數據狗 (DDOG) 2025 Q2 法說會逐字稿

Good day, and thank you for standing by. Welcome to the Q2 2025 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 speaker today, Yuka Broderick, SVP of Investor Relations.

您好，感謝您的支持。歡迎參加 2025 年第二季 Datadog 財報電話會議。（操作員指示）請注意，今天的會議正在錄音。現在，我想將會議交給今天的發言人、投資者關係高級副總裁 Yuka Broderick。

Thank you, DD. Good morning, and thank you for joining us to review Datadog's second quarter 2025 financial results, which we announced in our press release issued this morning. Joining me on the call today are Olivier Pomel, Datadog's Co-Founder and CEO; and David Obstler, Datadog's CFO.

謝謝你，DD。早安，感謝您與我們一起回顧 Datadog 2025 年第二季的財務業績，我們在今天早上發布的新聞稿中公佈了這一業績。今天參加電話會議的還有 Datadog 聯合創辦人兼執行長 Olivier Pomel 和 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 of the fiscal year 2025 and related notes and assumptions our gross margins and operating margins, our product capabilities and our ability to capitalize on market opportunities.

在本次電話會議中，我們將做出前瞻性陳述，包括與我們未來財務業績、2025 財年第三季度的展望以及相關說明和假設、我們的毛利率和營業利潤率、我們的產品能力以及我們利用市場機遇的能力有關的陳述。

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

預期、相信、繼續、估計、期望、打算、將和類似表達旨在識別前瞻性陳述或對未來預期的類似指示。這些聲明僅反映我們截至今天的觀點，並受到各種風險和不確定因素的影響，可能導致實際結果大不相同。

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, 2025. Additional information will be made available in our upcoming Form 10-Q for the fiscal quarter ended June 30, 2025, 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.

有關可能影響我們實際結果的重大風險和其他重要因素的討論，請參閱截至 2025 年 3 月 31 日的季度的 10-Q 表。更多資訊將在我們即將發布的截至 2025 年 6 月 30 日的財政季度的 10-Q 表以及向美國證券交易委員會提交的其他文件中公佈。這些資訊以及本次電話會議的重播也可在我們網站的「投資者關係」部分找到。

We will discuss non-GAAP financial measures, which are reconciled to their most directly comparable GAAP financial [present] in our earnings release, which is available at investors.datadog.hq.com.

我們將討論非 GAAP 財務指標，這些指標在我們的收益報告中與最直接可比較的 GAAP 財務[當前]相協調，該報告可在 investors.datadog.hq.com 上查閱。

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

說完這些，我想把電話轉給奧利維爾。

Thanks, Yuka, and thank you all for joining us this morning to go through our results for Q2. Let me begin with this quarter's business drivers. Overall, we saw trends for usage growth from existing customers in Q2 that were higher than our expectations. We experienced strong growth in our AI native cohort. The number of AI native customers are growing meaningfully with us as they see rapid user growth with their products.

謝謝，Yuka，也謝謝大家今天早上加入我們，共同討論第二季的業績。讓我先談談本季的業務驅動因素。整體而言，我們發現第二季現有客戶的使用量成長趨勢高於我們的預期。我們的 AI 原生群體經歷了強勁成長。隨著我們的產品用戶量快速成長，AI 原生客戶的數量也顯著成長。

Meanwhile, we saw consistent and steady usage growth in the rest of the business. We continue to see the overall demand environment as solid with an ongoing healthy pace of cloud migration and digital transformation. And churn has remained low, with gross revenue retention stable in the mid- to high 90s, highlighting the mission-critical nature of our platform for our customers.

同時，我們看到其餘業務的使用量持續穩定成長。我們持續看到整體需求環境穩固，雲端遷移和數位轉型持續健康發展。客戶流失率一直維持在較低水平，總收入保留率穩定在 95% 左右，凸顯了我們的平台對客戶的關鍵使命性質。

Regarding our Q2 financial performance and key metrics, revenue was $827 million, an increase of 28% year-over-year and above the high end of our guidance range. We ended Q2 with about 31,400 customers, up from about 28,700 a year ago. This includes about 150 new customers from our Eppo and Metaplane acquisitions.

關於我們第二季的財務表現和關鍵指標，營收為 8.27 億美元，年增 28%，高於我們預期範圍的高端。截至第二季度，我們擁有約 31,400 名客戶，較去年同期的約 28,700 名有所成長。其中包括我們收購的 Eppo 和 Metaplane 帶來的約 150 名新客戶。

We ended Q2 with about 3,850 customers with an ARR of $100,000 or more, up from about 3,390 a year ago. And this customers generated about 89% of our ARR. And we generated free cash flow of $165 million with a free cash flow margin of 20%.

截至第二季末，我們擁有約 3,850 名 ARR 為 10 萬美元或以上的客戶，而一年前這一數字約為 3,390 名。這些客戶創造了我們約 89% 的 ARR。我們產生了 1.65 億美元的自由現金流，自由現金流利潤率為 20%。

Turning to platform adoption. Our platform strategy continues to resonate in the market. At the end of Q2, 83% of customers were using to or more products, the same as last year. 52% of customers were using 4 or more products, up from 49% a year ago. 29% of our customers were using 6 or more products, up from 25% a year ago. And 14% of our customers were using 8 or more products, up from 11% a year ago.

轉向平台採用。我們的平台策略持續在市場上引起共鳴。截至第二季末，83% 的客戶使用了 2 種或更多產品，與去年持平。 52% 的客戶使用了 4 種或更多產品，高於一年前的 49%。 29% 的客戶使用了 6 種或更多產品，高於一年前的 25%。14% 的客戶使用 8 種或更多產品，高於一年前的 11%。

So our customers continue to add up more products, including our security offerings. As a reminder, our security customers can identify management vulnerabilities with code security, cloud security and sensitive data scanner and they can detect and protect from attacks with APP and API protection, [wallfare] protection and cloud SIEM.

因此，我們的客戶繼續添加更多產品，包括我們的安全產品。提醒一下，我們的安全客戶可以使用程式碼安全性、雲端安全性和敏感資料掃描器來識別管理漏洞，並且可以使用 APP 和 API 保護、[wallfare] 保護和雲端 SIEM 來偵測和防禦攻擊。

We are pleased that our security suite of products now generate over $100 million in ARR and is growing mid-40% year-over-year. While we are pleased to achieve this milestone, we are still just getting started in solving customer products in this area with new innovations such as [our Bits] AI Security Analyst.

我們很高興，我們的安全產品套件目前產生的 ARR 超過 1 億美元，並且比去年同期成長 40% 左右。雖然我們很高興實現這一里程碑，但我們仍剛開始利用 [our Bits] AI 安全分析師等新創新來解決該領域的客戶產品問題。

Moving on to R&D. We held our DASH User Conference in June, where we announced over 125 exciting new products and features for our users. So let's go through some of the announcements. First, we launched fully autonomous AI agents. including Bits AI SRE agent to investigate alerts and coordinated response. Bits AI data agent, an AI-powered coding assistant to proactively fix production issues. And Bits AI security analyst to triage our little cloud SIEM signals.

轉向研發。我們於 6 月舉行了 DASH 用戶大會，向用戶宣布了 125 多種令人興奮的新產品和功能。讓我們來看看一些公告。首先，我們推出了完全自主的AI代理，包括Bits AI SRE代理來調查警報和協調回應。Bits AI 資料代理，一個由人工智慧驅動的編碼助手，可以主動解決生產問題。Bits AI 安全分析師會對我們的小型雲端 SIEM 訊號進行分類。

To further accelerate our users incident response, we announced AI voice agent for incident response so users can quickly get up to speed and start taking action on their phones. We also announced hand-off notifications that make it easy to jump straight into the relevant context and quickly communicate with our responders. And Status Pages to enable automatic updates for customers undergrade incidents

為了進一步加快用戶的事件回應速度，我們宣布推出用於事件回應的 AI 語音代理，以便用戶能夠快速上手並在手機上開始採取行動。我們也宣布了交接通知功能，可以輕鬆直接進入相關內容並與我們的回應者快速溝通。狀態頁面可自動更新客戶未分級事件

Second, we delivered a series of products to help customers ship better software with confidence. With a Datadog internal developer portal, developers can ship better and faster by gaining a real-time view into their software systems and APIs with the subject catalog by provisioning infrastructure, scaffolding new services and managing co-changes and deployments with self-service actions and by following engineering and readiness standards with Scorecards.

其次，我們推出了一系列產品，幫助客戶滿懷信心地交付更好的軟體。借助 Datadog 內部開發人員門戶，開發人員可以透過配置基礎設施、建立新服務、透過自助服務操作管理共同變更和部署，以及透過記分卡遵循工程和準備標準，利用主題目錄即時查看其軟體系統和 API，從而更好、更快地交付產品。

We launched a Datadog MCP server to enable AI agents to obsess telemetry from Datadog and to act as a bridge between Datadog and MCP compatible AI agents, like OpenAI Codex, Cursor and Claude Code for Anthropic. We work together with OpenAI to integrate our MCP server within the OpenAI Codex CLI.

我們推出了 Datadog MCP 伺服器，使 AI 代理程式能夠從 Datadog 獲得遙測數據，並充當 Datadog 和 MCP 相容 AI 代理程式（如 OpenAI Codex、Cursor 和 Anthropic 的 Claude Code）之間的橋樑。我們與 OpenAI 合作，將我們的 MCP 伺服器整合到 OpenAI Codex CLI 中。

And the Datadog Cursor extension now gives developers access to the observability data directly within the Cursor ID. Third, we are reimagining observability to meet our customers' increasingly complex needs. Our APM Latency Investigator formulates and explores hypothesis in the background, helping teams to quickly isolate root causes and understand impact without combing through large amounts of data.

