數據狗 (DDOG) 2026 Q3 法說會逐字稿

Good day, and thank you for standing by. Welcome to the Third Quarter 2026 Datadog earnings conference call. (Operator Instructions) Please be advised that today's call is being recorded.

各位好，感謝您稍候。歡迎參加 Datadog 2026 年第三季財報電話會議。(接線員指示) 請注意，今天的電話會議將被錄音。

I will now hand it over to our first speaker, A.J. Ljubich, Senior Vice President, Strategic Finance and Investor Relations. Please go ahead.

現在我將把電話交給我們的第一位講者，策略財務與投資人關係資深副總裁 A.J. Ljubich。請開始。

Thank you, Victor. Good morning, and thank you for joining us to review Datadog's third quarter 2026 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. First, I'd like to thank Yuka Broderick for her excellent work leading our IR function over the last 5.5 years as she takes on a new role within Datadog.

謝謝你，Victor。各位早安，感謝各位加入我們，一同回顧 Datadog 2026 年第三季財務業績；我們已於今天早上發布的新聞稿中對外公布。今天與我一同出席電話會議的還有 Datadog 共同創辦人兼執行長 Olivier Pomel，以及 Datadog 財務長 David Obstler。首先，在 Yuka Broderick 於 Datadog 內部接任新職務之際，我想感謝她在過去 5.5 年領導我們投資人關係（IR）職能所做的出色工作。

I'm excited to step back into the IR function after having worked with many of you through the company's 2019 IPO. During this call, we will make forward-looking statements, including statements related to our future financial performance, our outlook for the fourth quarter and for the fiscal year of 2026 and related notes and assumptions, our product capabilities and our ability to capitalize on market opportunities.

我很高興在 2019 年公司 IPO 期間曾與許多各位合作之後，如今能再次回到 IR 職能。在本次電話會議中，我們將作出前瞻性陳述，包括與我們未來財務表現、對 2026 年第四季及 2026 會計年度的展望及相關註解與假設、我們的產品能力，以及我們把握市場機會的能力等相關陳述。

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

「anticipate（預期）」、「believe（相信）」、「continue（持續）」、「estimate（估計）」、「expect（預期）」、「intend（打算）」、「will（將）」及類似用語，旨在識別前瞻性陳述或對未來預期的類似指示。這些陳述反映我們截至今日的觀點，並受多項風險與不確定性影響，可能導致實際結果與上述陳述存在重大差異。

For a discussion of the material risks and other important factors that could affect our actual results, please refer to our Form 10-Q for the quarter ended June 30, 2026. Additional information will be made available in our upcoming Form 10-Q for the fiscal quarter ended September 30, 2026, 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 discuss non-GAAP financial measures, which are reconciled to the most directly comparable GAAP financial measures in our earnings release, which are available at investors.datadoghq.com.

關於可能影響我們實際結果的重大風險及其他重要因素之討論，請參閱我們截至 2026 年 6 月 30 日止季度的 Form 10-Q。更多資訊將於我們即將提交的截至 2026 年 9 月 30 日止會計季度之 Form 10-Q，以及其他向美國證券交易委員會（SEC）提交的文件中提供。這些資訊亦可於我們網站的投資人關係專區取得，並包含本次電話會議的重播。我們將討論非 GAAP 財務衡量指標；其與最直接可比的 GAAP 財務衡量指標之調節表已載於我們的財報新聞稿中，並可於 investors.datadoghq.com 取得。

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

接下來，我想把電話交給 Olivier。

Thanks, A.J., and thank you all for joining us today to go over our Q3 results. Let me begin with this quarter's business drivers. I am pleased to say that we executed a strong Q3. We saw strength across our business, including across our product lines and across our diverse customer base. Revenue growth from our non-AI customers accelerated for the fifth straight quarter and was in the high 20s percent year-over-year, up about 1 percentage point from last quarter and up from about 20% in the year ago quarter.

謝謝你，A.J.，也感謝各位今天加入我們，一同回顧第三季（Q3）業績。我先從本季的業務驅動因素談起。我很高興地表示，我們在第三季執行表現強勁。我們在整體業務上都看到強勢表現，包括各產品線以及多元的客戶基礎。非 AI 客戶帶來的營收成長已連續第五季加速，年增率落在 20% 高段，較上季約提升 1 個百分點，也高於去年同期約 20% 的水準。

Additionally, our AI customer cohort remains a strong growth driver. This cohort continue to grow and diversify, both in the number of customers we serve and in the scale of those customers. Overall, we continue to see healthy demand as customers from the most tech forward startups to the largest enterprises in the world are building with AI and with modern technologies, which we believe is fueling expanded adoption of the cloud and of the Datadog platform.

此外，我們的 AI 客戶族群仍是強勁的成長驅動力。該族群持續成長並更加多元化，無論是我們服務的客戶數量，或是這些客戶的規模皆然。整體而言，我們持續看到健康的需求；從最具科技前瞻性的新創公司到全球最大的企業，客戶都在以 AI 與現代化技術進行建置。我們相信，這正推動雲端以及 Datadog 平台的擴大採用。

Regarding our Q3 financial performance and key metrics. Revenue was $1.2 billion, an increase of 35% year-over-year and above the high end of our guidance range. We ended Q3 with about 33,500 customers, up from about 32,000 a year ago. We also ended with about 4,860 customers with an ARR of $100,000 or more, up from about 4,060 a year ago. These customers generated about 91% of our ARR. And we generated free cash flow of $251 million with a free cash flow margin of 21%.

關於我們第三季的財務表現與關鍵指標：營收為 12 億美元，年增 35%，並高於我們指引區間的上緣。第三季末我們約有 33,500 名客戶，高於一年前約 32,000 名。我們也在期末擁有約 4,860 名年經常性收入（ARR）達 10 萬美元或以上的客戶，高於一年前約 4,060 名。這些客戶貢獻了我們約 91% 的 ARR。此外，我們產生 2.51 億美元的自由現金流，自由現金流利潤率為 21%。

Turning to product adoption. Our platform strategy continues to resonate in the market. 61% of our customers now use four or more products, up from 54% a year ago. 41% of our customers use six or more products, up from 31% a year ago; and 16% of our customers use 10 or more products, up from 8% a year ago. So we're landing more customers and delivering value across more products through our unified platform.

接著談產品採用情況。我們的平台策略持續在市場上引起共鳴。目前有 61% 的客戶使用四項或以上產品，高於一年前的 54%。有 41% 的客戶使用六項或以上產品，高於一年前的 31%；另有 16% 的客戶使用 10 項或以上產品，高於一年前的 8%。因此，我們正在獲得更多客戶，並透過我們的統一平台在更多產品上交付價值。

One milestone we are very proud of, our infrastructure monitoring suite of products now exceeds $2 billion in ARR, growing in the mid-30s percent year-over-year. Infrastructure monitoring was the first product we brought to market more than 10 years ago. Today, our customers rely on Datadog for visibility across their environments, including cloud infrastructure, on-prem servers, containers, serverless applications and GPU fleets. And while we're thrilled to reach this milestone, we still feel that we're just getting started.

有一項我們非常自豪的里程碑：我們的基礎架構監控產品套件目前 ARR 已超過 20 億美元，年增率為 30% 中段。基礎架構監控是我們在 10 多年前推出的第一項產品。如今，客戶仰賴 Datadog 來掌握其環境的可視性，包括雲端基礎架構、地端伺服器、容器、無伺服器應用程式以及 GPU 叢集。雖然我們很高興達成此里程碑，但我們仍覺得這只是剛剛開始。

According to industry analysts, we are the market share leader in global observability, but with a percentage market share still in the teens. Moving on to R&D. As AI changes how our customers build and run software, we've been investing and executing on two fronts: AI for Datadog and Datadog for AI. First, AI for Datadog is our AI products and capabilities that make the Datadog platform more useful and increasingly automated for our customers. First off, our flagship agent, Bits AI, is off to a very strong start.

根據產業分析師的看法，我們是全球可觀測性（observability）市場的市占率領導者，但市占率百分比仍在十幾個百分點。接著談研發（R&D）。隨著 AI 改變客戶建置與運行軟體的方式，我們在兩個面向投入並執行：AI for Datadog 與 Datadog for AI。首先，AI for Datadog 指的是我們的 AI 產品與能力，讓 Datadog 平台對客戶更有用，並且日益自動化。首先，我們的旗艦代理（agent）Bits AI 開局非常強勁。

Since we launched Bits, it has become the fastest adopted product in our history. And in October, Bits testing became generally available. With a single click, it discovers a critical user flows and applications and generates tests to cover them. Meanwhile, our AI research team is hard at work pushing the boundaries of what Bits and our platform can do. Last month, we introduced Scout, our own reearning model specifically optimized for SRA tasks.

自從我們推出 Bits 以來，它已成為我們公司史上採用速度最快的產品。而在 10 月，Bits 測試功能已正式全面可用（generally available）。只需單擊一下，它就能發現關鍵使用者流程與應用程式，並產生測試來涵蓋這些流程。同時，我們的 AI 研究團隊正全力以赴，持續突破 Bits 與我們平台能力的邊界。上個月，我們推出了 Scout——我們自研的 reearning 模型，專為 SRA 任務最佳化。

Scout was built using our in-house distributed reinforcement learning infrastructure and now powers our investigations. This is exciting for two reasons. First, it performs on par or even better than Frontier models on alert investigations at a fraction of the inference cost. This is valuable as it allows Bits to investigate many more alerts, start earlier and go deeper on incidents as an always-on first responder for our customers. Second, this is also exciting as a demonstration of what we can achieve when we train models with our own production data, critical user workflows and internal tooling.

Scout 是使用我們自建的分散式強化學習基礎設施打造而成，並且目前已為我們的調查（investigations）提供支援。這令人振奮有兩個原因。第一，在告警調查方面，它的表現可與 Frontier 模型相當，甚至更好，但推論成本僅為其一小部分。這很有價值，因為它讓 Bits 能夠調查更多告警、更早開始，並在事件上鑽得更深，作為客戶的全天候第一線回應者。第二，這也展示了當我們使用自有的生產資料、關鍵使用者工作流程與內部工具來訓練模型時，所能達成的成果。

Scout is only the beginning. With the Adaptive ML team, which joined us in June, we are broadly accelerating our work on reinforcement learning and model post training. Over time, we expect our research efforts to drive a step change in capabilities across our platform and move us closer to our goal of autonomous operations. Moving on next to Datadog for AI. These are the products that deliver end-to-end observability and security across the new AI stack.

