free cash flow was $291 million for a free cash flow margin of 31%. And now for our outlook for the first quarter and the full fiscal year 2026.

現在來看資產負債表和現金流量表。本季末，我們持有現金、現金等價物及有價證券共44.7億美元。本季經營活動產生的現金流量為3.27億美元。計入資本支出和資本化軟體後，自由現金流為2.91億美元，自由現金流利潤率為31%。接下來，我們將展望2026財年第一季及全年業績。

Our guidance philosophy overall remains unchanged. As a reminder, we based our guidance on trends observed in recent months. and apply conservatism on these growth trends. For the first quarter, we expect revenues to be in the range of $951 million to $961 million, which represents a 25% to 26% year-over-year growth.

我們的整體績效指引策略維持不變。需要提醒的是，我們的業績指引是基於近幾個月觀察到的趨勢，我們對這些成長趨勢採取了保守的預期。我們預計第一季營收將在9.51億美元至9.61億美元之間，年增25%至26%。

Non-GAAP operating income is expected to be in the range of $195 million to $205 million, which implies an operating margin of 21%. Non GAAP net income per share is expected to be in the $0.49 to $0.51 per share range based on approximately 367 million weighted average diluted shares outstanding. And for the full fiscal year 2026, we expect revenues to be in the range of $4.06 billion to $4.10 billion which represents 18% to 20% year-over-year growth.

預計非GAAP營業收入將在1.95億美元至2.05億美元之間，這意味著營業利潤率為21%。基於約3.67億股加權平均稀釋後流通股，預計非GAAP每股淨收益將介於0.49美元至0.51美元之間。對於2026財年全年，我們預計營收將在40.6億美元至41億美元之間，年增18%至20%。

This includes modeling within our guidance that our business, excluding our largest customer, grows at least 20% during the year. Non-GAAP operating income is expected to be in the range of $840 million to $880 million, which implies an operating margin of 21%. And non-GAAP net income per share is expected to be in the range of $2.08 to $2.16 per share based on approximately 372 million weighted average diluted shares.

這包括我們業績指引中設定的模型，即除最大客戶外，我們的業務在年內至少成長20%。非GAAP營業收入預計在8.4億美元至8.8億美元之間，這意味著營業利潤率為21%。基於約3.72億股加權平均稀釋股份，非GAAP每股淨收益預計介於2.08美元至2.16美元之間。

Finally, some additional notes on our guidance. First, we expect net interest and other income for the fiscal year 2026 to be approximately $140 million. Next, we expect cash taxes in 2026 to be about $30 million to $40 million and we continue to apply a 21% non-GAAP tax rate for 2026 and beyond. Finally, we expect capital expenditures and capitalized software together to be in the 4% to 5% of revenue range in fiscal year 2026.

最後，關於我們的業績指引，還有一些補充說明。首先，我們預計2026財年的淨利息及其他收入約為1.4億美元。其次，我們預計2026年的現金稅款約為3000萬至4000萬美元，並且我們將繼續沿用21%的非GAAP稅率（適用於2026年及以後）。最後，我們預計2026財年的資本支出和資本化軟體支出合計將佔營收的4%至5%。

To summarize, we are pleased with our strong execution in 2025. Thank you to the Datadog teams worldwide for a great 2025, and I'm very excited about our plans for 2026. And finally, we look forward to seeing many of you on Thursday for our Investor Day.

總而言之，我們對2025年的出色執行感到滿意。感謝Datadog全球團隊在2025年的辛勤付出，我對2026年的計畫充滿期待。最後，我們期待週四的投資者日與各位相聚。

And now with that, we will open up our call for questions. Operator, let's begin the Q&A.

現在，我們將進入問答環節。接線員，問答環節正式開始。

Thank you. (Operator Instructions) Sanjit Singh, Morgan Stanley.

謝謝。 （操作說明）桑吉特‧辛格，摩根士丹利。

Thank you for taking the questions and congrats on the strong close of the year and a successful 2025. Olivier, I wanted to get your updated views in terms of where observability is headed. In the context of a lot of advancements when it comes to agentic frameworks, agentic deployments.

感謝您回答問題，並祝賀您圓滿結束今年，也祝福您2025年一切順利。 Olivier，我想了解您對可觀測性發展方向的最新看法，尤其是在代理框架和代理部署方面取得許多進展的背景下。

The stuff that we've seen from anthropic and the new frontier models from OpenAI, just in terms of like what this means for observability as a category, defensibility of it in terms of can customers use these tools to build homegrown solutions for observability? So just get your latest comments on defensibility of the category, and how Datadog may potentially have to evolve in this new sort of a agentic era?

我們從Athropotic和OpenAI的新前沿模型中看到的成果，對於可觀測性這一概念的意義，以及其可辯護性（例如，客戶能否使用這些工具構建自主的可觀測性解決方案）而言，都值得關注。您能否就這一概念的可辯護性發表一些最新看法，以及Datadog在這種新的智能體時代可能需要如何發展？

Yeah. I mean, look, the -- there's a few different ways to look at it. One is, there's going to be many more applications than they were before. Like people are building much more and they are building much faster. We covered that in previous calls, but we think that the -- this is nothing, but an acceleration of the increase of productivity for developers in general, so you can build a lot faster.

是的。我的意思是，這可以從幾個不同的角度來看。首先，應用程式的數量會比以前多得多。人們開發的應用程式更多，速度也更快。我們在之前的電話會議中討論過這一點，但我們認為，這只不過是開發人員整體生產力提升的加速過程，所以你可以更快地完成開發。

As a result, you create a lot more complexity because you build more than you can understand at any point in time. And you move a lot of the value from the act of writing the code, which now you actually don't do yourself anymore to validating, testing and making sure it works in production, making sure it's safe, making sure it interacts well with the rest of the world with end users, make sure it does what it's supposed to do for the business, which is what we do with observability. So we see a lot more volume there, and we see that as what we do basically where observability everybody can help.

因此，你會建構出更多複雜的東西，因為你建構的東西遠遠超出了你任何時候的理解能力。而且，你把很多價值從編寫程式碼本身轉移到了驗證、測試，確保程式碼在生產環境中正常運行，確保其安全性，確保它能與最終用戶良好交互，確保它能為業務實現預期功能——這正是我們利用可觀測性所做的工作。所以我們看到這方面的工作量大大增加，我們也認為，可觀測性基本上是每個人都能發揮作用的地方。

The other part that's interesting is that we -- a lot happens -- a lot more happens within these agents and these applications. And a lot of what we do as humans now starts to look like observability. Basically, we here to understand -- we try to understand what the machine does. We try to make sure it's aligned with us. We try to make sure the output is what we expected when we started, and that we didn't break anything. And so we think it's going to bring observability more widely in domains that it didn't necessarily cover before.