現在，Datadog Cursor 擴充功能允許開發人員直接在 Cursor ID 中存取可觀察性資料。第三，我們正在重新構想可觀察性，以滿足客戶日益複雜的需求。我們的 APM 延遲調查員在後台製定和探索假設，幫助團隊快速隔離根本原因並了解影響，而無需梳理大量數據。

Proactive app recommendations help users stay ahead of growing system complexity by analyzing APM data to detect issues and propose fixes before they become problems. We announced a Flex Frozen tier, so customers can keep logs and fully managed storage for up to seven years and be able to search with our data movement or rehydration. [Our icon Search] now enables Teams to query archive logs directly in cloud storage, Amazon S3 bucket like Flex Frozen tier. And Datadog now supports advanced analysis features within notebooks.

主動應用程式推薦透過分析 APM 資料來偵測問題並在問題出現之前提出修復建議，幫助使用者應對日益增長的系統複雜性。我們推出了 Flex Frozen 層，客戶可以保留日誌和完全託管的儲存空間長達七年，並能夠使用我們的資料移動或補液功能進行搜尋。 [我們的圖示搜尋] 現在支援 Teams 直接在雲端儲存（例如 Amazon S3 儲存桶）中查詢存檔日誌，就像 Flex Frozen 層一樣。Datadog 現在支援筆記本內的進階分析功能。

Fourth, our security products cover new AI attack vectors across the application, model and data layers. At the AI data layer, sensitive data scanner can now prevent the leakage of sensitive data in training data as well as LLM prompts and responses.

第四，我們的安全產品涵蓋了應用程式、模型和資料層的新型人工智慧攻擊媒介。在AI資料層，敏感資料掃描器現在可以防止訓練資料以及LLM提示和回應中的敏感資料的洩漏。

At the model layer, we have secured against supply chain attacks in open source models, and prevent model hijacking attacks. At the application layer, we have prevent prompt injection attacks and data poisoning in real-time.

在模型層，我們針對開源模型做了供應鏈攻擊防護，防止模型劫持攻擊。在應用層，我們即時阻止提示注入攻擊和資料中毒。

And finally, we showcased our new end-to-end AI and data observability capabilities. Engineers and machine learning teams can use GPU monitoring to gain visibility into GPU fleets across cloud, on-prem and GPU-as-a-service platform, such as [Cori] and Lambda Labs. With agent console, enterprises can monitor the behavior and interactions with any AI agent teams.

最後，我們展示了新的端到端人工智慧和資料可觀察性功能。工程師和機器學習團隊可以使用 GPU 監控來了解雲端、本機和 GPU 即服務平台（例如 [Cori] 和 Lambda Labs）上的 GPU 叢集。透過代理控制台，企業可以監控任何 AI 代理團隊的行為和互動。

We now offer LLM observability experiments to help understand how changes to prompt, models or AI providers influence in application outcomes. We added a new agentic flows visualization to LLM observability to capture and understand the decision path of AI agent.

我們現在提供 LLM 可觀察性實驗，以幫助了解提示、模型或 AI 提供者的變化如何影響應用結果。我們為 LLM 可觀察性添加了新的代理流視覺化，以捕捉和理解 AI 代理的決策路徑。

And last but not least, and accelerated by our recent acquisition of Metaplane, Datadog now offers a complete approach to data observability across the entire data life cycle from intention to transformation to downstream usage.

最後但同樣重要的一點是，透過我們最近收購 Metaplane，Datadog 現在提供了一種完整的資料可觀察性方法，涵蓋從意圖到轉換再到下游使用的整個資料生命週期。

So we continue to relentlessly innovate to solve more problems for our customers. In doing so, we are being rightfully recognized by independent research, and we are pleased that for the fifth year in a row, Datadog has been named as a leader in the 2025 Gartner Magic Quadrant for observability platforms. We believe that this validates our approach to deliver a unified platform, which silos across team.

因此，我們不斷創新，為客戶解決更多問題。透過這樣做，我們得到了獨立研究的認可，我們很高興 Datadog 連續第五年被評為 2025 年 Gartner 可觀察性平台魔力像限的領導者。我們相信，這驗證了我們提供統一平台的方法，該平台可以跨團隊合作。

Now let's move on to sales and marketing. We had a number of great new logo wins and customer expansions this quarter. So let's go through a few of those. First, we signed a seven-figure annualized expansion in a three-year contract worth more than $60 million with one of the world's largest banks. This company believes getting to the cloud is essential so they can use AI on their extremely rich dataset to improve how they manage risk and serve their customers.

現在讓我們討論銷售和行銷。本季度，我們獲得了許多出色的新標誌並拓展了客戶範圍。讓我們來看看其中的幾個。首先，我們與世界上最大的銀行之一簽署了一份為期三年、價值超過 6000 萬美元的七位數年化擴張合約。該公司認為，進入雲端至關重要，這樣他們就可以在其極其豐富的數據集上使用人工智慧來改善其風險管理和服務客戶的方式。

They are using Datadog as their strategic cloud availability platform, and we continue to migrate more applications to the cloud. This customer is expanding to 21 Datadog products with thousands of users who log into the Datadog platform every month.

他們正在使用 Datadog 作為其戰略雲端可用性平台，我們將繼續將更多應用程式遷移到雲端。該客戶正在將 Datadog 產品擴展到 21 個，每月有數千名用戶登入 Datadog 平台。

Next, we signed a seven-figure expansion to an eight-figure unrealized contract with a leading US insurance company. Datadog is supporting these customers' efforts to consolidate observability tools and expand their cloud-based products. By adopting Datadog, they are experiencing fewer and less severe incidents.

接下來，我們與一家領先的美國保險公司簽署了一份七位數擴展到八位數的未實現合約。Datadog 正在支援這些客戶整合可觀察性工具和擴展其基於雲端的產品的努力。透過採用 Datadog，他們遇到的事故越來越少，嚴重程度也減輕了。

With estimated savings of over $9 million per year in incident response costs and improving more than 100,000 customer transactions that would otherwise be impacted every year. With this expansion, this customer would adopt 19 Datadog products and we'll consolidate a couple of dozen tools across multiple business units.

預計每年可節省超過 900 萬美元的事件回應成本，並改善每年受影響的超過 10 萬個客戶交易。透過此次擴展，該客戶將採用 19 種 Datadog 產品，並且我們將整合多個業務部門的數十種工具。

Next, we signed a nearly seven-figure annualized expansion with the leading American media conglomerate. This customer has about 100 observability tools across more than 300 business units. And this tool fragmentation has resulted in inefficiencies in extra cost and loss engineering time. They are expanding to 21 Datadog products, including all of our security products and replacing their solution with Datadog and incident management.

接下來，我們與美國領先的媒體集團簽署了一份近七位數的年度擴張協議。該客戶擁有約 100 個可觀察性工具，涵蓋 300 多個業務部門。這種工具碎片化導致了額外成本和工程時間的損失。他們正在將 Datadog 產品擴展到 21 種，包括我們所有的安全產品，並用 Datadog 和事件管理取代他們的解決方案。

Next, we landed a seven-figure annualized deal with a leading Brazilian e-commerce company. This customer's previous observability vendor was unable to support them as they move to newer software platforms and modern cloud infrastructure. By replacing these tool with Datadog, the company was able to gain full visibility to its cloud and saw significant improvements in application stability and incident resolution times. This customer will start with seven Datadog products including DSCLOG.

接下來，我們與巴西一家領先的電子商務公司達成了一項七位數的年化交易。當該客戶轉向更新的軟體平台和現代雲端基礎設施時，他們先前的可觀察性供應商無法為他們提供支援。透過用 Datadog 取代這些工具，該公司能夠全面了解其云，並顯著提高應用程式穩定性和事件解決時間。該客戶將首先購買包括 DSCLOG 在內的七種 Datadog 產品。

Next, we landed a seven-figure annualized deal with the delivery app of a major American retailer. This customer run and allow tracking products to be immediately valuable finding an issue on the first day of the Datadog trial that they hadn't identified after month of searching with their old tool. [Let us see] Datadog with seven products to start, this customer will consolidate half a dozen tools while meeting their PCI compliance requirements.

接下來，我們與一家美國大型零售商的配送應用程式達成了一項七位數的年化交易。該客戶運行並允許追蹤產品立即發揮作用，在 Datadog 試用的第一天就發現了一個問題，而這個問題是他們使用舊工具搜尋一個月後仍未發現的。[讓我們看看] Datadog 一開始就有七種產品，客戶將整合六種工具，同時滿足其 PCI 合規性要求。

Finally, we welcome back a leading US mortgage company in a nearly seven-figure annualized deal. This customer had moved to using a dozen open source disconnected tools, which led to fragmented visibility, [alifaty] and poor customer experience. In returning to Datadog, they plan to add up 6 products including replacing their paging system with Datadog long goal. And that's it for another productive quarter from our go-to-market teams who are now very hard at work on BBQ3.

最後，我們歡迎一家領先的美國抵押貸款公司以近七位數的年化交易金額回歸。該客戶已轉而使用十幾種開源斷開連接的工具，這導致了可見性分散，[alifaty] 以及糟糕的客戶體驗。在回歸Datadog後，他們計劃增加6款產品，其中包括用Datadog替換他們的分頁系統作為長期目標。這就是我們的行銷團隊又一個有成效的季度，他們現在正努力開發 BBQ3。

Before I turn it over to David for a financial review, I want to say a few words on our longer-term outlook. There is no change to our overall view that digital transformation and cloud migration our long-term secular growth drivers of our business. As we think about AI, we are incredibly excited about our opportunities.

在我將財務審查交給大衛之前，我想談談我們的長期展望。我們的整體觀點並沒有改變，即數位轉型和雲端遷移是我們業務的長期成長動力。當我們思考人工智慧時，我們對我們的機會感到無比興奮。

First, AI is a tailwind for Datadog, as increased cloud consumption drives more usage of our platform. Today, we see this primarily in our AI native group of customers who are monitoring their cloud native applications with us. There are hundreds of customers in this group. They include more than a dozen that are spending over $1 million a year with us and more than 80 who are spending more than $100,000. And they include 8 of the top 10 leading AI companies.