Scout 只是開始。隨著 Adaptive ML 團隊於 6 月加入我們，我們正全面加速在強化學習與模型後訓練（post-training）方面的工作。隨著時間推進，我們預期研究投入將推動整個平台能力的躍升，並讓我們更接近自主化營運（autonomous operations）的目標。接下來談 Datadog for AI。這些產品可在新的 AI 技術堆疊上，提供端到端的可觀測性與安全性。

As AI moves from experimentation into production, customers today need to answer three key questions. How is it performing? Is it secure? But mostly ROI or is it worth of spend? And because AI doesn't run in isolation, they want these answers on the same platform as the rest of their tech stack.

隨著 AI 從實驗走向生產環境，客戶如今需要回答三個關鍵問題。它的表現如何？是否安全？但最重要的是投資報酬率（ROI）——是否值得投入這筆支出？而且由於 AI 並非孤立運行，他們希望能在與其其餘技術堆疊相同的平台上取得這些答案。

On performance, we cover the whole AI stack from compute all the way to agents. GPU monitoring now goes beyond hardware reliability with new capabilities that help customers maximize fleet utilization, increase training throughput and save inference at a lower cost per request. The number of GPU devices we monitor is up by a factor of eight so far this year. And some of our Frontier lab customers are working with us as event partners on these capabilities.

在效能方面，我們涵蓋從運算一路到代理（agents）的完整 AI 技術堆疊。GPU 監控如今已超越硬體可靠性，新增的能力可協助客戶最大化整體機群利用率、提升訓練吞吐量，並以更低的單次請求成本節省推論支出。截至今年目前為止，我們監控的 GPU 裝置數量已成長至原來的八倍。此外，我們的一些 Frontier 實驗室客戶正與我們合作，作為這些能力的活動合作夥伴。

Going up the stack, Agent Observability helps teams monitor and evaluate their agents in production, including the quality of their responses, latency and token usage. The number of spans sent to Agent Observability is now up by a factor of nine this year. We've also made Agent Observability available in the Anthropic marketplace, and we are a launch partner to OpenAI's new enterprise marketplace as well. So now customers can buy our products directly through the Frontier labs they already work with.

再往上到堆疊更高層，Agent Observability 可協助團隊在生產環境中監控並評估其代理，包括回應品質、延遲與 token 使用量。今年以來，送至 Agent Observability 的 span 數量已成長至原來的九倍。我們也已在 Anthropic marketplace 上提供 Agent Observability，同時也成為 OpenAI 全新企業級 marketplace 的首發合作夥伴。因此，客戶現在可以透過他們既有合作的 Frontier 實驗室，直接購買我們的產品。

On the security side, our AI Guard product discovers the custom agents running across an organization and protect them at one time against prompt injection and data exfiltration, bring on the same real-time data we collect to assess them. And on the cost side, companies everywhere are grappling with a very fast growth in token and AI spend. Beyond the three major clouds, AI providers are now the largest category of spend our customers monitor in Datadog Cloud Cost Management or CCM.

在資安方面，我們的 AI Guard 產品可發現組織內各處運行的自訂代理，並一次性保護它們免於提示注入（prompt injection）與資料外洩（data exfiltration），同時引入我們蒐集的同一套即時資料來進行評估。在成本方面，各地企業都正面臨 token 與 AI 支出快速成長的挑戰。除了三大雲端之外，AI 供應商如今已成為客戶在 Datadog Cloud Cost Management（CCM）中監控的最大支出類別。

Now with AI cost in CCM, customers can see which teams, models and agents drive their AI spend, can govern that spend with budgets, forecasts and anomaly alerts. More than a third of our CCM customers now track AI costs. The number of customers doing so is up more than 11 times. Moving up the stack again. Our agent console adds to our CCM data and shows exactly which agents are actually used by whom, for what tasks at what cost and most importantly, with what outcomes.

現在透過 CCM 的 AI 成本功能，客戶可以看見是哪些團隊、模型與代理推動其 AI 支出，並可透過預算、預測與異常警示來治理該支出。目前我們超過三分之一的 CCM 客戶已在追蹤 AI 成本。採用此功能的客戶數量已成長超過 11 倍。再往上到堆疊更高層。我們的 agent console 會補充 CCM 的資料，精確顯示哪些代理實際被誰使用、用於哪些任務、成本為何，以及最重要的——產生了什麼成果。

As we continue to innovate across AI for Datadog and Datadog for AI, independent research is taking note. Forrester named Datadog a leader in its 2026 Forrester Wave for AIOps platform. And Gartner's August 2026 AI Vendor Race report called Datadog the Company to Beat for observability platform. We believe this validates our leadership in observability as well as our ability to help customers manage complexity in the edge of AI. Let's move on to sales and marketing and look at a few of the deals our go-to-market team have closed in what has been a very strong quarter.

隨著我們持續在「Datadog 的 AI」與「AI 的 Datadog」兩個方向創新，獨立研究機構也注意到了。Forrester 在其 2026 年 Forrester Wave：AIOps 平台報告中，將 Datadog 評為領導者。而 Gartner 於 2026 年 8 月發布的《AI Vendor Race》報告則稱 Datadog 為可觀測性平台領域「最值得擊敗的公司」。我們相信，這驗證了我們在可觀測性方面的領導地位，以及協助客戶在 AI 前沿管理複雜度的能力。接下來我們進入銷售與行銷，看看我們 go-to-market 團隊在這個非常強勁的季度中完成的幾筆交易。

First, we signed a 7-figure annualized extension with a leading software company, more than doubling their annual commitment. This customer is moving AI agent into production and will replace a stand-alone commercial tool with Datadog Agent Observability to monitor their costs and quality. And by using Flex Logs, they can gather and correlate larger log volumes at a lower cost. In total, they will consolidate three tools onto Datadog and go to 20 Datadog products, including Oncore and Bits AI.

首先，我們與一家領先的軟體公司簽下年化七位數的續約擴展案，將其年度承諾金額提高到原來的兩倍以上。該客戶正將 AI 代理導入生產環境，並將以 Datadog Agent Observability 取代獨立的商用工具，以監控其成本與品質。同時透過 Flex Logs，他們能以更低成本蒐集並關聯更大量的日誌。整體而言，他們將把三套工具整併到 Datadog 上，並擴展至使用 20 項 Datadog 產品，包括 Oncore 與 Bits AI。

Next, we landed a 5-year 7-figure annualized deal with the US Federal Defense Agency. This customer runs thousands of cloud accounts across hundreds of mission owners. And as they've modernized, their manual tooling left them with pragmatic visibility and security blind spots. They chose Datadog as their enterprise observability and security platform and we'll start with nine products, including infrastructure monitoring and cloud security.

接著，我們與美國聯邦國防機構簽下為期 5 年、年化七位數的合約。該客戶在數百個任務負責單位之下，運行數千個雲端帳戶。隨著其現代化推進，原本的人工工具使其在可視性與安全性上留下了實務上的盲點。他們選擇 Datadog 作為其企業級可觀測性與安全平台，並將從九項產品開始導入，包括基礎設施監控與雲端安全。

I want to congratulate our public sector team on this milestone win, which was a direct result of our investment in government and the FedRAMP high certification we secured earlier in the year. And we are actively expanding our government platform to support workloads up to Impact Level 5 or IL5, the highest level of accreditation for unclassified data, which is part of our long-term investment in the US public sector. Next, we added a 7-figure annualized deal with a leading home health care software company.

我要恭喜我們的公部門團隊拿下這個里程碑式的勝利，這是我們對政府領域投資，以及我們在年初取得 FedRAMP High 認證的直接成果。我們也正積極擴展政府平台，以支援最高至 Impact Level 5（IL5）的工作負載——這是非機密資料的最高等級認證——這也是我們對美國公部門長期投資的一部分。接著，我們與一家領先的居家健康照護軟體公司新增一筆年化七位數的交易。

As this customer has grown, fragmented tools have slowed incident response and hurt customer experience. They will consolidate four tools into Datadog, replacing their legacy log and vendor with Flex Logs and Cloud SIEM and relying on our sensitive data scanner to help them stay HIPAA compliant. This customer will start with nine Datadog products, including incident management and Bits AI for faster resolution.

隨著該客戶成長，分散的工具拖慢了事件回應並損害客戶體驗。他們將把四套工具整併至 Datadog，以 Flex Logs 與 Cloud SIEM 取代其既有的日誌與供應商方案，並仰賴我們的敏感資料掃描器協助其維持 HIPAA 合規。該客戶將從九項 Datadog 產品開始導入，包括事件管理與 Bits AI，以加速問題解決。

Next, we signed a 7-figure annualized extension with a leading online entertainment company. This customer is seeing a surge in traffic with the season and is scaling effectively with Datadog. Their own AI agents already use our MCP server. And today, they are also adding Bits AI. This customer will grow to 23 Datadog products, including Agent Observability and Cloud Cost Management. Next, we landed a 7-figure analyze deals with two of Japan's most important banks.

接著，我們與一家領先的線上娛樂公司簽下年化七位數的續約擴展案。該客戶在旺季流量激增，並透過 Datadog 有效擴展。他們自有的 AI 代理已在使用我們的 MCP 伺服器。而今天，他們也將新增採用 Bits AI。該客戶將擴展至使用 23 項 Datadog 產品，包括 Agent Observability 與 Cloud Cost Management。接著，我們與日本最重要的兩家銀行拿下年化七位數的分析（analytics）交易。

As they modernize their core financial systems, both banks are standardizing on Datadog for unified visibility across cloud and on-prem environments. Both customers are adopting the full Datadog platform, including all three pillars: digital experience, security, software delivery, service management and Bits AI as they move towards AI-driven operations.

在現代化其核心金融系統的過程中，兩家銀行都選擇以 Datadog 作為雲端與地端環境的統一可視性標準。兩位客戶都在採用完整的 Datadog 平台，包括三大支柱：數位體驗、安全、軟體交付、服務管理，以及 Bits AI，並在邁向 AI 驅動的營運。

Next, we landed a 6-figure annualized deal with a top European AI lab. These customers developers were losing valuable time to slow build and fled tests with deed releases in a highly competitive market. They chose Datadog AI Observability and test optimization, replacing a commercial tool and freeing their own engineers to focus on shipping products. Their engineers also use Bits AI to build monitors and custom analytics. Next, we signed a 7-figure analyze expansion for an 8-figure analyze deal with a leading AI coding company.

