數據狗 (DDOG) 2026 Q2 法說會逐字稿

Good day, and thank you for standing by. Welcome to the Q2 2026 Datadog earnings conference call. (Operator Instructions) Please be advised that today's conference is being recorded. I would now like to hand the conference over to your first speaker today, Yuka Broderick, Senior Vice President of Investor Relations. Please go ahead.

大家好，感謝您稍候等候。歡迎參加 Datadog 2026 年第二季財報電話會議。(接線員指示) 請注意，今天的會議將被錄音。現在我想把會議交給今天的第一位講者，投資人關係資深副總裁 Yuka Broderick。請開始。

Thank you, Lauren. Good morning, and thank you for joining us to review Datadog's second-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 CFO.

謝謝你，Lauren。各位早安，感謝各位與我們一同回顧 Datadog 2026 年第二季財務結果；我們已於今早發布的新聞稿中對外公布。今天與我一同參與電話會議的有 Datadog 共同創辦人暨執行長 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 third quarter and the fiscal year 2026 and related notes and assumptions, our product capabilities, and our ability to capitalize on market opportunities. The words anticipate, believe, continue, estimate, expect, intend, will, and similar expressions are intended to identify forward-looking statements or similar indications of future expectations. These statements reflect our views today and are subject to a variety of risks and uncertainties that could cause actual results to differ materially.

在本次電話會議中，我們將發表前瞻性陳述，包括與我們未來財務表現、對 2026 年第三季及 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。「預期」、「相信」、「持續」、「估計」、「預計」、「打算」、「將」以及類似用語，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，可能導致實際結果出現重大差異。

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 March 31, 2026. Additional information will be made available on our upcoming Form 10-Q for the fiscal quarter ending June 30, 2026, and other filings for the SEC. This information is also available on the investor relations section of our website along with the 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.

關於可能影響我們實際結果之重大風險及其他重要因素的討論，請參閱我們截至 2026 年 3 月 31 日止季度的 Form 10-Q。更多資訊將於我們即將提交、截至 2026 年 6 月 30 日止會計季度的 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。上述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，已列於我們財報新聞稿的表格中。該新聞稿可於 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, Yuka, and thank you all for joining us to go over Q2 results. Let me begin with this quarter's business drivers. Our revenue growth in Q2 has accelerated across our customer base. On one hand, our AI-native customer cohort continued to grow and diversified, both in the number of customers we serve and the scale of those customers. But on the other hand, and as a great illustration of the breadth of trends across our business, revenue growth for our non-AI customers also accelerated again this quarter to the high 20% year over year, up from the mid-20s% last quarter, and 18% in the year-ago quarter.

謝謝你，Yuka，也感謝各位加入我們，一同回顧第二季的業績。我先從本季的業務驅動因素談起。我們第二季的營收成長在整體客戶基礎上有所加速。一方面，我們的 AI 原生客戶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。但另一方面，也很好地展現了我們業務趨勢的廣度：本季非 AI 客戶的營收成長也再次加速，年增率達到接近 30% 的高 20% 區間，高於上季的中 20% 區間，也高於去年同期的 18%。

Overall, we continue to see healthy trends in customer demand. Our broad base of customers from the most nimble start-ups to the largest and most established enterprises are all adopting AI. We think this is accelerating their usage of cloud and modern technologies as well as their usage of the Datadog platform to observe, secure, and act on their cloud and AI workloads.

整體而言，我們持續看到客戶需求呈現健康的趨勢。我們廣泛的客戶群，從最靈活的新創公司到最大、最成熟的企業，都在採用 AI。我們認為，這正在加速他們對雲端與現代化技術的使用，同時也加速他們使用 Datadog 平台來觀測、保護並對其雲端與 AI 工作負載採取行動。

Regarding our Q2 financial performance and key metrics, revenue was $1.12 billion, an increase of 36% year over year and above the high end of our guidance range. We ended Q2 with about 33,400 customers, up from about 31,400 a year ago. We also ended with about 4,720 customers with an ARR of $100,000 or more, up from about 3,850 a year ago. These customers generated about 91% of our ARR. And we generated free cash flow of $279 million with a free cash flow margin of 25%.

關於我們第二季的財務表現與關鍵指標，營收為 11.2 億美元，年增 36%，並高於我們指引區間的上緣。第二季結束時，我們約有 33,400 名客戶，高於一年前的約 31,400 名。我們也有約 4,720 名年經常性收入（ARR）達 10 萬美元或以上的客戶，高於一年前的約 3,850 名。這些客戶貢獻了我們約 91% 的 ARR。我們產生了 2.79 億美元的自由現金流，自由現金流利潤率為 25%。

Turning to product adoption. Our platform strategy continues to resonate in the market. For example, 58% of our customers now use four or more products, up from 52% a year ago. 37% of our customers used six or more products, up from 29% a year ago and 13% of our customers used 10 or more products, up from 7% a year ago. So we're landing more customers and delivering value across more products, and our products are broadly delivering strong growth in usage and ARR.

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。例如，目前有 58% 的客戶使用四個或以上的產品，高於一年前的 52%。有 37% 的客戶使用六個或以上的產品，高於一年前的 29%；另有 13% 的客戶使用 10 個或以上的產品，高於一年前的 7%。因此，我們不僅獲得更多客戶，也在更多產品上交付價值；我們的產品整體在使用量與 ARR 方面皆展現強勁成長。

As an example. RUM, or Real User Monitoring, now exceeds $200 million in ARR and accelerated at its scale to over 50% growth year over year. Our customers are sending more user sessions and using RUM in conjunction with our newer product analytics to optimize their business outcomes.

舉例來說。RUM（Real User Monitoring，真實使用者監控）的 ARR 現已超過 2 億美元，並在此規模下仍加速至年增超過 50%。我們的客戶正在傳送更多使用者工作階段，並將 RUM 與我們較新的產品分析功能結合使用，以優化其業務成果。

Moving on to R&D, we held our DASH User Conference in June where we announced over 100 exciting new products and features for our users. So let's go through some of the announcements. First, we expanded Bits AI to accelerate and automate the DevOps loop. This is the loop that goes from detection to investigation to remediation that engineers go through each time something breaks.

接著談研發方面，我們於 6 月舉辦了 DASH 使用者大會，並為使用者宣布了超過 100 項令人振奮的新產品與新功能。接下來我們快速回顧其中幾項公告。第一，我們擴展了 Bits AI，以加速並自動化 DevOps 迴圈。這個迴圈指的是每次系統出問題時，工程師從偵測到調查再到修復所經歷的流程。

At DASH, we announced a lot of new Bits capabilities for the DevOps loop. Bits can now create and maintain monitors, identify root causes within minutes of a negative signal, recommend and implement fixes, follow guardrails to add safety and controls, continuously learn, and improve from prior incidents, and detect symptomatic behaviors early to repair infrastructure issues before they escalate.

在 DASH 上，我們宣布了許多針對 DevOps 迴圈的全新 Bits 功能。Bits 現在可以建立並維護監控項目、在出現負面訊號後於數分鐘內找出根因、建議並實作修復措施、遵循護欄以增加安全性與控管、持續學習並從過往事件中改進，以及及早偵測症狀性行為，在問題升級前修復基礎設施問題。

Second, we announced Bits AI products to address the development loop. This is the loop that goes from coding to delivery to evaluation that developers navigate to get code to production. For this loop, Bits Release now acts as an AI-release validation agent. Analyzing the impact of code changes, running end-to-end checks, and verifying production rollouts. Bits Code generates code fixes grounding every fix in reproduction behavior and Bits Testing also automates synthetic test generation and maintenance.

第二，我們宣布了用於解決開發迴圈的 Bits AI 產品。這個迴圈是開發者將程式碼推向正式環境時，從撰寫到交付再到評估所走的流程。針對此迴圈，Bits Release 現在可作為 AI 發佈驗證代理。它能分析程式碼變更的影響、執行端到端檢查，並驗證正式環境的部署推送。Bits Code 會產生程式碼修復，並以可重現的行為作為每次修復的依據；Bits Testing 也能自動化合成測試的產生與維護。

Third, we expanded Datadog for AI on products that observe, secure, and optimize the AI stack from end-to-end. Data observability enables companies to trust the data being used by AI with Lineage quality monitoring and jobs monitoring. Based data analysis uses a rich data context to accurately answer business questions and Agent Console provides visibility to AI agent usage, cost, and effectiveness.

第三，我們擴展了 Datadog for AI，涵蓋可端到端觀測、保護並最佳化 AI 技術堆疊的產品。資料可觀測性透過血緣（Lineage）品質監控與作業（jobs）監控，使企業能信任 AI 所使用的資料。Based 資料分析利用豐富的資料情境來準確回答業務問題，而 Agent Console 則提供 AI 代理的使用情況、成本與成效的可視性。

In Agent Observability, our patterns capability automatically clusters user interactions into behavior groups to identify quality or cost issues, and Bits Evals handles the repetitive parts of the agent development loop in order to improve the outcomes of agents.

在 Agent Observability 中，我們的 patterns 功能會自動將使用者互動分群為行為群組，以識別品質或成本問題；而 Bits Evals 則處理代理開發迴圈中重複性的部分，以改善代理的成果。

Fourth, we are broadening our platform to ingest, correlate, analyze, and act on more data, whether on-prem or in the cloud. In network monitoring, we launched Network Path and Network Configuration Management to trace changes that cause complex network issues. Within database monitoring, Bits Database Optimizer now automatically simulates and evaluates the impact of AI-generated changes in order to optimize flow queries.

第四，我們正在擴展平台，以擷取、關聯、分析並對更多資料採取行動，無論資料是在地端或雲端。在網路監控方面，我們推出了 Network Path 與 Network Configuration Management，用以追蹤導致複雜網路問題的變更。在資料庫監控方面，Bits Database Optimizer 現在可自動模擬並評估 AI 生成變更的影響，以最佳化資料流查詢。

In log management, federating logs enables users to query external data stores, including Databricks and ClickHouse and with Bring Your Own Cloud or BYOC, customers can now use the full Datadog experience on logs that are kept within their infrastructure. And we've also announced that we are bringing BYOC to metrics and traces as well.

在日誌管理方面，日誌聯邦（federating logs）讓使用者能查詢外部資料儲存體，包括 Databricks 與 ClickHouse；而透過 Bring Your Own Cloud（BYOC），客戶現在可以在保留於其自有基礎設施內的日誌上，使用完整的 Datadog 體驗。我們也宣布將把 BYOC 延伸至指標（metrics）與追蹤（traces）。

In the digital experience space, journey monitoring automatically gives a single shared view for every critical user flow. And for custom metrics data, we introduced infinite cardinality metrics, which allow our users to answer arbitrarily complex questions as they generate larger amounts of data with AI agents without incurring any extra costs.