首先，人工智慧對 Datadog 來說是一大助力，因為雲端消費的增加推動了我們平台的使用率的提高。今天，我們主要在與我們一起監控其雲端原生應用程式的 AI 原生客戶群中看到了這一點。這個群組裡有幾百個客戶。其中有十幾位每年在我們這裡消費超過 100 萬美元，還有 80 多人每年在我們這裡消費超過 10 萬美元。其中包括十大領先人工智慧公司中的八家。

While we know there's a lot of attention in this cohort, we primarily see it as an indication of what's to come, as companies of every size and every single industry incorporate AI into applications. And we continue to see rising customer interest for next-gen AI observability and analysis. Today, over 4,500 customers use one or more Datadog integrations.

雖然我們知道這個群體受到了很多關注，但我們主要將其視為未來的一個跡象，因為各種規模和每個行業的公司都將人工智慧融入應用程式中。我們持續看到客戶對下一代人工智慧可觀察性和分析的興趣日益濃厚。如今，超過 4,500 名客戶使用一個或多個 Datadog 整合。

Second, next-gen AI introduces new complexity and new observability challenges. For AI observability products help our customers gain visibility and deploy with confidence across their entire AI stack, including GPU monitoring, LLM observability, AI agent observability and data [observability]. And we will, of course, keep innovating as the AI landscape develops further.

其次，下一代人工智慧帶來了新的複雜性和新的可觀察性挑戰。對於 AI 可觀察性產品，我們可以幫助我們的客戶獲得可見性，並在整個 AI 堆疊中充滿信心地部署，包括 GPU 監控、LLM 可觀察性、AI 代理可觀察性和數據[可觀察性]。當然，隨著人工智慧領域的進一步發展，我們將繼續創新。

Third, we are incorporating AI into the Datadog platform to deliver more value to our customers. As I discussed earlier, we launched SRE agent and security agent. We are seeing very good results with those with more improvements and new capabilities to come.

第三，我們正在將人工智慧融入 Datadog 平台，為客戶提供更多價值。正如我之前討論的，我們推出了 SRE 代理和安全代理。我們看到了這些產品所取得的非常好的成果，它們還具有更多的改進和新的功能。

Finally, as a SaaS platform, focus on our customers' critical workflows, we have a large volume of reach clear and detailed data, which allows us to conduct ground-breaking research. A great example of that is our Toto model for time series forecasting, which shows state-of-the-art performance on all benchmarks, even going well beyond specialized observability use cases. And you should expect to see more from us on that front in the future as well as taking novel research approaches and models straight into our products to improve customer outcomes.

最後，作為一個SaaS平台，我們專注於客戶的關鍵工作流程，擁有大量清晰詳細的數據，這使我們能夠進行開創性的研究。一個很好的例子就是我們的時間序列預測 Toto 模型，它在所有基準測試中都表現出最先進的效能，甚至遠遠超出了專門的可觀察性用例。您應該期待未來看到我們在這方面做出更多努力，並將新穎的研究方法和模型直接融入我們的產品中，以改善客戶成果。

So we are extremely excited about our progress so far against what we expect to be a generational growth opportunity. In other words, we're just getting started.

因此，我們對迄今為止的進展感到非常興奮，我們預計這將是一個世代成長的機會。換句話說，我們才剛開始。

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

說完這些，我將把權力移交給我們的財務長。戴維？

Thanks, Olivier. Q2 revenue was $827 million, up 28% year-over-year and up 9% quarter-over-quarter. Now to dive into some of the drivers of this Q2 revenue growth. First, overall, we saw trends for usage growth from existing customers in Q2 that were higher than our expectations. This included strong growth in our AI native cohort as well as usage growth from the rest of the business that was consistent with recent quarters amidst a healthy and steady cloud migration environment.

謝謝，奧利維爾。第二季營收為 8.27 億美元，年增 28%，季增 9%。現在深入探討第二季營收成長的一些驅動因素。首先，整體而言，我們發現第二季現有客戶的使用量成長趨勢高於我們的預期。這包括我們的 AI 原生群體的強勁成長，以及其他業務的使用量成長，這與最近幾季在健康穩定的雲端遷移環境下的表現一致。

We saw a continued rise in contribution from AI native customers in the quarter who represented about 11% of Q2 revenues up from 8% of revenues in the last quarter and about 4% of revenues in the year ago quarter. The AI native customers contributed about 10 points of year-over-year revenue growth in Q2 versus about 6 points last quarter and about 2 points in the year ago quarter.

我們看到本季來自 AI 原生客戶的貢獻持續上升，佔第二季營收的約 11%，高於上一季的 8% 和去年同期的 4%。人工智慧原生客戶在第二季度貢獻了約 10 個百分點的年收入成長，而上一季約為 6 個百分點，去年同期約為 2 個百分點。

Now as previously discussed, we do see revenue concentration in this cohort in recent quarters. But if we look at our revenue without the largest customer in the AI native cohort, our year-over-year revenue growth in Q2 was stable relative to Q1. We remain mindful that we may see volatility in our revenue growth on the backdrop of long-term volume growth in this cohort as customers renew with us on different terms and as they may choose to optimize cloud and observability usage over time.

正如之前所討論的，我們確實看到最近幾季的收入集中在這個群體中。但如果我們拋開 AI 原生群體中最大的客戶來看我們的收入，我們第二季的年比收入成長相對於第一季而言是穩定的。我們仍然意識到，在這一群體的長期數量增長的背景下，我們的收入增長可能會出現波動，因為客戶會以不同的條款與我們續約，並且他們可能會選擇隨著時間的推移優化雲和可觀察性的使用。

As you heard from Oli, we continue to believe that adoption of AI will benefit Datadog in the long term, and we believe that the growth of this AI native customer group is an indication of the opportunity to come as AI is adopted more broadly and customers outside the AI native group begin to operate AI workloads in production.

正如您從 Oli 那裡聽到的，我們仍然相信採用 AI 將使 Datadog 長期受益，並且我們相信，隨著 AI 得到更廣泛的應用，這個 AI 原生客戶群的增長預示著未來的機遇，AI 原生客戶群之外的客戶開始在生產中運行 AI 工作負載。

Now regarding usage growth by customer segment. In Q2, our year-over-year usage growth was fairly similar across segments relative to previous quarters. As and mid-market usage growth improved in Q2, while enterprise customer usage growth remain roughly stable. Note that we are excluding the AI native cohort for the purposes of this commentary.

現在討論按客戶群劃分的使用量成長。在第二季度，我們各部門的年比使用量成長與前幾個季度相比相當相似。第二季度，中階市場使用量成長有所改善，而企業客戶使用量成長基本上保持穩定。請注意，為了本評論的目的，我們排除了 AI 原生群體。

And as a reminder, we define enterprise as customers with 5,000 or more employees, mid-market as customers with 1,000 to 5,000 employees and SMB as customers with less than 1,000 employees. Regarding our retention metrics. Our 12-month trailing net retention percentage was about 120, higher than the high 110s last quarter, and our trailing 12-month gross revenue retention percentage remains in the mid- to high 90s.

提醒一下，我們將企業定義為擁有 5,000 名或更多員工的客戶，中型市場定義為擁有 1,000 至 5,000 名員工的客戶，SMB 定義為擁有少於 1,000 名員工的客戶。關於我們的保留指標。我們過去 12 個月的淨留存率約為 120%，高於上一季的 110% 左右，而過去 12 個月的總收入留存率仍維持在 90% 左右。

Now moving on to our financial results. First, billings were $852 million, up 20% year-over-year. And remaining performance obligations, or RPO, was $2.43 billion, up 35% year-over-year. Our current RPO growth was in the low [30%s] year-over-year and our RPO duration was up slightly year-over-year.

現在來看看我們的財務表現。首先，營業額為8.52億美元，年增20%。剩餘履約義務（RPO）為 24.3 億美元，年增 35%。我們目前的 RPO 成長率與去年同期相比處於較低水準 [30%]，我們的 RPO 持續時間與去年同期相比略有增加。

As previously mentioned, we continue to believe that revenue is a better indication of our business trends than billings and RPO, as those can fluctuate relative to revenue based on the timing of invoicing and the duration of customer contracts.

如前所述，我們仍然相信收入比帳單和 RPO 更能表明我們的業務趨勢，因為這些指標會根據開票時間和客戶合約期限相對於收入而波動。

And 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.

現在讓我們回顧一些關鍵的損益表結果。除非另有說明，所有指標均為非 GAAP。我們在收益報告中提供了 GAAP 與非 GAAP 財務狀況的對帳表。

First, gross profit in the quarter was $669 million for a gross margin of 80.9%. This compares to a gross margin of 80.3% last quarter and 82.1% in the year ago quarter. As we've discussed in the last call, we saw an increasing impact of our engineers cost savings efforts throughout this quarter as they delivered on cloud efficiency projects.

首先，本季毛利為 6.69 億美元，毛利率為 80.9%。相較之下，上一季的毛利率為 80.3%，去年同期的毛利率為 82.1%。正如我們在上次電話會議中所討論的那樣，我們看到我們的工程師在本季度交付雲端效率專案時，成本節約努力的效果越來越顯著。

And we are continuing our focus on cloud efficiency, and believe that we have further opportunity for gross margin improvement in the second half of the year. Our Q2 OpEx grew 30% year-over-year, up from 29% last quarter. As we've communicated over the past year, we plan to grow our investments to pursue our long-term growth opportunities, and this OpEx growth is an indication of our execution on our hiring plans.