接著，我們與一家歐洲頂尖 AI 實驗室拿下一筆年化六位數的交易。在競爭激烈的市場中，該客戶的開發人員因建置緩慢與測試不穩定（flaky tests），以及頻繁發布而耗費了寶貴時間。他們選擇 Datadog AI Observability 與測試最佳化，取代一套商用工具，讓自家工程師能專注於產品交付。他們的工程師也使用 Bits AI 來建立監控項目與自訂分析。接著，我們為一家領先的 AI 程式碼公司簽下年化七位數的分析（analytics）擴展案，對應其年化八位數的分析（analytics）交易。

This customer is growing rapidly and renewed early to keep pace with their expanding footprint. With this expansion, they will adopt Flex Logs and our new infinite cardinality metrics to scale cost effectively with volume. And they are also one of the largest users of our MCP server. This customer will grow to 13 Datadog products. Finally, we signed an 8-figure annualized expansion with the AI division of one of the world's largest technology companies, which nearly tripled their annual commitment.

該客戶成長迅速，並提前續約以跟上其不斷擴大的使用版圖。透過這次擴展，他們將採用 Flex Logs 與我們全新的無限基數（infinite cardinality）指標，以在高資料量下仍能具成本效益地擴展。他們同時也是我們 MCP 伺服器的最大使用者之一。該客戶將擴展至使用 13 項 Datadog 產品。最後，我們與全球最大科技公司之一的 AI 部門簽下年化八位數的擴展案，幾乎將其年度承諾金額提高到三倍。

Just two quarters after we landed them, they are scaling Datadog across a large number of AI training workloads. They are also adopting APM and Bits AI to further reduce engineering friction. Alongside these wins on the other side of the customer spectrum, we are leaning into the large number of new AI native companies we see create every day by investing in our Datadog for start-ups program.

在我們拿下他們僅兩個季度後，他們就正在將 Datadog 擴展到大量 AI 訓練工作負載。他們也在導入 APM 與 Bits AI，以進一步降低工程摩擦。除了在客戶光譜另一端的這些勝利之外，我們也正加大投入，透過 Datadog for start-ups 計畫，支持我們每天看到大量新成立的 AI 原生公司。

We are broadening its reach to a larger number of start-ups that are earlier in their life cycle and offering deeper engineering collaboration with the ones that are seeing explosive growth. This is a truly exciting time to partner with start-ups. Now before I turn it over to David for a financial review, I'd like to say a few words on the longer-term outlook. There is no change to our overall view that digital transformation and cloud migration are long-term growth drivers for our business.

我們正在擴大該計畫的覆蓋範圍，觸及更多處於更早期生命週期的新創公司，並對那些呈現爆發式成長的公司提供更深入的工程協作。現在確實是與新創公司合作的令人振奮時刻。在我把時間交給 David 進行財務回顧之前，我想先談幾句較長期的展望。我們對整體看法沒有改變：數位轉型與雲端遷移是推動我們業務長期成長的動能。

And AI is another large growth driver for us today as AI workloads move into production to drive more cloud consumption and more usage of the Datadog platform, and we see that today across our business. All 15 of the top 15 AI leaders are our customers. And more broadly, we now serve over 850 AI customers, up from about 750 last quarter. And across our broader customer base, we also see AI activity growing substantially.

而 AI 也是我們當前另一個重要的成長驅動力，因為 AI 工作負載進入正式生產環境，帶動更多雲端消耗以及 Datadog 平台的更多使用量，我們在整個業務中都看到了這一點。前 15 大 AI 領導者中的 15 家都是我們的客戶。更廣泛地說，我們目前服務超過 850 家 AI 客戶，高於上一季約 750 家。在我們更廣泛的客戶群中，我們也看到 AI 活動大幅成長。

About 1/3 of our customers now use one or more of our AI products, including 3/4 of our $100,000-plus customers with many of these customers still just getting started with these products, using a large opportunity for future expansion. And we continue to see rapid growth in Agent activity with a number of ACP tool calls up by a factor of 50 so far this year.

目前約有 1/3 的客戶使用我們一項或多項 AI 產品，其中包括我們年支出超過 10 萬美元客戶的 3/4；而這些客戶中有許多仍只是剛開始使用這些產品，為未來擴張帶來龐大機會。此外，我們持續看到 Agent 活動快速成長，今年迄今 ACP 工具呼叫次數已增加至 50 倍。

We are tremendously excited about where this is going. As AI writes and runs more of our customers' software, they're shipping more code faster across a growing mix of models, agents and infrastructure. Of course, it generates complexity that is very quickly outpacing what even the best engineering teams can track on their own. And that's exactly the problem we're investing to solve.

我們對此發展方向感到非常振奮。隨著 AI 撰寫並執行更多客戶的軟體，他們在日益多元的模型、代理（agents）與基礎設施組合下，以更快速度交付更多程式碼。當然，這也帶來了複雜度，並且很快就超越即使是最優秀工程團隊也能自行追蹤的範圍。而這正是我們投入資源要解決的問題。

AI for Datadog takes on more of that work for them. Datadog for AI help them run their AI with confidence and our AI research turns those data into models that will deliver more autonomy over time. Because of that, now more than ever, we feel ideally positioned to have customers of every size and every industry as well as every type of user, whether humans or AI agents, so they can transform, innovate and drive value through AI and cloud adoption.

Datadog 的 AI 會替他們承擔更多這類工作。Datadog for AI 協助他們有信心地運行其 AI，而我們的 AI 研究會將這些資料轉化為模型，隨時間推進提供更高的自主性。因此，比以往任何時候，我們都更覺得自己處於理想位置，能服務各種規模、各行各業的客戶，以及各類型使用者（無論是人類或 AI 代理），讓他們透過 AI 與雲端採用實現轉型、創新並創造價值。

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

接下來我把時間交給我們的 CFO。David？

Thanks, Olivier, and good morning to everybody. Our Q3 revenue was $1.197 billion, up 35% year-over-year. We continue to see robust usage growth from existing customers as well as a strong ramp in our new customers. Revenue growth accelerated within our broad base of customers, excluding AI customers, to the high 20s percent year-over-year, up about 1 percentage point from last quarter and up from about 20% in the year ago quarter. And we saw robust growth with broad-based strength across customer segments, spending bands and industries.

謝謝你，Olivier，也向各位道早安。我們第三季營收為 11.97 億美元，年增 35%。我們持續看到既有客戶的使用量強勁成長，同時新客戶也快速放量。在我們廣泛的客戶基礎中（不含 AI 客戶），營收成長率加速至年增接近 30% 的高 20% 區間，較上一季提升約 1 個百分點，也高於去年同期約 20% 的水準。我們也看到強勁成長，且在各客戶區隔、支出級距與產業皆呈現廣泛的強勢表現。

And excluding our largest customer, revenue growth accelerated again. As we communicated in August, our largest customer reduced their usage in the third quarter, while their usage remained above their minimum commitment. Meanwhile, our AI customers growth continues to significantly outpace the rest of the business. This over 850 strong group continues to diversify and grow, including 39 customers spending more than $1 million annually with us.

若排除我們最大的客戶，營收成長再次加速。如同我們在 8 月所溝通的，我們最大的客戶在第三季降低了使用量，但其使用量仍高於其最低承諾。同時，我們 AI 客戶的成長仍持續顯著快於其他業務。這個超過 850 家的客戶群持續多元化並成長，其中包括 39 家每年在我們這裡支出超過 100 萬美元的客戶。

And we continue to land larger and grow faster with new logos, contributing more to revenue growth. The portion of our year-over-year revenue growth that relates to new customers was about 30% in Q3, up from 25% in the year ago quarter. And now regarding our retention metrics. Our trailing 12-month net revenue retention percentage was in the low 120s%, similar to last quarter, and churn remains low, with gross retention stable -- gross revenue retention stable in the mid- to high 90s. We believe this metric highlights the mission-critical nature of our platform for our customers.

我們也持續在新客戶（new logos）上拿下更大案子並更快成長，對營收成長的貢獻提高。第三季與新客戶相關的年增營收成長占比約為 30%，高於去年同期的 25%。接著談我們的留存指標。過去 12 個月（trailing 12-month）的淨營收留存率落在 120% 出頭，與上一季相近；流失率仍低，總留存穩定——總營收留存率穩定在 90% 中段至高段。我們認為此指標凸顯了我們平台對客戶而言具備任務關鍵（mission-critical）的特性。

And now moving on to our financial results. Billings in the quarter were $1.16 billion, up 30% year-over-year. Remaining performance obligations, or RPO, was $3.85 billion, up 38% year-over-year, and current RPO grew in the mid-30s percent year-over-year. RPO duration increased slightly year-over-year. As a reminder, we continue to believe revenue is a better indication of our business trends than billings or RPO. Let's review some of the key income statement results. Unless otherwise noted, all metrics are non-GAAP.

接下來進入我們的財務結果。本季帳單金額（billings）為 11.6 億美元，年增 30%。剩餘履約義務（remaining performance obligations，RPO）為 38.5 億美元，年增 38%，而當期 RPO 年增約在 30% 中段。RPO 期間（duration）較去年同期略有增加。提醒一下，我們仍認為相較於 billings 或 RPO，營收更能反映我們的業務趨勢。接著我們來檢視損益表的一些關鍵結果。除非另有說明，所有指標皆為非 GAAP。

We have provided a reconciliation of GAAP to non-GAAP financials in our earnings release. For Q3, our gross profit was $972 million, and the gross margin was 81.2%. This compares to a gross margin of 79.6% last quarter and 81.2% in the year ago quarter. Q3 OpEx grew 25% year-over-year versus 26% last quarter and 32% in the year ago quarter. And Q3 operating income was $334 million or 28% operating margin compared to 23% last quarter and 23% in the year ago quarter.

我們已在財報新聞稿中提供 GAAP 與非 GAAP 財務數據的調節表。第三季毛利為 9.72 億美元，毛利率為 81.2%。相較之下，上季毛利率為 79.6%，去年同期為 81.2%。第三季營業費用（OpEx）年增 25%，相較上季為 26%，去年同期為 32%。第三季營業利益為 3.34 億美元，或 28% 的營業利益率，相較上季為 23%，去年同期亦為 23%。

And now turning to the balance sheet and cash flow statements. We ended the quarter with $5.2 billion in cash, cash equivalents and marketable securities. Cash flow from operations was $290 million in the quarter. After taking into consideration capital expenditures and capitalized software, free cash flow was $251 million with a free cash flow margin of 21%. Quarterly, the timing of collections and payments vary and on a 12 -- trailing 12-month basis, free cash flow margin was 26%.

