MongoDB, Inc. (MDB) 2027 Q2 法說會逐字稿

Hello, and welcome to MongoDB's second quarter fiscal '27 earnings call. (Operator Instructions) I would now like to hand the conference over to Jess Lubert, Vice President of Investor Relations. You may begin.

大家好，歡迎參加 MongoDB 2027 會計年度第二季財報電話會議。(接線員指示) 現在我想把會議交給投資人關係副總裁 Jess Lubert。您可以開始了。

Thank you, operator. Good afternoon, and thank you for joining us today to review MongoDB's second quarter fiscal 2027 financial results, which we announced in our press release issued after the close of market today. Joining me on the call today are CJ Desai, President and CEO of MongoDB; and Mike Berry, CFO of MongoDB.

謝謝，接線員。各位下午好，感謝今天加入我們，一同回顧 MongoDB 2027 會計年度第二季財務結果；我們已在今日收盤後發布的新聞稿中對外公布。今天與我一同出席電話會議的有 MongoDB 總裁暨執行長 CJ Desai，以及 MongoDB 財務長 Mike Berry。

During this call, we will make forward-looking statements, including statements related to our market and future growth opportunities, our opportunity to win new business, our expectations regarding Atlas assumption growth, the impact of EA and other business and multiyear license revenue and the long-term opportunity of AI, our financial guidance and underlying assumptions, including expectations regarding profitability and operating margin and our investments in growth opportunities in AI.

在本次電話會議中，我們將發表前瞻性陳述，包括與我們的市場與未來成長機會、贏得新業務的機會、對 Atlas 假設成長的預期、EA 與其他業務及多年期授權收入的影響，以及 AI 的長期機會、我們的財務指引與其基礎假設（包括對獲利能力與營業利益率的預期），以及我們在 AI 成長機會上的投資等相關陳述。

These statements are subject to a variety of risks and uncertainties, including the results of operations and financial conditions that could cause actual results to differ materially from our expectations.

這些陳述受到各種風險與不確定性影響，包括營運結果與財務狀況，可能導致實際結果與我們的預期出現重大差異。

For a discussion of material risks and uncertainties that could affect our actual results, please refer to the risks described in our quarterly report on Form 10-Q for the quarter ended July 31, 2026, filed with the SEC on September 1, 2026.

關於可能影響我們實際結果之重大風險與不確定性的討論，請參閱我們截至 2026 年 7 月 31 日止季度的 Form 10-Q 季度報告中所述風險；該報告已於 2026 年 9 月 1 日向美國證券交易委員會（SEC）提交。

Any forward-looking statements made on this call reflect our views only as of today and we undertake no obligation to update them, except as required by law.

本次電話會議中所作的任何前瞻性陳述僅反映我們截至今日的觀點；除法律要求外，我們不承擔更新之義務。

Additionally, we will discuss non-GAAP financial measures on this conference call. Please refer to the tables in our earnings release on the investor relations portion of our website for a reconciliation of these measures to the most directly comparable GAAP financial measures.

此外，我們將在本次電話會議中討論非 GAAP 財務衡量指標。請參閱我們網站投資人關係專區之財報新聞稿中的表格，以取得這些指標與最直接可比之 GAAP 財務衡量指標的調節表。

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

接下來，我想把電話會議交給 CJ。

Thank you, Jess, and thanks, everyone, for joining us today. I am pleased to share our very strong Q2 results. Total revenue of $772 million, up 30% year-over-year and representing the highest level of quarterly growth seen since fiscal year '24.

謝謝你，Jess，也謝謝各位今天加入我們。我很高興分享我們非常強勁的第二季業績。總營收為 7.72 億美元，年增 30%，並創下自 2024 會計年度以來最高的單季成長水準。

Atlas revenue grew approximately 29% year-over-year for the fifth straight quarter driven by large enterprise customers and building AI momentum. EA and Other had a standout quarter, growing 36% year-over-year due to widespread strength driven by our run anywhere capabilities.

Atlas 營收年增約 29%，已連續第五季成長，主要由大型企業客戶帶動，並建立 AI 動能。EA 與其他業務本季表現突出，年增 36%，原因在於我們「可在任何地方運行」能力所帶來的廣泛強勁需求。

We generated a non-GAAP operating margin of 24%, driven by the strong revenue growth we delivered. We ended the quarter with 70,600 customers, adding a record 2,900 net new customers in the period. Voyage customer count nearly doubled quarter-over-quarter and Atlas Vector Search adoption continues to outpace the growth of the rest of the company, showing our strong early momentum for AI workloads.

我們的非 GAAP 營業利益率為 24%，主要受惠於我們所交出的強勁營收成長。本季結束時客戶數達 70,600 家，期間淨新增客戶 2,900 家，創下紀錄。Voyage 客戶數季增近一倍，而 Atlas Vector Search 的採用成長持續快於公司其他業務的成長，顯示我們在 AI 工作負載方面具備強勁的早期動能。

Our core business remains strong, and our run-anywhere advantage is a key differentiator for this quarter's growth across both Atlas and EA. Enterprises across financial services, health care, tech, are running their most demanding mission-critical workloads on MongoDB, and we are winning more workloads each quarter.

我們的核心業務依然強勁，而「可在任何地方運行」的優勢是本季 Atlas 與 EA 兩大業務成長的關鍵差異化因素。金融服務、醫療保健、科技等產業的企業，正將其最嚴苛、最關鍵任務的工作負載運行在 MongoDB 上，而我們每一季都在贏得更多工作負載。

Increasingly, these same enterprises as well as AI natives are choosing our platform for AI workloads evidenced by the adoption of Atlas Vector Search and Voyage embeddings. My team and I spent another quarter with the C-suite of our customers discussing our data platform for their most pressing core AI and modernization needs.

越來越多這些企業以及 AI 原生公司，正選擇我們的平台來承載 AI 工作負載，這可從 Atlas Vector Search 與 Voyage embeddings 的採用情況得到印證。我和我的團隊在本季再次與客戶的高階主管團隊（C-suite）交流，討論我們的資料平台如何滿足他們最迫切的核心 AI 與現代化需求。

Our Q2 performance is exactly why I'm confident that we are emerging as the real-time intelligent data platform for modern application in the multi-cloud and AI era. I will begin with what I'm seeing in the enterprise.

我們第二季的表現正是我有信心我們正崛起為多雲與 AI 時代現代應用的即時智慧資料平台的原因。我將先從我在企業端所看到的情況談起。

For customers that already run a large part of their data estate on MongoDB, building an agent on top of that data is a natural extension because the data and agent actually needs is live operational data not a stale copy sitting in a warehouse.

對於已經將其大量資料資產（data estate）運行在 MongoDB 上的客戶而言，在這些資料之上建置代理（agent）是很自然的延伸，因為資料與代理實際需要的是即時的營運資料，而不是放在資料倉儲裡的一份過時副本。

Search, Vector Search and Embeddings are built in, not bolted on, so rather than agents connecting to many separate systems, they connect to one platform. We are seeing this show up across industries in a range of use cases, whether it's retrieval of internal knowledge, customer-facing chatbots and agents or fraud and identity workflows.

Search、Vector Search 與 Embeddings 是內建的，而不是後加上去的；因此代理不必連接許多彼此分離的系統，而是連到單一平台。我們看到這在各產業、各種使用情境中逐步浮現，無論是內部知識檢索、面向客戶的聊天機器人與代理，或是詐欺與身分識別工作流程。

It is still early, but we are seeing more of these workloads reach production, such as the Financial Times, which leverages us to power AI-driven discovery reaching millions of readers with interactive experiences at scale.

目前仍在早期階段，但我們看到更多這類工作負載進入正式上線（production），例如《金融時報》（Financial Times）便運用我們來驅動 AI 驅動的探索功能，以可擴展的互動體驗觸及數百萬讀者。

With Vector Search and Voyage, the Financial Times now unifies their operational data and Vector Embeddings on a single platform, building a hybrid full text and Symantec search solution, eliminating the complexity of sinking separate systems and accelerating time to production.

透過 Vector Search 與 Voyage，《金融時報》如今在單一平台上整合其營運資料與向量嵌入（Vector Embeddings），建置混合式的全文與 Symantec 搜尋解決方案，消除串接多套獨立系統的複雜度，並加速上線時程。

By indexing content with a high accuracy Voyage for model and serving 100,000 daily queries on the cost-efficient voice for light model, the Financial Times has significantly cut attributable cost with minimal performance impact. What used to take weeks of manual index monitoring is now finished in a day.