我們將繼續關注雲端效率，並相信下半年我們有進一步提高毛利率的機會。我們第二季的營運支出年增 30%，高於上一季的 29%。正如我們在過去一年中所傳達的那樣，我們計劃增加投資以尋求長期成長機會，而這項營運支出成長表明了我們招募計畫的執行情況。

Q2 operating income was $164 million for a 20% operating margin compared to 22% last quarter and 24% in the year ago quarter. Within that, as we've noted, we held our DASH user conference in June. And as expected, the event cost $13 million. We also experienced a rising impact from the weaker dollar and absorbed $6 million of negative FX impact during Q2. Excluding those expenses, operating income would have been 22% in Q2 or 200 basis points higher.

第二季營業收入為 1.64 億美元，營業利潤率為 20%，而上一季為 22%，去年同期為 24%。正如我們所提到的，我們在 6 月舉辦了 DASH 用戶大會。正如預期的那樣，這項活動花費了 1300 萬美元。我們也受到了美元疲軟的影響，第二季承受了 600 萬美元的負面外匯影響。除去這些費用，第二季的營業收入將成長 22%，即高出 200 個基點。

And now turning to the balance sheet and cash flow statements. We ended the quarter with $3.9 billion in cash, cash equivalents and marketable securities. And our cash flow from operations was $200 million in the quarter, after taking into consideration capital expenditures and capitalized software, free cash flow was $165 million for a free cash flow margin of 20%.

現在轉向資產負債表和現金流量表。本季末，我們擁有 39 億美元的現金、現金等價物和有價證券。本季我們的營運現金流為 2 億美元，在考慮資本支出和資本化軟體後，自由現金流為 1.65 億美元，自由現金流利潤率為 20%。

And now for our outlook for the third quarter and the remainder of fiscal 2025. First, our guidance philosophy overall remains unchanged. As a reminder, we base our guidance on recent trends observed and apply conservatism on these growth trends. For the third quarter, we expect revenues to be in the range of $847 million to $851 million, which represents a 23% year-over-year growth.

現在來展望一下 2025 財年第三季和剩餘時間的前景。首先，我們的指導理念大致保持不變。提醒一下，我們的指導基礎是最近觀察到的趨勢，並對這些成長趨勢採取保守態度。對於第三季度，我們預計營收將在 8.47 億美元至 8.51 億美元之間，年增 23%。

Non-GAAP operating income is expected to be in the range of $176 million to $180 million, which implies an operating margin of 21%. And non-GAAP net income per share is expected to be $0.44 to $0.46 per share based on approximately 364 million weighted average diluted shares outstanding.

非公認會計準則營業收入預計在 1.76 億美元至 1.8 億美元之間，這意味著營業利潤率為 21%。基於約 3.64 億股加權平均稀釋流通股，非 GAAP 每股淨收益預計為 0.44 美元至 0.46 美元。

For fiscal 2025, we expect revenue to be in the range of $3.312 billion to $3.322 billion, which represents a 23% to 24% year-year growth. Non-GAAP operating income is expected to be in the range of $684 million to $694 million, which implies an operating margin of 21%. And non-GAAP net income per share is expected to be in the range of $1.80 to $1.83 per share based on approximately 364 million average diluted shares.

對於 2025 財年，我們預計營收將在 33.12 億美元至 33.22 億美元之間，年增 23% 至 24%。非公認會計準則營業收入預計在 6.84 億美元至 6.94 億美元之間，這意味著營業利潤率為 21%。基於約 3.64 億股平均稀釋股份，非 GAAP 每股淨收益預計介於 1.80 美元至 1.83 美元之間。

Some additional notes on our guidance. We expect net interest and other income for fiscal 2025 to be approximately $150 million. And due to the impact of the recent federal tax legislation, we now expect cash taxes for 2025 to be about $10 million to $20 million. We continue to apply a 21% non-GAAP tax rate for 2025 and going forward. And finally, we expect capital expenditures and capitalized software together to be 4% to 5% of revenues in fiscal year 2025.

關於我們的指導的一些補充說明。我們預計 2025 財年的淨利息和其他收入約為 1.5 億美元。而且由於最近聯邦稅收立法的影響，我們現在預計 2025 年的現金稅將約為 1,000 萬至 2,000 萬美元。我們將在 2025 年及以後繼續採用 21% 的非 GAAP 稅率。最後，我們預計資本支出和資本化軟體合計將佔 2025 財年收入的 4% 至 5%。

To summarize, we are pleased with our execution in Q2, including the many products and features we launched at DASH. We are well positioned to help our existing and prospective customers with their cloud migration and digital transformation journeys, including their adoption of AI.

總而言之，我們對第二季的執行感到滿意，包括我們在 DASH 推出的許多產品和功能。我們完全有能力幫助現有和潛在客戶進行雲端遷移和數位轉型，包括採用人工智慧。

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

我要感謝全球所有 Datadog 的努力。現在，我們將開始提問。接線員，我們開始問答。

(Operator Instructions)

（操作員指示）

Raimo Lenschow, Barclays.

巴克萊銀行的 Raimo Lenschow。

Perfect. Two quick questions from me. Olivier, like you talked about the AI contribution and slowly broadening out. How should we think about it in terms of when this goes much broader into inference, et cetera? So does that everyone like Barclays, JPMorgan, et cetera, they all kind of need to do more around observability because they're going to do more inference, et cetera?

完美的。我想問兩個簡單問題。奧利維爾，就像你談到的人工智慧的貢獻和慢慢擴大的範圍一樣。當這個問題涉及更廣泛的推理等等時，我們應該如何思考這個問題？那麼，巴克萊、摩根大通等所有人，是否都需要在可觀察性方面做更多工作，因為他們將進行更多的推理等等？

So in a way, like OpenAI, et cetera, is just setting the scene for the future? And what do you think about the market opportunity there? And then, David, in the second half of last year, you hired a lot of extra sales guys. Can you talk a little bit about that ramp and where they are in their productivity curve?

那麼從某種程度上來說，像 OpenAI 等等只是為未來奠定基礎嗎？您認為那裡的市場機會如何？然後，大衛，去年下半年，你僱用了很多額外的銷售人員。您能否稍微談談這個坡道以及他們在生產力曲線上的位置？

Yeah. On the AI opportunity. So there's really multiple layers to it. The first layer is largely what we see today, which is companies that are running their inference stacks and the application in cloud environment. So that's the case of the model makers or you think of the companies that are doing coding agents, things like that. That is what we see today. And it looks a lot like normal compute. So you have normal machine CPUs, some GPUs quite a few other components, web service, things like that. So that's the bulk of what we see today. And there will be more of it as the AI applications come into production.

是的。關於人工智慧機會。所以它其實有多個層次。第一層基本上就是我們今天所看到的，在雲端環境中運行其推理堆疊和應用程式的公司。這就是模型製作者的情況，或者你想到從事編碼代理商等業務的公司的情況。這就是我們今天所看到的。它看起來很像普通的計算。所以你有普通的機器 CPU、一些 GPU、相當多的其他元件、網路服務等等。這就是我們今天所看到的大部分內容。隨著人工智慧應用投入生產，這種情況還會越來越多。

There are more specialized inference workloads and even training work within some situations that rely on instrumenting GPUs. And for that, we have a new product out there that does GPU monitoring that we announced at DASH. But all that I would call the infrastructure layer of AI.

在某些情況下，有更多專門的推理工作負載，甚至還有依賴 GPU 的訓練工作。為此，我們在 DASH 上發布了一款可以進行 GPU 監控的新產品。但我把這一切都稱為人工智慧的基礎設施層。

Then on top of that, there's new problems in terms of understanding what the applications themselves are doing and the applications are largely nondeterministic anymore. They're either are run by a model that is nondeterministic by nature or they run in code that was not as carefully written as it used to be, it's not completely written by humans, just largely written by AI agent.

除此之外，在理解應用程式本身的功能方面還存在新的問題，而且應用程式在很大程度上是不確定的。它們要么由本質上不確定的模型運行，要么運行在不像以前那樣仔細編寫的程式碼中，它不是完全由人類編寫的，只是主要由人工智慧代理編寫。

And as a result, you also need to spend a lot more time understanding how that code is working and that largely happens in production. So that's a brand-new area of observability, which is how you deal with applications that have not been completely defined in development and that have to be evaluated in production.

因此，您還需要花費更多的時間來了解程式碼的工作原理，而這主要發生在生產中。所以這是一個全新的可觀察性領域，即如何處理在開發中尚未完全定義並且必須在生產中進行評估的應用程式。

And what we think is the whole market is going there, not just the AI natives, the AI natives are definitely doing that today, both applications are running on models and code that has been largely written by agent, but the rest of the market is going there. And the best proof point you see of that is the very, very broad adoption today, both of the API gated AI model and of the coding agents, which you see in every single large enterprise there.

我們認為整個市場都在朝著這個方向發展，不僅僅是人工智慧原生企業，人工智慧原生企業今天肯定在這樣做，這兩個應用程式都在由代理商編寫的模型和程式碼上運行，但其餘市場也在朝著這個方向發展。而您看到的最好的證明點就是如今非常廣泛的採用，包括 API 門控 AI 模型和編碼代理，您可以在每個大型企業中看到它們。

Yeah. And as to sales capacity, we have been successful in increasing both our number of salespeople and our ramp sales capacity. We started that, as you said, in the last part of 2025. And we are seeing evidence of that through our new logo production and our pipeline. We need to, as we talked about previously, go through the ramping of that. But in looking at the size and productivity and performance, we see some good signs that, that quota capacity is becoming productive.

是的。至於銷售能力，我們成功地增加了銷售人員的數量並提高了銷售能力。正如您所說，我們在 2025 年末開始實施此計劃。我們透過新標誌的製作和管道看到了這一點的證據。正如我們之前談到的，我們需要逐步實現這一目標。但從規模、生產力和績效來看，我們看到一些好跡象，顯示配額產能正在變得有生產力。

Thank you.

謝謝。

Sanjit Singh, Morgan Stanley.