接著看資產負債表與現金流量表。本季末我們持有 52 億美元的現金、約當現金與有價證券。本季營運活動現金流為 2.90 億美元。在考量資本支出與資本化軟體後，自由現金流為 2.51 億美元，自由現金流利潤率為 21%。以季度來看，收款與付款的時間點會有所波動；以過去 12 個月（trailing 12-month）計，自由現金流利潤率為 26%。

And now for our outlook for the fourth quarter and for fiscal year 2026. First, our guidance philosophy overall remains unchanged. As a reminder, we base our guidance on trends observed in recent months and apply conservatism on these growth trends. Regarding our largest customer, our Q4 guidance contemplates their revenue at their minimum commitment.

接下來是我們對第四季與 2026 會計年度的展望。首先，我們整體的指引理念維持不變。提醒一下，我們的指引是基於近幾個月觀察到的趨勢，並在這些成長趨勢上採取保守假設。關於我們最大的客戶，我們的第四季指引假設其營收落在其最低承諾水準。

Now for the fourth quarter, we expect revenue to be in the range of $1.19 billion to $1.2 billion, which represents a 25% to 26% year-over-year growth. Excluding our largest customer, Q4 revenue growth implied in our guidance is in the low to mid-30s percent range. Non-GAAP operating income is expected to be in the range of $280 million to $290 million, which implies an operating margin of 24%. The fourth quarter includes large marketing investments, including AWS Reinvent.

就第四季而言，我們預期營收介於 11.9 億至 12.0 億美元之間，代表年增 25% 至 26%。若排除我們最大的客戶，指引所隱含的第四季營收成長率約在年增 30% 出頭至中段 30% 的區間。非 GAAP 營業利益預期介於 2.80 億至 2.90 億美元之間，隱含營業利益率為 24%。第四季包含較大的行銷投資，包括 AWS Reinvent。

Our non-GAAP net income per share is expected to be in the $0.69 to $0.71 per share range based on approximately 379 million weighted average diluted shares outstanding. For the fiscal year 2026, we expect revenue to be in the range of $4.515 billion to $4.525 billion, which represents approximately 32% year-over-year growth. And non-GAAP operating income is expected to be in the range of $1.095 billion to $1.105 billion, which implies an operating margin of 24%.

我們預期非 GAAP 每股淨利介於每股 0.69 至 0.71 美元之間，係以約 3.79 億股加權平均稀釋流通股數計算。就 2026 會計年度而言，我們預期營收介於 45.15 億至 45.25 億美元之間，代表約 32% 的年增成長。非 GAAP 營業利益預期介於 10.95 億至 11.05 億美元之間，隱含營業利益率為 24%。

Finally, non-GAAP net income per share is expected to be in the range of $2.73 to $2.75 per share based on approximately 375 million weighted average diluted shares outstanding. Now some additional notes on our guidance. We expect net interest and other income for the fiscal year 2026 to be approximately $200 million. We expect cash taxes to be about $30 million to $40 million, and we continue to apply a 21% non-GAAP tax rate for 2026 and going forward.

最後，非 GAAP 每股淨利預期介於每股 2.73 至 2.75 美元之間，係以約 3.75 億股加權平均稀釋流通股數計算。再補充幾點關於我們指引的說明。我們預期 2026 會計年度的淨利息與其他收入約為 2.00 億美元。我們預期現金稅約為 3,000 萬至 4,000 萬美元，且我們將在 2026 年及往後持續採用 21% 的非 GAAP 稅率。

And finally, we expect capital expenditures and capitalized software together to be in the 3% to 4% of revenue range in fiscal year 2026. To summarize, we are pleased with our execution in Q3, which combines strong growth with strong profitability. We continue to invest to capture our long-term opportunity in the cloud and in AI. And I want to thank Datadogs worldwide for their efforts.

最後，我們預期在 2026 會計年度，資本支出與資本化軟體合計將落在營收的 3% 到 4% 區間。總結來說，我們對第三季的執行成果感到滿意，兼具強勁成長與強勁獲利能力。我們將持續投資，以把握在雲端與 AI 的長期機會。也要感謝 Datadog 全球同仁的努力。

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

接下來，我們將開放提問。接線員，讓我們開始問答環節。

(Operator Instructions)

(接線員指示)

Sanjit Singh, Morgan Stanley.

Sanjit Singh，摩根士丹利。

Congrats on the strong results on Q3. David, I want to start with the Q4 guide. If we wind back 90 days ago, has anything changed with respect to your underlying assumptions between the different cohorts, so between the core business and the sort of largest customers versus 90 days ago? Or is this outlook just sort of a reflection of that sort of minimum commitment methodology that you're applying in Q4? Just want to get a sense of has anything changed with respect to the outlook.

恭喜第三季繳出強勁成果。David，我想先從第四季指引談起。如果我們把時間倒回 90 天前，在不同客群（也就是核心業務與最大客戶等）之間，你們對於底層假設有任何改變嗎？相較於 90 天前有變嗎？還是說，這個展望只是反映你們在第四季採用的「最低承諾」方法？我想了解的是，對於展望本身，有沒有任何改變。

There are a couple of things to talk about. First of all, I think we said versus our largest customer that they renewed and they were spending above the minimum commit, and that happened in Q3. We said that in our guidance, we were going to only include the minimum commit despite the fact that in Q3, they were spending above the minimum commit. The -- as we've talked about, we've accelerated in Q3, excluding the largest customer.

有幾點可以談。首先，關於我們最大的客戶，我想我們先前提到他們已完成續約，而且實際支出高於最低承諾，這件事在第三季發生。我們也在指引中說過，儘管第三季他們的支出高於最低承諾，但在指引上我們只會納入最低承諾。另外，如同我們討論過的，若排除最大客戶，我們在第三季的成長是加速的。

So I would say the only thing that has changed was in sort of Q3, we did some renewals extensions with some large and large AI customers where as normal, we essentially extend and increase the commit, but do better pricing for that. And that happened in a number of large customers. It's a normal behavior, and that's really the only thing that changed since our last communication. Oli, do you want to add anything?

所以我會說，唯一的改變是在第三季，我們與一些大型、以及大型 AI 客戶做了一些續約延展；如同一般情況，我們基本上會延長並提高承諾額度，同時提供更好的價格。而這在多家大型客戶身上都有發生。這是正常行為，這也確實是自上次溝通以來唯一的變化。Oli，你要補充嗎？

Yes. There's a few -- we discussed on the call, I could bring up a few of those large customers where we saw renewals way earlier than we thought we would. There's another one that happened in October that we're not discussing. So these are all good news, but they do create like headwind into the short term into the revenue, and that's one change from last quarter. Other than that, like everything has played out pretty much the way we were expecting with some happy surprises increasing, in particular, the continued acceleration of the non-AI customer, which has been very good at all.

是的。有幾點——我們在電話會議中提到過，我可以再提幾個大型客戶的例子：我們看到他們比我們原先預期更早就進行續約。另外還有一個在 10 月發生的案例，我們不打算討論。這些都是好消息，但短期內會對營收形成一些逆風，這是相較上一季的一個變化。除此之外，整體發展大致符合我們的預期，也有一些令人欣喜的意外上行，特別是非 AI 原生客戶持續加速，表現非常好。

Understood. And if we think about the ex AI native customer growth, that's been on an accelerating trend for several quarters now. As we think about this core non-AI native business, you obviously have a lot of tailwinds on the AI portfolio. It seems like the $100,000 customer quarter is more of the AI product. Do you feel that, that can continue to drive sort of sustained trajectory? Or are we -- after multiple quarters of acceleration, do you sort of see a leveling off in that ex AI native cohort?

了解。如果我們看 AI 原生客戶以外的成長，這幾季一直呈現加速趨勢。就這個核心的非 AI 原生業務而言，你們在 AI 產品組合上顯然有很多順風。看起來，單季 10 萬美元以上客戶的增加更多是由 AI 產品帶動。你們覺得這能否持續推動一條更可持續的成長軌跡？或者在多季加速之後，你們是否看到這個非 AI 原生客群的成長開始趨於持平？

I mean, look, in the -- if you zoom out, all of these customers are adopting AI and they continuing more cloud and they're building more software and they are changing the way they work. So we expect a long period of growth for these customers. Now with the overall growth rate is, does it keep going up a lot, does it stay stable? It's a very high growth rate already, and we feel pretty good about it. It's hard to tell where it's going to go. But we're very confident in the broader trend that is underpinning that.

我的意思是，如果你把視角拉遠，所有這些客戶都在採用 AI，也在持續更多上雲、建置更多軟體，並改變他們的工作方式。因此我們預期這些客戶會有一段很長的成長期。至於整體成長率會如何，是會繼續大幅上升、還是維持穩定？目前成長率已經很高，我們對此感覺相當不錯。但要判斷它會走到哪裡，確實不容易。不過，我們對支撐這一切的更大趨勢非常有信心。

Gabriela Borges, Goldman Sachs.

Gabriela Borges，高盛。

David, I want to stay on this point on the AI native cohort. Because I think you've been very transparent in how you talk about your largest customers versus the rest of the cohort. And my question for you is you have these funky sequentials because you have had this incredible growth rate in this cohort over the last 12 months. And now you've got this renewal dynamic with your largest customer, you're also talking about the renewal dynamics on the early renewals.

David，我想延續 AI 原生客群這個點。因為我認為你們在談最大客戶與其他客群的差異時一直很透明。我的問題是：過去 12 個月這個客群的成長率非常驚人，因此你們的季對季變化看起來有點「不規則」。而現在你們又有最大客戶的續約動態，同時也在談一些提早續約的動態。

So my question for you is, what can you tell us about how to think about modeling the sequentials in this business? Is it that we get through the next, call it, two quarters and then we're back to steady state? Is it that maybe we see another step function down every year in the sequential because you have this early renewal dynamic, which again, is a really good place to be because you're getting really large deals that the only way you get better pricing is because the dollar volume is going up.

所以我的問題是：你能否談談我們應該如何在模型中思考這個業務的季對季變化？是否是說，我們度過接下來大概兩季後，就會回到穩態？還是說，因為有提早續約的動態，我們可能每年都會在季對季表現上看到一次階梯式下移？而這其實也是很好的狀態，因為你們拿到非常大的合約，而能拿到更好價格的方式，正是因為美元量在上升。

So maybe just the puts and takes to how we think about the sequentials for the AI native cohort and when we will be back to something that looks like normal, which I get is a tricky definition of the word normal anyway.

所以也許請你談談，AI 原生客群的季對季變化有哪些拉動與抵銷因素，以及我們何時會回到看起來比較「正常」的狀態——我也理解「正常」本身就是個很難定義的詞。

Yes. So good question. So in terms of the largest customer, I think as we said, the customer renewed the customer, we decided to put only the commitment, the minimum commitment. So the customer in Q3 spent substantially above the minimum commitment. So one thing that is very important in the sequentials is what happens there.