藉由使用高準確度的 Voyage four 模型為內容建立索引，並以具成本效益的 voice for light 模型提供每日 100,000 次查詢服務，《金融時報》在對效能影響極小的情況下，大幅降低可歸因成本。過去需要數週的人工作業索引監控，如今一天就能完成。

Moving on to the momentum we are seeing with Frontier labs who are both customers and partners for us. Multiple leading labs leverage Atlas for workloads that are mission critical to how they ship their products. One lab uses us for inference and chat workloads, after moving away from post guess due to performance legs and outages affecting user experience. They migrated their chat memory system on to Atlas in just four weeks and now run at 10x faster REITs than PostgreSQL. Beyond that, labs uses for research workloads to store experimental results, evaluation data and training artifacts for model development.

接著談談我們在 Frontier labs（前沿實驗室）所看到的動能；他們同時是我們的客戶與合作夥伴。多家領先的實驗室使用 Atlas 來承載對其產品交付方式至關重要的關鍵任務工作負載。其中一家實驗室在因效能瓶頸與當機影響使用者體驗而不再使用 post guess 之後，改用我們來支援推論與聊天工作負載。他們僅用四週就把聊天記憶系統遷移到 Atlas，現在的 REITs 速度比 PostgreSQL 快 10 倍。除此之外，實驗室也將我們用於研究工作負載，以儲存實驗結果、評估資料與模型開發的訓練產物。

These relationships are still early and engagement varies lab-by-lab, but we are energized by the traction we are seeing with them.

這些關係仍在早期階段，各實驗室的合作深度也不盡相同，但我們對目前看到的進展感到振奮。

As partners with these Frontier labs, we are enabling the developers and agents building on their platforms to leverage Atlas. Just recently, we launched a fully managed MCP server making it easier for developers and agents to connect directly to MongoDB when they are using Cloud Code, Codex and Grok Build as well as popular coding tools like Cursor and Devon from Cognition. This is how we stay embedded in the AI supply chain for how new applications get built.

作為這些 Frontier labs 的合作夥伴，我們正讓在其平台上建置的開發者與代理能夠運用 Atlas。就在最近，我們推出了全代管的 MCP 伺服器，讓開發者與代理在使用 Cloud Code、Codex 與 Grok Build，以及像 Cursor 與 Cognition 的 Devon 等熱門程式開發工具時，更容易直接連接 MongoDB。這就是我們如何在新應用程式的建置方式上，持續嵌入 AI 供應鏈。

Paul Smith, Chief Commercial Officer at Anthropic, described our technology partnership and recent integration with Claude by noting the best AI applications need a strong database which is why we have long pointed to developers building on Claude to MongoDB Voyage for embeddings. More recently, demand from those developers drove MongoDB to build a new managed MCP server which has seen fast adoption since launch, and now lets developers explore query and manage their MongoDB data without ever leaving Claude.

Anthropic 商務長 Paul Smith 在談到我們的技術合作夥伴關係以及近期與 Claude 的整合時指出，最好的 AI 應用需要強大的資料庫；這也是為什麼我們長期以來一直引導在 Claude 上開發的開發者使用 MongoDB Voyage 來做 embeddings。最近，這些開發者的需求促使 MongoDB 建置新的代管 MCP 伺服器；自推出以來迅速被採用，並讓開發者無需離開 Claude，就能探索查詢並管理其 MongoDB 資料。

The final piece of the AI opportunity is AI native. Companies whose data layer determines whether the product can support rapid scale. Some choose us from day one, others start elsewhere like pro-driven development platforms and migrate to us as they hit scaling limits and real usage arise. That pattern is showing up in the numbers. We added a record 2,900 net new customers this quarter, and many of them are AI natives.

AI 機會的最後一塊是 AI 原生（AI native）。也就是那些其資料層決定產品能否支援快速擴張的公司。有些公司從第一天就選擇我們；另一些則先從其他方案開始，例如 pro-driven development platforms，並在觸及擴展上限且真實使用量出現後遷移到我們這裡。這種模式正反映在數據上。本季我們淨新增 2,900 家客戶，創下紀錄，其中許多都是 AI 原生公司。

Fireflies, a unicorn AI-native start-up is building what it caused the number one AI assistant for work, helping people unlock the knowledge, but it in their conversations. Fireflies serves more than 20 million users across 1 million plus organizations and has processed over 7 billion meeting minutes. Fireflies choose Atlas from day one for its flexible document model over a rigid relational schema and totally runs today runs more than 40 micro services with change streams powering real-time pipelines for analytics and growth intelligence. That lean, scalable foundation has helped their, fuel their hyper growth seamlessly.

Fireflies 是一家獨角獸級、AI 原生的新創公司，正在打造其所稱的第一名工作用 AI 助理，協助人們解鎖對話中的知識，但也在他們的對話之中。Fireflies 服務超過 2,000 萬名使用者，遍及 100 萬多家組織，並已處理超過 70 億分鐘的會議內容。Fireflies 從第一天起就選擇 Atlas，因其彈性的文件模型優於僵化的關聯式綱要；而且如今完全在其上運行，運作超過 40 個微服務，並以變更串流（change streams）驅動即時的分析與成長情報管線。這個精實、可擴展的基礎，已協助他們無縫地推動超高速成長。

We are also seeing strong traction with Voyage, our embedding and rebanking models, which consistently rank at the top of independent leaderboards. In August, we brought Automated Voyage Embeddings to Atlas for one-click vector search setup, launched Voyage Code 4, a model purpose-built for Core and shipped an upgraded reranking API, all keeping Atlas retrieval accuracy for AI ahead of the market. Voice traction is showing up on both ends of the market. Some of our largest existing Atlas customers are beginning to adopt Voyage for the AI use cases, while a large majority of new voyage customers are AI native and have no prior relationship to MongoDB.

我們也看到 Voyage（我們的嵌入與重排序模型）有強勁的市場牽引力，並且在各項獨立排行榜上持續名列前茅。8 月，我們將自動化 Voyage Embeddings 帶到 Atlas，提供一鍵式向量搜尋設定；推出 Voyage Code 4（專為程式碼而打造的模型）；並發布升級版的重排序 API，這些都讓 Atlas 的 AI 檢索準確度持續領先市場。Voyage 的牽引力在市場兩端都很明顯。我們一些最大的既有 Atlas 客戶開始採用 Voyage 來支援 AI 使用情境；同時，多數新的 Voyage 客戶是 AI 原生，先前與 MongoDB 並無合作關係。

EU is one of them. Our Unicorn AI native that automates legal case intake, medical chronologis and demand letter drafting for plaintiff law firms. Eve uses Atlas embedding and reranking API part by Voyage AI’s Rerank ranked 2.5 to surface the most relevant evidence from large sets of case documents. This improves retrial quality directly into use rag layer while simplifying the infrastructure needed to build and evolve these AI experience.

Eve 就是其中之一。我們的獨角獸級 AI 原生公司，為原告律師事務所自動化法律案件受理、醫療時序整理以及求償函（demand letter）撰寫。Eve 使用 Atlas 的嵌入與重排序 API，部分採用 Voyage AI 的 Rerank（排名第 2.5）來從大量案件文件中找出最相關的證據。這能直接提升檢索品質並導入其 RAG 層，同時簡化建置與演進這些 AI 體驗所需的基礎設施。

Turning to Enterprise Advanced. This quarter's strength was widespread across our installed base particularly within financial services, tech and the public sector. Two patterns in how customers are using EA stand out and both point to why this business is strategic for us. The first is AI in governed self-managed environment. This quarter, we brought Search and Vector Search to EA closing a gap between our cloud and self-managed experiences.

接著談 Enterprise Advanced。本季的強勁表現廣泛出現在我們的既有客戶群中，特別是在金融服務、科技與公部門。客戶使用 EA 的方式有兩個模式特別突出，而兩者都指向為何這項業務對我們具有策略性。第一個是：在受治理的自管環境中導入 AI。本季我們將 Search 與 Vector Search 帶到 EA，補上我們雲端與自管體驗之間的落差。

Demand came in immediately and across industries from customers looking to take a consolidated approach to building AI in their own govern, self-managed environment. A major US bank shows what that looks like in practice. EA already serves as the standardized data platform for more than 100 production applications across payments, fraud detections, document processing, customer and account services.

需求立刻湧現，且跨越各行各業；客戶希望在其自有、受治理的自管環境中，以更整合的方式建置 AI。一家美國大型銀行展示了實務上會是什麼樣子。EA 已作為標準化資料平台，支援超過 100 個正式環境（production）應用，涵蓋支付、詐欺偵測、文件處理、客戶與帳戶服務。

This quarter, that bank extended that same environment to GenAI and semantic search for employee advisers chatbots, product search and document intelligence. By bringing operational data, Search and Vector retrievable together, self-managed with EA they keep sensitive customer and conversational data inside their own government environment without sending up separate systems. That gives them a practical foundation to expand AI and across the bank on the same platform already running their most critical operations.