摩根士丹利的 Sanjit Singh。

Congrats on the really stellar results this quarter. David, when I look at the guide, I mean, this is probably one of the more impressive guys coming out of the Q2 that I've seen in a couple of years. If I square that against the commentary that you guys made on the AI native cohort that look, there could be volatility from this cohort.

恭喜本季取得的出色業績。大衛，當我看指南時，我的意思是，這可能是我幾年來見過的 Q2 中最令人印象深刻的人物之一。如果我將其與你們對人工智慧原生群體的評論進行對比，那麼這個群體可能會出現波動。

When I kind of put those two together, the guidance is really strong. And so when I think about that potential risk, is it fair to assume that it's not something that you're seeing right now and may come to play later on down the road? Because the guidance seems really strong, it doesn't seem to, at least on the face, doesn't it seem to anticipate that much volatility from the AI native cohort.

當我將這兩者結合起來時，指導作用就非常強了。因此，當我考慮這種潛在風險時，是否可以公平地假設它不是您現在看到的事情，並且可能會在以後發揮作用？因為該指導看起來確實很強勁，至少從表面上看，它似乎沒有預料到人工智慧原生群體會出現那麼大的波動。

Yeah. I think we gave metrics indicating that based on what we saw in the quarter and we're seeing now that the AI cohort continues to grow quite rapidly, and we're winning a good market share in that, and so how we incorporate that into the guidance is, as we discussed previously, we know that there might be volatility in usage or in -- as we negotiate contracts in unit rates. And so therefore, we adopt conservative assumptions as to that performance in the remainder of the year. It's not something, as you can tell from the growth metrics that we see yet in our results. But as we learned in the previous cycle of cloud natives, there can be volatility, and we want to make sure we incorporate that in our guidance.

是的。我認為我們給出的指標表明，根據我們在本季度看到的情況，我們現在看到人工智慧群體繼續快速增長，並且我們在該領域贏得了良好的市場份額，因此我們如何將其納入指導中，正如我們之前討論過的，我們知道使用情況或——在我們以單位費率談判合約時，可能會出現波動。因此，我們對今年剩餘時間的表現採取保守的假設。從我們在結果中看到的成長指標就可以看出，這並不是什麼大事。但正如我們在上一個雲端原生週期中所了解的那樣，可能會存在波動，我們希望確保將其納入我們的指導中。

Perfect. And then, Olivier, with the new security disclosures, congrats on crossing the $100 million threshold, is there any sort of change in the volume behavior there's been consolidation in the industry? You guys have been advancing your portfolio quite significantly, you guys have fully autonomous security agents. What's your prospect for this pool of the business, this part of the business to drive growth for the balance of the year and going into 2026?

完美的。然後，奧利維爾，隨著新的安全披露，祝賀您突破了 1 億美元的門檻，行業整合的交易量行為是否有任何變化？你們的投資組合取得了顯著進步，你們擁有完全自主的安全代理。您認為這部分業務、這部分業務將如何推動今年餘下時間以及 2026 年的成長？

Yes. So we have a very good product set. I mean we mentioned we have three different products in there. There are a couple of those products that are really, I would say, reaching an inflection point in terms of what they're doing on the customer side.

是的。所以我們有一套非常好的產品。我的意思是我們提到我們有三種不同的產品。我想說，其中有幾款產品在客戶端的表現確實已經達到了一個轉捩點。

When I think of where we're successful today in security, we're very successful at getting broad adoption like a large number of customers, a few customers that are spending $1 million-plus on security with us. So we're good with the -- we're happy with the proof points we have there.

當我想到我們今天在安全方面取得的成功時，我們非常成功地獲得了廣泛的採用，例如大量客戶，一些客戶在我們這裡花費了 100 多萬美元用於安全。因此，我們對此感到滿意——我們對現有的證據點感到滿意。

What we haven't done very well yet is getting standardized adoption wall-to-wall in large enterprises, and that's the next focus for us on the security side. And some of that is product work, but a lot of it is customizations to go to market there, so we get better at selling enterprise-wide security top down, which is not something we have done a lot of in the past.

我們目前還未做好的是在大型企業中全面採用標準化，這是我們在安全方面的下一個重點。其中一些是產品工作，但很多是進入市場的客製化工作，因此我們可以更好地自上而下地銷售企業範圍的安全性，而這不是我們過去做過很多的事情。

So that's sort of where we are as a product. So happy with where we are. A lot of groundwork has been done on the product side, but there's quite a bit more work to be done and a ton more opportunity in front of us. So we're -- that's why we're focusing on it.

這就是我們的產品現狀。我們對現狀非常滿意。在產品方面我們已經做了很多基礎工作，但是還有很多工作要做，我們面前還有大量的機會。所以我們——這就是我們關注它的原因。

Thank you.

謝謝。

Kash Rangan, Goldman Sachs.

高盛的卡什·蘭根（Kash Rangan）。

This is Matt Martino on for Kash Rangan. David, you called out enterprise consumption volatility the last quarter. It sounds like that may have been consistent this time around, while SMB continues to improve. So could you perhaps characterize any discernible trends between these two customer demographics, what went right relative to your expectations heading into 2Q? And really how that informs your second half guide? That's it for me.

這是 Kash Rangan 的 Matt Martino。大衛，你指出上個季度企業消費出現波動。聽起來這次可能一直如此，而 SMB 則繼續改進。那麼您能否描述這兩個客戶群之間的任何明顯趨勢，相對於您對第二季度的預期，哪些方面取得了進展？那麼這對您的下半年指南有何指導作用？對我來說就是這樣。

Yes. I think broadly, we're calling out that the usage trends across the segments were roughly consistent with the previous quarters. We said we did see some more concentrated. This is not a comment about AI, this is comment about enterprise take down -- less and less consumption relative to a spike, but we saw that stabilize. And we've seen small but gradual improvement of the SMB as it results of their usage of our products.

是的。我認為，整體而言，我們認為各個細分市場的使用趨勢與前幾季大致一致。我們說我們確實看到了一些更集中的情況。這不是關於人工智慧的評論，而是關於企業下滑的評論——相對於高峰而言，消費越來越少，但我們看到它趨於穩定。我們看到，由於使用我們的產品，中小企業的狀況正在逐漸改善。

Mark Murphy, JPMorgan.

摩根大通的馬克墨菲。

I'll add my congrats. So Olivier, I actually wanted to ask you about those announcements, it looks like you're bringing very serious AI research to a space where it is applicable. And opening it up very broadly, the size of the data set is vast. I'm curious what type of response do you expect to see here? And just help us understand maybe how that can sustain growth in future years? And I have a quick follow-up for David.

我要表達我的祝賀。所以奧利維爾，我實際上想問你關於這些公告的問題，看起來你正在將非常嚴肅的人工智慧研究帶到一個適用的領域。將其廣泛開放，數據集的規模是巨大的。我很好奇您希望在這裡看到什麼類型的回應？能否幫助我們了解如何在未來幾年維持成長？我有一個關於大衛的快速跟進問題。

Look, we think there's so much opportunity in automation with autonomous AI agents. Like we really broke it out in three different categories so far and one is the SRE and responding to alerts and investing in alerts and those issues.

看，我們認為自主人工智慧代理在自動化領域有很多機會。就像我們到目前為止將其分為三個不同的類別一樣，一個是 SRE 和響應警報以及對警報和這些問題進行投資。

Second one is coding, fixing issues that we find in the code that happened in production and verifying the fixes ourselves. And the last one is security on investigating it in security signals on our own so that customers don't have to do that themselves.

第二個是編碼，修復我們在生產中發現的程式碼問題並自己驗證修復。最後一點是，我們自行調查安全訊號，以確保客戶不必自己做這件事。

There's so much and that can happen there. A lot of it is going to depend on great research, which is why we built a research team, and which is why we developed and released with open weight research models already. Of course, the next step after releasing this research model is to incorporate them into the product, so that's also one other thing we're working on right now.

那裡有太多的事情可以發生。這在很大程度上取決於出色的研究，這就是我們建立研究團隊的原因，也是我們開發和發布開放重量研究模型的原因。當然，發布這個研究模型之後的下一步是將它們融入產品中，這也是我們現在正在努力的另一件事。

But there's just so much opportunity in front of us there that at this point, we're happy we got a great start, we've got fantastic results in our first release as a research output is really state-of-the-art model that beats every single other model in a category that has seen quite a bit of action over the years, time series forecasting is -- has very wide applicability in a lot of different domains.

但我們面前有如此多的機會，在這一點上，我們很高興我們有了一個很好的開端，我們在第一次發佈時就取得了出色的成果，因為研究成果是真正最先進的模型，它擊敗了多年來在這一類別中已經看到相當多行動的所有其他模型，時間序列預測 - 在許多不同領域具有非常廣泛的適用性。

So I think it shows that we can perform at the highest level there. And I think it's a great sign of things to come in terms of AI automation and AI agents.

所以我認為這表明我們可以在那裡發揮出最高水準。我認為這是人工智慧自動化和人工智慧代理未來發展的良好訊號。

Okay. And then, David, we keep pointing out that Datadog is one of the only software companies that are investing seriously in headcount growth. And it feels like that is paying top line dividends pretty tremendously today. We noticed the R&D spending is up noticeably in Q2. Just wondering what are the mechanics that are driving that on the R&D line? And then -- the flip side is what's allowing you to guide operating income so much higher in Q3 than you had guided that for Q2?

好的。然後，大衛，我們一直指出，Datadog 是唯一一家認真投資於員工人數成長的軟體公司之一。感覺今天這正在帶來相當豐厚的收入紅利。我們注意到第二季的研發支出明顯增加。我只是想知道在研發線上推動這項進程的機制是什麼？那麼，另一方面，是什麼讓您預測第三季的營業收入比第二季高得多？

Yeah. In R&D, as we talked about, we had an aggressive investment plan and we've been able to execute. And I think our recruitment, credit to our recruitment team, we've been able to get people in the door, the right people earlier in the year. There are some things within that around FX that weigh a little bit on it because, as you know, we do have a significant R&D center in Paris.