是的，這是個好問題。就最大客戶而言，如同我們所說，客戶已完成續約，而我們決定只納入承諾額度，也就是最低承諾。因此該客戶在第三季的支出顯著高於最低承諾。所以在季對季變化中，有一個非常重要的因素就是那邊會發生什麼。

We don't control that. The customer can't go below the minimum commitment, but can be various degrees above the minimum commitment. So that is one factor that we -- so essentially, one thing that will create less of a sequential guidance or actual would be that, that number stabilizes and isn't running above the minimum commitment. So that's one.

這不是我們能控制的。客戶不可能低於最低承諾，但可能高於最低承諾的幅度會有不同程度。所以這是我們——本質上，若那個數字趨於穩定、且不再高於最低承諾在上方跑，那麼指引或實際結果的季對季波動就會變小。這是其一。

I would say -- the point here is there's one part that is very usual, which is that we have all these customers that are expanding and they're growing and they're renewing. And then when they renew, they get better deals and they also adjust their traffic over time with an underpinning of growth, which is what we've seen since the beginning of the company. There's something that is new that we have some of these companies that are scaling incredibly fast to a level that we've never seen in the industry or any industry really before, which creates new dynamics.

我會說——這裡有一部分是非常常見的情況：我們有很多客戶在擴張、成長並續約。而當他們續約時，他們會拿到更好的條件，同時也會隨時間調整他們的流量使用，但其底層仍由成長所支撐——這是我們自公司成立以來一直看到的現象。另外也有一個新的情況：我們有一些公司以極快速度擴張到一個程度，這在這個產業、甚至任何產業中都幾乎前所未見，因而帶來新的動態。

This is why we can be a bit surprised when we see customers renew early just a few quarters into there starting to add up the product. In general, though, we're getting a larger number of these customers like the AI native cohort is broadening. We have many more customers, many more large customers into that cohort. We called out a few wins that we made another AI lab, for example, this quarter that's in the note. So we -- the long term, this is going to broaden. This is going to represent a much larger fraction of our customer base.

這也是為什麼當我們看到客戶在開始大量採用產品後、僅僅幾個季度就提早續約時，我們會有點意外。但整體而言，我們正在獲得更多這類客戶，AI 原生客群正在擴大。我們在該客群中有更多客戶、也有更多大型客戶。例如，我們在本季也點出幾個贏單，其中之一是我們拿下另一家 AI 實驗室，這在附註中有提到。所以——長期來看，這個客群會持續擴大。它將占我們客戶基礎更大的一部分。

And as a result, there will be more stability over time. I still don't think it's a very unstable business. I mean we're talking about the change of a few percentage quarter-to-quarter in terms of where we end up versus where we think we're going to be. But we think at the end of the day, this is a usage-based model, and we get more visibility into the mid- to long term than we do into the very short term.

因此，隨著時間推移，整體也會更穩定。我其實不認為這是一個非常不穩定的業務。我的意思是，我們談的是季對季幾個百分點的變動，也就是最終落點相對於我們原先預期的位置差異。但我們認為，歸根結底這是一個按使用量計費的模式，我們對中長期的能見度其實高於對非常短期的能見度。

I do want to remind everybody that all things being equal, the sequential between Q3 and Q4 tends to be less because of the late December effect when our usage-based products and vacations and some code freezes. So that can be transparent based on the other factors or it might be underneath of that, but we do have that effect in Q4, which we don't have in the other quarters.

我確實想提醒大家，在其他條件相同的情況下，Q3 到 Q4 的季比增幅通常會比較小，因為 12 月下旬效應：我們的按使用量計費產品、假期，以及一些程式碼凍結都會影響使用量。因此，這可能會在其他因素之上呈現得更透明，或是被掩蓋在其下，但我們在 Q4 的確會有這個效應，而其他季度沒有。

Raimo Lenschow, Barclays.

Raimo Lenschow，巴克萊。

Can I stay on one last time -- one last question on this one. So if I think about the customers renewed early. So they get better pricing. But obviously, the volume is going kind of going crazy there, but it kind of creates a short-term headwind because the same volume is now on a lower price, but then the volume grows and from then on in a couple of quarters, we should see like the normal progression. Is that kind of the right thing to think about? I think we have that in the past, but I just wanted to clarify that. And then I have one follow-up.

我想最後再追問一次——就這個議題最後一個問題。如果我從客戶提前續約來看，他們拿到更好的價格。但顯然那邊的用量成長得很瘋狂，不過短期內會形成逆風，因為同樣的用量現在是以較低的價格計算；但之後用量會成長，接下來幾個季度我們應該會看到回到正常的成長軌跡。這樣理解對嗎？我想過去我們也遇過這種情況，但我只是想確認一下。然後我還有一個追問。

Yes, that's totally what happened. There's a few other things that typically happen in those situations, which is they try to understand what is that going to mean. So they look at what they're using and they think, okay, this part, maybe we don't need as much of this part, we're probably more of or maybe in some situations, we tell them, hey, maybe you should use this different tier of retention for this type of data because of the way you're using it.

是的，完全就是那樣發生的。在那種情況下通常還會發生幾件事，他們會試著理解這代表什麼。所以他們會檢視自己正在使用的內容，然後想：好，這一部分也許不需要那麼多；我們可能更偏向——或者在某些情況下，我們會告訴他們，嘿，針對這類資料，基於你們的使用方式，也許應該改用不同層級的保留（retention）方案。

So there's a bit of account optimization there when we work with them. But the -- that this is on the background of growth. These customers are growing and they're deploying much larger workloads, and they're looking at doubling, tripling, decoupling their footprint with us over time, and this is basically we're working on with them.

所以我們在合作時，會有一些帳戶最佳化的動作。但——這一切都是在成長的背景之下發生的。這些客戶正在成長，他們部署更大規模的工作負載，並且著眼於隨時間將在我們這裡的用量版圖加倍、三倍，甚至以解耦的方式擴張，而這基本上就是我們正在與他們共同推進的事情。

Okay. Perfect. And then the main question for me was on the ex AI part, like historically, we would think about that growth coming from cloud migration, people modernize and doing more stuff. You mentioned another aspect, which is kind of because of AI, et cetera, we can code more, there's more application modernization, all these stuff happening.

好的。完美。然後我主要的問題是關於不含 AI（ex AI）的部分：過去我們通常會認為那個成長來自雲端遷移、人們現代化改造並做更多事情。你提到另一個面向，也就是因為 AI 等因素，我們可以寫更多程式碼，有更多應用程式現代化改造，這些事情都在發生。

So is this kind of changing the way it's kind of evolving for you from classically with people modernize, you have to go to Datadog. Thank you very much. Well, now actually the companies are working differently, and that's also starting to drive the ex AI business. Can you speak to that, please?

所以這是否正在改變你們那邊的演進方式？過去典型的情況是人們做現代化改造，就必須用 Datadog。非常感謝。但現在其實公司工作的方式不同了，而這也開始推動不含 AI（ex AI）的業務。可以請你談談這點嗎？

Yes. I mean -- well, the one thing I didn't mention on the call, that's always true, we also do more things for our customers. We have consolidated a lot of what they were using. We have more products to cover more of the surface area. As it comes to customers building more with AI, like it also brings some of our other products more into the front and center.

是的。我的意思是——嗯，有一件我在電話會議上沒提到、但一直都成立的事：我們也為客戶做更多事情。我們把他們原本在用的很多東西整合起來了。我們有更多產品可以覆蓋更大的範圍。當客戶用 AI 建置更多東西時，也會讓我們其他一些產品變得更核心、更受關注。

So an example is customers are building a lot more software. So they're putting a lot more pressure on their CI and deployment and testing. And so they're trying also to adopt more of our CI products. I think we called out a few wins around that too in the call. So that opens the door for more products beyond core observability for us.

舉例來說，客戶正在開發更多軟體。因此他們的 CI、部署與測試承受更大壓力。所以他們也在嘗試採用更多我們的 CI 產品。我想我們在電話會議中也提到了一些相關的贏單。因此，這為我們打開了核心可觀測性（observability）之外更多產品的機會。

Samik Chatterjee, JPMorgan.

Samik Chatterjee，摩根大通。

I had a couple. Maybe for the first one, I know you mentioned your largest customer did spend above their commit in Q3. Is there any way to tell us how much of the beat relative to your guidance in Q3 was because of the large customer spending of the commit relative to the rest of the business depending on how you forecasted them in Q3? And I have a quick follow-up.

我有幾個問題。先問第一個：我知道你提到你們最大客戶在 Q3 的支出高於其承諾額（commit）。你們能否說明一下，相對於你們 Q3 指引的超出（beat）中，有多少是因為這個大客戶支出高於承諾額、相對於你們在 Q3 對他們的預測假設，以及相對於其他業務的部分？我還有一個簡短的追問。

Yes. We're not answering -- we're not being specific, but it is true. It just -- it is true that if you're basically guiding like in below there -- we said we'd be conservative. In Q3, we said conservative relative to what they were spending. And in Q4, we've been explicit and said minimum commitment. It is true that if they spend more than that, that would be implicit beat mathematically. Yes.

是的。我們不會回答——我們不會提供具體數字，但確實如此。只是——確實，如果你們看我們的指引——我們說過會保守。在 Q3，我們相對於他們當時的支出水準給了較保守的假設。而在 Q4，我們已經明確說是以最低承諾額（minimum commitment）為基礎。確實，如果他們支出高於那個水準，從數學上來說就會隱含形成超出（beat）。是的。

Again, we didn't change the assumption there over time. So we get everything unchanged. I think brought up earlier, the one difference since the last quarter is some positive trends on the overall customer growth, but also some early renewals of larger customers, which includes some other AI customers. That's the one thing you should remember as being the difference.

再說一次，我們並沒有隨時間改變那個假設。所以我們把其他一切都維持不變。我想先前也提到，自上季以來唯一的差異是整體客戶成長出現一些正向趨勢，另外也有一些較大客戶提前續約，其中也包含一些其他 AI 客戶。這是你應該記住、作為差異點的那一件事。

Yes, sure. And for my follow-up, I mean, the contribution of revenue from new customers continues to ramp up steadily. Can you just talk about sort of what is the average land size that you're seeing now with new customers and how that's changed? And what sort of behavior you're seeing on that front? How you're thinking about it as you sort of move into next year in terms of how much new customers contribute to your overall growth?