另一個有趣的地方在於，在這些智慧體和應用程式中，會發生很多事情。我們人類現在所做的很多事情，看起來都像是可觀測性的體現。基本上，我們在這裡是為了理解——我們試圖理解機器的運作機制。我們試圖確保它與我們的目標一致。我們試圖確保輸出結果符合我們最初的預期，並且沒有造成任何破壞。因此，我們認為這將把可觀測性更廣泛地應用到以前未曾涉及的領域。

So we think that these are accelerants, and we -- I mean, obviously, we have a horse in this ramp up, we think that observability and the contact between the code, the applications and the real world and product environment and real user and the real business is the most interesting, the most important part of the whole AI development life cycle today.

所以我們認為這些都是加速器，而且——我的意思是，很明顯，我們在這個加速過程中投入了大量資源，我們認為可觀察性以及程式碼、應用程式與現實世界、產品環境、真實用戶和真實業務之間的聯繫，是當今整個人工智慧開發生命週期中最有趣、最重要的部分。

And maybe just one follow-up on that line of thinking. In a world where there's a greater mix between human SREs and agentic SREs, is there any sort of evolution that we need to think about in terms of whether it's UI or how workflows work in observability and how maybe Datadog sort of tries to align with that evolution that's likely to come in the next couple of years?

或許可以就此思路再補充一點。在人類SRE和智慧SRE日益融合的時代，我們是否需要考慮在使用者介面或可觀測性工作流程方面做出某種演進，以及Datadog如何嘗試適應未來幾年可能出現的這種演進？

Yeah, there's going to be an evolution, that's certain. There's going to be a lot more automation. We see today, like we see the -- all the signs we see [0.2] everything will be faster, more data and more interactions, more systems, more releases, more breakage, more resolutions of those breakages, more bugs, more venerability, everything. So we see an acceleration there. At the end of the day, the humans will still have some form of UI to interact with all that.

是的，肯定會有一場變革。自動化程度會大大提高。從今天我們能看到的種種跡象來看，一切都會更快，資料量更大，互動更多，系統更多，版本發布更多，故障更多，故障修復也更多，bug更多，可靠性更高，等等。所以我們看到發展速度正在加快。但最終，人類仍然需要某種形式的使用者介面來與所有這些系統互動。

And a lot of the interaction will be automated by agent. So we're building the products to satisfy both conditions. So we have a lot of UIs, and we are able to present the humans with UIs that represent who the one works, what their options are, give them some their ways to go through problems and tomorrow the world. And we also are exposing a lot of our functionality to agents directly.

很多互動將由智能體自動完成。因此，我們正在建構的產品旨在滿足這兩個條件。我們擁有大量的使用者介面，能夠向使用者展示哪些人負責工作、有哪些選項，並為他們提供解決問題和展望未來世界的方法。同時，我們也直接向智能體開放了許多功能。

We mentioned on the call, we have an MCP server that is currently in preview and that is really seeing explosive growth of usage from our customers. And so it's a very likely future that part of our functionality is delivered to agents through MCP servers or the likes. Part of our functionality is directly implemented by our own agents, and part of our functionality is delivered to humans with UI.

我們在電話會議中提到，我們有一個目前處於預覽階段的MCP伺服器，客戶的使用量正在爆炸式增長。因此，未來我們很可能會透過MCP伺服器或其他類似方式向客服人員提供部分功能，部分功能由我們自己的客服人員直接操作，還有部分功能則透過使用者介面提供給使用者。

Raimo Lenschow, Barclays.

雷莫·倫肖，巴克萊銀行。

Thank you, congrats from me as well. Staying on a little bit on that AI theme, Olivier, the 8-figure deal for a model company is really exciting. I assume they try to do it with some open source tooling, et cetera, but -- and actually went from like almost paying not a lot of money to paying you more money. What drove that thinking? What do you think what they saw that kind of convince them to do that?

謝謝，也恭喜你。奧利維爾，我們繼續聊聊人工智慧這個話題，你和一家模型公司簽下八位數合約真是太令人興奮了。我猜他們打算用一些開源工具之類的東西來實現，但是——而且他們一開始幾乎沒打算付多少錢，後來卻給了你更高的價錢。是什麼促使他們做出這樣的決定？你覺得他們看到了什麼，才讓他們下定決心這麼做？

And it's now the second one after the other very big model provider, so clearly, that whole debate in the market between oh, you can do that on the chip somewhere is not kind of quite valid. Could you speak to that, please?

現在它是繼另一家大型模型供應商之後的第二家，所以很明顯，市場上關於「哦，你可以在晶片上實現這個功能」的爭論並不完全成立。您能談談這方面嗎？

Thank you.

謝謝。

I mean the situation is just very similar to every single customer we land. Every customer we and has some -- has had some at home grown. They have some personal run some open stores. like that's typically where we see everywhere. The -- it's cheaper to do it yourself is really not the case.

我的意思是，我們遇到的每個客戶的情況都非常相似。每個客戶都有一些自家種植的作物，或是自己經營一些店。這種情況我們到處都能看到。所以，自己動手做更便宜的說法其實並不成立。

So your engineers are typically are very well compensated and the big part of the spend in this company. The velocity is the -- is what gates just about anything else in the business. And so usually, when we come in, customer starts engaging with us, we can very quickly show value that way. So it's not any different from what we see with any other customer.

所以，你們的工程師通常薪水很高，而且他們占公司支出的大部分。速度－幾乎是決定公司其他一切的關鍵因素。因此，通常情況下，當我們介入並開始與客戶互動時，我們很快就能展現價值。這與我們服務其他客戶的情況並無不同。

And also within the AI cohort, it's not original at all like or AI cost in general is who's who of the company that are growing very fast and that are shaping the world in AI and they all had a single product for all the same reasons, sometimes the different volumes because those complete are different, but the logic is the same.

而且在人工智慧領域，也沒有什麼原創性可言，或者說人工智慧成本總體上就是那些快速成長、正在塑造人工智慧世界的公司，它們都出於相同的原因推出了單一產品，有時銷量不同，因為這些產品各不相同，但邏輯是一樣的。

Thank you. Gabriela Borges, Goldman Sachs.