本季，該銀行將同一套環境延伸到 GenAI 與語意搜尋，用於員工顧問聊天機器人、產品搜尋與文件智慧。透過把營運資料、Search 與 Vector 檢索能力整合在一起，並以 EA 自行管理，他們能將敏感的客戶與對話資料保留在自有的受治理環境內，而不必再另外導入獨立系統。這讓他們能在同一個平台上（也就是已承載其最關鍵營運的那個平台）建立可行的基礎，將 AI 擴展到全行。

The second is hybrid deployment, more and more of my customer conversations involve running across multiple clouds and self-managed environments at the same time. For customers on both EA and Atlas, it is an and, not an or.

第二個是混合式部署：我與客戶的對話中，越來越常見的是同時在多個雲端與自管環境中運行。對同時使用 EA 與 Atlas 的客戶而言，這是「兩者都要」，而不是「二選一」。

For example, one of the largest cybersecurity companies runs a substantial estate across both Atlas and EA, and both parts grew meaningfully in the quarter. Nationwide UK, the world's largest building society is also a good example.

例如，其中一家最大的資安公司在 Atlas 與 EA 上都運行了相當可觀的系統版圖，而本季兩個部分都顯著成長。英國 Nationwide（全球最大的建築互助社）也是一個很好的例子。

They now run their growing speed layer application across both EA and Atlas simultaneously giving members real-time access to account and transaction data across every digital channel and supporting more than 24 million weekly app logins.

他們現在同時在 EA 與 Atlas 上運行其成長中的速度層（speed layer）應用，讓會員能在所有數位通路即時存取帳戶與交易資料，並支援每週超過 2,400 萬次的 App 登入。

Splitting that workload across EA and Atlas gives a nationwide stronger operational resilience and help satisfy UK regulatory requirements while simplifying an estate that used to be far more fragmented. Nationwide already runs with us for faster payments, up to 3 million transactions and GBP1.5 billion in value on a peak day on a self-managed dual Cloud EA cluster.

將該工作負載分散在 EA 與 Atlas 之間，使 Nationwide 具備更強的營運韌性，並有助於滿足英國監管要求，同時也簡化了過去更為碎片化的系統版圖。Nationwide 已使用我們的方案來支援 Faster Payments：在尖峰日於自管的雙雲 EA 叢集上，交易量可達 300 萬筆、金額達 15 億英鎊。

Bringing AI self-managed opens net new demand for us and hybrid deployment often means that the strong estate opens the door to net new Atlas conversations within the same customers. EA's profitability also lets us invest more heavily in R&D and go-to-market furthering Atlas growth and our AI road map.

在自管環境中導入 AI 為我們帶來全新的需求，而混合式部署往往也意味著：同一客戶內，既有的強大系統版圖會為全新的 Atlas 對話打開大門。EA 的獲利能力也讓我們能在研發與市場推進上投入更多資源，進一步推動 Atlas 成長與我們的 AI 路線圖。

Finally, I feel great about the leadership team driving innovation across both Atlas and EA. Ben Cefalo owns core products, and Pablo Stern-Plaza owns AI and emerging products. And on the go-to-market side, Ryan Mac Ban has hit the ground running as our new CRO, giving me real confidence in our ability to capture the opportunity ahead.

最後，我對帶領 Atlas 與 EA 兩端創新的領導團隊感到非常有信心。Ben Cefalo 負責核心產品，Pablo Stern-Plaza 負責 AI 與新興產品。在市場推進方面，Ryan Mac Ban 作為我們的新任 CRO 上任後迅速展開工作，讓我對我們把握前方機會的能力充滿信心。

Before I close, I would like to remind everyone that we will be hosting our Investor Day in New York City on September 29, and our local New York user event on September 30. We look forward to seeing many of you there.

在我結束之前，我想提醒大家：我們將於 9 月 29 日在紐約市舉辦投資人日（Investor Day），並於 9 月 30 日舉辦紐約在地使用者活動。期待在現場見到各位中的許多人。

With that, I will turn it over to Mike.

接下來，我把時間交給 Mike。

Thank you, CJ, and good afternoon, everyone. I will walk through the second quarter fiscal '27 results and then turn to our outlook for the third quarter and the balance of the fiscal year. As always, I will be discussing both GAAP and non-GAAP results.

謝謝你，CJ，各位下午好。我將先說明 2027 會計年度第二季的業績，接著談第三季以及本會計年度剩餘期間的展望。一如往常，我會同時討論 GAAP 與非 GAAP 的結果。

As CJ noted, we had another very strong quarter and came in above all of our guidance ranges. Given this performance and the strong momentum across the business, we are rolling the beat from Q2 and raising our second half fiscal '27 guidance, largely driven by strength in Atlas.

如 CJ 所提，我們又迎來一個非常強勁的季度，實際表現高於我們所有的指引區間。基於這項表現以及全公司強勁的動能，我們將第二季的超預期表現延續到下半年，並上調 2027 會計年度下半年的指引，主要由 Atlas 的強勢所帶動。

Before getting into the details, I want to highlight a few key takeaways for the quarter. First, total revenue growth accelerated to 30%, the first time we've reached that level since fiscal '24. Second, this is the fifth consecutive quarter with Atlas growth of approximately 29%.

在進入細節之前，我想先強調本季幾個關鍵重點。第一，總營收成長加速至 30%，這是自 2024 會計年度以來首次達到這個水準。第二，這是 Atlas 約 29% 成長的連續第五個季度。

Third, EA and Other had an exceptional quarter, growing 36% year-over-year driven by EA's growing strategic importance to many of our largest customers and early traction from our Q2 launch of Search and Vector Search on EA. And finally, as a result of these trends, we significantly outperformed our operating margin and EPS guidance, reflecting the strength of our operating model.

第三，EA 與其他（Other）表現非常出色，在 EA 對我們許多最大客戶的策略重要性提升，以及我們於第二季在 EA 上推出 Search 與 Vector Search 所帶來的早期牽引力推動下，年增 36%。最後，受惠於這些趨勢，我們的營業利益率與 EPS 指引均顯著超越，反映出我們營運模式的強勁。

Moving on to the results. Total revenue in the second quarter was $772 million representing 30% year-over-year growth compared to 24% growth in the year ago quarter. Turning to our product breakdown. Atlas revenue grew approximately 29% year-over-year and exceeded our guidance by approximately 300 basis points. Consumption was strong resulting in a third straight beat consistent with our guidance framework.

接著看業績結果。第二季總營收為 7.72 億美元，年增 30%，相較去年同期為 24% 的成長。接著看產品拆分。Atlas 營收年增約 29%，並較我們的指引高出約 300 個基點。使用量（consumption）表現強勁，帶來連續第三次超預期，且符合我們的指引框架。

This is the sixth straight quarter of year-over-year dollar growth in Atlas, adding a record $127 million in the quarter. Our main growth driver this quarter continued to be strength in North America and our largest customers, particularly those in the $100,000 plus ARR cohort consistent with the broader upmarket momentum we have discussed in recent quarters. This continued strength is reflected in our total company net ARR expansion rate, which increased to 122% for the quarter, compared to 119% a year ago and 121% last quarter.

這是 Atlas 連續第六個季度在年對年美元金額上成長，本季新增金額創紀錄達 1.27 億美元。本季主要成長動能仍來自北美與我們最大型的客戶，特別是年經常性收入（ARR）達 10 萬美元以上的客群，與我們近幾季討論的整體向上市場（upmarket）動能一致。這股持續的強勢也反映在全公司淨 ARR 擴張率上：本季提升至 122%，相較一年前為 119%，上一季為 121%。

The quarter-over-quarter increase in net ARR expansion rate was driven by strength in both Atlas and EA. We also continue to see momentum in the AI native cohort and across AI signals, including adoption of Vector Search, new Voyage customers and a continued increase in clusters connecting through MCP.

淨 ARR 擴張率的季對季提升，主要由 Atlas 與 EA 的強勁表現所帶動。我們也持續看到 AI 原生客群與各項 AI 訊號的動能，包括 Vector Search 的採用、新增 Voyage 客戶，以及透過 MCP 連線的叢集數持續增加。

Turning to EA and other revenue. We saw very strong results with revenue growing approximately 30% year-over-year, our strongest quarter in three years. We saw early demand for the Search and Vector Search capabilities we launched on EA Q2, adding retrievable capabilities that enhance our ability to support AI workloads.