是的。在研發方面，正如我們所談到的，我們有一個積極的投資計劃，並且我們已經能夠執行。我認為我們的招募團隊值得稱讚，我們能夠在今年早些時候找到合適的人才。其中圍繞 FX 的一些事情對此有一點影響，因為如你所知，我們在巴黎確實有一個重要的研發中心。

But I think the overall trend is the execution and recruiting. We talked about some of the factors in Q2 that caused the operating income to increase -- greater to increase at a rate of 36%. And some of those are things like the timing of DASH, we talked about $13 million; the FX. And I think that we have good line of sight on the drivers in R&D, both in terms of as we talked about and some of the operating expenses are -- have some seasonality in it.

但我認為總體趨勢是執行和招募。我們討論了第二季導致營業收入增加的一些因素——成長率高達 36%。其中一些是 DASH 的時間安排，我們談到了 1300 萬美元；FX。我認為，我們對研發的驅動因素有很好的了解，無論是就我們所討論的方面而言，還是就一些營運費用而言，都有一定的季節性。

The one thing I would add, which is that we also are spending more on AI training and inference in R&D if you compare it to past years. And the output of that is things such as total or the next versions of it that we're training right now and experiments we're running to train agents, run simulations agents and things like that. You shouldn't expect the overall picture of R&D investment to change in the future, although I think we expect the same envelope to be what we use moving forward.

我想補充一點，與過去幾年相比，我們在人工智慧訓練和推理方面的研發投入也更多了。其輸出就是我們現在正在訓練的總數或下一個版本，以及我們正在進行的訓練代理、運行模擬代理等的實驗。你不應該期望未來研發投資的整體情況會發生變化，儘管我認為我們期望未來會使用相同的範圍。

Yes, I'll add that and really call out to our R&D team and our FinOps that we said last quarter that we were going to focus on how we use cloud. That applies to both the gross margin. And as you know, we use a lot of our applications internally. And we were quite successful in Q2 in that run rate we expect to continue forward in optimizing our cloud usage, which is -- will have an effect on the margins and the OpEx growth rates as we proceed through the year.

是的，我要補充一點，並真正呼籲我們的研發團隊和 FinOps，我們上個季度說過，我們將專注於如何使用雲端。這適用於毛利率。如您所知，我們在內部使用了很多應用程式。我們在第二季度取得了相當大的成功，我們預計將繼續優化我們的雲端使用率，這將對我們全年的利潤率和營運支出成長率產生影響。

Thank you.

謝謝。

Koji Ikeda, Bank of America.

美國銀行的 Koji Ikeda。

We all see that the second quarter was really, really strong. Guidance for 2025 looks really, really great. And so I wanted to ask you about contract visibility. How are you feeling about contract visibility specifically with your large AI native customers?

我們都看到第二季的表現確實非常強勁。2025 年的指導看起來真的非常棒。所以我想問您有關合約可見性的問題。您對與大型 AI 原生客戶的合約可見性有何感受？

I have to imagine you're very close to these customers and having lots of conversations with them. And so I know there is some concern about there. And David, you mentioned potential volatility. So I really want to ask about how you're feeling about contracted visibility?

我必須想像你與這些客戶非常親近並且與他們進行了很多交談。所以我知道那裡存在一些擔憂。大衛，您提到了潛在的波動性。所以我真的很想問一下你對於合約可見性有何感受？

I mean, look, we can't really speak about any specific customers. As a reminder, any individual customer can do whatever they want. They're the heroes of their own stories, and we can't really speak for them.

我的意思是，看，我們實際上不能談論任何特定的客戶。提醒一下，任何個人客戶都可以做任何他們想做的事情。他們是自己故事中的英雄，我們無法真正代表他們說話。

I would say we have strong product engagement from our top customers, in general. We are working on making develop the very best platform for every company at any scale, including scale that have never been seen before in companies with high growth. And I would say it's about it. When you look at the way we forecast the business, remember that we are overall, extremely high retention product.

我想說，總體而言，我們的頂級客戶對我們的產品有著很高的參與度。我們致力於為每家公司開發最好的平台，無論規模大小，包括高成長公司前所未有的規模。我想說的就是這個。當您查看我們預測業務的方式時，請記住我們總體上是具有極高保留率的產品。

For most customers, it's not rational to do it themselves, build their own solutions. We have many customers who did churn to themselves to come back afterwards and we named one on the call today. So we feel confident about the way we forecast the business and the mid- to long term there.

對於大多數客戶來說，自己動手、建立自己的解決方案是不合理的。我們有很多客戶後來都主動流失了，我們在今天的電話會議上提到了其中一位。因此，我們對業務和中長期前景的預測充滿信心。

Of course, as we renegotiate with customers, as they increased volume, et cetera, et cetera, what typically happens is we see short-term drops and long-term growth in the revenue that associated with them, and that's the where we always deployed it.

當然，當我們與客戶重新談判時，隨著他們增加交易量等等，通常會發生的情況是，我們會看到與之相關的收入在短期內下降，但在長期內增長，而這正是我們始終部署它的地方。

Thanks so much. And I did have a follow-up on security. And so it sounds -- I mean, great to hear about the milestones, $100 million around 40%. And so thinking about the product set, how are you thinking about expanding the capabilities from here? Are you focused on more organic, inorganic? And maybe an update to your M&A philosophy? Or I mean, I guess the question here is, are you willing to go much bigger to supplement your security strategy?

非常感謝。我確實對安全問題進行了跟進。聽起來——我的意思是，很高興聽到這些里程碑，1 億美元約佔 40%。那麼考慮一下產品組合，您打算如何從這裡擴展功能？您更關注的是有機物還是無機物？或許您的併購理念會有所更新？或者我的意思是，我想這裡的問題是，您是否願意採取更大的措施來補充您的安全策略？

I mean, look, we're looking at a number of different things in security. There's a lot of companies out there. There's a lot of product areas we cover already in a lot of more product areas we can cover. It's also a space where you need to cover a lot of the -- how we call them, must have table takes futures on one end, but also there's quite a bit of investment in the future with the way the whole field is being disrupted with AI. So there's quite a bit of work to be done there. You should expect us to do more M&A around that as we do in the rest of the business, as there is a lot of assets out there, and there's a lot of opportunities to grow.

我的意思是，看，我們正在研究安全方面的許多不同事物。那裡有很多公司。我們已經涵蓋了許多產品領域，我們還可以涵蓋更多的產品領域。這也是一個需要涵蓋很多內容的領域——我們如何稱呼它們，一方面必須有表格來記錄未來，但隨著整個領域被人工智慧顛覆，未來也需要相當多的投資。所以那裡還有很多工作要做。您應該期待我們像在其他業務領域一樣，圍繞該領域進行更多的併購，因為那裡有很多資產，並且有很多成長機會。

Thank you so much.

太感謝了。

Karl Keirstead, UBS.

瑞銀的卡爾·基爾斯特德（Karl Keirstead）。

Okay. Great. Maybe I'll direct this to David and link the AI native exposure to margins. So David, now that the AI natives are 11% of Datadog's revenue mix. I think it's fair to ask whether the revenues from that cohort are coming at similar margins as the rest of the business? Or do you think that this could be even short term, a modest source of margin pressure?

好的。偉大的。也許我會把這個問題轉達給大衛，並將人工智慧原生曝光與利潤聯繫起來。所以 David，現在 AI 原生代佔了 Datadog 收入組合的 11%。我認為我們應該問一問，該群體的收入是否與其他企業的收入利潤率相似？或者您認為這甚至可能是短期的、適度的利潤壓力來源？

Yeah. I would say like we talked about last quarter, this isn't about the AI in margins, the AI cohort versus AI cohorts. We price based on volume and on term. So to the extent you would have an AI customer who's doing much of the same things as our other customers in the use of the product, has similar volumes and similar terms to the non-AI, it would be similar margins.

是的。我想說，就像我們上個季度討論的那樣，這與利潤中的人工智慧無關，也不是人工智慧群體與人工智慧群體之間的問題。我們的定價是基於數量和期限。因此，在某種程度上，如果你有一個人工智慧客戶，他在使用產品時與我們的其他客戶做很多相同的事情，與非人工智慧客戶有相似的數量和相似的條款，那麼利潤率也會相似。

To the extent that we have a larger customer in there, given our price grids, that customer would get a better discount, that's the way we've always priced. So it really is related to customer size rather than AI native or non-AI native.

如果我們有更大的客戶，根據我們的價格表，該客戶將獲得更好的折扣，這就是我們一直以來的定價方式。因此，這確實與客戶規模有關，而不是與 AI 原生或非 AI 原生有關。

Yes. And I will in commercial. So we did see, as we mentioned last quarter, we were seeing gross margins going down a little bit further than we would like them to. So what happened is we task our engineering teams with optimizing the cloud usage, which goes across all of our customer base.

是的。我也會從事商業活動。因此，正如我們在上個季度提到的那樣，我們確實看到毛利率下降的幅度比我們希望的要大一些。因此，我們要求我們的工程團隊優化雲端的使用，這涉及我們所有的客戶群。

What we did is we turned to our own product, we turned to our cloud cost management products and our profiling product largely. And then, in a matter of months, we really turned up like substantial improvements of savings on our bills and improvements in performance and efficiency of our systems, while we're still shipping new features. And that's something that we're working right now to bring to all of our customers so they can get the same effect and they can see their margins go up as well.

我們所做的是轉向我們自己的產品，我們主要轉向我們的雲端成本管理產品和我們的分析產品。然後，在短短幾個月內，我們確實在帳單節省和系統性能和效率方面取得了顯著改善，同時我們仍在推出新功能。我們現在正在努力為所有客戶提供這項服務，以便他們能夠獲得相同的效果，並且利潤率也能上升。

Got it. And maybe the natural follow-up there is, David, you mentioned that you're optimistic about gross margins in the second half. Is that because of what Olivier just mentioned? Or are there some other drivers you have in mind?