謝謝。加布里埃拉·博爾赫斯，高盛集團。

Hi, good morning. Congratulation on the quarter. Olive, I want to follow-ups on Sandeep's question on how to think about where the line is between what an LLM can do longer term and the domain experience that you have in capability? If I think about some of Anthropic's recent announcements, we're talking about LLMs as a broader anomaly detection type tool, for example, on the security vulnerability margin side.

您好，早安。恭喜您本季業績出色。 Olive，我想就Sandeep提出的問題做個後續探討，即如何界定LLM的長期功能與您在領域經驗方面的能力之間的界限？如果我回顧一下Anthropic最近的一些公告，我們會發現他們正在將LLM作為一種更廣泛的異常檢測工具，例如，在安全漏洞檢測方面。

How do you think about the limiting factor to using LLMs as a normal detection tool that could potentially take share from the severability of the time in the category? And how do you think about the moat that Datadog has that offers customers a better solution relative to whether we map and LLMs can go long term?

您認為將LLM作為常規檢測工具的限制因素是什麼？這種限制因素可能會因為該類別中時間的可分割性而搶佔市場份額。您又如何看待Datadog的競爭優勢，它能為客戶提供比我們現有的地圖繪製和LLM更優的解決方案，以及它們能否長期有效運作？

Thank you.

謝謝。

Yeah. So that's a very good question. We see -- we definitely see that LLMs are getting better and better, and we'll bet on them getting significantly better every few months as we've seen over the past couple of years. And as a result, they are very, very good at looking at broad sets of data. So if you feed a lot of data for an LLM analysis you're very likely to get something that is very good and that is going to get even better.

是的，這是一個很好的問題。我們看到——我們確實看到LLM模型越來越好，正如過去幾年所見，我們相信它們每隔幾個月就會有顯著的提升。因此，它們非常擅長處理大數據集。所以，如果你輸入大量資料進行LLM分析，你很可能會得到非常好的結果，而且這個結果還會越來越好。

So when you think of what we have finally mode here, there's two parts. One is how we are able to assemble that contact, so we can feed it into those intelligence engines. And that's how we aggregate all the data we get, we parse out the benefits, we understand where we think fits together and we can fit that into the LMM. That's in part what we do, for example, today, we expose these kinds of functionality behind our MCP server. And so customers can recombine that in different ways within different intelligence tools.

所以，當我們最終建構出這樣的模式時，它包含兩個部分。一部分是我們如何整合這些聯絡訊息，以便將其輸入到情報引擎中。我們透過這種方式匯總所有獲取的數據，解析出其中的優勢，了解哪些資訊能夠相互關聯，並將其整合到LMM模型中。例如，目前我們正是透過這種方式，在MCP伺服器背後開放這些功能。這樣，客戶就可以在不同的情報工具中以不同的方式重新組合這些資訊。

But the other part that we think where the world is going for over observability is that right now, we are -- the CLC is accelerating a lot, but it's still somewhat slow. And so it's okay to have incidents and run post hoc analysis on those incidents and maybe use some outside tooling for them. Where the world is going is you're going to have many more changes and more things.

但我們認為，就可觀測性而言，世界發展的另一個方向是，目前——CLC（控制生命週期）正在加速發展，但速度仍然相對緩慢。因此，發生事件並對這些事件進行事後分析，或許可以藉助一些外在工具，這都是可以接受的。但世界的發展趨勢是，未來將會出現更多變化和更多的事情。

You cannot actually afford to have incidents to look at for everything that's happening in your system. So you need to be proactive, you'll need to run analysis in stream as all the data flows through, you'll need to run detection and resolution before you actually have outages materialize.

實際上，你不可能等到系統出現所有問題時才去查看故障報告。所以你需要主動出擊，在所有資料流經系統時進行即時分析，在實際發生故障之前就進行檢測和修復。

And for that, you'll need to be embedded into the data plane, which is what we run. And you also need to be able to run specialized models that can act on that data as opposed to just taking everything and summarizing everything after the fact in 15 minutes level. And that's what we're uniquely positioned to do.

為此，你需要融入我們的資料平面。你還需要能夠運行專門的模型來處理這些數據，而不是僅僅事後將所有數據匯總成15分鐘的粗略版本。而這正是我們獨有的優勢所在。

We are building that. We're not quite there yet, but we think that a few years from now, that's what the was going to run, and that's what makes us significantly different in terms of who we can apply and detection, intelligence and preemptive resolution into our systems.

我們正在建構這個系統。雖然還沒完全實現，但我們認為幾年後，它就能運行，而這正是我們在系統應用範圍、檢測、情報和先發製人解決問題等方面與我們顯著不同的原因。

That makes a lot of sense. My follow-up.

這很有道理。我的後續問題是：

By the way, the data plans were to very real time. And there are many others of magnitude larger in terms of data flows that are volumes that would you typically feed into an LLM. So it's a bit of a different problem to solve.

順便一提，這些數據計劃是即時性的。還有許多其他計劃，其資料流量要大得多，這些資料量通常會輸入到LLM系統中。所以，這是一個需要解決的不同問題。

Yeah. Super interesting. My follow-up for both you, Oli and David, you've mentioned a couple of times now and what's the conversations you have with customers about value creation within the Datadog platform.

是的，非常有趣。 Oli 和 David，你們之前也多次提到過，我想問你們和客戶在 Datadog 平台內如何創造價值，這是你們想了解的重點。

Talk a little bit about how some of those conversations evolve when the customer sees that in order to do observability for more AI usage, perhaps that Datadog [bill] is going up. What are some of the steps that you can take to make sure the customer still feels like they're getting a ton of value out of the Datadog platform?

請談談當客戶發現為了實現更多人工智慧應用的可觀測性，Datadog 的費用可能會增加時，雙方的溝通會如何發展。您可以採取哪些措施來確保客戶仍然覺得他們能從 Datadog 平台獲得巨大的價值？

Thank you.

謝謝。

Well, there's a few things. I mean, first, again, the rule of software always applies. There's only two reasons that people buy your product is to make more money or to save money. So whatever you do, when the customers uses a new product, they need to see a cost savings somewhere or they need to see that they're going to get to customers that wouldn't get to otherwise. So we have to prove that.

嗯，有幾點要考慮。首先，軟體產業的普遍規律依然適用。人們購買你的產品只有兩個原因：不是為了賺更多錢，就是為了省錢。所以無論你做什麼，當客戶使用新產品時，他們要嘛需要看到成本節省，要嘛需要看到產品能幫助他們接觸到原本接觸不到的客戶。因此，我們必須證明這一點。

We always prove that. Any time a customer buys a product, that's what these are happening behind the scenes. The -- in general, when customers add to our platform as opposed to bringing another banner in or another product deal, they also spend less by doing it in our platform.