在數位體驗領域，journey monitoring 會針對每一個關鍵使用者流程，自動提供單一且共享的視圖。而針對自訂指標資料，我們推出了無限基數（infinite cardinality）指標，讓使用者在 AI 代理產生更大量資料時，仍能回答任意複雜的問題，且不會產生任何額外成本。

Finally. We launched a number of innovations to secure the AI stack and design against a new class of AI-powered attacks. AI Guard Agent Discovery finds and maps every known and unknown custom agent so security teams can see what is protected and what is not.

最後。我們推出多項創新，以保護 AI 技術堆疊並在設計上防範一類全新的 AI 驅動攻擊。AI Guard Agent Discovery 會找出並繪製所有已知與未知的自訂代理，讓資安團隊能看見哪些受到保護、哪些尚未受到保護。

AI Guard for custom agents provides runtime protections to block attacks that can only be detected with real-time observability data. AI Guard for coding agents applies the same deep observability to block malicious skills and packages in code.

AI Guard for custom agents 提供執行期防護，以阻擋只能透過即時可觀測性資料才能偵測到的攻擊。AI Guard for coding agents 則運用同樣的深度可觀測性，阻擋程式碼中的惡意技能與套件。

And we also announced runtime prioritization engine to cut vulnerability noise by over 95%. And finally, we expanded Bit Security Analyst to run on known Datadog SIEMs so customers can benefit from the smarts and the learnings of our pro dataset regardless of which SIEM they deploy.

此外，我們也宣布推出執行期優先排序引擎，可將弱點雜訊降低超過 95%。最後，我們擴展 Bit Security Analyst，使其可在既有的 Datadog SIEM 上運行，讓客戶無論部署哪一種 SIEM，都能受益於我們專業資料集的智慧與學習成果。

As we continue to innovate, we are being rightfully recognized by independent research. We are pleased to see that for the sixth year in a row, Datadog has been named a leader in the 2026 Gartner Magic Quadrant for Observability Platforms.

隨著我們持續創新，我們也理所當然地獲得獨立研究機構的肯定。我們很高興看到，Datadog 連續第六年在 2026 年 Gartner《Magic Quadrant》可觀測性平台（Observability Platforms）中被評為領導者。

Let's move on to sales and marketing and look at a few of the deals our GTM teams have closed in what has been a very strong quarter. First, we landed a six-digit annualized deal with a Fortune 10 company. This company is expanding its e-commerce business, and they plan to use Datadog Log Management alongside 10 other Datadog products to improve customer experience and business outcomes. This wins validates our expertise go-to-market approach to focus on the world's largest companies and win opportunities in the most complex environment.

接下來談談銷售與行銷，看看我們 GTM 團隊在這個表現非常強勁的季度中完成的幾筆交易。首先，我們與一家《財富》前 10 大企業拿下一筆年化六位數的交易。該公司正在擴展其電子商務業務，並計畫在使用 Datadog Log Management 的同時，搭配另外 10 項 Datadog 產品，以提升客戶體驗與業務成果。這項勝利驗證了我們以專業能力為導向的 GTM 策略：聚焦全球最大型企業，並在最複雜的環境中贏得機會。

Next, we landed seven-figure analyze deals with two NeoLabs. These AI labs are rapidly scaling their AI model training workloads and preparing for major product launches. By deploying observability using Datadog, they gain visibility across their training infrastructure and GPU fleets and can iterate faster on their AI models. They are also using Bits AI to rapidly build monitors, dashboards, and alerts for deep observability context.

接著，我們與兩家 NeoLabs 拿下七位數的 Analyze 交易。這些 AI 實驗室正快速擴張其 AI 模型訓練工作負載，並為重大產品發表做準備。透過部署 Datadog 的可觀測性能力，他們能掌握訓練基礎設施與 GPU 叢集的全貌，並更快迭代其 AI 模型。他們也使用 Bits AI 來快速建立監控項、儀表板與警示，以取得更深層的可觀測性脈絡。

Next, we landed a seven-figure annualized deal with a South American bank. The bank's fragmented legacy-monitoring stack and manual triaging caused significant application downtime that was often called in by customers. By consolidating into Datadog with 11 products, this customer enables visibility from their mainframe all the way to their microservices and has already reduced mean time to resolution on live production incidents. They are adopting Cloud SIEM and data security. And evaluating other Datadog security products to improve their security posture.

接著，我們與一家南美銀行簽下年化七位數的交易。該銀行分散且老舊的監控技術堆疊，以及仰賴人工分流與判讀，導致應用程式嚴重停機，且往往是由客戶回報才發現。透過整合 11 項產品到 Datadog，該客戶得以從大型主機一路到微服務都具備可視性，並已降低線上正式環境事件的平均修復時間（MTTR）。他們正在採用 Cloud SIEM 與資料安全。並評估其他 Datadog 資安產品，以提升其安全態勢。

Next, we signed a seven-figure annualized expansion for an eight-figure annualized deal with a Fortune 100 health insurance company. These customers' biggest pain point is to deliver great experience to their members throughout their care while protecting PII across dozens of business units.

接著，我們為一家《財富》前 100 大的健康保險公司，在原本年化八位數的交易基礎上，簽下年化七位數的擴充。這些客戶最大的痛點，是在其照護流程中為會員提供優質體驗，同時在數十個事業單位間保護個人可識別資訊（PII）。

Datadog's HIPAA compliance and PII handling in RUM, log management, and cloud SIEM allowed us to differentiate and win over competitive solutions. And Bits AI investigation is already speeding up incident resolution and reducing expensive escalations. Its customer will expand to 19 Datadog products.

Datadog 在 HIPAA 合規，以及於 RUM、Log Management 與 Cloud SIEM 中對 PII 的處理能力，讓我們得以差異化並擊敗競品方案。而 Bits AI investigation 已在加速事件解決並降低昂貴的升級處理。該客戶將擴展至使用 19 項 Datadog 產品。

Next, we signed a multi-year over $30 million TCV deal with one of the world's largest online media companies. This customer chose to standardize on Datadog across its business, displacing four commercial and internal tools. Datadog also proved value beyond core observability with product analytics, CI visibility, data observability, and cloud cost management. This deal includes our largest win to date for Bring You On Cloud, displacing their legacy commercial logging tool at a petabyte scale.

接著，我們與全球最大型的線上媒體公司之一簽下多年期、TCV 超過 3,000 萬美元的交易。該客戶選擇在其整個業務中以 Datadog 作為標準，取代四套商用與內部工具。Datadog 也在核心可觀測性之外，透過產品分析、CI 可視性、資料可觀測性與雲端成本管理，證明了額外價值。這筆交易包含我們迄今為止最大的一筆 Bring You On Cloud 勝利，以 PB 等級規模取代其既有的商用日誌工具。

And finally, we found a nine-figure renewal with a leading AI company. This long-time, very large customer, uses 17 data products to enable unified visibility on production workloads at a very large scale, albeit with a usage reduction starting in Q3, which we considered in our guidance and which David will speak to.

最後，我們與一家領先的 AI 公司完成一筆九位數的續約。這位長期且規模非常大的客戶使用 17 項資料產品，以在極大規模下對正式環境工作負載提供統一可視性；不過其使用量將自第三季開始下降，我們已在財測指引中納入考量，David 也會進一步說明。

Before I turn it over to David for a financial review, let me offer a few words on our longer-term outlook. There is no change to our overall view that digital transformation and cloud migration are long-term secular growth drivers for our business. But we now have an additional growth driver with AI as we help our customers deliver value with this transformative new technology.

在我把時間交給 David 進行財務回顧之前，我想先談幾句我們較長期的展望。我們對於數位轉型與雲端遷移是推動本公司長期結構性成長動能的整體看法沒有改變。但現在，隨著我們協助客戶運用這項具變革性的全新技術創造價值，AI 也成為額外的成長驅動力。

We are tremendously excited about our opportunities in AI. To summarize where we are and where we're going, first, AI is a tailwind for Datadog today. As cloud consumption grows and drives more usage of our platform. As of Q2, over 750 AI customers use Datadog to monitor and improve their tech stacks.

我們對 AI 帶來的機會感到非常振奮。總結我們目前的位置與未來方向：第一，AI 目前對 Datadog 來說是順風。因為雲端消費成長，帶動我們平台的使用量提升。截至第二季，已有超過 750 家 AI 客戶使用 Datadog 來監控並改善其技術堆疊。

When we look at the largest companies driving AI, all 10 of the top 10 AI leaders are Datadog customers. Beyond AI natives, we see AI activity growing across our broader customer base. We're also seeing signs of rapid growth in agentic activity with the number of MCP tool calls quadrupling again quarter-over-quarter and growing more than 22 times when compared to Q4 2025.

當我們觀察推動 AI 的最大型企業時，前 10 大 AI 領導者全部都是 Datadog 客戶。除了 AI 原生公司之外，我們也看到 AI 活動在更廣泛的客戶群中成長。我們也看到代理式（agentic）活動快速成長的跡象：MCP 工具呼叫次數再度較前一季成長四倍，與 2025 年第四季相比更成長超過 22 倍。

Second, we are delivering AI for Datadog to deliver more value and greater platform capability to our customers. This includes our Bits AI products, chat, investigation, detection, code, testing, release, and many, many others.

第二，我們正在交付「AI for Datadog」，為客戶帶來更高價值與更強的平台能力。這包括我們的 Bits AI 產品：聊天、調查、偵測、程式碼、測試、發佈，以及更多更多。

Third, next-gen AI introduces new complexity and observability challenges. We are addressing this with what we call Datadog for AI to observe and secure the AI stack from end to end. This includes GPU monitoring, Agent Observability, Agent Console, Data Observability, AI Guard, and many other products.

第三，新一代 AI 帶來新的複雜度與可觀測性挑戰。我們以「Datadog for AI」來因應，從端到端觀測並保護 AI 技術堆疊。這包括 GPU 監控、Agent Observability、Agent Console、Data Observability、AI Guard，以及許多其他產品。

Finally, our AI research team and our large volume of rich data using critical workflows enable us to conduct groundbreaking research. We have shown some of our work already with the second version of our time series model, Toto, in May. Toto version two was exciting for two reasons. First, we've shown it to be state-of-the-art on key benchmarks, but more importantly, we've demonstrated for the first time true scalability for time series models, allowing us to target the same improvement path language models have followed since 2020.