好的，了解。那我的追問是：來自新客戶的營收貢獻持續穩定提升。你能否談談目前你們看到的新客戶平均初始導入規模（average land size）大概是多少，以及它如何變化？在這方面你們觀察到什麼樣的行為？以及當你們展望明年時，對於新客戶對整體成長的貢獻，你們是怎麼思考的？

We haven't provided guidance on we said that over time, we've seen an increase of average land size due to the increase of the platform and as we give the metrics on the adoption of products, the adoption of more parts of the platform. Oli, do you want to --

我們沒有提供相關指引；我們只是說，隨著時間推移，因為平台能力擴大，以及我們提供的產品採用度指標——也就是平台更多部分的採用——我們看到平均初始導入規模有所增加。Oli，你要不要——

Yes. I think -- look, this is something we see in the customer base that we see that one is with the new and the AI native companies, some of them start with more scale earlier, like we've discussed it before, but we see companies scaling faster than used to. So they have -- they represent more of the revenue faster. But we also are developing new segments and landing large customers from day one. So we called out a few on the script today.

是的。我想——你看，這是我們在客戶群中看到的現象：其中一點是，對於新的、AI 原生的公司，有些公司在更早期就以更大規模起步，正如我們之前討論過的；而且我們看到公司擴張速度比過去更快。所以他們——更快地貢獻更多營收。但同時，我們也在開發新的客群區隔，並且從第一天就拿下大型客戶。所以我們今天在講稿裡也點名了幾個。

We mentioned, I think, a defense agency in the US government. We mentioned Japanese banks, like two of the three largest Japanese banks in the same quarters. Those are all sizable low land deals. And if you compare the business to what it was, say, five years ago, like this is definitely a new side of business that we didn't necessarily have before. And by the way, those wins I mentioned, they don't even register into the revenue yet because these are land, but these are not customers that are ramp users yet.

我們提到，我想，是美國政府的一個國防機構。我們提到日本的銀行，例如同一季度裡日本前三大銀行中的兩家。這些都是相當可觀的低起始導入（low land）交易。如果你把現在的業務和例如五年前相比，這絕對是我們以前不一定有的一個新面向。順帶一提，我提到的那些贏單，甚至還沒有反映到營收裡，因為這些是導入（land），但這些客戶還不是用量快速爬升（ramp）的使用者。

Matt Hedberg, RBC.

Matt Hedberg，RBC。

I wanted to start with training. You noted in your script, you had an 8-figure, I think, annualized expansion with an AI division of a large software company. It sounds like they're using you guys for AI training workloads. I guess this is -- to us, it seems like a really interesting growth opportunity. Can you step back and kind of talk about what that training opportunity is? And what could it mean for your growth?

我想先從訓練（training）談起。你在講稿中提到，你們與一家大型軟體公司的 AI 部門有一筆我記得是年化八位數的擴張。聽起來他們是在用你們來支援 AI 訓練工作負載。我想這——對我們來說，這看起來是個非常有趣的成長機會。你能否退一步，談談這個訓練機會是什麼？以及它可能對你們的成長意味著什麼？

I mean it's been evolving positively. If you look a year or two years ago, basically nobody was doing training. It was only a few Frontier labs who were doing their own training and nobody else was. Now we see an order of magnitude more large-ish labs or labs that are doing training at a large scale. And then there's another whole category of companies below that, that are starting to do their own, including us.

我的意思是，它一直在正向演進。如果你看一年前或兩年前，基本上沒有人在做訓練。只有少數前沿實驗室在做自己的訓練，其他人都沒有。現在我們看到，進行大規模訓練的、規模較大的實驗室或實驗室數量增加了一個數量級。然後在那之下還有另一整類公司，也開始做自己的訓練，包括我們在內。

We started doing training at a pretty decent scale, and we showed some of the results, some of the results on the call as well. So I think there's some interesting potential development there where training becomes a part of everybody's business and everybody's customizing model in building their own. And so we are investing. I would say it's still early to call it as a mass market. Right now, we see some interesting indications of it. We also see that we can be extremely useful to some very large customers. But I think we still have to invest. We have to build. We have to see how that market develops.

我們開始以相當不錯的規模進行訓練，也展示了一些成果，在電話會議上也分享了部分結果。所以我認為這裡有一些有趣的潛在發展：訓練會成為每家公司業務的一部分，每家公司都在客製化模型、打造自己的模型。因此我們正在投資。我會說現在要把它稱為大眾市場還為時過早。目前我們看到一些有趣的跡象。我們也看到，我們能對一些非常大型的客戶提供極大的價值。但我認為我們仍需要投資。我們必須建設。我們必須看看那個市場如何發展。

Got it. And actually, David, if I could just have one more follow-up. On your largest customer, you used the word substantially above their minimums, which I find interesting. And I know you're assuming only the minimums for 4Q, but philosophically, looking at this customer and their recent consumption patterns, does this perhaps change your opinion about how they might use Datadog longer term, maybe new use cases above and beyond what they're using today that could make that growth opportunity really interesting for this customer?

了解。其實，David，如果我可以再追問一個問題。關於你們最大的客戶，你用了「大幅高於其最低承諾」這個說法，我覺得很有意思。我知道你們對第四季只假設最低承諾，但從理念上來看，觀察這位客戶近期的使用量模式，這是否可能改變你對他們長期使用 Datadog 的看法？例如，是否可能出現超出他們目前使用範圍的新使用情境，讓這位客戶的成長機會變得非常有吸引力？

I think that's true for any customer that there are many different use cases. For that, in particular, we -- as we've said all along for many quarters, we don't control. We have the contract, the renewal, and we're not in the business of projecting that. But it is true from -- across our customer base that there are many use cases in a company, whether it be division, end user, et cetera. Oli, do you want to --

我認為對任何客戶而言都確實如此，因為有許多不同的使用情境。就那位客戶而言——正如我們過去好幾季一直說的——我們無法控制。我們有合約、有續約，但我們不做那方面的預測。但就我們整體客戶群而言確實如此：在一家公司裡有很多使用情境，不論是部門、終端使用者等等。Oli，你要不要——

Karl Keirstead, UBS.

Karl Keirstead，瑞銀。

Okay. Great. Maybe, Olivier, for you. You mentioned in your comments that a lot of customers are grappling with higher token costs, higher AI spend and are trying to get it under control. I'm just wondering, based on all your customer interactions, how enterprise spending behavior is changing in light of the fact that they're trying to get their token costs under control.

好的。很好。也許這題給你，Olivier。你在評論中提到，很多客戶正面臨更高的 token 成本、更高的 AI 支出，並試圖把它控制住。我想請教，根據你與客戶的互動，在他們試圖控制 token 成本的背景下，企業支出行為正在如何改變？

And to the extent that any of the customers that early renewed to try to get price discounts were not AI natives. They were traditional enterprises. Do you think that might be because they're trying to grapple with budget pressure?

另外，就那些為了爭取價格折扣而提早續約的客戶而言，如果其中有些並非 AI 原生公司，而是傳統企業。你是否認為，這可能是因為他們正在承受預算壓力、試圖應對？

Yes. So I think, look, this is a very widespread problem. I think what we've seen is there's been an explosion of adoption of AI, in particular, coding agents. And that was not planned largely. A year ago, nobody had the amount they spend in their budget on AI. So they really had to try and figure out what that works and what part of that they actually need and what they get from that. So there's a strong need for tooling, which is something we provide.

是的。所以我認為，你看，這是一個非常普遍的問題。我們看到的是 AI 的採用出現爆發式成長，特別是程式碼代理（coding agents）。而這在很大程度上並非事先規劃的。一年前，沒有人在預算中編列他們如今在 AI 上的支出金額。因此他們必須真正去釐清：哪些有效、哪些部分他們實際需要、以及能從中獲得什麼。所以對工具的需求非常強烈，而這正是我們所提供的。

It's a great opportunity for us, and we see great growth and adoption of our Cloud Cost Management products on that or agent console that is our products that -- now in terms of the early renewals that we see, they're not directly related to that. I think they come from large customers that are either AI labs or that are focused on that are tech companies in general, I would say. And that doesn't really have anything to do with their own something has more to do with their own growth as a business.

這對我們是很好的機會，我們也看到我們的雲端成本管理（Cloud Cost Management）產品在這方面有很強的成長與採用；以及作為我們產品的 agent console——現在至於我們看到的提早續約，並不是直接與此相關。我認為那些更多是來自大型客戶：要嘛是 AI 實驗室，要嘛是聚焦於此的科技公司，整體而言我會這麼說。而這其實與他們自身的某些因素無關，更多是與他們作為一個企業的自身成長有關。

Fatima Boolani, Citi.

Fatima Boolani，花旗。

This is Joel on for Fatima. I'll maybe just ask two real quick. So firstly, just on margins and operating and gross income leverage, maybe for you, David. Strong results this quarter, at least relative to our expectations, but we do know there's also certain areas that you are investing fairly heavily in.

我是 Joel，代替 Fatima 提問。我可能快速問兩個。首先，關於利潤率以及營業與毛利的槓桿，可能給你，David。本季結果很強，至少相對於我們的預期而言，但我們也知道你們在某些領域投入相當多。

So just curious what some of the drivers of the performance were specifically this quarter? And then what your updated or what your latest investment posture is? And then just the quick second question, I'll just throw it in now is any data points on Bits AI penetration specifically in the customer base?

所以想請教，本季表現的驅動因素具體是哪些？以及你們更新後或最新的投資姿態是什麼？第二個小問題我也先一起問：在客戶群中，Bits AI 的滲透率有沒有任何數據點可以分享？

Do you want to go on margins first?

你想先談利潤率嗎？

I'll go margins first, yes. It's very similar to what we said. We are investing aggressively in the go-to-market, we continue to have white space uncovered, both from a geographical or an end market, for instance, Fed. And we are continuing to be successful in scaling our quota capacity and you see that in the growth. In addition, we're continuing to invest in R&D.

我先談利潤率，是的。這和我們之前說的非常類似。我們在 go-to-market 上積極投資；我們仍有尚未覆蓋的白地，不論是地理區域或終端市場，例如聯邦（Fed）。我們也持續成功擴大配額產能（quota capacity），你可以從成長中看到。此外，我們也持續投資研發。

In this case, it's a combination of AI, of investments in a combination of AI and people. We see lots of opportunities. I think Oli will now go talk about the products, but we continue to see a lot of opportunity for us to get market share, get platform penetration and increase our feet on the street or on a global basis.

在這裡，這是 AI 投資與人員投入的組合。我們看到很多機會。我想接下來 Oli 會談產品，但我們仍看到許多機會，讓我們取得市佔、提升平台滲透率，並在全球範圍增加我們的一線人力（feet on the street）。

Yes. And on this, I think we've mentioned on the call, we see amazing adoption of it, like this is -- when you look at the number of individual customers or users that are using it, this has been the strongest launch we've ever had as a product. So we feel very good about it. We've also revamped the way we package it over the past quarter. So we used to charge specifically for investigations.