接著談 EA 與其他營收。我們看到非常強勁的成果，營收年增約 30%，為三年來最強的一季。我們看到對於我們在 EA 第二季推出的 Search 與 Vector Search 功能的早期需求，新增可檢索能力，提升我們支援 AI 工作負載的能力。

This strength was broad-based reflecting momentum across a number of deals rather than any single transaction with particular strength in financial services, public sector and technology. This continued momentum highlights the strategic importance of EA as customers continue to expand their self-managed footprints to support both traditional and AI applications.

這項強勁表現具廣泛性，反映多筆交易的動能，而非任何單一交易的特別強勢；其中以金融服務、公部門與科技產業尤為突出。這股持續動能凸顯 EA 的策略重要性，因為客戶持續擴大其自我管理的部署規模，以支援傳統與 AI 應用。

EA and other ARR, which normalizes for the impact of duration grew approximately 11% year-over-year, the third consecutive quarter of double-digit ARR growth.

EA 與其他 ARR（已將合約期間長短的影響標準化）年增約 11%，為連續第三季達成雙位數 ARR 成長。

Moving down the P&L. Total non-GAAP gross margin was 75.9%, up approximately 210 basis points year-over-year, and subscription gross margin was 78.3%, up approximately 70 basis points year-over-year. The increase in subscription gross margin was primarily driven by the higher EA revenue mix in Q2.

接著往下看損益表。總非 GAAP 毛利率為 75.9%，年增約 210 個基點；訂閱毛利率為 78.3%，年增約 70 個基點。訂閱毛利率的提升主要來自第二季 EA 營收占比提高。

Moving to profitability. We are excited that Q2 marks our third consecutive quarter of GAAP EPS profitability, and our full year guidance incorporates our expectation to be GAAP EPS profitable for fiscal '27. Non-GAAP income from operations was $186 million for an operating margin of 24% compared to 15% in the year ago period. We continue to be very pleased with our operating margin results, which benefited from the strong revenue performance this quarter.

接著談獲利能力。我們很高興第二季是我們連續第三季達成 GAAP 每股盈餘（EPS）獲利，而我們的全年指引也納入了我們預期在 2027 會計年度達成 GAAP EPS 獲利的假設。非 GAAP 營業利益為 1.86 億美元，營業利益率為 24%，相較去年同期為 15%。我們對本季的營業利益率表現仍感到非常滿意，受惠於本季強勁的營收表現。

Second quarter non-GAAP net income was $163 million or $1.90 per share based on 85.8 million fully diluted shares outstanding. This compares to net income of $87 million or $1 per share on 87.1 million fully diluted shares outstanding in the year ago period.

第二季非 GAAP 淨利為 1.63 億美元，或每股 1.90 美元，係以 8,580 萬股完全稀釋流通股數計算。相較之下，去年同期淨利為 8,700 萬美元，或每股 1 美元，係以 8,710 萬股完全稀釋流通股數計算。

Our remaining performance obligations, which we define as obligations for contracts with a duration greater than 12 months ended the quarter at $1.52 billion, representing year-over-year growth of 91% with the current portion growing 73%.

我們的剩餘履約義務（RPO；我們定義為合約期間超過 12 個月之合約義務）在本季末為 15.2 億美元，年增 91%，其中流動部分年增 73%。

We had a very strong quarter for new customers, adding approximately 2,900 customers sequentially, bringing our total customer count to $70,600, up from 59,900 in the year ago period. Growth continues to be driven primarily by Atlas, which had 69,300 customers at the end of the second quarter compared to 58,500 in the year ago period.

本季新增客戶表現非常強勁，較前一季約增加 2,900 名客戶，使我們的客戶總數達到 70,600 名，高於去年同期的 59,900 名。成長仍主要由 Atlas 帶動；第二季末 Atlas 客戶數為 69,300 名，相較去年同期為 58,500 名。

Within Atlas, Voyage customers roughly doubled quarter-over-quarter for the second consecutive quarter, continuing the encouraging signs of the demand for our AI embedding capabilities. We continue to feel good about the momentum we are seeing with new customers and would remind you that this metric will fluctuate from quarter-to-quarter.

在 Atlas 之中，Voyage 客戶數已連續第二季季對季約翻倍，持續展現市場對我們 AI 嵌入（embedding）能力需求的正向訊號。我們對新客戶動能仍感到樂觀，也提醒各位此指標將會隨季度而波動。

We ended the quarter with nearly 3,000 customers with at least $100,000 in ARR representing 17% year-over-year growth. Revenue growth from this cohort continues to be strong and outpaced total company revenue growth consistent with our move-up market. We also continue to see strong Atlas performance adoption.

本季末我們有接近 3,000 名 ARR 至少 10 萬美元的客戶，年增 17%。該客群帶來的營收成長持續強勁，且增速超過公司整體營收成長，符合我們往中大型市場（move-up market）拓展的策略。我們也持續看到 Atlas 的強勁採用表現。

Of our Atlas customers generating at least $100,000 in ARR, 48% are leveraging two or more features on our platform, which is up from 42% in the year ago quarter, driven largely by Vector and tech search adoption.

在 ARR 至少 10 萬美元的 Atlas 客戶中，有 48% 使用我們平台上的兩項或以上功能，高於去年同期的 42%，主要由 Vector 與技術搜尋（tech search）的採用所帶動。

Turning to the balance sheet and cash flow. We ended the second quarter with $2.4 billion in cash, cash equivalents and short-term investments. During the quarter, we allocated $100 million towards share repurchases and $59 million to settle taxes on employee RSUs.

接著談資產負債表與現金流。第二季末我們持有 24 億美元的現金、約當現金與短期投資。本季我們配置 1 億美元用於庫藏股回購，並支付 5,900 萬美元以結清員工 RSU 的稅款。

Operating cash flow was $142 million compared to $72 million in the year-ago period and free cash flow was $138 million compared to $70 million a year ago. We remain committed to driving meaningful and durable cash flow and through the first half of fiscal '27 we have generated $344 million in operating cash flow and $335 million in free cash flow.

營業現金流為 1.42 億美元，相較去年同期為 7,200 萬美元；自由現金流為 1.38 億美元，相較一年前為 7,000 萬美元。我們仍致力於推動具意義且可持續的現金流；在 2027 會計年度上半年，我們已產生 3.44 億美元的營業現金流與 3.35 億美元的自由現金流。

Now I'd like to share some of the assumptions driving our third quarter outlook and provide some additional detail into how we're thinking about the rest of fiscal '27. As I mentioned earlier, we continue to be pleased with the strong and consistent Atlas growth.

接下來我想分享一些推動我們第三季展望的假設，並補充我們對 2027 會計年度剩餘期間的思考細節。如我先前提到的，我們持續對 Atlas 強勁且一致的成長感到滿意。

Our growth to date has been driven primarily by continued strength with our largest enterprise customers and we expect that to continue in the second half of fiscal '27. Based on this continued momentum, we expect Atlas growth of approximately 26% in Q3 and we are raising our full year growth expectation to approximately 27%, an increase of 300 basis points from the midpoint of our prior guidance.

迄今為止，我們的成長主要由最大型企業客戶的持續強勁表現所帶動，我們預期這股趨勢將在 2027 會計年度下半年延續。基於這股持續動能，我們預期第三季 Atlas 成長約 26%，並將全年成長預期上調至約 27%，較先前指引中點提高 300 個基點。

Our second half guidance raise for total revenue is primarily driven by the strength we are seeing in Atlas. The strength in Atlas is highlighted by the sixth straight quarter of increasing revenue dollar growth year-over-year, strong net ARR expansion rate increasing multiproduct penetration and early signs of adoption of AI workloads. We discussed over the last several quarters in as Atlas has gotten larger, it has become more predictable and less sensitive to revenue movements by any individual customer or cohort. This can be seen in the consistency of the results we have delivered over the last three quarters where we have seen approximately 200 to 300 basis points of outperformance relative to our initial guidance. We used the same guidance framework for our Q3 outlook understanding that Q3 is our toughest compare of the year for Atlas.