最後，我們的 AI 研究團隊，以及在關鍵工作流程中累積的大量高品質資料，使我們能進行突破性的研究。我們已在 5 月展示部分成果：時間序列模型 Toto 的第二版。Toto 第二版令人振奮有兩個原因。第一，我們證明它在關鍵基準測試上達到最先進水準；更重要的是，我們首次展示時間序列模型真正的可擴展性，使我們能走上與語言模型自 2020 年以來相同的改進路徑。

So now, beyond Toto, we are working on larger and more ambitious dedicated models, both training models to power Bits AI and bringing other modalities beyond time series data into world models that we think can lead to a step change in capabilities for our customers. And we plan to accelerate these research efforts with the acquisitions of Adaptive ML, which will close in June.

因此，除了 Toto 之外，我們也在開發更大、更具企圖心的專用模型：一方面訓練模型以驅動 Bits AI，另一方面也將時間序列資料以外的其他模態納入我們所稱的世界模型（world models）中；我們認為這能為客戶帶來能力上的躍升。我們也計畫透過收購 Adaptive ML 來加速這些研究工作，該交易將於 6 月完成交割。

Because of all of that, now more than ever, we feel ideally positioned to have customers of every size and every industry, as well as all types of users, whether humans or AI agents, so they can transform, innovate, and drive value through AI in cloud adoption.

基於以上種種，如今比以往任何時候，我們都更有信心自己處於理想位置，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），讓他們在雲端採用的過程中，透過 AI 進行轉型、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財務長 David。

Thanks, Olivier. Our Q2 revenue was $1.12 billion, up 36% year over year. Within that, our 11% quarter-over-quarter revenue growth is the highest since Q2 2022, and our quarter-over-quarter revenue added of $115 million is a record by a significant margin.

謝謝你，Olivier。我們第二季營收為 11.2 億美元，年增 36%。其中，11% 的季增營收成長率是自 2022 年第二季以來最高，而本季營收季增額 1.15 億美元也以顯著差距創下紀錄。

We continued to see robust usage growth from existing customers as well as a strong ramp in our new customers. Revenue growth accelerated with our broad base of customers, excluding AI customers, to the high 20s% year-over year, up from the mid-20s% percent last quarter and 18% in the year-ago quarter. We saw robust growth across our customer base with broad-based strength across customer size, spending bands, and industries.

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。在排除 AI 客戶後，我們廣泛客戶基礎的營收年增率加速至接近 20% 後段（high 20s%），高於上季的 20% 中段（mid-20s%）以及去年同期的 18%。我們在整體客戶群中看到強勁成長，且在不同客戶規模、支出區間與產業別皆呈現廣泛的強勢表現。

Meanwhile, our AI customers continue to grow rapidly and diversify in the quarter. This 750-strong customer group includes a broad range of AI start-ups as it has in the past, but now also includes hyperscalers using Datadog for in-house AI labs. In Q2, this includes 31 customers spending more than $1 million annually, of which eight customers spent more than $10 million annually.

同時，我們的 AI 客戶在本季持續快速成長並更加多元化。這個約 750 家的客戶群如同以往涵蓋廣泛的 AI 新創公司，但現在也包括使用 Datadog 支援其內部 AI 實驗室的超大規模雲端服務商（hyperscalers）。在第二季，其中有 31 家客戶年支出超過 100 萬美元，且其中 8 家客戶年支出超過 1,000 萬美元。

We also achieved strong new logo dollar bookings with particular strength in enterprise where new logo annualized bookings more than doubled from a year ago. And we are seeing new logos ramping faster, contributing more to revenue growth. The portion of our year-over-year revenue growth that relates to new customers was about 30% in Q2, up from 25% in Q1.

我們也在新客戶（new logo）美元訂單（bookings）方面取得強勁表現，尤其在企業客戶領域，新客戶年化訂單較去年同期增加逾一倍。此外，我們看到新客戶放量速度更快，對營收成長的貢獻也更高。第二季，我們年對年營收成長中與新客戶相關的部分約為 30%，高於第一季的 25%。

Geographically, we're performing well in all regions with growth acceleration across the regions. We see particular strength in the Americas as much of the AI activity is occurring in the US as well -- as in addition, we are executing strongly in LatAm.

就地理區域而言，我們在所有地區表現良好，且各區域成長皆呈現加速。我們在美洲地區看到特別強勁的動能，因為許多 AI 活動同樣發生在美國；此外，我們在拉丁美洲（LatAm）的執行也相當出色。

Regarding retention metrics, our trailing 12-month net revenue retention percentage was in the [low-$120], similar to last quarter, and churn remains low with gross revenue retention in the mid- to high-90s%. We believe this metric highlights the mission-critical nature of our platform for our customers.

就留存指標而言，我們過去 12 個月的淨營收留存率落在 120% 出頭（low-$120），與上季相近；流失率仍維持低檔，總營收留存率在 90% 中段至高段（mid- to high-90s%）。我們認為此指標凸顯了我們平台對客戶而言具備關鍵任務（mission-critical）的特性。

Now moving on to our financial results. Billings were $1.18 billion, up 38% year over year. Remaining Performance Obligations, or RPO, was $3.47 billion, up 43% year over year. Current RPO grew about 40% year over year and RPO duration increased year over year. As we previously mentioned, we continue to believe revenue is a better indication of our business trends than billing an RPO.

接著談我們的財務結果。帳單金額（Billings）為 11.8 億美元，年增 38%。剩餘履約義務（Remaining Performance Obligations，RPO）為 34.7 億美元，年增 43%。當期 RPO 年增約 40%，且 RPO 期限年對年拉長。如先前所述，我們仍認為相較於 Billings 與 RPO，營收更能反映我們業務趨勢。

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. Our future gross profit was $892 million for gross margin of 79.6%. This compares to gross margin of 80.2% last quarter and 80.9% in the year-ago quarter.

現在我們來檢視幾項損益表的關鍵結果。除非另有說明，所有指標皆為非 GAAP。我們已在財報新聞稿中提供 GAAP 與非 GAAP 財務數據的調節表。本季毛利為 8.92 億美元，毛利率為 79.6%。相較之下，上季毛利率為 80.2%，去年同期為 80.9%。

As we've discussed in the past, our gross margin varies from quarter to quarter with investments into innovations for our customers offset by efficiency efforts. There's no change in our expectations for gross margin, which has been in the 80% plus or minus range historically. Q2 OpEx grew 26% year over year versus 31% last quarter and 36% in the year ago quarter.

如同我們過去所討論的，我們的毛利率會因季度而異，主要是對客戶創新投入與效率提升措施彼此抵銷所致。我們對毛利率的預期沒有改變，歷史上大致落在 80% 上下的區間。第二季營業費用（OpEx）年增 26%，相較上季為 31%，去年同期為 36%。

We held our DASH Conference, user conference, in June, and as expected, the event cost about $15 million. Q2 operating income was $257 million for a 23% operating margin compared to 22% last quarter and 20% in the year-ago quarter.

我們於 6 月舉辦了 DASH 大會（使用者大會），如預期該活動成本約 1,500 萬美元。第二季營業利益為 2.57 億美元，營業利益率為 23%，相較上季為 22%，去年同期為 20%。

Turning to our balance sheet and cash flow statements, we ended the quarter with $5 billion in cash, cash equivalents, and marketable securities. Cash flow from operations was $316 million in the quarter. After taking into consideration capital expenditures and capitalized software, free cash flow was $279 million for a free cash flow margin of 25%.

接著看資產負債表與現金流量表，本季末我們持有 50 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 3.16 億美元。在考量資本支出與資本化軟體後，自由現金流為 2.79 億美元，自由現金流利潤率為 25%。

And now for our outlook for the third quarter and the fiscal year 2026. Our guidance philosophy overall remains unchanged. As a reminder, we base our guidance on trends observed in recent months and imply conservativism on these growth trends. Regarding our largest customer, we have seen a usage reduction, which is incorporated in our Q3 and full-year 2026 guidance. As Olivier noted, this customer has recently renewed with us.

接下來是我們對第三季與 2026 會計年度的展望。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。就我們最大客戶而言，我們已看到使用量下降，並已納入第三季與 2026 全年指引之中。如 Olivier 所提到，該客戶近期已與我們完成續約。

For the third quarter, we expect our revenue to be in the range of $1.135 billion to $1.145 billion, which represents a 28% to 29% year-over-year growth. Non-GAAP operating income is expected to be in the range of $260 million to $270 million, which implies an operating margin of 23% to 24%. And non-GAAP net income per share is expected to be in the $0.63 to $0.65 per share range based on approximately $378 million weighted average diluted shares outstanding.

就第三季而言，我們預期營收將落在 11.35 億至 11.45 億美元之間，代表年增 28% 至 29%。非 GAAP 營業利益預期為 2.60 億至 2.70 億美元，隱含營業利益率為 23% 至 24%。非 GAAP 每股淨利預期為每股 0.63 至 0.65 美元，係以約 3.78 億股加權平均稀釋流通股數計算。

For the full fiscal year 2026, we expect revenue to be in the range of $4.45 billion to $4.47 billion, which represents a 30% year-over-year growth.

就 2026 會計年度全年而言，我們預期營收將落在 44.5 億至 44.7 億美元之間，代表年增 30%。

Non-GAAP operating income is expected to be in the range of $1.01 billion to $1.03 billion, which implies an operating margin of 23%. And non-GAAP net income per share is expected to be in the range of $2.50 to $2.54 per share, based on approximately $376 million average diluted shares outstanding.

非 GAAP 營業利益預期為 10.1 億至 10.3 億美元，隱含營業利益率為 23%。非 GAAP 每股淨利預期為每股 2.50 至 2.54 美元，係以約 3.76 億股平均稀釋流通股數計算。

And for some additional notes on guidance, we expect net interest and other income for the fiscal year 2026 to be approximately $180 million. We expect cash taxes in 2026 to be about $30 million to $40 million. We continue to employ a 21% non-GAAP tax rate for 2026 and going forward. And finally, we expect CapEx and capitalized software together to be in the 4% to 5% of revenue range in the fiscal 2026.