是的。關於這點，我想我們在電話會議上提到過，我們看到它的採用非常驚人——當你看使用它的個別客戶或使用者數量時，這是我們作為產品推出以來最強的一次上市表現。所以我們對此感覺非常好。我們也在過去一季重新調整了它的打包方式。我們過去是針對調查（investigations）單獨收費。

Now we have a model of AI credits that cover a whole different set of use cases that involve bit as our agent and we've seen great results thanks to that. So it's super encouraging. I would say it's still early because there's still so much that we were building into this. So many things our customers want us to do for them, so many opportunities to automate their workflows. But we are -- we feel very, very good about the current status.

現在我們採用 AI 點數（AI credits）的模式，涵蓋一整套不同的使用情境，這些情境涉及 Bit 作為我們的代理（agent），而我們也因此看到很好的成果。所以這非常令人鼓舞。我會說現在仍然偏早期，因為我們還在把很多功能建進去。客戶希望我們為他們做的事情還有很多，有很多機會去自動化他們的工作流程。但我們——我們對目前的狀態感到非常、非常滿意。

Let me just circle back on one thing. I think I neglected to go over gross margin, as you mentioned. I think it's very similar to what we said before that through a combination of the penetration of our platform as well as our engineering, we have been plus or minus in this 80% range. I think you see in this quarter, -- we ended up around 81%. And so this is common practice for us. We invest, optimize, do engineering projects and what was communicating gross margin indicates stability there, which we've evidenced in previous quarters.

我再回到一點。我想我剛才忽略了你提到的毛利率。我認為這與我們之前說的非常類似：透過我們平台滲透率的提升以及工程面的努力，我們的毛利率一直大致維持在 80% 左右、上下小幅波動。我想你在本季看到——我們最後大約是 81%。所以這對我們來說是常態做法。我們投資、優化、做工程專案；而我們所溝通的毛利率顯示其穩定性，這在前幾季也已經證明。

Kirk Materne, Evercore ISI.

Kirk Materne，Evercore ISI。

I guess, David, on -- or Oli, on this native AI cohort that's renewing a little bit early. Obviously, they're coming back because they're spending so much with you all and obviously want to get better economics. I'm just curious, what's the sort of attach rate on some of those newer renewals?

我想問一下，David——或是 Oli，關於這個原生 AI 客群（cohort）有點提早續約的情況。很明顯，他們會回來是因為在你們這裡花了很多錢，也顯然想要更好的經濟效益。我只是好奇，這些較新的續約案在一些新產品上的附加採用率（attach rate）大概是多少？

When they come back and renew early, do you still have time to sort of cross-sell them some of the other products? I guess what are you seeing in that dynamic? I realize it creates some near-term headwinds from a revenue perspective, but it would also, I think, open up the opportunity to have a discussion about you expanding their usage across multiple products.

當他們回來提早續約時，你們是否仍有時間向他們交叉銷售其他產品？你們在這種互動動態上看到的是什麼？我知道從營收角度看，短期會帶來一些逆風，但我也認為這同時會打開機會，讓你們討論把他們的使用擴展到多個產品上。

Yes. I mean, look, if you look at these cohorts, and if you compare them in particular to the native cohort we might have had like five or six years ago, like these are adopting many more products. So we see much broader product adoption and platform adoption with those customers than we saw five, six years ago. So that's definitely something that is happening. Some of that is happening at renewal time, sometimes just happening organically as new needs emerge and all we spread to more teams inside those companies.

是的。我的意思是，你看這些客群，如果特別拿它們跟我們大概五、六年前的原生客群相比，這些客戶採用的產品數量多得多。所以我們看到這些客戶的產品採用與平台採用都更廣泛，遠超過五、六年前的情況。所以這確實正在發生。其中一部分是在續約時發生，有時則是隨著新需求出現而自然發生，然後我們擴散到那些公司內部更多團隊。

But I think it's important to clarify, these are renewals, but they are at higher levels than their previous commit, which is how they're getting the volume discounts. But we are renewing these customers at higher commits.

但我覺得需要澄清的是，這些是續約，但續約的承諾等級（commit）高於他們先前的承諾，這也是他們能拿到量大折扣的方式。但我們確實是以更高的承諾額度在續約這些客戶。

Okay. And David, I guess just for you, I mean, this would seem this cohort coming up for renewal earlier is all good over the long term. I'd imagine just visibility is a little bit harder from a sequential perspective. Is that sort of -- to Oli's comments on the long term, I'd imagine it's all good from a longer-term perspective, but it just creates a little more volatility for you on a sort of quarter out guidance perspective. Is that fair?

了解。那 David，我想再問一下，這批客群提早進入續約期，從長期來看似乎都是好事。我猜只是從季度環比的角度，可見度會比較難一些。就像 Oli 對長期的評論一樣，我想長期看都是好事，但它會讓你們在以一季為單位的指引上出現更多波動。這樣理解公平嗎？

I would say it has a small amount of variation in the timing of the revenue path of these customers. The sequential here was really three things. It was really, one, that factor. Two, I think we mentioned the largest customer in the guide, which we're not predicting what they're going to do. And three is we know about the small amount of seasonality. So it's one of the factors. We try to get a good sense of that. But of course, as our RPO and as our contract, as we extend, it does create some volatility around the edges in the sequential nature.

我會說，這會讓這些客戶的營收路徑在時間點上出現小幅變動。這次的環比主要是三個因素。第一，就是那個因素。第二，我想我們在指引中提到最大客戶，我們並不預測他們會怎麼做。第三，我們知道有少量的季節性。所以這是其中一個因素。我們會努力掌握一個合理的判斷。但當然，隨著我們的 RPO 以及合約期限拉長，確實會在環比的邊際上帶來一些波動。

But again, it's all good news. Look, we use model and when we deal with growth customers, these customers themselves don't know exactly how they're going to grow over the next few months and what part of their business and how their consumption is going to change. So we happily work with them and make sure that we support their growth and we do that track.

但再說一次，這都是好消息。你看，我們使用模型；而當我們面對成長型客戶時，這些客戶自己也不完全知道接下來幾個月會怎麼成長、會在哪一部分業務成長，以及他們的用量會如何變化。所以我們很樂意與他們合作，確保我們支持他們的成長，並且沿著那條軌道前進。

Beyond this time we think about it in difficult terms, when we prefer for earnings call, we have to figure out, okay, so what part can we communicate, what part do we know, part do we not know. But overall, these are all great news, great customers, great growth, great adoption, amazing trends that are underpinning the industry as a whole. So we feel very good about it.

除此之外，當我們要在財報電話會議上準備時，會用比較困難的方式去思考：好，哪些部分我們可以溝通、哪些部分我們知道、哪些部分我們不知道。但整體而言，這些都是非常好的消息：很棒的客戶、很好的成長、很好的採用度，以及支撐整個產業的驚人趨勢。所以我們感覺非常好。

Yes. And this is similar to what we -- in the company's existence that you talked about. I mean our motion is to increase the commitment of customers, broaden the platform and extend the contract. So that's what we do all the time. It has some variability, but it's very similar to what we've been doing in the company throughout its history, and it's a good thing.

是的。而且這也類似於你提到的——在公司存在期間我們一直在做的事情。我的意思是，我們的運作方式就是提高客戶的承諾額度、擴大平台覆蓋，並延長合約期限。所以我們一直都在做這件事。它會有一些變動性，但這跟我們公司歷史上一直在做的事情非常相似，而且是好事。

Peter Weed, Bernstein Research.

Peter Weed，Bernstein Research。

You talked about it a little bit, but it's really exciting to hear about the release of the Bits AI Scout and the strong early adoption. Could you help numerate that a little bit? And maybe remind us how the pricing works there? Is it kind of directly monetized? Or does it really drive the adoption or use of Bits AI overall? It would be really exciting to kind of appreciate how broad Bits AI adoption is now and how it might move the needle on overall growth and perhaps become another major pillar for the business.

你們稍微提到了一點，但聽到 Bits AI Scout 的發布以及早期強勁的採用情況，真的很令人振奮。你能否把這個量化一下？也請再提醒我們那邊的定價方式是怎麼運作的？它是屬於直接變現嗎？還是它主要是推動 Bits AI 整體的採用或使用？如果能理解目前 Bits AI 的採用範圍有多廣，以及它可能如何帶動整體成長、甚至成為業務的另一個主要支柱，會非常令人期待。

Yes. So it's actually a bit of both. So look, it largely sits on the -- all of the data we acquire and the way we can process it and all the various products we have. So that's the core. But we also have a model where we charge for AI creative specifically for Bits on top of that. And the model we've chosen is a model where we stay fairly close to inference costs there so that we can pass on the savings as the models become more efficient to our customers, and so we can run many more investigations.

是的。其實兩者都有。你看，它很大程度上是建立在——我們所取得的所有資料、我們處理資料的方式，以及我們擁有的各種產品之上。那是核心。但除此之外，我們也有一個模式，會針對 Bits 的 AI 創作（AI creative）在其上另外收費。而我們選擇的模式是讓價格與推理成本（inference costs）保持相當接近，這樣當模型變得更有效率時，我們就能把節省下來的成本回饋給客戶，也能進行更多的調查（investigations）。

And the reason we've done that is the field is changing so fast, and we're improving the efficiency of those models so fast that it's not very productive to have a fixed price on you do as you expect the volume of things you do to increase by orders of magnitude while the price goes down by orders of magnitude as well.

我們之所以這麼做，是因為這個領域變化非常快，而我們提升模型效率的速度也非常快；因此，如果你預期你做的事情的量會以數量級增加，同時價格也會以數量級下降，那麼採用固定價格其實不太有生產力。

And so we on that model, which works really well. So long term, we expect both to monetize it directly as we do today and to drive broader adoption of the platform and deliver more value to our customers. In terms of what becomes the dominant monetization, it's hard to tell, and we don't really care. Again, we're fully user based. So as long as we get more usage, deliver more value, we'll be fine.

所以我們採用那個模式，而且運作得非常好。長期來看，我們預期一方面會像今天一樣直接變現，另一方面也會推動平台更廣泛的採用，並為客戶帶來更多價值。至於哪一種會成為主導的變現方式，很難說，我們其實也不太在意。再說一次，我們完全是以使用者為基礎（user based）。所以只要我們帶來更多使用、交付更多價值，我們就會沒問題。

Koji Ikeda, Bank of America.