我們上調下半年總營收指引，主要由我們在 Atlas 看到的強勁表現所驅動。Atlas 的強勁表現可由以下面向凸顯：連續第六季年對年營收美元成長率提升、強勁的淨 ARR 擴張率、多產品滲透率提高，以及 AI 工作負載採用的早期跡象。我們在過去幾季也討論過，隨著 Atlas 規模變大，其表現變得更可預測，且對任何單一客戶或客群的營收變動敏感度降低。這可從我們過去三季交出的穩定成果看出：相對於初始指引，我們約有 200 至 300 個基點的超預期表現。我們在第三季展望中沿用相同的指引框架，同時也理解第三季是本年度 Atlas 最具挑戰性的比較基期。

For EA and Other, given the strength we saw in the first half, including the demand we are seeing for the Search and Vector Search capabilities we launched on EA Q2 we are raising our full year expectations for EA and Other revenue to approximately 11% growth in fiscal '27 up from our prior guidance of mid-single-digit growth. This is the first time in three years, EA and Other is projected to grow at a double-digit rate for the full year. Our second half guide is consistent with what we shared last quarter. We continue to expect EA and Other revenue to be approximately flat in the second half, with growth in the mid-single digits in the third quarter. Because multiyear deals are inherently hard to predict, we will continue to be prudent in how we guide the EA business.

至於 EA 與其他，由於上半年表現強勁（包括我們在 EA 第二季推出的 Search 與 Vector Search 功能所帶來的需求），我們將 2027 會計年度 EA 與其他營收的全年預期上調至約 11% 成長，高於先前指引的中個位數成長。這是三年來首次，EA 與其他預期在全年以雙位數速度成長。我們對下半年的指引與上季分享的內容一致。我們仍預期 EA 與其他營收在下半年約持平，第三季為中個位數成長。由於多年期合約本質上難以預測，我們將持續以審慎態度提供 EA 業務指引。

We are excited about the growth we are seeing in EA and would encourage you to focus on the full year growth rather than any single quarter since performance will naturally move around period to period.

我們對 EA 的成長感到振奮，並建議各位聚焦於全年成長，而非任何單一季度，因為表現自然會在不同期間之間波動。

Turning to profitability. You can see in the first half fiscal '27 results the leverage in the business model and the ability to drive incremental profitability while still investing in growth initiatives specifically engineering and product innovation. We remain committed to driving both revenue growth and improved profitability. We now expect to expand operating margin by approximately 250 basis points in fiscal '27, 100 basis points higher than the high end of our previous range. We will achieve this expansion while continuing to invest in key growth initiatives across both products and go-to-market.

接著談獲利能力。從 2027 會計年度上半年的結果可以看到商業模式的槓桿效應，以及在持續投資成長計畫（特別是工程與產品創新）的同時，推動增量獲利能力的能力。我們仍致力於同時推動營收成長與獲利能力改善。我們目前預期在 2027 會計年度將營業利益率擴張約 250 個基點，較先前區間上緣高出 100 個基點。我們將在持續投資產品與市場拓展（go-to-market）等關鍵成長計畫的同時，達成此一擴張。

Our product investment remains focused on enhancing our AI and core database capabilities, including on EA, and you will hear more about our new product innovations at our upcoming Investor Day.

我們的產品投資仍聚焦於強化我們的 AI 與核心資料庫能力（包括在 EA 上），您將在即將到來的投資人日聽到更多關於我們新產品創新的消息。

On the go-to-market side, we are investing in accelerating adoption of new product innovations and continuing to focus on our highest growth opportunities by geography and customer segment. We will also continue to invest in quota-carrying headcount, marketing programs and developer awareness. On cash flow, given our strong first half performance, we now expect full year free cash flow to conversion to be at the upper end of our long-term target range of 80% to 100%.

在市場推進（go-to-market）方面，我們正投資以加速新產品創新的採用，並持續聚焦於依地理區域與客戶細分所界定的最高成長機會。我們也將持續投資於具配額責任的員工編制、行銷計畫以及開發者認知。在現金流方面，鑑於我們上半年表現強勁，我們現在預期全年自由現金流轉換率將落在我們長期目標區間 80% 至 100% 的上緣。

Now let's shift how this translates to guidance for the third quarter and fiscal '27. To reiterate, this second half raise is being driven mainly by strength in Atlas. For the third quarter, we expect total revenue of $756 million to $761 million representing 20% to 21% year-over-year growth. We expect non-GAAP income from operations of $152 million to $156 million for an operating margin of approximately 20.5% at the high end of guidance. We expect non-GAAP net income per share of $1.57 to $1.61, based on 87.1 million diluted shares outstanding.

現在讓我們轉到這如何轉化為第三季與 2027 會計年度的財測指引。再次強調，這次下半年上調主要是由 Atlas 的強勁表現所帶動。就第三季而言，我們預期總營收為 7.56 億美元至 7.61 億美元，代表年增 20% 至 21%。我們預期非 GAAP 營業利益為 1.52 億美元至 1.56 億美元，在指引上緣的營業利益率約為 20.5%。我們預期非 GAAP 每股淨利為 1.57 美元至 1.61 美元，係以 8,710 萬股稀釋後流通股數為基礎。

For fiscal '27, we now expect total revenue of $2.99 billion to $3.03 billion representing full year growth of 21% to 23%, which would be the second straight year of total revenue acceleration at the high end of guidance. We expect non-GAAP income from operations of $616 million to $636 million for an operating margin of approximately 21% at the high end of guidance. With the combination of 23% revenue growth and 21% operating margin, we are targeting a Rule of 44 performance at the high end of our fiscal '27 outlook. We expect non-GAAP net income per share of $6.39 to $6.58 based on 86.4 million diluted shares outstanding.

就 2027 會計年度而言，我們現在預期總營收為 29.9 億美元至 30.3 億美元，代表全年成長 21% 至 23%，這將是連續第二年在指引上緣實現總營收成長加速。我們預期非 GAAP 營業利益為 6.16 億美元至 6.36 億美元，在指引上緣的營業利益率約為 21%。在 23% 營收成長與 21% 營業利益率的組合下，我們的目標是在 2027 會計年度展望的上緣達成「44 法則（Rule of 44）」表現。我們預期非 GAAP 每股淨利為 6.39 美元至 6.58 美元，係以 8,640 萬股稀釋後流通股數為基礎。

In closing, I want to thank the entire MongoDB team for another quarter of strong execution. We are pleased with the results, confident in the durability of our growth and remain focused on driving long-term shareholder value as we continue to invest responsibly in the business. Last but not least, we look forward to seeing many of you later this month at our Investor Day. You can find more information on how to register for the live event or listen to the live stream on our IR website.

最後，我要感謝整個 MongoDB 團隊在本季再次展現強勁的執行力。我們對成果感到滿意，對我們成長的持久性充滿信心，並將在持續以審慎方式投資業務的同時，專注於推動長期股東價值。最後但同樣重要的是，我們期待在本月稍晚的投資人日與各位中的許多人見面。您可在我們的投資人關係（IR）網站上找到更多關於如何註冊現場活動或收聽直播的資訊。

With that, operator, let's open it up for questions.

那麼，接線員，讓我們開放提問。

(Operator Instructions) Raimo Lenschow with Barclays.

（接線員指示）Barclays 的 Raimo Lenschow。

Perfect. Congrats on the great quarter. The question I had was on Atlas. If I look at your, if I listen to your guidance comments, the strength driven by Atlas. What are the factors that you're considering there?

很好。恭喜本季表現出色。我想問的是關於 Atlas。如果我看你們的——如果我聽你們對指引的評論，強勁表現是由 Atlas 帶動。你們在那裡考量的因素有哪些？

And what's driving your confidence?

以及是什麼驅動你們的信心？

Thanks for the question, Raimo. It's Mike. So as we talked about, we feel very good about the Atlas business. And what we look at is this was the fifth straight quarter of approximately 29% year-over-year growth, very consistent. We've increased the full year guidance by 300 basis points from the previous guide.

謝謝你的問題，Raimo。我是 Mike。如同我們談到的，我們對 Atlas 業務感覺非常好。我們觀察到的是，這已是連續第五個季度約 29% 的年增率，非常一致。我們也將全年指引較先前指引上調了 300 個基點。

And that is also buttressed by a record net new $127 million net new Atlas dollars as well as the increase in the net ARR expansion rate and now Atlas is almost a $2.3 billion run rate. So as we look forward, we continue to expect really good growth from our larger enterprise customers, especially in the US. We started to see some benefit from AI even though it's small, but we are excited about the momentum and we do expect consumption to continue to be consistent with what we've seen during the first half of the year.

此外，這也受到創紀錄的新增淨額 1.27 億美元 Atlas 淨新增美元、以及淨 ARR 擴張率提升的支撐，而目前 Atlas 的年化執行率（run rate）已接近 23 億美元。展望未來，我們仍預期來自大型企業客戶（尤其在美國）的成長會非常不錯。我們開始看到一些 AI 帶來的助益，雖然目前仍小，但我們對這股動能感到振奮，並確實預期用量（consumption）將持續與我們在上半年所見保持一致。

Alex Zukin with Wolfe Research.