我們一直都在證明這一點。每當客戶購買產品時，背後都會發生這樣的事情。通常情況下，客戶會在我們平台上購買，而不是引入其他橫幅廣告或產品推廣活動，他們的花費也會更少。

Ittai Kidron, Oppenheimer & Company.

伊泰·基德隆，奧本海默公司。

Thanks and congrats quite an impressive finish for the year. David, I wanted to dig in a little bit into your '26 guide. I just want to make sure I understand some of your assumptions. So maybe you could talk about the level of conservatism that you've built into the guide for the year?

謝謝，恭喜你，今年的成績真是令人印象深刻。大衛，我想稍微深入探討一下你的2026年指南。我只是想確保我理解你的一些假設。所以，你能談談你在這一年指南中採用的保守程度嗎？

And also, you've talked about at least 20% growth for the core, excluding the largest customer, but what is it that we should assume for the large customer? And now when you look at the AI cohort, excluding this large customer, are there any concentrations evolving over there given your strong success there?

此外，您提到核心業務（不包括最大客戶）至少成長了20%，那麼對於這位大客戶，我們該作何預期？現在，當您審視人工智慧領域（不包括這位大客戶）時，鑑於您在該領域的巨大成功，是否存在任何集中化趨勢？

Yeah. There are three questions in the first is overall on guidance, except what we're going to speak about next, we took the same approach as we looked at the organic growth rates and the attach rates and then the logo accumulation rates and discounted that. So for the overall business, which is quite diversified, we talked about diversification by industry, by geography, by SMB, mid-market and enterprise, we took the same approach.

是的。第一個問題是關於整體績效指引的，除了接下來我們要討論的內容之外，我們採用了相同的方法，考察了有機成長率、附加成長率和客戶累積率，並進行了相應的調整。因此，對於整體業務（業務相當多元化），我們討論了按行業、地理、中小企業、中型市場和大型企業劃分的多元化情況，我們採用了相同的方法。

We noted that with the guidance being 18% to 20% and the non-AI or heavily diversified business being 20% plus, that would imply that the growth rate of that core business assumed in the guidance is higher than the growth rate of the large customer. It doesn't mean the large customer is growing any which way.

我們注意到，公司給出的預期成長率為18%至20%，而非人工智慧或高度多元化業務的成長率則超過20%，這意味著預期中假設的核心業務成長率高於大客戶的成長率。但這並不意味著大客戶的成長方向有任何改變。

It's just that, in our consumption model, we essentially don't control that. And so we took a very conservative assumption there. And the last point, I think you mentioned is the highly diversified. We said 650 names in the AI is quite diversified, essentially would be very similar to our overall business, which we have a range of customers, but not the concentration level. And what we're seeing there is significant growth.

只是在我們的消費模式中，我們基本上無法控制這一點。所以我們在這方面採取了非常保守的假設。最後一點，我想您也提到了，就是高度多元化。我們說過，人工智慧領域擁有650個客戶已經相當多元化了，這與我們整體業務非常相似，我們擁有廣泛的客戶群，但客戶集中度不高。而且我們看到的是顯著的成長。

But like our overall distributed customer base, growth and then potentially some working on how the product is being used, but nothing out of the ordinary relative to the overall customer base in the very diversified AI set of customers outside the largest customer.

但與我們整體分散的客戶群一樣，成長，然後可能會對產品的使用方式進行一些研究，但相對於除最大客戶之外的非常多元化的 AI 客戶群的整體客戶群而言，沒有什麼不尋常的。

Yeah. And can you give us the percent of revenue of the AI cohort this quarter?

是的。您能告訴我們本季人工智慧業務的營收佔比嗎？

We didn't -- haven't put it in there.

我們沒有——還沒把它放進去。

Thank you. Todd Coupland, CIBC.

謝謝。托德·庫普蘭，加拿大帝國商業銀行。

Thank you and good morning. I wanted to ask you about competition and how the LLM rise is impacting share shifts. Just talk about that and how Datadog will be impacted?

謝謝，早安。我想問您關於市場競爭以及LLM上漲對市場佔有率變化的影響。請您談談這方面，以及Datadog會受到怎樣的影響？

Thanks a lot.

多謝。

Yeah. I mean, there hasn't been -- in the market with customers, there hasn't been any particular change in competition in that we see the same kind of folks and the positioning are relatively similar. And we are pulling away. We're taking share from anybody who has scale. And I know there's been no -- there were a couple of M&A deals that came up, and we got some questions about that.

是的。我的意思是，市場上的客戶群並沒有發生什麼特別的變化，競爭格局也基本上沒變，我們面對的還是同一批人，市場定位也相對相似。但我們正在拉開差距，從那些有規模的公司手中奪取市場份額。我知道最近確實出現了一些併購交易，我們也收到了一些相關的問題。

The company is in there were not particularly winning companies, not companies that we saw in deals that in had a large market impact. And so we don't see that as changing the competitive dynamics for us in the near future.

這家公司並沒有參與那些特別成功的交易，也沒有看到任何對市場產生重大影響的交易。因此，我們認為這在短期內不會改變我們所面臨的競爭格局。

We also know that competing in observability is a very, very full-time job. It's a very innovative market. And we know exactly what we have to do and have to do to keep pulling away the where we are. And so we're very confident in our approach and we're going to do in the future there.

我們也知道，在可觀測性領域競爭是一項非常非常耗費精力的工作。這是一個充滿創新的市場。我們非常清楚自己必須做什麼，以及為了保持領先地位必須採取哪些措施。因此，我們對我們的方法以及未來將要採取的措施都非常有信心。

With the rise of LLM, there's clearly more functionality to deal and there are new ways to serve customers. We mentioned our LLM product. There are a few other products on the market for that. I think it's still very early for that part of the market, and that market is still relatively undifferentiated in terms of the kinds of products they are, but we expect that to shake out more into the future.

隨著LLM的興起，顯然需要處理的功能更多了，服務客戶的方式也更加多元了。我們之前提到過我們的LLM產品。市場上還有其他一些同類產品。我認為這個市場領域目前還處於早期階段，產品類型也相對單一，但我們預期未來市場格局會更加清晰。

We think, in the end, there's no reason to have observability for your LLM that is different from the rest of your system in great part because you LLM don't work in isolation. The way they implement their parts is by using tools, the tools are your applications and your existing applications or new applications you built for that purpose. And so you need everything to be integrated in production, and we think we stand on a very strong foot in there.