另外補充幾點指引說明：我們預期 2026 會計年度的淨利息與其他收益約為 1.8 億美元。我們預期 2026 年現金稅負約為 3,000 萬至 4,000 萬美元。我們在 2026 年及往後將持續採用 21% 的非 GAAP 稅率。最後，我們預期 2026 會計年度的資本支出（CapEx）與資本化軟體合計將約占營收的 4% 至 5%。

Now finally, to summarize, we are pleased with our execution in Q2. Our investments in R&D and go-to-market are yielding positive results, and they position us well for continued execution.

最後總結一下，我們對第二季的執行成果感到滿意。我們在研發與市場拓展（go-to-market）上的投資正帶來正面成果，並使我們具備持續良好執行的有利位置。

I want to thank all the Datadog’s worldwide for their efforts. And with that, we'll open the call for questions. Operator, let's begin the Q&A.

我要感謝 Datadog 全球所有同仁的努力。接下來，我們將開放提問。接線員，我們開始問答環節。

(Operator Instructions) Sanjit Singh, Morgan Stanley.

(接線員指示) Sanjit Singh，摩根士丹利。

Thank you for taking any questions, and that's on the acceleration and running of growth again this quarter. David, thank you for giving us a color on some of the guidance assumptions, particularly headed into Q3 with respect to the largest customer. I was wondering if you could share any additional details in terms of the new contract, was it of a similar duration? And in terms of the lower usage, is that a function of the customer getting lower unit price because of making a new commitment, or where is there some turn or down sell that we've missing through, not only for Q3, but for the balance of the year?

感謝讓我提問。我想問的是本季成長再度加速與延續的情況。David，也謝謝你就部分指引假設提供更多說明，特別是進入第三季、關於最大客戶的部分。我想請教你是否能分享新合約的更多細節，例如合約期限是否相近？另外，使用量下降是否是因為客戶在做出新的承諾後取得更低的單位價格所致，或是我們是否忽略了某些轉換或降級（down sell）的因素，不僅影響第三季，也影響今年其餘期間？

Yeah, so maybe I'll take this one. I think we -- overall, we -- as usual, we don't want to comment too much on any specific customer because we also don't really control what's happening with any specific customer.

好的，這題我來回答。我想我們——整體而言，我們——如同往常，我們不希望對任何特定客戶評論太多，因為我們也無法真正掌控任何特定客戶正在發生的事情。

We wanted to be transparent about this on the call. Because we did see a reduction in usage and we to deliberately to fully [deris] the guidance for the rest of the year with respect to that customer. And again, the reason for that is we don't control what's happening to a specific customer, but we do have a grand amount of control of what's happening to everything else in the business, and the business is booming and we don't want that to overshadow basically the acceleration we see pretty much everywhere else in the business.

我們希望在電話會議上對此保持透明。因為我們確實看到使用量有所下降，因此我們刻意就該客戶的情況，將今年剩餘期間的指引完全去風險化。再重申一次，原因在於我們無法控制某個特定客戶正在發生的事情，但我們對於業務中其他所有事情的發展有相當大的掌控度；而且整體業務正蓬勃成長，我們不希望這件事掩蓋我們在幾乎所有其他業務領域所看到的加速成長。

So as we mentioned on the call, we renewed the customer, we -- it's a long-time customer using many of our products, but there's not a lot more we can share.

所以如同我們在電話會議上提到的，我們已與該客戶完成續約——他們是長期客戶，使用我們許多產品，但我們沒有太多其他資訊可以分享。

I think just to get specific on the guidance, we, last quarter, and previous quarter said that we essentially have a level of commit, and we can de-risk our guidance by using that. And then, as in most of our large customers, we have variability relating to the commit, so take that into consideration.

我想就指引再具體一點：我們在上一季以及再前一季都說過，我們基本上有一定程度的承諾量（commit），我們可以利用它來降低指引風險。然後，如同我們多數大型客戶一樣，承諾量之上仍會有使用量的波動性，所以請把這點納入考量。

Yeah. The last thing I will say, because I know it's on people's minds is, if you back out our largest customer from our growth, you get pretty much the same growth rate. As the rest of the business has been accelerating very steadily, actually, we've seen, I think (inaudible) quarters of continuous acceleration from the rest of the business. And we feel very good about what we see in the market.

是的。我最後再補充一點，因為我知道大家都在想：如果把我們最大客戶從成長中扣除，你得到的成長率幾乎是一樣的。由於其餘業務一直非常穩定地加速成長，實際上，我們看到其餘業務已經連續（聽不清）季都在持續加速。而且我們對於市場上看到的狀況感到非常有信心。

Yeah, no, I appreciate the thought. Let's talk about maybe the rest of the business. What we've seen in the past couple of years, sort of AI native sort of leading the charge. It sounds like the enterprises are getting on board with their AI initiatives. And so just in terms of like the enterprise AI app dev cycle, what does that look like for Datadog over the last couple of quarters?

是的，不，我理解你的想法。我們來談談也許是其餘業務。過去幾年我們看到，某種程度上由 AI 原生（AI native）帶頭推動。聽起來企業也開始加入他們的 AI 計畫。所以就企業 AI 應用開發週期而言，過去幾季 Datadog 看到的是什麼樣的情況？

Well, we do see broad adoption, and we see it in two ways. One is we see it manifest itself in just more transformation, more cloud adoption, more workloads, more modernization from customers. And that's what drives the majority of the known AI customer acceleration. And also we mentioned also we see continuous acceleration from customers that (inaudible) for AI and that are not majority AI businesses.

我們確實看到廣泛採用，而且主要以兩種方式呈現。第一種是，它體現在更多的轉型、更多的上雲採用、更多的工作負載、以及客戶更多的現代化。這些因素推動了我們所知的 AI 客戶加速成長的大部分。另外我們也提到，我們也看到那些（聽不清）用於 AI、但本身並非以 AI 為主體業務的客戶，仍在持續加速。

And it has been pretty remarkable, like the acceleration that we have the numbers in the call, but the acceleration since last year has been constant and very significant and it keeps happening as far as we can tell. So it's a very positive trend there. That's the first thing we see.

而且這相當令人驚訝——我們在電話會議中有提到相關數字，但自去年以來的加速一直是持續且非常顯著的，而且就我們所能判斷，這種情況仍在持續發生。所以這是一個非常正向的趨勢。這是我們看到的第一件事。

The same thing we see is very rapid increase in the usage of all of our AI-first surfaces. So that would be the products that measure agents and LLMs. We see an explosion of traffic in terms of the LLM and tool calls we're getting. That would be the amount of calls we're getting to our MCP endpoints. So we see that not explode completely over the past two quarters.

我們看到的另一件事是：所有以 AI 優先（AI-first）的介面/產品使用量都在非常快速地增加。也就是那些用來衡量代理（agents）與 LLM 的產品。我們看到在 LLM 與工具呼叫方面的流量爆發式成長。也就是我們的 MCP 端點所收到的呼叫量。所以我們看到在過去兩季並沒有完全爆炸式成長。

Appreciate the thoughts.

感謝分享看法。

Raimo Lenschow, Barclays.

Barclays 的 Raimo Lenschow。

Perfect, thank you. Could I stay on that AI team, please?

太好了，謝謝。我可以繼續談 AI 這個主題嗎？

At the moment, if you think about the large customers, there's a lot of model training, et cetera, but if you broaden it out, the inference is really becoming the bigger part. Can you talk a little bit about how much more observability is needed? And I'm thinking there, if I do inference, I need to think about vector databases, I need to think guardrails, all of these agents are going to be in containers that need to be monitored, et cetera.

目前如果你看大型客戶，會有很多模型訓練等等，但如果把範圍放大，推論（inference）其實正成為更大的部分。你能談談需要多少更多的可觀測性（observability）嗎？我想到的是，如果我做推論，我需要考慮向量資料庫，需要考慮護欄（guardrails），所有這些代理都會在需要被監控的容器中運行等等。

So what do you see in real life at the moment in terms of if some people do more inference, how much more observability gets triggered by inference? Is that kind of an opportunity that we should probably pay more attention than that one renewal? And I had one follow-up.

所以就你們目前在真實世界看到的情況而言，如果有些人做更多推論，推論會觸發多少額外的可觀測性需求？這是否是一個我們應該比那一次續約更值得關注的機會？我還有一個追問。

Oh, there is opportunity at every layer of the stack in inference. So we do think, at the end of the day, inference will be the dominant workload. Anytime you train, you probably will want to infer more than you train as a rule of thumb.

喔，在推論的技術堆疊每一層都有機會。所以我們確實認為，最終推論會成為主導性的工作負載。經驗法則是：只要你訓練，你通常會想要做的推論次數遠多於訓練次數。

We see opportunity at the low level when it comes to the infrastructure, the GPUs, and the consumption you have there. There's opportunity at the very top end when you measure what the agents are doing and whether you're getting the right outcomes and whether you're getting the right alignment. And there's opportunities at every layer in between, just looking at the LLM itself, just looking at the tool calls and the applications that are being called by the agents, like everything is an opportunity in there.

在基礎設施層面，我們看到機會，包括 GPU 以及你在那裡的消耗。在最上層，我們也看到機會：衡量代理在做什麼、你是否得到正確的結果、以及是否達到正確的對齊（alignment）。而在中間的每一層也都有機會——只看 LLM 本身、只看工具呼叫、以及代理所呼叫的應用程式——基本上每一個環節都是機會。

We see growing adoption from the products we already have there. We mentioned our GPU monitoring product is actually getting quite a bit of usage in a number of new labs and AI-first types of customers.

我們也看到既有產品的採用正在成長。我們提到過，我們的 GPU 監控產品在一些新的實驗室以及 AI-first 類型的客戶中，實際上獲得了相當多的使用。

We're also seeing an explosion of volume in our agent-monitoring product and so we're well positioned there, but we think this market is going to change quite a bit and the preoccupations of customers, they also change over time. So for example, last year, our customers were mostly trying to validate correctness and validate that they were getting some form of autumn that it could then scale up.

我們也看到我們的代理監控產品在量上出現爆發式成長，因此我們在那裡的布局很到位；但我們認為這個市場會有相當大的變化，而客戶關注的重點也會隨時間改變。例如，去年我們的客戶主要在嘗試驗證正確性，並驗證他們是否能取得某種形式的「autumn」，之後再把它擴大規模。

I would say three to six months ago, the focus has moved quite a bit toward cost. Now, customers were spending a lot on AI and they were wondering what to optimize cost. And I think we'll see some variations in the concerns over time as customers get further into the adoption and new products emerged for them.