Wolfe Research 的 Alex Zukin。

I guess maybe, CJ, if I look at the business, right, on the first half, clearly, Atlas accelerating subscription revenue growth is accelerating but it felt like 2Q, maybe it was a slight decel on Atlas, the guide for the rest of the year, particularly Q4 implies a pretty meaningful deceleration in Atlas.

我想也許，CJ，如果我看這個業務，對吧，上半年很明顯 Atlas 加速，訂閱營收成長也在加速，但感覺在第二季，Atlas 可能有些微放緩；而你們對今年剩餘期間的指引，特別是第四季，暗示 Atlas 會有相當明顯的減速。

And I understand conservatism, but if we're kind of early and rolling down the hill with some of the AI natives and labs. What are some of the dynamics? Is it possible that EA is flipping some deals to Atlas like happened in Q4 of last year. What's kind of the dynamic that maybe we're not seeing?

我理解保守，但如果我們算是早期、正隨著一些 AI 原生公司與實驗室（labs）的趨勢往下推進。其中有哪些動態？是否可能 EA 正把一些交易轉到 Atlas，就像去年第四季發生的那樣？有什麼我們可能沒看到的動態？

Okay. So Alex, thank you. Let me address there are quite a few questions in there. I would say, first, to see consistent 29% growth in Atlas now, as Mike outlined, is extremely encouraging and that execution, whether it's in the enterprise or with AI-native cohort is overall very encouraging for us. And like you called out, we have seen the acceleration in the first half compared to what we guided in the beginning of March. So that's number one.

好的。Alex，謝謝。我來回應，你這裡面其實有不少問題。我會先說，Atlas 現在如 Mike 所概述，能維持一致的 29% 成長是非常令人鼓舞的，而這樣的執行力——不論是在企業端或 AI 原生客群——整體都讓我們非常振奮。而如你所指出，我們確實看到上半年相較於 3 月初我們給出的指引有所加速。這是第一點。

Number two, I want to be very clear that the growth of EA self-managed MongoDB is not coming at expense of Atlas. Atlas actually continues to grow, and we are Alex meeting customers where they are. When I originally joined, and I outlined in the first earnings call is that customers asked us that we want to run for these large massive workloads that they run on MongoDB EA. CJ, we want to get this AI ready and hence, the team should build Search and Vector Search on it because these kind of workloads for a variety of reasons, whether it's data solvent, whether they don't want to move it to public cloud for other reasons, will run in our self-managed environment. So we did that, and we delivered that on June 30, and we saw that, that was received really well in our customer base.

第二點，我想非常清楚地說，EA 自行管理（self-managed）的 MongoDB 成長並不是以犧牲 Atlas 為代價。Atlas 其實仍在成長，而我們也在——Alex——在客戶所在之處與他們相遇。我剛加入時，在第一次財報電話會議上就提到，客戶告訴我們，他們希望能在 MongoDB EA 上執行他們運行的這些大型、龐大的工作負載。他們說，CJ，我們希望讓它具備 AI 就緒（AI ready），因此團隊應該在其上建置 Search 與 Vector Search，因為這類工作負載基於各種原因——不論是資料主權（data sovereignty），或是他們因其他原因不想搬到公有雲——將會在我們的自行管理環境中運行。所以我們做到了，並在 6 月 30 日交付，我們也看到客戶群對此反應非常好。

And as Mike called out this was a widespread stregth on our self-managed MongoDB, and it was not concentrated in a single customer and across industries. So point number one is that I feel very good about Atlas consumption trends in the first half going into the second half. Number two, EA growth that we are seeing from a self-managed perspective, whether they are running in neo clouds, whether they are running on-prem in their colors, whether there are sometimes some customers run EA in a public cloud in certain regions around the world feel very good that, that is not coming at expense of Atlas. And a couple of examples that I highlighted, we are actually seeing that from an operational resilience perspective, some of the large banks or government customers have said, that this is a strength of MongoDB data platform versus one over the other from Atlas on EA perspective.

而如 Mike 所提到，這是我們自行管理 MongoDB 的廣泛性強勁表現，並非集中在單一客戶，且橫跨各產業。所以第一點是，我對 Atlas 在上半年的用量趨勢、以及進入下半年的走勢感到非常良好。第二點，從自行管理角度來看，我們看到的 EA 成長——不論他們是在新型雲（neo clouds）上運行、是在其機房內部（on-prem）運行、或是有些客戶在全球某些地區於公有雲上運行 EA——我都很有信心這並不是以犧牲 Atlas 為代價。而我提到的幾個例子顯示，從營運韌性（operational resilience）的角度，一些大型銀行或政府客戶表示，這是 MongoDB 資料平台的優勢，而不是 Atlas 與 EA 之間二選一的問題。

Now in terms of guidance, I'll let Mike comment on it. But we raised the guidance by 300 basis points for the year on Atlas. You know where we started in March, and now we are at 27% growth. We are always going to be prudent about it. And for Q4 specifically, it is still in conduction dynamics that is still weighs away from our perspective.

就指引而言，我先讓 Mike 來評論。不過，我們把 Atlas 全年指引上調了 300 個基點。你知道我們 3 月起步在哪裡，而現在我們達到 27% 的成長。我們一向會在這方面保持審慎。而就 Q4 而言，從我們的角度看，仍然存在一些導入（conduction）動態，仍在拖累。

We need to see how things play out in the month of September, in month of October, which becomes the baseline then the holidays are coming in Q4, which does impact our consumption. So we are trying to be prudent in how we guide and I am optimistic on what I'm seeing, both from the cohort perspective on Atlas as well as what we are seeing on the AI native side? And Mike, do you want to comment on the guidance on Atlas?

我們需要看看 9 月、10 月的情況如何發展；那會成為基準，接著 Q4 進入假期，這確實會影響我們的消費。所以我們在給出指引時力求審慎；我對目前看到的情況感到樂觀，無論是 Atlas 的同群（cohort）角度，還是我們在 AI 原生（AI native）端看到的表現。Mike，你要不要評論一下 Atlas 的指引？

Yes. Thank you. Great answer, CJ. I just want to underline what he said is our guidance philosophy, Alex, has not changed. In terms of how we guided the rest of the year, we'll always be prudent more than a quarter out, and that's what's reflected in the guidance.

好的。謝謝。回答得很好，CJ。我只想強調他所說的：我們的指引理念（guidance philosophy），Alex，沒有改變。就我們如何指引今年剩餘時間而言，對於超過一季以後的展望，我們一向會保持審慎，而這也反映在指引之中。

I also want to address your comment about Q4 and just be super clear on the call hey, there were no large bundled deals in the quarter. There was none of that Q4 dynamic this quarter.

我也想回應你對 Q4 的評論，並在電話會議上把話說得非常清楚：本季沒有大型綑綁式（bundled）交易。本季完全沒有那種 Q4 的動態。

Matt Martino with Goldman Sachs.

高盛的 Matt Martino。

CJ, for you, this is the second quarter you've highlighted strong momentum in Voyage customer count. And I think you made an interesting comment in the prepared remarks, where a variety of Voyage customers are net new to MongoDB. It seems like a great funnel to win some hypergrowth workloads among AI natives? How would you characterize the success in converting some of those customers to a broader platform sale thus far?

CJ，想請教你：這是你第二季強調 Voyage 客戶數的強勁動能。而且我覺得你在事先準備的講稿裡有個很有意思的評論：各式各樣的 Voyage 客戶中，有不少是 MongoDB 的全新客戶（net new）。這看起來像是一個很好的漏斗，能在 AI 原生客戶中贏得一些超高速成長（hypergrowth）的工作負載？到目前為止，你會如何描述把其中一些客戶轉換為更廣泛的平台型銷售（broader platform sale）的成效？

Yes. So Matt, I would approach this in two buckets, okay? Bucket number one, we are really, really energized by the new customer count for MongoDB that is coming via Voyage. And you are absolutely correct, and that's why those remarks were made explicitly that many of them are actually not MongoDB customers, okay? So that is absolutely true.

是的。Matt，我會把這件事分成兩個面向來看，好嗎？第一個面向：我們對於透過 Voyage 帶來的 MongoDB 新客戶數，感到非常、非常振奮。而你說得完全正確；這也是為什麼我在講稿裡特別明確提到：其中很多其實原本不是 MongoDB 客戶，好嗎？所以這點完全屬實。

And the acquisition, as you know, was done in February of 2025. So we are only 18 months into the acquisition.