我們認為，最終沒有必要為您的LLM（生命週期管理）設定與系統其他部分不同的可觀測性，這主要是因為LLM並非獨立運作。它們透過工具來實現各自的功能，這些工具就是您的應用程序，包括您現有的應用程式以及為此目的構建的新應用程式。因此，您需要將所有內容整合到生產環境中，而我們認為我們在這方面擁有非常強大的優勢。

Thank you. Mark Murphy, JPMorgan

謝謝。馬克墨菲，摩根大通

Thank you, Olivier, Amazon is targeting $200 billion in CapEx this year. If you include Microsoft and Google, that CapEx is going to exceed $500 billion this year for the big three hyper scalers that it's growing 40% to 60%. I'm wondering if you've collected enough signal from the last couple of years of CapEx that trend to estimate how much of that is training related and when it might convert to inferencing, where Datadog might be required?

謝謝 Olivier，亞馬遜今年的資本支出目標是 2,000 億美元。如果算上微軟和谷歌，三大超大規模資料中心營運商今年的資本支出將超過 5,000 億美元，成長率在 40% 到 60% 之間。我想知道，您是否已經從過去幾年的資本支出趨勢中收集到足夠的信息，來估算其中有多少用於訓練，以及何時會轉向推理，屆時是否需要 Datadog？

In other words, are you looking at this wave of CapEx and able to say it's going to create a predictable ramp in your LLM observability revenue, maybe what inning of that are we in? And then I have a follow-up.

換句話說，您是否正在審視這波資本支出浪潮，並能夠斷言它將為您的LLM可觀測性收入帶來可預測的成長？我們現在處於這個過程的哪個階段？接下來我還有一個問題。

I think -- I think it's pretty too reductive to tack that LLM observability. I think it points to way more applications, way more intelligence, way more of everything into the future. Now it's kind of hard to directly the CapEx on those companies into what part of the infrastructure is actually going to be used to deliver value 2- or 3- or 4-years from now.

我認為——我認為僅僅關注LLM的可觀測性過於簡化了。我認為它指向的是未來更多的應用、更多的智慧、更多的一切。現在很難將這些公司的資本支出直接投入基礎建設的哪些部分，以便在未來2年、3年甚至4年內創造價值。

So I think we'll have to see on what the conversion rate is on that. But look, it definitely points to very, very, very large increases in the complexity of the systems, the number of systems and the reach of the systems in the economy. And so we think it's going to be like it's going to be of great help to our business, let's put it this way.

所以我覺得我們得看看轉換率是多少。但你看，這無疑表明系統複雜性、系統數量以及系統在經濟中的覆蓋範圍都將大幅增加。因此，我們認為這將對我們的業務大有裨益，這麼說吧。

Yeah. Great help. Okay. And then as a quick follow-up, there is an expectation developing that OpenAI is going to have a very strong competitor, which is Anthropic kind of closing the gap, producing nearly as much revenue as OpenAI in the next 1- to 2-years. You mentioned an 8-figure land with an AI model company.

是的，幫了大忙。好的。另外，還有一個問題：目前普遍預期 OpenAI 將迎來一個非常強勁的競爭對手，Anthropico 正在縮小差距，預計在未來一到兩年內，其營收將幾乎與 OpenAI 持平。您曾提到一家人工智慧模型公司獲得了數千萬美元的投資。

I'm wondering, if we step back, do you see an opportunity to diversify that AI customer concentration, whether sometimes it might be a direct customer relationship there? Or it could be some of the products like cloud code being adopted globally, just kind of creating more surface area to drive business to data? Can you comment on maybe what is happening there among the larger AI providers or whether you can diversify that out?

我想知道，如果我們退一步來看，您是否認為有機會分散人工智慧客戶群的集中度？例如，有時可以透過直接的客戶關係來實現嗎？或者，一些產品，例如雲端程式碼，在全球範圍內得到應用，從而創造更多機會將業務與數據連接起來？您能否談談大型人工智慧提供者在這方面正在發生的事情，或者您可以考慮分散客戶群？

Yeah. I mean, look, we've never been -- we're not built as a business to be concentrated on a couple of customers. That's not who we become successful. That's probably not how we'll be successful in the long term. So yes, I mean we -- at the end of the day, it should be irrational for customers, for all customers in the AI court not to use our product.

是的。我的意思是，你看，我們從來就不是——我們的業務模式就不是為了專注於少數幾個客戶。我們靠的不是少數客戶，長遠來看，這也可能不是我們成功的關鍵。所以，是的，我的意思是，歸根結底，對於人工智慧領域的客戶來說，不使用我們的產品才是不理智的。

So we see -- we have some great successes with the customers currently in that cohort. We see more. By the way, we have more that are more inbound there and more customers that are talking to us from the largest even hyper scaler level AI lab. And we expect to drive more business there in the future. I think there's no question about that.

所以我們看到——我們目前在該群體中的客戶取得了一些巨大的成功。我們看到了更多。順便說一句，我們有更多的主動諮詢客戶，還有更多來自規模最大的超大規模人工智慧實驗室的客戶正在與我們洽談。我們預計未來將在該領域推動更多業務成長。我認為這點毋庸置疑。

And you're seeing that in some of the metrics we've been giving in terms of the number of native customers, the size of some of these customers. So to echo what Olive said, we are essentially selling to many of the largest players, which results in greater size of the cohort and more diversification.

從我們提供的部分指標也可以看出這一點，例如原生客戶的數量以及部分客戶的規模。正如Olive所說，我們實際上是在向許多大型企業銷售產品，這使得客戶群規模更大，產品也更加多元化。

Thank you. Matt Hedberg, RBC

謝謝。馬特赫德伯格，加拿大皇家銀行

Great, thanks for taking my question, guys. Congrats from me as well. Dave, a question for you. Your prior investments are clearly paying off with another quarter of acceleration, and it seems like you're going to continue to invest in front of the future opportunity. I think op margins are down maybe 100 basis points on your initial guide. I'm curious if you can comment on gross margin expectations this year, and how you also might realize incremental OpEx synergies by using even more AI internally?

太好了，謝謝各位回答我的問題。也祝賀你們！ Dave，我有個問題想問你。你之前的投資顯然已經見效，又一個季度業績加速成長，而且看來你還會繼續投資以把握未來的機會。我認為營業利益率比你最初的預期下降了大約100個基點。我很想知道你對今年的毛利率預期有何看法，以及你打算如何透過在內部更多地運用人工智慧來實現營運成本的協同效應？

Yeah. On the gross margin, I think what we said is, plus or minus, the 80% mark. We try to engineer when we see opportunities for efficiency, we've been quite good at being able to harvest them. At the same time, we want to make sure we're investing in the platform. So I think what we're essentially -- where we are today is very much sort of in line with what we said we're targeting.