我會說大約三到六個月前，焦點已相當程度轉向成本。當時客戶在 AI 上花了很多錢，他們在思考如何最佳化成本。而我認為，隨著客戶更深入採用、以及新的產品為他們出現，我們會看到關注點隨時間有所變化。

I just want to add that when you look at what we described as some of our deals in the quarter and you look down our description, you'll see that a number of them have the AI products included. And so that is indication that those large enterprises are using the platform and buying the AI products as well.

我只想補充：當你看我們本季描述的一些交易，並往下看我們的描述，你會看到其中有不少包含 AI 產品。因此，這也顯示那些大型企業正在使用這個平台，並且也在購買 AI 產品。

Okay, perfect. Thank you. And then, David, one for you, it's like, it's obviously you're always in a tough position if you have to guide and there's these large contracts. How did you do it historically? So did you always kind of put in the base level and then what happened or has that approach changed or, I don't envy you on having to do this.

好的，完美。謝謝。接著，David，我有一題給你：顯然當你必須提供指引、又有這些大型合約時，你總是處在很艱難的位置。你們過去歷史上是怎麼做的？所以你們是否總是先放入基礎水位，然後看後續發生什麼？或者這個做法有改變嗎？我不羨慕你必須做這件事。

No, we essentially use, as we've talked about over the many years, we kind of use the inputs of what we see and what we said, I think, in the last quarter or two is that we have certain base levels, as we have a commitment and a usage model and we've factored that in and providing our guidance, so as we said in the prepared remarks. Our methodology for guidance hasn't changed, we've always used those inputs and looked at, the commitment and the usage in doing that.

沒有，我們基本上如同多年來一直談到的，我們會使用我們所看到的輸入因素；而且我想我們在上一季或前兩季說過，我們有某些基礎水位，因為我們有承諾量與使用量模型，我們在提供指引時已把這些納入考量，如同我們在事先準備的講稿中所說。我們的指引方法論沒有改變；我們一直都是用這些輸入，並在過程中觀察承諾量與使用量。

Yeah, I mean, the one thing I say is, in this case, we did chose to fully de-risk our largest customer. And the reason for that is we don't want that to be an overhang on what is otherwise a business that is accelerating and performing extremely well, so we extended that we have, we have the same overall conservatism as we always do when we look at our numbers, but in this case, we also weighted this one a little bit differently.

是的，我的意思是，我要說的一點是：在這個案例中，我們確實選擇將我們最大客戶完全去風險化。原因是我們不希望它成為一個陰影，影響到其他本來就在加速、且表現極佳的業務；因此我們延伸了——我們在看數字時仍維持一如既往的整體保守性，但在這個案例中，我們也對這一項給了稍微不同的權重。

Okay, perfect. That's very clear.

好的，完美。這非常清楚。

Thank you.

謝謝。

Gabriela Borges, GS.

Gabriela Borges，GS。

Hey, good morning. Thank you. I wanted to ask you both about one of our observations at DASH, which is the insurance love the pace of innovation. They talk very positively about the product. The CFOs love to complain a little bit about their Datadog bills. So my question for you is talk to us a little bit about how the CFO level conversations are evolving.

嗨，早安。謝謝。我想向兩位請教我們在 DASH 的一項觀察，也就是保險業很喜歡創新的速度。他們對產品的評價非常正面。財務長們則很愛稍微抱怨一下他們的 Datadog 帳單。所以我想請你們談談，財務長層級的對話是如何演變的。

Clearly, the ROI is there, but maybe give us a little bit more on where the budget is coming from and something like infinite cardinality, is that now part of the conversation with CFOs in solving some of those very particular cardinality cost questions? Thank you.

顯然投資報酬率（ROI）是存在的，但能否再多談一些預算是從哪裡來的？以及像「無限基數」（infinite cardinality）這樣的議題，現在是否也已成為與財務長對話的一部分，用來解決那些非常特定的基數成本問題？謝謝。

At the high level, there's only two reasons people buy software. It makes them more money or it saves them money. And anytime we sell, anytime we got a renewal, we got enough sale, we land a new customer, that's because we do one of those two things for them. And we always have to make that case. So I wouldn't say that's any different from what we've seen before.

從高層次來看，人們購買軟體只有兩個原因。要嘛讓他們賺更多錢，要嘛幫他們省錢。而我們每一次銷售、每一次續約、每一次拿到足夠的訂單、每一次拿下新客戶，都是因為我們為他們做到這兩件事之一。而我們也總是必須把這個價值主張講清楚。所以我不會說這跟我們以前看到的有什麼不同。

What we do for our customers today, especially as they keep adopting AI, is we help them save a lot of the money they would spend on building, running operations or running AI agents.

我們今天為客戶做的事情，特別是當他們持續採用 AI 時，是幫助他們省下原本會花在建置、營運或運行 AI 代理（AI agents）上的大量成本。

When we have concerns with customers, that's the one thing they kept mentioning, is, how can you help me? Ran in my AI costs. This is going very fast. I don't have any control on it and I don't know whether I'm reaching the right outcomes with that. And so that's one of the reasons we've invested in all those products we've mentioned earlier. And also we're seeing some of the great returns on that products already.

當我們與客戶交流、聽到他們的顧慮時，他們一直提到的一件事是：你要怎麼幫我？把我的 AI 成本控制住。這進展得非常快。我對它沒有任何控制，而且我也不知道我是否正在達成正確的成果。因此，這也是我們投資在先前提到那些產品上的原因之一。而且我們也已經在那些產品上看到一些很好的回報。

In terms of infinite cardinality, that's, I would say it's been one of the longest standing source of frustration for customers when sometimes they send more data or they send more fine-grained tags with their data and to get some unpredictability on the bills because of that, because it increases the capability of the data we're getting.

至於無限基數，我會說這一直是客戶長期以來的挫折來源之一；有時候他們送出更多資料，或在資料中送出更細粒度的標籤（tags），因此帳單會出現一些不可預測性，因為這會提高我們所接收資料的基數能力。

And we saw that from a technical perspective and from a commercial perspective by packaging our metrics a little bit differently. And we think it's particularly important and relevant as customers are building more applications with AI and as they want to send basically more tags, more information and ask more complex questions and get more fine-grained answers to those questions.

我們從技術面與商業面來看，透過以稍微不同的方式來封裝（packaging）我們的指標（metrics）來處理這件事。而我們認為，當客戶用 AI 建置更多應用、並且希望送出更多標籤、更多資訊，提出更複雜的問題，並取得更細緻的答案時，這點特別重要且相關。

That fits well within their plans, basically. So we've got great feedback on that so far, but it's too early. Sometimes we get it right, sometimes we get it slightly wrong, and when we get it slightly wrong, we fix it. That's something different from what we've done in the past.

基本上這很符合他們的規劃。到目前為止我們收到很好的回饋，但現在還太早。有時候我們做對了，有時候會稍微做錯；而當我們稍微做錯時，我們就會修正。這跟我們過去做法有所不同。

That all makes sense. Thank you for the detail.

這些都很合理。謝謝你提供的細節。

Mike Cikos, Needham.

Mike Cikos，Needham。

Thanks for taking the questions, guys. I wanted to come back to the significant size of the labs that you had this quarter. And it's great to see the sustained traction, especially with those AI labs. But if I'm thinking about the [2, 7] figure AI labs that you landed this quarter and then going to David's commentary around winning some of these in-house AI labs with the hyperscalers, are those one in the same here or are those two separate customers that were customer sets we're talking to?

謝謝你們回答問題，各位。我想回到你們本季 labs 的顯著規模。很高興看到持續的動能，尤其是那些 AI labs。但如果我在想本季你們拿下的「[2, 7]」規模的 AI labs，然後再連結到 David 對於在超大規模雲端服務商（hyperscalers）那邊贏得一些內部 AI labs 的評論，這兩者是同一批嗎？還是我們談的是兩組不同的客戶／客群？

These are different customers. The ones we mentioned on the new lands are NeoLabs. So these are companies that didn't exist a few years ago. And what's interesting about them on the use case -- there is that very often we land customers when they go into production and they release products and they start selling their customers.

這些是不同的客戶。我們在新客戶落地（new lands）中提到的是 NeoLabs。也就是幾年前還不存在的公司。而他們在使用情境上的有趣之處在於——我們很多時候是在他們進入正式上線（production）、發布產品並開始向他們的客戶銷售時，才拿下這些客戶。

In this case, these are customers we're getting as they are training models and they're using us to observe and improve and optimize the training of the models. And so that's an exciting new area that was not really a business area for us a couple of years ago, and we've seen a number of new proof points about that.

但在這個案例中，我們是在他們訓練模型時就取得這些客戶；他們使用我們來觀測、改進並最佳化模型訓練。因此這是一個令人興奮的新領域，幾年前對我們來說還不算真正的業務領域，而我們已經看到一些新的實證案例（proof points）。

In addition to that, and we've mentioned in previous calls, we've also landed the AI lab or super-intelligent labs of a number of hyperscalers. And I would say the workloads are similar in that it's largely training of the models, but the customers are a bit different, these are very large companies that in that case previously had a lot.

此外，我們也在先前的電話會議中提過，我們也拿下了多家超大規模雲端服務商的 AI lab 或超智慧實驗室（super-intelligent labs）。我會說工作負載相似，主要都是模型訓練，但客戶有些不同；這些是非常大型的公司，而在那種情況下，他們先前有很多

Homegrown technology to observe and run workflows.

自研（homegrown）的技術來觀測並運行工作流程（workflows）。

Excellent. And for a follow-up, I know you had cited the new logos ramping more strongly than what we've seen historically. And correct me if I'm wrong, but I feel like that's a newer phenomenon that you guys are calling up this quarter. When I think about those new logos ramping, is that a function of pull through where maybe some of these AI capabilities are pulling through the broader platform or is it vice versa?

很好。接著追問一下，我知道你們提到新客戶（new logos）的擴張速度比歷史上更強。如果我沒記錯的話，這似乎是你們這一季才特別提出的較新現象。當我想到這些新客戶的擴張，是因為某種拉動效應（pull through）——也許是一些 AI 能力帶動了更廣泛的平台採用——還是反過來？

Anything you can do to help us think through what is creating that catalyst? If you will, when the new logos are contributing to the model.

你們能否幫我們理解一下，是什麼在創造那個催化劑？也就是說，當新客戶在模型中開始貢獻時。

It's been happening and building up to the number that we have in our queues, which is the percent from customers of growth that we didn't have a year ago, that number we said has gone from 25 to 30. So this has been building and we wanted to point that out because of that disclosure indicating that the customers that were landing that it's not only the new logos. But it's also the growth of the new logos that we've added over the last couple of years. So last year, sorry. So it's a compounding of that.