Koji Ikeda，美國銀行（Bank of America）。

I wanted to ask a question on infrastructure monitoring. So you guys disclosed it's now exceeding $2 billion in ARR and growing in the mid-30s. Could you quantify how different that growth rate is from when you last disclosed the scale of this business at $1.5 billion earlier this year? How much of that growth is coming from AI infrastructure? And how sustainable is that growth rate as GPU efficiency improves?

我想問一個關於基礎設施監控（infrastructure monitoring）的问题。你們披露這部分現在 ARR 已超過 20 億美元，且以 30% 多的增速成長。你能否量化一下，這個成長率相較於你們今年稍早上次披露該業務規模為 15 億美元時，有多大的差異？其中有多少成長來自 AI 基礎設施？以及當 GPU 效率提升時，這個成長率有多可持續？

So I mean, look, we've seen an acceleration of the business in general. And not just thanks to AI native, but also thanks to the rest of the customer base. So we mentioned that, and that applies to infrastructure monitoring also.

所以我的意思是，你看，我們整體上確實看到業務加速。而且不只是因為 AI 原生客戶，也包括其他客戶群。我們提到過這點，而這也同樣適用於基礎設施監控。

Today, that growth is not actually related directly to GPUs. GPUs are still largely on -- we see them for our customers on training. We have products there, but we don't monetize those products widely yet. And so for us, that's an opportunity we have in front of us. That's not something that's baked into the current growth and the current numbers.

目前，這個成長其實並不是直接與 GPU 相關。GPU 對我們客戶而言仍主要用在訓練（training）。我們在那裡有產品，但我們還沒有廣泛地把那些產品變現。所以對我們來說，那是一個擺在眼前的機會。那並不是已經反映在目前成長與目前數字裡的東西。

Patrick Colville, Scotiabank.

Patrick Colville，豐業銀行（Scotiabank）。

To me, one of the most interesting points in the prepared remarks is about Datadog training, your own Frontier models. And then Olivier, your comments around behavior is not mass market, but seeing how the market develops. I guess I want to ask you because you're at the forefront of how things are changing. How does this affect Datadog's use of proprietary tokens internally or for customers versus open weight tokens?

對我來說，事先準備的發言中最有意思的一點之一，是關於 Datadog 的訓練、你們自家的 Frontier 模型。接著 Olivier，你提到的觀點是：行為並非面向大眾市場，而是觀察市場如何發展。我想我想問你，因為你正站在變革最前線。這會如何影響 Datadog 在內部或為客戶使用自有 token，相較於使用開放權重模型的 token？

And then I guess, David, if this shift that Olivier is describing picks up steam, what might that mean for Datadog profitability that it nicely increased this quarter, but is there a potential that this might help the bottom line as well?

然後我想，David，如果 Olivier 所描述的這種轉變加速發酵，這對 Datadog 的獲利能力可能意味著什麼？本季獲利能力確實有不錯提升，但是否也可能進一步幫助損益表？

So look, what we see internally and what we see with customers is -- there's a mix of models that are being used. So I mean I can say we include both customers and us into that collective we because the behaviors are fairly similar. We are big consumers of the top labs, top models, but we also consume open web models, a growing amount of open web models, and we train our own post train on. And I think it's probably representative of where the industry as a whole is going.

所以你看，我們在內部以及在客戶端看到的是——正在使用的模型是混合的。我的意思是，我可以說我們把客戶和我們自己都包含在這個「我們」的集合裡，因為行為相當類似。我們大量使用頂尖實驗室、頂尖模型，但我們也使用開放網路模型，且開放網路模型的使用量正在增加，我們也會訓練自己的模型並進行後訓練。我認為這大概也代表了整個產業的走向。

We do that because there are certain things you can only get from the top-of-the-line models that are being generally trained by others. And there's a number of things that are good enough and very cost efficient with open web models, you can just pick up and run. And there's finally a number of other things that you can make way more efficient with your own data, your own workflow and your own post training, which is what we've done with some of the models we announced. So I expect that mix to remain about the same.

我們這麼做是因為有些事情你只能從那些通常由他人訓練的最頂級模型中取得。也有不少事情，用開放網路模型就已經夠好且非常具成本效率，你可以直接拿來用並運行。最後，還有一些事情你可以用自己的資料、自己的工作流程以及自己的後訓練做得更有效率，這也是我們在部分已宣布模型上所做的事。所以我預期這種組合大致會維持不變。

In terms of the impact on margin, look, at the end of the day, you get some control on what you spend your money on. If it's very proprietary to you and you can deliver superior economy, you should absolutely do so. If not, like if it's very undifferentiated and applies broadly in some parts of your business that are the same to millions of other businesses, then it's fine probably to use off-the-shelf models and keep consuming those.

就對毛利率的影響而言，你看，歸根結底，你能對花錢的項目有一些掌控。如果某件事對你而言非常專有，且你能提供更優的經濟性，那你就絕對應該這麼做。如果不是——例如它非常不具差異化，而且廣泛適用於你業務中的某些部分，而那些部分與數以百萬計其他企業都相同——那麼使用現成模型並持續採用它們大概就可以。

Alex Zukin, Wolfe Research.

Alex Zukin，Wolfe Research。

Oli, maybe a quick one for you and David, a follow-up. Just to what extent are you seeing the demand for agent observability from both AI natives and the core driving demand for offerings like your Traces product? And what is the -- like what has changed here longer term just given the kind of elevated adoption levels? And maybe also any comments on cybersecurity and the plans there?

Oli，可能先給你和 David 一個簡短問題，作為追問。你們在多大程度上看到來自 AI 原生公司以及核心客群對「代理可觀測性」的需求，進而帶動像你們 Traces 產品這類產品的需求？而且從長期來看——考量到採用水準提高——這裡有什麼變化？另外也想請你們談談資安以及相關規劃？

Yes. So we see growing demand for agent observability. And what -- so there was always interesting demand from the AI, et cetera. But what's most interesting here is the non-AI natives are consuming more and more. And because they're getting deeper into AI, we see real demand, real volumes and really exciting -- really exciting pipeline for products actually.

是的。所以我們看到對代理可觀測性的需求正在成長。而且——所以一直以來來自 AI 等領域都有有趣的需求。但這裡最有意思的是，非 AI 原生的客戶正在越用越多。而因為他們更深入導入 AI，我們看到真實的需求、真實的量，以及非常令人振奮——非常令人振奮的產品管線。

So we think that this is crossing the chasm, so to speak, and that's exciting for this product, even though we didn't mention any hard numbers today on that. On cybersecurity, look, there's so many new opportunities with AI. We're very happy to see that we can land the full platform with large customers. We mentioned that for a defense agency.

所以我們認為這正在跨越鴻溝（crossing the chasm），可以這麼說，這對這個產品很令人興奮，儘管我們今天沒有提到任何硬數字。至於資安，你看，AI 帶來了非常多新的機會。我們很高興看到，我們能在大型客戶中落地完整平台。我們提到過一家國防機構的案例。

We mentioned that also a couple of other wins when we landed not just observability, but our security platform at the same time. So we feel good about the market fit there, and we feel good about the way the whole retooling and rebuild that is taking place with AI is playing in our favor.

我們也提到另外幾個案子，我們不只是落地可觀測性，同時也落地了我們的安全平台。所以我們對那裡的產品市場契合度感到樂觀，也對整體因 AI 而進行的重新工具化與重建，正朝對我們有利的方向發展感到樂觀。

Perfect. And then, David, maybe for you, just a clarification. If we think about -- first of all, it's great to hear about the renewal with your largest customer. And I think we totally understand the guiding for the minimum for Q4. Any pearls of wisdom on how to think about modeling that for '27?

完美。然後 David，可能請你再澄清一下。如果我們思考——首先，很高興聽到你們與最大客戶完成續約。我想我們完全理解你們對 Q4 最低值的指引。對於 2027 年的建模，有沒有什麼建議或思路可以分享？

Because presumably, that's a 1-year contract. And should -- is that something we should think about kind of maintaining at some level? Is that something that you guys are going to take out of the model? And then if I think about how you're guiding with respect to some of those other early renewals for the AI natives that get renewed, are those also at the minimum commitment level? Are those slightly above? Just any help on those two items, I think, would be helpful for --

因為推測那是一份 1 年期合約。那我們是否應該把它視為在某個水準上維持？還是你們會把它從模型中剔除？另外，如果我再看你們對其他一些 AI 原生客戶提前續約的指引，那些續約也是以最低承諾水準計入嗎？還是略高一些？我想在這兩點上給一些協助會很有幫助——

Yes. Reminding everybody -- yes, sorry. Yes, on the -- we'll give guidance for 2027 on our year-end call. I think we've said in the last couple of quarters that we're guiding at whatever the contractual minimums, and we've repeated that this quarter. We'll give some more flavor on that when we do '27.

是的。提醒大家——是的，抱歉。是的，關於——我們會在年終電話會議上提供 2027 年的指引。我想我們在過去幾季已經說過，我們的指引是以合約最低承諾（contractual minimums）為基礎，而我們本季也再次重申。等到我們談 2027 年時，會再提供更多說明。

And in terms of the rest of the business, what we've been -- what we do is, and we've said this over the many years, is we take the growth rate of those customers and we discount that. So that would be how we apply it across the customer base in taking the most recent organic growth and then discount it. And we would certainly be doing that for both the early renewal customers and our broader customer base.

至於其餘業務，我們一直——我們的做法是，而且多年來我們也一直這麼說：我們會取那些客戶的成長率，然後對其做折扣調整。所以這就是我們如何在客戶群中套用：取最近的自然成長（organic growth），再加以折扣。而我們當然也會對提前續約的客戶以及更廣泛的客戶群都這麼做。

All right. And I think that's it for our questions today. So -- thank you all for listening. I also want to thank everybody at Datadog for a great Q3. I know we are right in the middle of an even greater Q4.

好的。我想我們今天的提問就到這裡。所以——感謝各位收聽。我也想感謝 Datadog 的每一位同仁帶來出色的第三季。我知道我們正處在更精彩的第四季正中間。

All very, very busy. We're meeting -- I'm going to meet a lot of our customers at the largest US conference in a few weeks. And we hope to have many more exciting product announcement to share soon. So thank you very much all, and see you next quarter.

大家都非常、非常忙。幾週後我會在美國最大的一場會議上——我將會見到很多我們的客戶。我們也希望很快能分享更多令人振奮的產品公告。非常感謝各位，我們下季見。

Thank you for your participation in today's conference. This does conclude the program. You may now disconnect. Everyone, have a great day.

感謝各位參與今天的電話會議。本次議程到此結束。您現在可以中斷連線。祝各位有美好的一天。