而且如你所知，這項收購是在 2025 年 2 月完成的。所以我們收購後至今也才 18 個月。

Between the Voyage team that is making sure that we are best-in-class embedding model when you look at external data, benchmarks and so on. But most importantly, there are some customers who come in as voyage customers and they become Atlas customers, but it is still early because we just started making sure that we can now cross-sell upsell, whatever the right term you want to use. But I see this as a massive opportunity for Atlas long term that we are getting this Voyage customers and almost always, when I look at the names of the kind of customers we are getting, whether they are in San Francisco Bay Area, whether they're large enterprise, whether they are in London or Tel Aviv or Seattle, they tend to be driven by AI workloads. And when the team did analysis on where is the referral for our Voyage is coming, as you would have imagined, most of this referral is coming via coding agents.

Voyage 團隊一方面確保我們在嵌入模型（embedding model）方面達到同級最佳（best-in-class），你看外部資料、基準測試等都能印證。但更重要的是，有些客戶先以 Voyage 客戶的身分進來，之後也成為 Atlas 客戶；不過目前仍屬早期，因為我們才剛開始確保我們現在能夠交叉銷售、向上銷售（cross-sell/upsell），或不管你想用什麼合適的詞。但我把這視為 Atlas 長期的一個巨大機會：我們正在獲得這些 Voyage 客戶；而且幾乎每次當我看我們拿到的客戶名單，不論是在舊金山灣區、不論是大型企業、不論是在倫敦、特拉維夫或西雅圖，他們往往都是由 AI 工作負載所驅動。而當團隊分析 Voyage 的推薦流量來源時，如你所想像，大多數推薦是透過程式碼代理（coding agents）而來。

Number one, codex, sorry, Claude and number two Codex is driving most of the referral traffic for Voyage. So we have like multiple things happening. Coding agents love Voyage they're recommending us, and we are getting this new customer cohort.

第一是 Codex——抱歉，是 Claude；第二是 Codex，這兩者帶來了 Voyage 大部分的推薦流量。所以我們同時有多件事在發生。程式碼代理很喜歡 Voyage、會推薦我們，而我們也因此獲得這批新的客戶同群。

Second is that becomes top of the funnel, like you said, that we will cross sell, upsell our alliance team have a plan for Atlas customers, and number three, almost always, these are AI workloads. So overall, early but super encouraging.

第二，正如你所說，這會成為漏斗頂端（top of the funnel）；我們會進行交叉銷售、向上銷售，我們的聯盟團隊（alliance team）對 Atlas 客戶也有一套計畫。第三，幾乎總是 AI 工作負載。

Karl Keirstead with UBS.

UBS 的 Karl Keirstead。

Okay. Great. Maybe I'll direct this one to CJ. CJ, you said that MongoDB is seeing some early momentum with AI workloads. As all of us try to monitor the timing and magnitude of the pending AI pull-through to Mongo.

好的。很好。我想把這題交給 CJ。CJ，你提到 MongoDB 在 AI 工作負載上看到一些早期動能。我們大家都在嘗試觀察 AI 對 Mongo 的拉動效應（pull-through）的時間點與幅度。

I'm just wondering if you could elaborate on what kind of AI use cases or workloads have you found have the greatest pull-through to Mongo, I was intrigued by a comment that Mike made intra-quarter where he flag customer-facing enterprise workloads.

我想請你更詳細說明：你們發現哪些 AI 使用案例或工作負載，對 Mongo 的拉動效應最大？我對 Mike 在季中提到、他特別點出的「面向客戶的企業工作負載」那段很感興趣。

And I know in your prepared remarks, you mentioned customer support use cases. But perhaps you could elaborate a little bit on specific use cases that have the most powerful pull-through so we can all watch for those.

我也知道你在事先準備的講稿裡提到客服支援（customer support）的使用案例。不過也許你可以再多談一點：哪些具體使用案例的拉動效應最強，讓我們都能針對那些方向持續觀察。

Absolutely. So what I have seen, and this is across many conversations Karl is I'm just going to first look at the bucket of enterprise, okay? Enterprises as in, whether you want to say Global 2000 or Fortune 500 or Fortune 100.

當然可以。Karl，我在許多對話中看到的情況是——我先從企業（enterprise）這一類來看，好嗎？企業指的是，不管你想稱之為 Global 2000、Fortune 500 或 Fortune 100。

When I look at those, MongoDB was always almost always database platform that was used for customer-facing workloads, whether it's say, insurance claims, health care policies, whether it's credit card transactions and getting the past data for the end users. MongoDB always shines when it is a massive workload that is customer-facing.

當我看這些企業時，MongoDB 幾乎一直都是用於面向客戶的工作負載的資料庫平台，例如保險理賠、醫療保單，或信用卡交易，以及為終端使用者調取歷史資料。當它是龐大且面向客戶的工作負載時，MongoDB 一直表現特別出色。

What I'm seeing is initially say you're a wealth manager at a bank, and there are lots and lots of knowledge-based articles that you want to vectorize, use our embeddings and then use as a chatbot for folks that are doing wealth management and talking to clients real time. That is one very specific example where a particular large bank is using MongoDB. There are also other examples where because there are lots and lots of documents, employee-facing use cases where knowledge-based articles so that employees can leverage, do a search because now search is fully integrated into the operational data and documents get loaded and then embedding make the vectorization better. That will be another large enterprise example where we are seeing use cases.

我看到的情況是：一開始，假設你是銀行的財富管理人員，你有大量的知識型文章想要向量化（vectorize），使用我們的嵌入（embeddings），然後做成聊天機器人，讓做財富管理、與客戶即時對談的人使用。這是一個非常具體的例子：某家大型銀行正在使用 MongoDB 來做這件事。也有其他例子：因為文件量非常大，出現一些面向員工的使用案例——例如知識型文章讓員工可以運用、進行搜尋；因為現在搜尋已完整整合到營運資料中，文件被載入後，嵌入會讓向量化效果更好。這會是我們看到的另一個大型企業使用案例。

But the clarity that I got was that it was almost always, hey, we want to use MongoDB where the scale matters on the agents that we are trying to create for our customer-facing activities, whatever the customer-facing activities are. We are not seeing early traction with, hey, I created a co-pilot kind of a thing that appeals to a couple of hundred employees we are not seeing MongoDB being used because they are like, hey, this is too big. MongoDB, of course, gives us scale and all these other functionalities. So that's number one.

但我得到的明確結論是：幾乎總是——「嘿，我們想在建立用於面向客戶活動的代理（agents）時，在規模（scale）很重要的地方使用 MongoDB」，不管那些面向客戶的活動是什麼。我們尚未看到早期牽引力來自那種「我做了一個類 co-pilot 的東西，只服務幾百名員工」的情境；在那種情況下，我們沒有看到 MongoDB 被使用，因為他們會覺得「這太大了」。當然，MongoDB 能提供規模，以及所有其他功能。所以這是第一點。

And Karl, the other thing I would say is when you look at AI natives, including I'm going to put Frontier labs in there, but you look at the example that I shared, which was on EU or whether it was Fireflies which are agents in production. But when you look at these agents in production, we have used 11 labs in the past and others, these are millions of agents in production that are doing something that is customer-facing, and they would say, we want to use MongoDB for scale, performance and of course, run anywhere, and that's where you're using it. So these are the two vectors that I'm seeing in enterprises are agents going into production where it makes sense on Atlas, okay, let's do that.

另外，Karl，我還想說的是：當你看 AI 原生公司（AI natives），我也把 Frontier labs 放在其中；再看我分享過的例子，不管是在 EU 的那個案例，或是 Fireflies——那些是在生產環境（production）中的代理。但當你看這些在生產環境中的代理，我們過去用過 11 labs 和其他公司；這些是在生產環境中運作的數百萬個代理，做的事情是面向客戶的；他們會說，我們想用 MongoDB 來取得規模、效能，當然還有「可在任何地方運行（run anywhere）」，這就是他們使用它的原因。所以我在企業端看到的兩個方向就是：代理進入生產環境，且在 Atlas 上具備合理性——好，我們就這麼做。

And on EA that I touched on in my prepared remarks, make no mistake as these regulated industries are trying to get their operational data AI-ready, was also the reason year growth was driven across the industry, including tech, where customer says hey, I'm building an AI agent for my tech, whatever technology platform uses MongoDB or technology company, and we saw the growth there. So that would be my overall summary on where we are seeing, and I'm going to have Mike comment anything additional, if you need it.

至於我在事先準備的發言中提到的 EA，千萬別搞錯：這些受監管產業正努力讓其營運資料具備 AI 就緒（AI-ready）的能力，這也是推動全產業年增長的原因之一，包含科技業在內；客戶會說，嘿，我正在為我的技術打造一個 AI 代理（AI agent），不管是哪個技術平台使用 MongoDB 或是哪家科技公司，我們都在那裡看到成長。以上是我對我們目前看到情況的整體總結；如果你需要的話，我也會請 Mike 補充評論。

No. Great answer. Thank you.