是的。關於毛利率，我們之前說的是上下浮動80%。我們努力尋找提高效率的機會，而且我們在這方面做得相當不錯。同時，我們也希望確保對平台進行投資。所以我覺得我們目前的狀況，基本上和之前設定的目標一致。

There may be opportunities in longer term, but we also are trying to balance those opportunities with investment in the platform. And in terms of AI, to date, we are using it in our internal operations. So far, it's -- the first signs of what we're seeing is productivity and adoption. We will continue to update everybody as we see opportunities in terms of the cost structure.

長遠來看，或許存在一些機遇，但我們也在努力平衡這些機會與平台投資之間的關係。就人工智慧而言，目前我們主要將其應用於內部營運。就目前來看，我們初步觀察到的成效體現在生產力和應用率的提升。一旦發現成本結構的機會，我們將持續向大家報告。

Olive, anything else you want to go over?

奧利佛，還有什麼要補充的嗎？

Yeah. I mean, look, we -- the expectation in the short midterm anyway should be that we keep investing heavily in R&D. We're getting a lot -- we see great productivity gains with AI there, but at this point it does, it helps us be more faster and get to sell more problems for our customers. And -- but we're very busy adopting AI for the organization.

是的。我的意思是，你看，至少在中短期內，我們應該繼續大力投資研發。人工智慧在研發方面帶來了顯著的生產力提升，目前來看，它確實能幫助我們更快解決客戶面臨的更多問題。而且，我們目前正忙於在組織內部推廣人工智慧。

Thank you. Koji Ikeda, Bank of America.

謝謝。池田浩二，美國銀行。

Yeah, hey guys, thanks so much for taking the question. Olivier, maybe a question for you. A year ago, you talked about how -- while some customers do want to take observability in-house, it's really a cultural choice. It may not be rational unless you have tremendous scale, access to talent and growth is not limited by innovation bandwidth, which most companies do not.

嗨，各位，非常感謝你們回答這個問題。奧利維爾，或許我有個問題想問你。一年前，你談到過——雖然有些客戶確實希望將可觀測性管理納入公司內部，但這實際上是一種文化選擇。除非公司規模龐大、人才濟濟且成長不受創新能力的限制（而大多數公司並不具備這些條件），否則這樣做可能並不理性。

And so it is a year later, and it does seem like the industry and the ecosystem and everything has changed quite a bit. So I was hoping to get your updated views on these thoughts, if it has changed at all over the past year and why?

一年過去了，產業、生態系統以及一切似乎都發生了很大的變化。所以我想聽聽您對這些想法的最新看法，過去一年您的看法是否有所改變，以及改變的原因是什麼？

Thank you.

謝謝。

No. I mean, look, it's something that happens sometimes, but it's a small minority of the cases. Like the general notion is customers start with some homegrown or attempts to do things themselves, then they move to our product and they care with our products. Sometimes they optimize a little bit along the way, but the general notion is they do more and more with us, they're relying on us for more of their -- solving more of their problems, and they outsource the problem and increasingly the outcomes to us. So I don't think that's changing.

不。我的意思是，你看，這種情況偶爾會發生，但只是少數。通常情況下，客戶一開始會嘗試自己開發或做一些事情，然後他們會轉向我們的產品，並且會認真對待我們的產品。有時他們會在過程中進行一些優化，但總的來說，他們越來越多地使用我們的產品，越來越依賴我們來解決他們越來越多的問題，他們把問題本身以及最終結果都外包給我們。所以我認為這種情況不會改變。

Look, we'll still see customers here and there that choose to resource it into themselves, again, usually for cost reasons. I would say, economically from a focus perspective, it doesn't make sense for the very vast majority of companies. And we even see teams at hyperscalers that have all the tooling in the world, all the money in the world, all the knowledge in the world and that still choose to use our product because it gives them a more direct path to solving their problems.

你看，我們還是會看到有些客戶選擇自行開發，通常也是出於成本考量。但從經濟效益和專注度的角度來看，這對絕大多數公司來說並不划算。我們甚至看到一些超大規模資料中心的團隊，他們擁有世界上所有的工具、資金和知識，卻仍然選擇使用我們的產品，因為它能讓他們更直接地解決問題。

Thank you. Brad Reback, Stifel.

謝謝。布拉德雷巴克，斯蒂費爾公司。

Great, thanks very much, great. Olive, this sustained acceleration in the core business is pretty impressive. Obviously, you all have invested very aggressively and go to market over the last kind of 18- to 24-months. Can you give us a sense of where you are in that productivity curve? And if there's additional meaningful gains, you think? Or is it incremental? And maybe where you see additional investments in the next 12- to 18-months?

太好了，非常感謝。奧利佛，核心業務的持續加速發展確實令人印象深刻。顯然，在過去的18到24個月裡，你們進行了非常積極的投資和市場拓展。能否請您談談目前你們的生產力處於什麼階段？您認為未來是否還有顯著的成長空間？還是只是漸進式成長？未來12到18個月，你們計劃在哪些方面進行額外的投資？

Thanks.

謝謝。

Yeah. I mean we feel good about the productivity. I think the main drivers from us in the future is we still need to scale, and we're still scaling the go-to-market team. We're not at the scale we need to be in every single market segment we need to be in the world right now. And so we keep scaling there.

是的。我的意思是，我們對目前的生產力感到滿意。我認為未來我們的主要驅動力仍然是需要擴大規模，我們也不斷擴大市場推廣團隊。目前，我們還沒有達到在全球所有細分市場所需的規模。所以我們會繼續擴大規模。

So the focus now is not necessarily to improve productivity, it's to scale while maintaining productivity. And of course, there's so many, many things we can do. Actually, we -- even though we love our performance, there's always a bunch of things that could be better, territories that could be better, productivity that could be better, things like that.

所以現在的重點不一定是提高生產力，而是在維持生產力的同時擴大規模。當然，我們可以做的事情還有很多。事實上，儘管我們對目前的業績很滿意，但總是有很多方面可以做得更好，例如可以更好地拓展業務區域、提高生產力等等。

So we have or tons of things we want to do on the some things improve. But overall, we feel good about what happened. We feel good about scaling, and you should expect more scaling for us on the go-to-market side in the year to come.