這件事一直在發生，並逐步累積到我們在指標中看到的數字，也就是來自一年前還不是我們客戶、但現在貢獻成長的客戶所占的百分比；我們說這個數字已從 25% 上升到 30%。所以這是逐步累積的，我們想指出這點，因為這項揭露顯示，帶來成長的客戶不僅僅是新客戶（new logos）。也包括我們在過去幾年新增的新客戶本身的成長。所以是去年，抱歉。因此這是一種複利式的累積。

Excellent. Thank you.

很好。謝謝。

Thank you.

謝謝。

Alex Zukin, Wolfe Research LLC.

Alex Zukin，Wolfe Research LLC。

Hey, guys. Thanks for taking the question. Ollie, maybe first for you just on the. A lot of headlines around security over the course of the last few weeks, particularly AI-breaking containment. And it occurs to me that with your positioning and observability and security increasingly, the notion of a guardian model and development around that could meaningfully increase kind of your ambit on what you can do and achieve for clients, both AI natives legacy can you maybe talk to what the increasing opportunity around this crossover in this AI age and what that means for Datadog? And then I've got a quick follow-up for David.

嗨，各位。謝謝你們回答問題。Ollie，也許先請教你，關於……過去幾週有很多與資安相關的頭條，特別是 AI 突破隔離（AI-breaking containment）。我想到的是，隨著你們在可觀測性與資安上的定位日益加深，「守護者模型」（guardian model）的概念以及相關開發，可能會實質擴大你們能為客戶做到與達成的範圍，無論是 AI 原生或傳統（legacy）客戶。你能否談談在這個 AI 時代，這種交叉領域帶來的機會增加，以及這對 Datadog 意味著什麼？然後我還有一個給 David 的簡短追問。

I mean, look, there's a complete. Switch in the way the security products need to work. So you can't wait, basically, for putting humans in the loop. You can't have the typical path when you have 12 or 15 different products that are going to aggregate signal, then you put that signal into a system and prioritize them for humans, then humans will review them when they can. You need to integrate everything a lot more. You need to operate a lot closer to the application and to the infrastructure.

我的意思是，你看，資安產品需要運作的方式正在發生徹底的轉變。所以你基本上不能再等著把人放在流程中（humans in the loop）。你不能再走典型路徑：用 12 或 15 種不同產品去彙整訊號，然後把訊號放進一個系統裡替人做優先排序，再由人類在有空時去審查。你需要把所有東西更緊密地整合。你需要在更貼近應用與基礎設施的地方運作。

And you need to have AI agents solve the issues first. So it's a complete reveal for most of the industry. And I think it plays into our approach, which is to have an integrated platform and have all the different data streams come directly from observability straight into the security agent and have all that be integrated from end to end.

而且你需要先讓 AI 代理先把問題解決。所以對於產業中的大多數人來說，這是一個完整的揭示。我認為這也契合我們的做法，也就是打造一個整合式平台，讓所有不同的資料流直接從可觀測性進入安全代理，並且把這一切端到端整合起來。

So obviously, the field is moving very fast. We see new. New classes of issues pretty much every week at this point. We are quite busy building that up, but we think it displays into our strengthening to where we are basically already are and we're building for our security products.

所以很明顯，這個領域發展非常快。我們看到新的——新的問題類別幾乎每週都會出現。我們正忙著把這些能力建起來，但我們認為這也反映出我們在既有基礎上的強化方向，並且我們正在為我們的安全產品持續建設。

Perfect. And then David, maybe just for you. On the largest customer renewal, is there anything you can tell us around maybe just any changes around the duration or anything that makes this new contract maybe a little stickier in terms of the discounted rate card, the amount of products that they're able to kind of use for better value, anything that increases the conviction level around stickiness?

很好。接著 David，可能想請教你。關於最大客戶的續約，你能否談談合約期間是否有任何變化，或是有沒有什麼讓這份新合約在黏著度上更強的因素，例如折扣費率表、他們能使用的產品數量以取得更好的價值，或任何能提高對黏著度信心的因素？

I'll comment on this other than to say that most of our enterprise customers, as we've talked about for a long time, have annual plus, and then the pricing.

我可以評論的是，我們大多數企業客戶，如同我們長期以來談到的，通常是年度以上的合約，然後再加上定價。

Is generally volume-based pricing. So I would say overall, our customers transact with us in that way, and then we have that level of commitment. And then as we talked about over a lot of years, then there's usage, and then we transact. So it's similar to what we have with most of our larger enterprise customers. Anything you want to add there?

一般而言是以用量為基礎的定價。所以我會說整體來看，我們的客戶就是以這種方式與我們交易，並且我們也有那樣的承諾程度。然後如同我們多年來談到的，接著就是使用量，然後我們再進行結算交易。所以這和我們大多數大型企業客戶的情況相似。你還想補充什麼嗎？

No, I think there's a lot of continuity in that in that renewal. I think that's what you're doing. It's one way to put it.

沒有，我認為那次續約在很多方面都很有延續性。我想這就是你在做的事。可以這麼說。

Perfect. Thank you, guys.

很好。謝謝各位。

Eric Heath, KeyBanc Capital Markets.

Eric Heath，KeyBanc Capital Markets。

Hi. This is Tracy Kashif on for Eric Heath. I would love to hear more color on your [Tracy] guys specifically. Seems like it's a little below your sequential levels of how you've guided your previous two views. So we'd love to just hear more about what trends you're seeing going into two view and maybe what some of the assumptions the guide are.

嗨。我是 Tracy Kashif，代 Eric Heath 提問。我很想聽到更多關於你們指引的細節，特別是你們這邊的看法。看起來它比你們先前兩次所給的序列水準略低一些。所以我們很想多了解你們在進入第二季時看到的趨勢，以及這份指引背後的一些假設。

Yeah, I think it's similar to the methodology we take what we see and provide some conservativism. And I think we had mentioned in the script that while we've been renewed our largest customer, but we've seen use just declines relative to the previous quarter, we said that. So that's all taken into consideration in trying to develop a guidance that is consistent with the methodology of conservativism that we've used as a public company.

是的，我認為這與我們一貫的方法類似：根據我們所看到的情況，並加入一些保守性。而且我想我們在講稿裡也提到，雖然我們已完成最大客戶的續約，但我們看到使用量相較前一季有所下降，我們也說了這點。所以在制定指引時，這些都會納入考量，以符合我們作為上市公司一直採用的保守方法論。

Gotcha. And if I could just ask, I'd love to just get your thoughts on the impact of diversification of AI model usage in your customers and what you're seeing there.

了解。如果我可以再問一個，我想聽聽你們對客戶端 AI 模型使用多元化所帶來影響的看法，以及你們目前看到的情況。

Well, we think it's great. Like, there's a lot more options for customers to choose from in general. That creates -- opens up a lot of doors and opportunities for them. That also creates a lot of complexity, and we're here to help deal with that complexity. So for us, these are great opportunities.

我們覺得這很棒。整體而言，客戶可選擇的選項多了很多。這為他們打開——帶來很多大門與機會。同時也帶來很多複雜度，而我們就在這裡協助處理這些複雜度。所以對我們來說，這些都是很好的機會。

And by the way, we see that we've had that thesis since the early days of AI that we would not just end up with one or two big AI companies and everybody using them the same way we didn't just end up with one or two big cloud companies and everybody just using software from them. Like the ecosystems are very, very, very rich.

順帶一提，我們從 AI 早期就有這個論點：最後不會只剩下一兩家大型 AI 公司、然後所有人都用同樣的方式使用它們；就像雲端也沒有只剩下一兩家大型雲端公司、然後大家都只用它們的軟體一樣。這些生態系非常、非常、非常豐富。

There are lots of providers. There are very large providers, there are smaller providers and everything in between, and there are many compositions of those different systems that are used by any given customer. And so we think the same is going to happen in AI.

供應商很多。有非常大的供應商，也有較小的供應商，以及介於兩者之間的各種規模；而且任何一個客戶都可能使用這些不同系統的多種組合。所以我們認為 AI 也會發生同樣的情況。

We think also that the multiplication of models and open source models in particular opens the door to customers doing a lot more training on their own. And so that's a new market for us. We see some signs that we have a very good role to play there, and so we're building towards that as well. So overall, it's very positive for everyone.

我們也認為，模型數量的增加，尤其是開源模型，為客戶自行做更多訓練打開了大門。因此這對我們來說是一個新市場。我們看到一些跡象顯示我們在那裡可以扮演非常好的角色，所以我們也正朝那個方向建設。總體而言，這對所有人都非常正面。

Koji Ikeda, Bank of America.

Koji Ikeda，美國銀行。

Yeah, hey guys, thanks so much for taking my question. Just one for me here. I wanted to ask on Bits AI, all the commentary that you guys are saying on Bits AI and all the work that we've been doing intra-quarter sounds like Bits AI is really taking off for you guys.

是的，嗨各位，謝謝你們回答我的問題。我這邊只有一個問題。我想問 Bits AI。你們對 Bits AI 的所有評論，以及我們在季度內所做的所有工作，聽起來 Bits AI 對你們來說真的正在快速起飛。

And so just thinking that Bits AI is going to be increasingly automating activities that historically has created observability workflows. I'm curious and really wonder, how do you ensure that greater automation that might be driven by Bits AI doesn't eventually reduce the volume of activity that traditionally grows Datadog consumption? Thank you.

因此，考慮到 Bits AI 將會愈來愈多地自動化那些過去會產生可觀測性工作流程的活動，我很好奇也想知道，你們如何確保由 Bits AI 驅動的更高自動化，最終不會降低傳統上推動 Datadog 用量成長的活動量？謝謝。

Well, look, if we provide more value, we get more -- as I was saying earlier in the call, we sell more software by helping customers make more money or save money or both. And I think if we can automate more and let them do more, we'll provide more value. That's as simple as that.

嗯，你看，如果我們提供更多價值，我們就會得到更多——就像我在電話會議前面說的，我們透過幫助客戶賺更多錢或省更多錢，或兩者兼具，來賣出更多軟體。我認為如果我們能自動化更多、讓他們能做更多事，我們就能提供更多價值。就這麼簡單。

I think. The future of the observability is not just observing, it's fixing. It's not waking up people in the middle of the night because something broke, but fixing it for them. It's not letting people do damage control on a security incident because an attacker is in. It's preventing the attacker from getting in to start with by auto-remediating issues.