知道了。也許自然的後續問題是，大衛，你提到你對下半年的毛利率持樂觀態度。這是因為 Olivier 剛才提到的嗎？或者您還想到了其他一些車手嗎？

No, it's because of what Olivier mentioned. So we said we were engaging in these efforts. And as we were more successful in the quarter, we will be carrying that run rate forward, which wasn't fully in Q2 as well as, what Olivier mentioned, using cloud cost management and our projects to have further opportunities going forward. So it's really about our progress in pace, which has been successful in our cloud efficiency going forward.

不，是因為奧利維爾提到的事。所以我們說我們正在參與這些努力。由於我們在本季度取得了更大的成功，我們將保持第二季度未能完全實現的運行率，正如 Olivier 所提到的，我們將利用雲端成本管理和我們的專案來獲得更多的發展機會。因此，這實際上與我們的步伐進步有關，這對我們未來的雲端效率產生了成功的影響。

Thank you.

謝謝。

Mike Cikos, Needham.

麥克·西科斯，尼德姆。

I just wanted to double back on the enterprise segment and just -- this is for Oli. But if I'm thinking about it, I know that we have the enterprise demonstrating this stable growth, is it fair to assume -- like is the analogy for enterprises who are more traditional using CPU versus the AI native companies are growing investment in GPUs is it analogous to like 15 years ago where we saw, hey, on-prem continues to see investment, but maybe more dollars are going towards cloud. Is that like a fair analogy when we think about what sort of behavior is exhibited by these different customers and where Datadog is headed?

我只是想再次回到企業領域——這是給奧利的。但如果我考慮一下，我知道我們的企業表現出了這種穩定的增長，是否可以公平地假設 - 就像那些更傳統地使用 CPU 的企業與 AI 原生公司在 GPU 上的投資不斷增加一樣，這是否類似於 15 年前我們看到的，嘿，本地部署繼續獲得投資，但也許更多的資金流向了雲端運算。當我們思考這些不同客戶表現出什麼樣的行為以及 Datadog 的發展方向時，這是否是一個合理的類比？

I don't know if you can say it exactly this way because at the time, the on-prem versus cloud is tended to be different customers -- this was to be the same customers, whereas today, like the and the enterprise are different companies altogether.

我不知道您是否可以這樣說，因為當時，本地部署和雲端往往針對不同的客戶——這是相同的客戶，而今天，和企業完全是不同的公司。

I think the main difference is the AI natives have businesses that are growing very, very fast and infrastructures that are growing very, very fast in sales, whereas the enterprises are still going through a controlled migration from on-prem into the cloud.

我認為主要的差異在於，人工智慧原生企業的業務成長非常快，基礎設施的銷售額成長也非常快，而企業仍在經歷從本地到雲端的受控遷移。

And the rate there is more limited by their bandwidth to undergo that migration as opposed to being driven by an explosion of traffic on the demand side for them. If I look at our Enterprise segment, in general, we see great trends in terms of the bookings, in terms of new products attach new customers, things that these customers are buying from us that are net new.

而且那裡的遷移速率更多地受到其頻寬的限制，而不是由需求方流量的爆炸性增長所驅動。如果我看一下我們的企業部門，總體而言，我們會看到預訂量、新產品吸引新客戶以及這些客戶從我們這裡購買的全新產品方面的巨大趨勢。

But we see that the usage growth is a bit more moderate than that at this point. And I think that speaks to the bandwidth on there and just to move the workload and to go there, and that relates in part to the fact that a lot of their attention is spent on figuring out what AI technology they're going to adopt and how they're going to ship these AI applications into production.

但我們發現，目前的使用量成長略顯溫和。我認為這說明了那裡的頻寬以及工作負載的轉移，這在一定程度上與他們將大量注意力放在弄清楚他們將採用什麼人工智慧技術以及如何將這些人工智慧應用程式投入生產有關。

Overall, we see that rate as stable. So we think this is healthy. But we think, this is -- we think we will see more growth from these enterprise customers as they actually get into production with the AI applications in the future.

總體而言，我們認為該利率是穩定的。所以我們認為這是健康的。但我們認為，隨著這些企業客戶未來真正開始使用人工智慧應用進行生產，我們將看到他們實現更多的成長。

Understood. And congrats on the security. I didn't want to leave hanging. I don't know if we got commentary on it, but could we please get an update on Flex Logs? I know it was a shining star if I go back a quarter ago, but just wanted to see how progress is tracking on the Flex Logs side of the house?

明白了。祝賀你們的安全。我不想留下任何懸念。我不知道我們是否對此有評論，但我們能否獲得有關 Flex Logs 的更新？我知道如果我回到一個季度前，它是一顆耀眼的明星，但我只是想看看 Flex Logs 這邊的進展如何？

Yeah. All of the big deals with enterprise customers now involve Flex Logs in some form. And that's a story that resonates very well when we -- especially when we have customers that want to migrate from the legacy solutions from logs. So there's a number of things that we're working on with them, in particular, making sure the migration is painless for them that there's a number of things that we're investing in on that side.

是的。現在與企業客戶的所有大型交易都以某種形式涉及 Flex Logs。當我們有客戶想要從日誌的傳統解決方案遷移時，這個故事引起了我們的強烈共鳴。因此，我們正在與他們合作進行多項工作，特別是確保遷移對他們來說毫無痛苦，我們在這方面進行了許多投資。

But Flex Logs is a big draw for them as it really changes the picture economically and the predictability of the observability cost for them, which is a major concern for [data intensive class Flex Logs].

但 Flex Logs 對他們來說具有很大的吸引力，因為它確實改變了經濟狀況，並提高了可觀察性成本的可預測性，這是他們的主要擔憂[資料密集型類別 Flex Logs]。

Great. Thank you.

偉大的。謝謝。

Jake Roberge, William Blair.

傑克羅伯格、威廉布萊爾。

There's obviously been a lot of talk about AI natives around the business. I know you've talked about potential for optimization for several quarters, but we continue to see really strong growth in that segment. So if you were to see optimization, when would you expect that to happen? And as you get a wider swath of customers in that AI native cohort, do you think you're at a place where you could actually digest in optimization by one or two of those customers?

顯然，業界已經有很多關於人工智慧原生代的討論。我知道您已經談論了幾個季度的優化潛力，但我們仍然看到該領域的強勁成長。那麼，如果您看到優化，您預計它什麼時候會發生？隨著您在 AI 原生群體中擁有更廣泛的客戶群，您是否認為您實際上可以透過一兩個客戶來消化優化？

Well, I mean, look, if I knew when it was going to happen, I would tell you. The nature of our customers is they grow, they have their own businesses to run, they have their own constraints. We're here to have them delivered our services, and that's what we work on every single day. Now every now and then, there's a renegotiation in a renewal on the cadence for customers to figure out what you need to optimize, what you need to do for the future. But we never know whether it's going to happen this quarter and next quarter and three quarters next year, never. That's really hard to tell.

嗯，我的意思是，如果我知道什麼時候會發生，我會告訴你。我們的客戶的本質是他們在成長，他們有自己的業務要經營，他們有自己的限制。我們來這裡是為了向他們提供我們的服務，這就是我們每天的工作。現在，時不時地，我們會在續約節奏上進行重新談判，以便客戶弄清楚需要優化什麼，以及未來需要做什麼。但我們永遠不知道這是否會在本季、下個季度以及明年三個季度發生。這確實很難說。

Okay. That's helpful. And then could you also talk about the uptake and feedback that you're getting for your own AI solutions like Bits AI, the new observability agents? And when do you think those could really start layering into the model?

好的。這很有幫助。然後，您能否談談您自己的 AI 解決方案（例如 Bits AI 和新的可觀察性代理）的接受度和回饋？您認為這些什麼時候才能真正開始融入模型中？

Yes. So look, the initial response to the AI agents is really very positive. So the AI actually works surprisingly well. If you think of how far technology has grown in a number of a couple of years. And so right now, we're basically shipping it to as many customers as we can and customer with it.

是的。所以看起來，對人工智慧代理的初步反應確實非常積極。所以人工智慧實際上運行得出奇地好。如果您想想幾年內科技取得了多大的進步。所以現在，我們基本上正在將其運送給盡可能多的客戶。

And that's a big area of focus in the business as well. I think will develop by a fairly small team, the actual product that we ship. And now we're busy scaling that up as fast as we can, so we can sell all those customers. That's a core focus of the business today. So the initial response is very positive. We've had customers purchase an pretty quickly in that trial. And so we feel very good about it.

這也是我們業務關注的重點領域。我認為我們將由一個相當小的團隊來開發我們實際交付的產品。現在我們正忙於盡快擴大規模，以便能夠向所有客戶銷售產品。這是當今業務的核心重點。因此最初的反應非常積極。在試用期間，我們的客戶很快就購買了產品。因此我們對此感到非常高興。

Great. Helpful. Thanks for taking the question. And congrats on the results.

偉大的。很有幫助。感謝您回答這個問題。並對所取得的成果表示祝賀。

Brent Thill, Jefferies.

布倫特·蒂爾（Brent Thill），傑富瑞集團。

David, just on the quota-carrying rep capacity, I know you've been investing aggressively ahead of the curve. But when you think about 2025 or -- are you accelerating that count based on the great results you've seen? Are you digesting that count given those reps are on board? Just give us a sense of flavor of what that quota rep count looks like through the rest of the year? And if you can shape how that looks versus '24?