所以我們有很多事情想做，有些方面需要改進。但總的來說，我們對目前的情況感到滿意。我們對規模化發展充滿信心，你們可以期待我們在未來一年在市場拓展方面取得更大的進展。

Thank you. Howard Ma, Guggenheim.

謝謝。霍華德·馬，古根漢美術館。

Great, thanks for taking the question. I have one for Olivier. The core APM product, growing in the mid-30% growth. That is pretty impressive, and I think better than maybe a lot of us expected. The question is, is that a reacceleration?

太好了，謝謝你回答這個問題。我還有一個問題想問奧利維爾。核心APM產品實現了30%左右的成長率。這相當驚人，我認為比我們許多人預期的都要好。問題是，這是否意味著成長速度的再次加快？

And is the growth driven by AI native companies that are using Datadog's real user monitoring and other DM features as compared to -- where as opposed to rather core enterprise customers that are building more applications?

這種成長是由使用 Datadog 真實用戶監控和其他資料管理功能的 AI 原生公司推動的，還是由建立更多應用程式的核心企業客戶所推動的？

Yeah. I think -- I mean, look, APM, in general, I think has always been a bit of a steady eddy in terms of the growth, like it's a product that takes a little bit longer to deploy than others, which is further into the applications. And so it takes a little bit longer to penetrate within the customer environment. That being said, we did -- a number of different things we did that have with globe there. One is we invested a lot in actually making that onboarding, deployment a lot simpler and faster.

是的。我認為——我的意思是，總的來說，APM（應用效能管理）的成長一直比較緩慢，因為它部署起來比其他產品要慢一些，需要更深入地應用到各個應用程式層面。因此，它需要更長的時間才能滲透到客戶環境中。話雖如此，我們確實做了很多事情，其中​​一項就是投入大量資源，讓APM的上線和部署變得更簡單、更快捷。

So we think we'll have the best in the market for that and it shows. Second, we invested a lot in the digital experience side of it. And it's very differentiated, something our customers love is driving a lot of adoption of the broader APM suite, and we expect to see more of that in the future. And third, we make investments in go-to-market, we cover the market better. And so we're getting into more looks at more deals in more parts of the world.

所以我們相信我們在這方面會擁有市場上最好的產品，而事實證明確實如此。其次，我們在數位體驗方面投入了大量資金。我們的數位化體驗非常獨特，深受客戶喜愛，也推動了更廣泛的APM套件的普及，我們預計未來這一趨勢會更加明顯。第三，我們加大了市場推廣力度，更能覆蓋了市場。因此，我們在世界各地獲得了更多機會，也看到了更多交易的進展。

And so all of that combined helps that product reaccelerate growth quite a bit. And so we feel actually very, very good about it, which is why we keep investing. Overall, we still only have a small part of the pure APM market, like that product, we scale at about $10 billion, including the EM, but the market is larger. And so we think there's a lot more we can do there.

因此，所有這些因素加在一起，極大地促進了該產品的成長。所以我們對它非常有信心，這也是我們持續投資的原因。總體而言，我們目前在純APM市場（例如該產品）的份額仍然很小，我們的規模約為100億美元（包括新興市場），但市場規模更大。因此，我們認為我們在這個領域還有很大的發展空間。

I want to add, we talked about -- as Olive just mentioned, that we're not penetrated across our customer base, and therefore, we're continuing to consolidate onto our platform. So we have quite a number of wins where we already have other products. We already have infra and logs, and we're consolidating APM.

我想補充一點，正如Olive剛才提到的，我們之前討論過，我們的產品尚未覆蓋所有客戶，因此，我們正在繼續整合到我們的平台上。我們已經取得了一些成功，因為我們已經有其他產品。我們已經擁有基礎設施和日誌功能，現在正在整合APM（應用程式效能管理）功能。

Thank you guys. It, David, as a follow-up for you on margin, are the large AI-native customers significantly dilutive to gross margin? And when you think about the initial 2026 margin guidance, how much of that reflects potentially lower gross margin type to those customers versus incremental investments?

謝謝各位。 David，關於利潤率，我想跟你追問一下，大型人工智慧原生客戶是否會顯著稀釋毛利率？另外，在考慮2026年的初始利潤率預期時，其中有多少是由於這些客戶可能導致的毛利率下降，又有多少是由於新增投資造成的？

On weighted average, they're not. As we've always said, for larger customers, it isn't about the AI-natives or non-AI-natives, it has to do with the size of the customer we have a highly differentiated diversified customer base. So I would say we're essentially expecting a similar type of discount structure in terms of size of customer as we have going forward.

加權平均值來看，並非如此。正如我們一直強調的，對於大型客戶而言，關鍵不在於他們是人工智慧原生用戶還是非人工智慧原生用戶，而是客戶規模。我們擁有高度差異化且多元化的客戶群。因此，我認為我們預計未來將延續目前基於客戶規模的折扣結構。

And there are consistent ongoing investments in our gross margin, including data centers and development of the platform. So I think it's more or less what we've seen over the past couple of years, not really affected by AI or non-AI native.

而且我們一直持續投資於提高毛利率，包括資料中心和平台開發。所以我覺得這和過去幾年我們看到的情況基本上一致，並沒有真正受到人工智慧或非人工智慧原生技術的影響。

Thank you. Peter Weed, Bernstein Research.

謝謝。彼得‧韋德，伯恩斯坦研究公司。

Okay, thank you, yeah, apologies for the last time. Great quarter. Looking forward, I think one of your most interesting exciting opportunities really is around its AI and I'd love to hear kind of like how you think that opportunity shapes up? Like how do you get paid the fair value for the productivity you're bringing to the SRE and the broader operations team and really how you see competition playing out in that space? Because obviously, we've seen start-ups coming in, there's questions about Anthoropic and where they want to go.

好的，謝謝。是的，最後一次打擾您了，非常抱歉。這個季度很棒。展望未來，我認為你們最令人興奮的機會之一確實圍繞著人工智慧展開，我很想聽聽你們對這個機會的看法。例如，您如何才能獲得與您為SRE和更廣泛的維運團隊帶來的生產力相符的合理報酬？您如何看待這個領域的競爭格局？因為很明顯，我們看到一些新創公司湧入，大家都在關注Anthoropic的未來發展方向。

How does Datadog really capture this value and protect it for the business?

Datadog 究竟是如何取得並保護這種價值，進而為企業創造效益的？

Yeah. I mean, look, the way we currently say out a lot of these products is you show like the difference in time spent. And what the alternative is you try and solve a problem yourself and you have an outage and you you start a bridge and you have 20 people on the bridge and they look for 3 hours for the root cause and you work people night for that. It's very expensive. It takes a lot of time.