我認為。可觀測性的未來不只是觀測，而是修復。不是在半夜把人叫醒，因為某些東西壞了，而是替他們把它修好。不是讓人們在發生資安事件、攻擊者已經入侵時才做損害控制。而是透過自動修復問題，從一開始就防止攻擊者進入。

And we're very busy building all of that. And we're super confident that this will yield great business outcomes for us in the end. And that's what we see from customers in the market. Like when they use Bits AI, they use more of our product. They deploy more of it. They create more dashboards and alerts and everything else. They have more users inside of our product. Like you say, it's not a zero-sum game.

而我們正非常忙碌地打造這一切。我們也非常有信心，最終這會為我們帶來很好的商業成果。而這也是我們從市場上的客戶所看到的。例如，當他們使用 Bits AI 時，他們會使用更多我們的產品。他們會部署更多。他們會建立更多儀表板、警示以及其他一切。他們在我們產品內會有更多使用者。就像你說的，這不是零和遊戲。

Samik Chatterjee, JPMorgan.

Samik Chatterjee，摩根大通。

Thanks, and thanks for taking my question. Maybe just on the non-AI part and the acceleration that you're seeing related to non-AI part of the business, just wanted to get your thoughts on the sustainability? And whether this good acceleration that you're seeing is driven by some of the new customer logos that you're pointing out or more usage going up and CFOs get more sort of cautious around their budgets? Do you see more sensory around non-AI eventually related to some of the AI products and how they're doing at this point? And I have a quick follow-up. Thank you.

謝謝，也謝謝你們回答我的問題。也許先談非 AI 的部分，以及你們在非 AI 業務相關看到的加速，我想聽聽你們對其可持續性的看法？以及你們看到的這波良好加速，是由你們提到的一些新客戶標誌所驅動，還是更多來自使用量上升，且 CFO 對預算變得更謹慎？你們是否看到非 AI 的部分，最終會因為某些 AI 產品以及它們目前的表現而出現更多關聯性？我還有一個簡短的追問。謝謝。

I mean, from what we can tell, it's very broad-based. And it's largely driven by existing customers because that's the majority. When you think of what it takes to move that number, that's basically the majority of our business.

我的意思是，就我們所能判斷的，這是非常廣泛的。而且主要是由既有客戶所驅動，因為那是大宗。當你思考要讓那個數字變動需要什麼時，基本上那就是我們業務的大部分。

We're not just going to move that with a few newer customers. It's largely driven by the existing customers. And it's driven by both increases in volume and because they're moving more workloads to the cloud and adoption of our newer products as they consolidate onto us.

我們不可能只靠少數新客戶就把這個推上去。這主要是由既有客戶所帶動。而且驅動因素同時來自用量增加，以及他們把更多工作負載遷移到雲端，並在整合到我們平台的同時採用我們較新的產品。

We think it's sustainable for one thing. If you compare to what we have seen in the 80 days of 2021 or the growth rates are accelerating, but they're still far below what we're seeing at that time. And so we don't create the same issue of customers having to digest very large increases multiple years in a row. I think in this case, we're very well within the range of sustainability.

我們認為這是可持續的，這是一點。如果你拿我們在2021年前80天所看到的情況來比較，當時成長率是在加速，但仍遠低於我們那時看到的水準。因此，我們不會造成客戶必須連續多年消化非常大幅度增長的同樣問題。我認為在這種情況下，我們完全處於可持續性的範圍之內。

And as has been a theme in this call, remember like when customers adopt and they consolidate, they have an eye towards the financial side of the equation, basically how much money are they going to make or save by doing that at the end. And we are very good at helping customers understand that and making that case and helping them save money at the end of the day. So we feel good about that.

而且如同本次電話會議的一個主題，請記得，當客戶採用並進行整合時，他們會關注這個等式的財務面——基本上，最後他們透過這麼做能賺到或省下多少錢。而我們非常擅長協助客戶理解這一點、建立這個論述，並在最終幫助他們省錢。所以我們對此感到很有信心。

And I want to just add one thing, and we talked about this last quarter, that some of this has to do with the investments that we're making in our platform and our product, but it also has to do with the investments that we're making in our go-to-market. We've successfully expanded growth capacity, the geography of it, and essentially, that's, as we talked about last quarter, providing returns. So that's also being a growth driver in our non-AI or enterprise-type business. That's right.

我還想補充一點，我們上季也談過，其中一部分與我們在平台與產品上的投資有關，但也與我們在市場推進（go-to-market）上的投資有關。我們已成功擴大成長產能與其地理覆蓋範圍，而基本上，正如我們上季所說，這些正在帶來回報。因此，這也正在成為我們非AI或企業型業務的成長驅動因素。沒錯。

And you see it also an opportunity to investment there. So we keep investing in our review, usually, because we're shipping more products that are successfully being adopted and consolidated into by our large number of using customers, but we also are adding to our go-to-market teams. We still not at this scale we want to be in terms of getting to all of the customers worldwide in all of the segments that are relevant to us. So we're investing as we see the return of those investments.

你也看到那裡同樣是一個投資機會。因此我們持續投資於我們的（業務）評估/檢視（review），通常是因為我們推出了更多產品，且被大量既有客戶成功採用並整合進來；同時我們也在擴編我們的市場推進團隊。就觸達全球所有客戶、涵蓋所有與我們相關的細分市場而言，我們的規模仍未達到我們希望的程度。所以我們會在看到投資回報的同時持續加碼投資。

And from a quick follow-up here, you talked about the FedRAMP high certification last quarter. Just curious if there's anything to sort of update us on the pipeline and how if there's any momentum on that front of the pipeline yet?

快速追問一下，你們上季提到FedRAMP High認證。想請教是否能更新一下相關商機管線（pipeline）的情況，以及在這條管線上是否已經看到任何動能？

Yeah, we're investing quite a bit in the build-up of our federal and government sales in general and we see pipeline there. In general, these are not deals that happen overnight, but this is a very large market and we see great traction there and we're investing to take full advantage of it.

是的，我們在聯邦與政府銷售的整體建置上投入相當多，也看到那邊的商機管線。一般來說，這些交易不會一夕之間完成，但這是一個非常大的市場；我們在那裡看到很好的進展，也正在投資以充分把握這個機會。

A lot of that was a build up to get to the right level of specification so we can deliver SaaS to various levels of government. And we've done quite a bit there, there's actually even more we're planning to do there. But we're happy with the results so far.

其中很大一部分是為了達到正確的規格水準所做的建置，讓我們能向不同層級的政府交付SaaS。我們在這方面已做了不少，實際上我們也規劃要做更多。但就目前的成果而言，我們很滿意。

Howard Ma, Guggenheim Securities.

Howard Ma，Guggenheim Securities。

Great. Thank you. And congrats on the strong quarter and for your guidance raise. I have two questions. I'll just ask them together. The first is on Bits AI. I'm curious how adoption and contribution compares to the previous major feature expansions in the past.

很好。謝謝。也恭喜你們本季表現強勁並上調指引。我有兩個問題。我就一起問。第一個是關於Bits AI。我想了解它的採用情況與貢獻度，和過去幾次重大功能擴展相比如何。

And then my other question is the $30 million TCV deal with the I think you guys said it's the largest online or sorry, one of the largest online media companies. I'm assuming this company did mostly DIY before, so if you could share some light on the decision-making process and if they're using multiple Datadog products and why now? that'd be really helpful.

第二個問題是那筆3,000萬美元TCV的交易，對象我想你們說的是最大（或其中之一）的線上媒體公司。我猜這家公司之前主要是DIY自建，所以如果你們能分享一下他們的決策過程、是否採用多個Datadog產品，以及為什麼是現在，會非常有幫助。謝謝。

Yeah, I'm sorry, I missed some part of you, some part of your secondary question.

好的，不好意思，我漏聽了你第二個問題的一部分。

It was, are they taking multiple products, I think, right, Howard?

是問他們是否採用多個產品，我想是這個對吧，Howard？

Are they, yeah, the nature of the sale, yeah, why now?

對，他們是否採用多個產品——也就是這筆交易的性質，以及為什麼是現在？

Yeah, so, I mean, I would say, so first on Bits AI, so, yes, and one thing that happened is Bits AI used to be fairly specific, it used to be dedicated to alerts, like you would pick up an alert and would run an investigation for you. Now the surface of contact is a lot wider with the customer, so you can access it through chat, you can run, you can of course still do the investigations, and we've done quite a bit more there. You can have Bits AI, manage your monitoring, and manage your detection for you.

好的，所以我會說，先談Bits AI：是的。有一件事是，Bits AI過去相對比較特定，主要是針對告警（alerts）——你收到一個告警後，它會替你跑一個調查。現在它與客戶的接觸面大幅擴大，你可以透過聊天介面存取它；你也可以執行——當然仍然可以做調查——而且我們在這方面做了更多。你可以讓Bits AI替你管理監控，並替你管理偵測。

You can have it code for you. You can have it generate managed test. There's all sorts of different use cases that we built into it that brought on the surface of contact. And we see a lot of adoption across all of those different areas. We also are changing the way we package it. So we have a new model with AI credit that we're rolling out just because the surface of contact is so much wider now than the specific feature.

你可以讓它替你寫程式碼。你可以讓它產生代管測試（managed test）。我們把各式各樣的使用情境建進去，讓接觸面更廣。而且我們看到在所有這些不同領域都有很高的採用度。我們也正在改變它的打包方式。因此我們推出一個新的AI點數（credit）模型並逐步上線，因為現在的接觸面比起原本的單一功能大得多。

So we -- there's quite a bit that is going on there. The explosion of activity that I mentioned earlier about other parts of our other AI surfaces is happening also in Bits AI. So that's something we're looking forward to.

所以——這裡有相當多事情正在進行。我先前提到我們其他AI接觸面的活動量爆發，在Bits AI上也同樣正在發生。所以這是我們很期待的一件事。

That's on that. On the second one, on the products that are being adopted in the sale, I mean, look, we typically land with two or more products that the balance we try to strike there is always to lend enough of the platform without slowing down the deals too much because the more you try to do at once, the more stakeholders you get and the longer it takes. And so we found that two products in general is a good land and then we can expand from there.

以上是第一題。第二題，關於這筆交易中採用的產品：你看，我們通常會以兩個或以上的產品切入；我們在這裡要拿捏的平衡點，是提供足夠的平台能力，但又不要讓交易進度變得太慢，因為你一次想做越多，就會牽涉越多利害關係人，成交時間也會拉長。因此我們發現一般而言，以兩個產品切入是個不錯的方式，之後再從那裡擴張。

On the calls, we tend to mention a lot of consolidation deals because they tend to be the larger ones, like if you lend with 12 products, you're going to be larger than if you lend with two in general.