大衛，就配額代表能力而言，我知道您一直在積極投資。但是當您考慮 2025 年或——您是否會根據所看到的出色成果來加速這一計數？考慮到這些代表都參與其中，您是否能理解這個數字？能否讓我們大概了解一下今年剩餘時間內配額代表數量的情況？你能將它與 24 進行比較嗎？

Yeah. What we're doing is we're executing the plan, we entered the year with. We knew -- I think we said that we had underinvested in go-to-market and looked at that with the white space, et cetera. And I would say we're successfully executing that. The plan was a little more front-weighted given our appetite for taking advantage of that opportunity, but we're executing that. And we will look at the -- towards the end of the year as we plan for next year on the metrics around that and try to calibrate how we look at that growth next year.

是的。我們正在做的就是執行我們進入新的一年的計劃。我們知道——我想我們說過我們在市場進入方面投資不足，並且從空白領域等方面來看待這個問題。我想說我們成功地執行了這一點。考慮到我們渴望利用這個機會，這個計劃有點過於前傾，但我們正在執行。我們將在年底前製定明年的計劃，並嘗試調整我們對明年成長的看法。

Okay. And Olivier, I'm just curious, many CEOs are either holding headcount flat or down. We've seen Meta headcount down from two years ago, Microsoft headcount flat, others Palantir they're saying they're going to shrink headcount in 10x revenue. Do you believe you can become more efficient with fewer or do you think that, that model doesn't apply that you're seeing with other software companies?

好的。奧利維爾，我只是好奇，許多執行長要么保持員工人數不變，要么減少員工人數。我們發現 Meta 的員工數量較兩年前有所下降，微軟的員工數量持平，其他公司 Palantir 則表示，他們將以 10 倍的收入比例縮減員工數量。您是否相信可以用更少的資源提高效率，或者您認為該模型並不適用於其他軟體公司？

I mean, look, there's definitely -- the spend is shifting a little bit on the engineering side. As I said, we do see more AI, AI training in France. And so that's definitely changing a bit of the balance between what you have humans do and what you GBUs. That being said, we're still perfectly constrained by the amount of product we can put out there.

我的意思是，你看，工程方面的支出確實在發生一些變化。正如我所說，我們確實在法國看到了更多的人工智慧和人工智慧培訓。因此，這肯定會改變人類所做的事情和 GBU 所做的事情之間的平衡。話雖如此，我們所能推出的產品數量仍然受到嚴格限制。

There's a ton of opportunity in every single direction we look, whether that's on the AI automation, whether it's on the security side, whether that's in the new areas that observability or experimentation that we're going after. And so for us, very strong ROI in the ads that we're making at the moment.

我們所關注的每一個方向都蘊藏著大量的機會，無論是在人工智慧自動化領域，還是在安全方面，還是在我們所追求的可觀察性或實驗的新領域。因此，對我們來說，我們目前製作的廣告的投資報酬率非常高。

Great. Thanks.

偉大的。謝謝。

[Andrew DeGasperi], BNP Paribas.

[安德魯·德加斯佩里]，法國巴黎銀行。

First, on the ramp-up in terms of sales capacity, would you say that's been broad-based in terms of the productivity across both international and domestic?

首先，就銷售能力的提升而言，您是否認為這在國際和國內生產力方面都具有廣泛的基礎？

As we talked about previously, we have a less developed international footprint. And so our growth rate internationally is running higher. We have markets we've talked about before like Brazil and India and parts of AP and Middle East that we have opportunities to grow our footprint.

正如我們之前談到的，我們的國際影響力還不夠大。因此，我們的國際成長率正在不斷提高。我們之前討論過的市場包括巴西、印度以及亞太地區和中東的部分地區，我們有機會擴大我們的影響力。

So we are executing in that way. We're doing a bottoms up. As always, we're looking at the accounts. We're looking at the TAM, and we're looking at how much we're covering it. So that produces a result of a little more investment intensity internationally versus in North America, but there are lots of opportunities in North America as well.

所以我們就是按照這種方式執行的。我們正在乾杯。像往常一樣，我們正在查看帳目。我們正在研究 TAM，並研究我們涵蓋了多少 TAM。因此，與北美相比，國際投資強度略高，但北美也有很多機會。

That's helpful. And then on the enterprise side. I mean, given some of these reps are obviously on the ground, should we expect the number of attach rates in terms of the three or four more products per customer sort of accelerate at this level? I know they've been picking up about 1 point every quarter. Just wondering if that's something we should be seeing?

這很有幫助。然後是企業方面。我的意思是，考慮到其中一些銷售代表顯然是在實地工作，我們是否應該預期，以每個客戶增加三到四種產品計算的附加率會在這個水平上加速？我知道他們每季都會提高大約 1 分。只是想知道這是否是我們應該看到的東西？

Well, I think broadly, we expect the trends that we've seen of landing with some of the core products in the pillars and then expanding to continue. As the platform has expanded, we tended to land with more products, but those trends that we evidenced in the script are we expect to continue in the geographies.

嗯，我認為從總體上看，我們預計我們所看到的趨勢將會繼續下去，即一些核心產品將進入支柱領域，然後繼續擴大。隨著平台的擴展，我們傾向於推出更多產品，但我們預計腳本中所體現的趨勢將在各個地區繼續延續。

And keep in mind, a lot of the -- so when you're in the field, it's always easy to upsell a customer than to land a new customer. And a lot of the work we do in territory management and in comp planning for the sales teams is really to make sure that there's enough of an incentive to go and look for new customers, so we keep driving a number of new customers as well. So there's this balance always between do you direct the sales force at upsetting customers or any new customers.

請記住，很多——所以當你身處該領域時，向客戶追加銷售總是比獲得新客戶更容易。我們在區域管理和銷售團隊薪酬規劃方面所做的大量工作實際上是為了確保有足夠的動力去尋找新客戶，因此我們也能不斷吸引大量新客戶。因此，在指導銷售人員應對不滿意的客戶和新客戶之間始終存在著平衡。

Thank you.

謝謝。

Patrick Colville, Scotiabank.

加拿大豐業銀行的派崔克‧科爾維爾。

And I guess I just wanted to say before I ask my question, congrats on the S&P 500 index inclusion. I mean that's a really nice milestone for you guys. The question we get consistently from investors is on competition. I mean, you referred to your views on competition kind of tangentially in other kind of answers. But maybe more specifically, I mean, what are you seeing competitively in observability? And the one we get asked about a lot is versus Grafana and Chronosphere.

我想在提問之前我只想說，祝賀它被納入標準普爾 500 指數。我的意思是，這對你們來說是一個非常好的里程碑。投資者經常向我們提出的問題是關於競爭。我的意思是，您在其他回答中間接提到了您對競爭的看法。但也許更具體地說，我的意思是，您在可觀察性方面看到了什麼競爭力？我們常被問到的是 Grafana 和 Chronosphere 之間的對比。

Yes. I mean, look, there's always been competition in the field. As I like to say, when I first fundraise for Datadog the world that was coming back to the every single time with every single new was getting from our VCs was crowded space. And so throughout the company, they've been not only incumbents that we mostly have been in the market now, but also a steady stream of new entrants that we also have year after year have beaten in the market.

是的。我的意思是，你看，這個領域一直存在著競爭。正如我想說的，當我第一次為 Datadog 籌集資金時，隨著我們創投公司每次提供的新資金，世界變得十分擁擠。因此，在整個公司中，他們不僅包括我們現在在市場上佔據主導地位的現有企業，而且還包括我們年復一年在市場上擊敗的源源不斷的新進入者。

There's always new companies, always new folks that are building new things in observability. I think it's very attractive for engineers to build that. I would know something about it. Generally speaking, the companies landscape hasn't changed much in the past 10 to 15 years, about the same.

總有新的公司、新的人在可觀察領域建構新事物。我認為建造這個對工程師來說非常有吸引力。我會知道一些關於它的事情。整體來說，過去10到15年，公司格局沒有太大變化，差不多一樣。

The way we win and we will keep winning is by offering an integrated platform that as many problems as possible for our customers end-to-end, so we don't just focus on one data or one specific that our customers now want to use, we solve the whole problem for them end to end.

我們獲勝的方式以及我們將繼續獲勝的方式是提供一個整合平台，為我們的客戶端到端解決盡可能多的問題，因此我們不僅僅關注客戶現在想要使用的一個數據或一個特定數據，我們還為他們端到端解決整個問題。

And then in the long run, we win by being more innovative by having an economic model that lets us invest more in R&D, develop more products, build our existing products into the future faster than anybody else can do and cover more adjacencies faster than anybody else can do, so they can have the broadest platform. So that's the reason we win. And if you look at all of the companies you mentioned, none of them are in a position to do the same. And so that's where we're going to end up in the end.

從長遠來看，我們透過更具創新性的經濟模式獲得勝利，這種經濟模式讓我們能夠投入更多資金進行研發，開發更多產品，以比任何人都更快的速度將現有產品推向未來，以比任何人都更快的速度覆蓋更多相鄰領域，從而擁有最廣泛的平台。這就是我們獲勝的原因。如果你看看你提到的所有公司，你會發現沒有一家公司能夠做到這一點。這就是我們最終的結局。

And I think that's at the end of the call. So that would be the last question. And just to close out, I want to thank our customers for working with us to bring all of those great new products to market. So we had a lot on our plate this year.

我想通話到此結束。這是最後一個問題。最後，我要感謝我們的客戶與我們合作，將所有這些優秀的新產品推向市場。所以今年我們要做的事情很多。

You've seen that at DASH. It was amazing, by the way, to see all these customers and meet with them DASH and see the reception we get for all these new products. And so I want to thank them. And I know we're working with many of them on how these products are going to be adopted and what's going to happen in Q3 and Q4. So again, thank you, and I'll see you next quarter.

你已經看到 DASH了。順便說一句，看到所有這些客戶並與他們會面，看到我們對所有這些新產品的接受度，真是太棒了。所以我要感謝他們。我知道我們正在與其中許多公司合作，研究如何採用這些產品以及第三季和第四季將會發生什麼。再次感謝您，下個季度再見。

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

今天的電話會議到此結束。感謝您的參與，您現在可以斷開連接了。