是的。我的意思是，你看，我們現在對許多這類產品的評價方式是，要展示它們在節省時間上的優勢。而另一個選擇是，你嘗試自己解決問題，結果係統宕機了，你啟動了一個臨時維修站，20個人擠在維修站裡，花了3個小時查找根本原因，你得為此通宵工作。這非常昂貴，而且耗時。

There's a lot of customer impact because the outages are long. And if the alternative is in 5 minutes, you have the answer, you only get three people look at that are the right folks and you have a fix within 10 minutes, you -- shorter impact on the customer, many, many, many less folks internally involved, lower cost. So it's very easy to make that case. And so that's what we sell the value there.

停機時間長，會對客戶造成很大影響。但如果另一個方案是5分鐘內就能找到解決方案，只需要三個合適的人員介入，10分鐘內就能修復，那麼對客戶的影響會更小，內部參與的人員也會少得多，成本也會更低。所以很容易就能證明這一點。這就是我們所強調的價值所在。

Longer term, as I was saying earlier, I think the -- right now, the state-of-the-art for resolution is both part, you have an incident and you look into it. And you diagnose it and then you resolve it. So yes, maybe you could be the customer impact from 1 hour to 15 minutes. But you still have an issue, you still have impact, you still distract the team you still have humans working on that.

從長遠來看，正如我之前所說，我認為目前最先進的解決方案是這樣的：發生事件後，你需要進行調查、診斷，然後解決問題。沒錯，或許可以將對客戶的影響從 1 小時縮短到 15 分鐘。但問題依然存在，仍會造成影響，仍會分散團隊的精力，仍需要手動處理。

I think longer term, the what's going to happen is the systems will get in front of issues. They will auto diagnose issues. They will help pre-mitigate or pre-remediate potential issues. And for that, the analysis will have to be run in stream, which is a very different thing. You can massage data and give it to an LLM for post-hoc analysis and a lot of the value is going to be in the gathering the data, but you also have quite a bit of value in the smarts that are done in the back end by the LLM for that. And that's something that is done by the anthropic in the end, the opening eyes of the world today.

我認為從長遠來看，系統將會主動發現問題，自動診斷問題，並幫助預先緩解或修復潛在問題。為此，分析必須在即時運行，這與以往截然不同。你可以對資料進行處理，然後將其交給生命週期管理（LLM）進行事後分析，雖然資料收集本身俱有很高的價值，但LLM在後台進行的智慧處理也同樣重要。歸根究底，這正是人類學最終發揮作用的地方，也是當今世界開啟智慧之眼的關鍵所在。

I think as you look at being in stream looking at three, four, five orders of magnitude more data, looked this data in real time, and I think judgment in real time on what's normal, what's [anomalous] and what might be going wrong doing that hundreds, thousands, millions of times per second, I think that's what is going to be our advantage and where the -- it's going to be much harder for others to compete, especially in general purpose AI platforms.

我認為，當你觀察數據流時，要處理比現在多三、四、五個數量級的數據，實時查看這些數據，並實時判斷什麼是正常的，什麼是異常的，以及可能出了什麼問題，每秒要處理成百上千、數百萬次這樣的數據，我認為這將是我們的優勢所在，也是其他公司更難與之競爭的地方，尤其是在通用平台領域。

Thank you. Brent Thill, Jefferies.

謝謝。布倫特‧蒂爾，傑富瑞集團。

David, I think many gravitate back to that mid-20% margin you put up a couple of years ago. And I know the last couple of years, including the guide are looking at low 20%. Can you talk to maybe your true north how you're thinking about that? Obviously, growth being number 1, but how you're thinking about the framework on the bottom line?

大衛，我覺得很多人都會回到你幾年前提出的20%左右的利潤率目標。我知道過去幾年，包括你發布的指導方針在內，利潤率都在20%左右。可以跟你的核心領導談談你對這個目標的看法嗎？當然，成長是首要目標，但你是如何考慮最終利潤的框架的呢？

Thanks.

謝謝。

Yeah, the frame because we try to plan with more conservative revenues, understanding that if the revenues exceed above the targets that we give, it's difficult in the short term to invest incrementally. So we're trying to do is invest first in the revenue growth and then layer in additional investment as we see if we see excess of target.

是的，之所以採用這種框架，是因為我們盡量以較保守的收入預期來制定計畫。我們明白，如果收入超過我們設定的目標，短期內很難進行額外的投資。所以我們先投資在營收成長，然後根據實際收入是否超過目標來逐步追加投資。

So generally, it reflects, one, the continued investment, which we think is paying off, both in terms of the platform and R&D as well as in and including AI as a go-to-market. And then as we've seen over the years in our our being raised, we've tended to have some of that flow through into the margin line and then re-up again for the next phase of growth.

總的來說，這反映了兩點：第一，我們持續的投資正在取得成效，無論是在平台建設、研發，還是將人工智慧作為市場推廣手段等方面。第二，正如我們多年來融資的經驗所示，部分融資收益將轉化為利潤，並在下一階段成長之前再次投入資金。

And any big changes in the go-to-market or big investments you need to make, David, this year to address what's happened in the AI cohort or not?

大衛，為了因應人工智慧領域的變化，今年你們在市場推廣策略或投資方面是否需要做出任何重大改變？

We're continuing. It's very similar to what we're doing, which is to try to work with clients to prove value over time that reflects -- that manifests itself in our account management and our CS as well as our enterprise. So no, I think for this year, we are looking at capacity growth, including geographic, deepening the ways we interact with customers, expanding channels, very much similar to what we've done in the previous years.

我們正在繼續推進。這和我們目前所做的非常相似，即努力與客戶合作，隨著時間的推移證明我們的價值，這種價值體現在我們的客戶管理、客戶服務以及企業營運中。所以，我認為今年我們將著眼於能力成長，包括地理擴張，深化與客戶的互動方式，拓展管道，這與我們前幾年所做的非常相似。

Thanks. All right, and that's going to be it for today. So on that, I'd like to thank all of you for listening on this call, and I think well many of you on Thursday for our Investor Day. So thank you all. Bye.

謝謝。好的，今天就到這裡。感謝各位收聽本次電話會議，也感謝各位週四參加我們的投資者日活動。謝謝大家。再見。

Thank you.

謝謝。

Thank you for your participation. You may now disconnect. Everyone, have a great day

感謝您的參與。您現在可以斷開連線了。祝大家今天過得愉快！