在電話會議上，我們常提到很多整合型交易，因為它們通常規模更大；例如你若以12個產品切入，一般就會比以兩個產品切入更大。

That's not the majority of the deal. The consolidation typically happens later than when we land. But these make for very interesting examples of what our customers are doing when they consolidate on us all at once.

但那不是大多數交易的情況。整合通常發生在我們切入之後的較後期。不過這些案例很能說明，當客戶一次性全面整合到我們平台時，他們會怎麼做。

Andrew Sherman, TD Cowen.

Andrew Sherman，TD Cowen。

Great. Thank you, and congrats on the core growth acceleration. Ali, CPUs have had a renaissance lately driven by agentic AI. It would be great to hear your thoughts on this topic if it can be an incremental growth driver for your infrastructure monitoring? Have you seen any evidence of this yet? That's it for me. Thanks.

很好。謝謝，也恭喜核心成長加速。Ali，最近在代理式AI（agentic AI）的帶動下，CPU出現了復興。很想聽聽你對這個主題的看法：它是否可能成為你們基礎設施監控的額外成長驅動因素？你們是否已經看到任何相關跡象？我就這些。謝謝。

We do see an acceleration of consumption of our products in general and also that's at a high level, we do see that across the customer base. I don't know that if we see specifically the CPUs that get attached to GPUs in the new build out. I think a lot of it has more to do with the fact that the AI agents are largely spending a good amount of their time, like sometimes a majority of their time, coding tools, and tools are just applications that already existed and those applications typically run on CPUs, and so we see quite a bit of that.

我們確實看到整體而言我們產品的使用量在加速，而且在高層次上，我們也在整個客戶群中看到這種情況。我不確定我們是否特別看到在新一輪建置中，那些與GPU搭配的CPU所帶來的影響。我認為其中很大一部分更與這樣的事實有關：AI代理有相當多時間——有時甚至是大部分時間——在使用寫程式工具；而工具其實就是既有的應用程式，而這些應用程式通常跑在CPU上，所以我們看到相當多這類情況。

Brad Reback, Stifel.

Brad Reback，Stifel。

Great. Thanks very much. Ali, given your commentary around how strong the core is and that your largest customer was not additive to growth here in 2Q. Should we assume that if we X out the sequential downtick in that customer, that the core guide would have been probably 300 or 400 basis points higher?

很好。非常感謝。Ali，鑑於你對核心業務強勁的評論，以及你們最大的客戶在第二季並未對成長帶來增量。我們是否可以假設，如果把該客戶的季比下滑因素剔除，那麼核心指引可能會高出約300到400個基點？

Well, I can't, speculate, but what I will say, look, the business overall is growing at the same rate.

嗯，我不能臆測，但我想說的是，你看，整體業務仍以相同的速度成長。

If you expect that customer, as I said, and all the business has been accelerating. Overall. So that's why we feel good. Like when we look at whether we're getting the right returns and the right outcomes for our investments in R&D or investments in go-to-market and [whatnot]. And we look at our pipelines and all of the signs we have about the business, we feel great about the business.

如果你預期那位客戶，如我所說，而且所有業務都在加速。整體而言。所以這就是為什麼我們感覺很好。例如，當我們看我們在研發上的投資或在市場推進（go-to-market）上的投資，[等等]是否帶來正確的回報與正確的成果。而且我們看我們的銷售管線，以及我們掌握的所有業務訊號，我們對業務非常有信心。

So it's a good time to be in business.

所以現在是做生意的好時機。

Yeah, I think we commented in the remarks that the non-AI has accelerated and the AI, excluding the largest customer, continues. So I think we gave those friends in the in describing the business.

是的，我想我們在發言中提到，非AI業務已加速，而AI業務（不含最大客戶）仍在延續成長。所以我想我們在描述業務時已提供了這些資訊。

Of course, absolutely. Customers are growing a lot faster than the AI.

當然，絕對是。客戶的成長速度比AI快得多。

Yeah. And AI is growing. Exactly.

是的。而AI也在成長。沒錯。

Perfect. Thank you, guys.

完美。謝謝各位。

Ittai Kidron, Oppenheimer & Co.

Ittai Kidron，Oppenheimer & Co.。

Thanks for the help. You guys had a great quarter. I wanted to ask about new customer additions. This probably was the weakest quarter I ever remember for you guys, especially in the quarter where you had DASH, where historically DASH has been an accelerant of new customer additions. A holiday would be great.

謝謝協助。你們這一季表現很棒。我想問一下新客戶新增的情況。這可能是我記得你們最弱的一季，尤其是在你們有DASH的季度；歷史上DASH一直是推動新客戶新增加速的因素。如果能有個假期就好了。

Yeah, I think we essentially it's very similar to what we talked about before our gross customer additions continue to be strong and on trend line and that's the vast majority of our revenues.

是的，我想本質上這跟我們之前談到的非常類似：我們的總客戶新增仍然強勁，並且符合趨勢線，而這部分構成了我們絕大多數的營收。

We have at the very low end, the border between free and contract, and that has variability, very low effect on revenue. So if you that -- that accounts, as we talked about in many quarters, that accounts for the variability of the customer count, and it really has to do with something that has very little effect on revenues.

在非常低端，也就是免費與合約之間的邊界，那裡會有波動，對營收影響非常小。所以如果你——正如我們在很多季度談到的，那部分解釋了客戶數量的波動性，而它其實與對營收影響很小的事情有關。

Yeah. When you look at the customers above certain thresholds, like whether it's above a million, above 100K, above 10K, like all of those are trending very well.

是的。當你看某些門檻以上的客戶，例如超過100萬、超過10萬、超過1萬等，這些都呈現非常好的趨勢。

Very good. And then as a follow-up, Ollie, for you perhaps, I want to follow-up on the questions around Bits, which sounds super interesting. I guess the longer-term, and as you try to push deeper also into the security side of things, could this be evolving to some a broader AI SOC automation kind of platform? Is that a reasonable direction to think that this is where it's going to go?

非常好。接著追問一下，Ollie，可能是問你：我想延續關於Bits的問題，聽起來非常有趣。我想從更長期來看，當你們也試圖更深入切入資安領域時，這是否可能演進成更廣泛的AI SOC自動化類平台？把它視為未來可能發展方向，這樣想合理嗎？

Well, that's definitely we've taken moves towards that, right? So we initially we built, so we built the team first, for that, then we built the agent into the team. So Bits AI figured it out.

嗯，這確實是我們正在朝那個方向採取行動，對吧？所以一開始我們先建立了團隊，然後我們把代理（agent）建到團隊裡。所以Bits AI把它做出來了。

And now we've actually separated the agent from our team so the customers can use it with other teams. And we do that because the agent performs just so well. And it's been such a differentiator when we pitch a team that we're limiting our sales market wise if we just go after customers that want to re-platform their team and they can have a much better appeal as an AI company. So we are definitely taking moves towards that.

而現在我們其實已經把代理從我們的團隊中分離出來，讓客戶可以搭配其他團隊使用。我們這麼做是因為這個代理的表現實在太好。而且當我們推銷一個團隊時，它一直是很大的差異化；如果我們只鎖定想要把團隊重新平台化（re-platform）的客戶，我們在銷售市場面會受到限制，而若作為一家AI公司，吸引力會更大。所以我們確實正在朝那個方向採取行動。

Very good. I appreciate it. Thank you.

非常好。感謝。謝謝。

Andrew DeGasperi, BNP Paribas.

Andrew DeGasperi，BNP Paribas。

Thanks for fitting me in. I just wanted to ask a question on the non-AI natives, specifically in terms of the growth that you saw in the quarter. I was wondering, did you see rising demand for the AI-monitoring tool, particularly with open source tools being deployed across enterprises?

謝謝讓我插個問題。我想問一個關於非AI原生（non-AI natives）的問題，特別是就你們本季看到的成長而言。我想了解的是，隨著開源工具在企業中部署，你們是否看到對AI監控工具的需求上升？

Sorry, I need to soon back to your question.

抱歉，我需要很快回到你的問題。

The in terms of the AI monitor.

就AI監控而言。

I think you're asking about within that the AI, the what we used to call AI monitoring, I think you're asking [LM], etcetera, the growth trend there, yeah.

我想你問的是在AI之內、我們過去稱為AI監控的那部分；我想你問的是[LM]等等，那裡的成長趨勢，是的。

And look the volume like the (inaudible) used to be very little volume a year ago. It started growing quite a bit to the second-half of last year. And now it's been very rapidly accelerating over the past couple of quarters. So we've seen like an explosion basically of the volume we're getting there. And we get more users from different kinds of companies. So we definitely see that. We see it also across traditional companies and some more recent AI natives. So it's still a bit of both.

你看，像（聽不清）這類的量，一年前其實非常少。它在去年下半年開始成長不少。而在過去幾個季度又非常快速地加速。所以我們基本上看到那裡的量出現爆發式成長。而且我們從不同類型的公司獲得更多使用者。所以我們確實看到這點。我們也在傳統公司以及一些較新的AI原生公司中看到。所以仍然兩者都有。

I would say for that category, it's still super early. Like we expect the products to change quite a bit. We expect the user and maybe also the packaging to change over time quite a bit.

我會說，就那個類別而言，現在仍然非常早期。我們預期產品會有相當大的變化。我們也預期使用者，甚至可能還有包裝方式（packaging），都會隨時間有很大的變化。

Got it. Thank you.

了解。謝謝。

All right. So I think that was the last question. I want to thank all of you for attending the call today. I also want to, again, thank the teams everywhere at Datadog. I think everybody has been doing a fantastic job both on the product side and the go-to-market side.

好的。所以我想那是最後一個問題。我要感謝各位今天參加這通電話會議。我也想再次感謝Datadog各地的團隊。我認為大家在產品端與市場推進端都做得非常出色。

I know we have a lot more lined up for the end of the year on the product side, and I know also we have very large and very happy pipelines to tend to on the go-to-market side. So I hope to talk to you again in the quarter. Thank you all.

我知道在今年年底前，我們在產品端還有很多安排；我也知道在市場推進端，我們還有非常龐大且非常令人滿意的銷售管線需要持續跟進。所以希望下個季度再與各位交流。謝謝大家。

Thank you for your participation in today's conference. This does conclude the program. You may now disconnect.

感謝您參與今天的會議。本次議程到此結束。您現在可以中斷連線。