不用。回答得很好。謝謝。

Sanjit Singh with Morgan Stanley.

Morgan Stanley 的 Sanjit Singh。

I appreciate you taking the question. CJ so I wanted to focus on Enterprise Advance? I think under your tenure, the EA growth profile has definitely been uplifted while Atlas growth has, to your point, sustained at a very attractive rate at 25%.

感謝讓我提問。CJ，我想把焦點放在 Enterprise Advance（EA）上。我認為在你任內，EA 的成長輪廓確實被拉升了；而 Atlas 的成長也如你所說，維持在 25% 這個非常具吸引力的水準。

And some of the things that we've been hearing from customers is that if Mongo wants these customers to ultimately get to Atlas, right, advancing EA's capabilities with Search and Vector Search and potentially Voyage as well, that's really important.

我們從客戶那邊聽到的一些回饋是：如果 Mongo 想讓這些客戶最終走向 Atlas，對吧，那麼透過 Search、Vector Search，甚至可能也包括 Voyage，來強化 EA 的能力就非常重要。

Do you have a perspective one on the time line on when you get these customers on the new AI features what the ultimate, if you want to call it, an upgrade or migration to Atlas, what that timing would look like? That's the first part of the EA question.

你能否分享一下觀點：第一，讓這些客戶用上新的 AI 功能的時間表是什麼？以及最終如果你要稱之為升級或遷移到 Atlas，那個時點大概會是什麼樣子？這是 EA 問題的第一部分。

The second part of the EA question is which cohorts are implemental using it. So you mentioned kind of the large enterprises, the financial institutions that not surprising. But do you see an opportunity, I think you guys had hinted that neo cloud using EA as well. Do you, is there a world where the AI have started to use EA because maybe under a theme of like data sovereignty or some other reason on why they become adopters of EA as well. So that's my few question of EA.

EA 問題的第二部分是：哪些客群正在實際採用它。你提到像大型企業、金融機構，這不意外。但你是否看到機會——我想你們曾暗示新雲（neo cloud）也在使用 EA。是否存在一種情況：AI 公司開始使用 EA，可能是出於像資料主權（data sovereignty）或其他原因，因而也成為 EA 的採用者？以上是我關於 EA 的幾個問題。

Sounds good. So I'm going to up-level this a little bit, Sanjit. And we are a very customer-driven company. And the reason we invested in EA road map that we outlined and it is nice to see it's working out is that customers said to us, many, many customers, even in my early days that you must invest in EA and if EA gets to being AI ready with Search, Vector Search and so on, they are asking, hey, can we also make a Voyage available in a self-managed type of an environment. that was very customer-driven, and we are meeting customers where they are, okay? So that's my number one thing.

好的。Sanjit，我先把層次拉高一點來回答。我們是一家非常以客戶為導向的公司。我們之所以投資於我們所概述的 EA 路線圖，而且很高興看到它正在奏效，是因為客戶對我們說——非常非常多的客戶，甚至在我剛加入的早期就這麼說——你們必須投資 EA；如果 EA 能透過 Search、Vector Search 等做到 AI 就緒，他們也在問：嘿，我們能否也在自我管理（self-managed）的環境中提供 Voyage。這完全是由客戶驅動的，而我們是在客戶所在之處與他們會合，好嗎？這是我想講的第一點。

Second thing, as Mike shared, three quarters of double-digit EA growth gives me optimism that now we have two growth drivers, Atlas and EA. And as I shared, not coming at expense of each other because that's a very important thing. Sometimes customers will say, I need to do this for operational resiliency. Sometimes customers will say, I don't see this workload moving to Atlas but given what you're doing, and I'm seeing it on Search and Vector Search being unified here is a new workload that we want to try it on Atlas. So our momentum on EA is also driving potential additional use cases that a bank or a public sector organization a government organization is using Atlas be very specific, in the second quarter, we have a customer in public sector who decided to then expand their usage of our self-managed MongoDB as an EA.

第二點，如 Mike 所分享，連續三個季度的 EA 兩位數成長讓我更有信心：現在我們有兩個成長引擎，Atlas 和 EA。而且如我所說，兩者並非互相犧牲，這非常重要。有時客戶會說，我需要這樣做是為了營運韌性（operational resiliency）。有時客戶會說，我不認為這個工作負載會搬到 Atlas，但鑑於你們正在做的事情，而且我看到 Search 和 Vector Search 在這裡被整合統一，這是一個我們想在 Atlas 上嘗試的新工作負載。因此，我們在 EA 上的動能也在推動潛在的額外使用案例，例如某家銀行或公共部門組織、政府組織使用 Atlas；非常具體地說，在第二季，我們有一個公共部門客戶決定擴大他們對我們自我管理 MongoDB（作為 EA）的使用。

But in addition, we are currently working with them because they see some benefits of MongoDB core base. And they're like, CJ, if you guys are going to manage it and provide security patches and all other things, they currently have a pilot for Atlas in the government cloud that they are running.

此外，我們目前也正在與他們合作，因為他們看到了 MongoDB 核心基礎（core base）的一些好處。他們說，CJ，如果你們要負責管理並提供安全性修補（security patches）以及其他所有事情，他們目前在政府雲（government cloud）上正在進行 Atlas 的試點。

So from my standpoint, having now two growth drivers on behalf of MongoDB Corporation for both Atlas and EA is very, very encouraging. Now in terms of the time line, one thing is that when we introduced this functionality for Search and Vector Search, which was driven by the AI demand, we are charging our customers extra for that feature set that we are providing in EA. So that's number one.

所以就我而言，MongoDB Corporation 現在同時擁有 Atlas 與 EA 兩個成長引擎，這非常、非常令人鼓舞。至於時間表，有一點是：當我們推出 Search 與 Vector Search 的這些功能（由 AI 需求所驅動）時，我們會就 EA 中所提供的這套功能向客戶額外收費。這是第一點。

Number two, in terms of time to value, Sanjit, I would say the time to value is pretty fast. It's not like months, but it's weeks on the way we have release these features for our customers. It's just that they are self-managing versus when we manage in Atlas. Like what we saw on my financial times use case, that I shared that the time to value for them to leverage Vector Search and Embedding was in weeks, not in months and years to get AI ready for searches and others that happen. So that would be my overall perspective.

第二點，關於價值實現時間（time to value），Sanjit，我會說相當快。不是以月計，而是以週計——取決於我們向客戶釋出這些功能的方式。只是他們是自我管理，而不是像在 Atlas 中由我們來管理。就像我分享的 Financial Times 使用案例，我們看到他們要利用 Vector Search 與 Embedding 的價值實現時間是以週計，而不是以月或以年計，才能讓搜尋等場景具備 AI 就緒能力。這就是我的整體看法。

And then the last thing I would say is that specifically in banking and health care, what I'm also seeing is, Hey, CJ, we are going to use Atlas, but we are going to potentially fail over to EA because of our resiliency that you guys provide, which is definitely world-class and our big advantage. So that's the summary that I see this as a durable growth driver for MongoDB Corporation. It is driven based on customer demand and the customer demand is we want to run anywhere, sometimes we'll self-manage. Sometimes it's MongoDB managed.

最後我想說的是，特別是在銀行與醫療保健領域，我也看到的是：嘿，CJ，我們會使用 Atlas，但我們可能會因為你們提供的韌性而切換故障移轉（fail over）到 EA；這確實是世界級的，也是我們的一大優勢。所以總結來說，我認為這對 MongoDB Corporation 而言是一個可持續的成長驅動因素。它是由客戶需求所驅動，而客戶需求是：我們想要能在任何地方運行，有時我們會自我管理。有時則由 MongoDB 代管。

Ryan MacWilliams with Wells Fargo.

Wells Fargo 的 Ryan MacWilliams。

Two part question here. First one for CJ. Are you seeing customers come back to you ahead of their scheduled renewal and renew at a higher rate compared to a year ago? Like are they truing up sooner? Or are they seeing the consumption trends improve more strongly?

這裡有兩個問題。第一個給 CJ。你是否看到客戶在原定續約時間之前就回來找你們，並且以比一年前更高的水準續約？例如他們是否更早進行用量對齊（true-up）？或者你們是否看到消費（consumption）趨勢更明顯地改善？

And then for Mike, I know your guidance philosophy hasn't changed, but given less history with quarterly Atlas guides. Getting some questions on the implied 4Q Atlas guide. Can you just help us with some inputs into that guide and how we should think about it as investors?

然後給 Mike：我知道你們的指引（guidance）哲學沒有改變，但由於按季度提供 Atlas 指引的歷史較短，市場對隱含的第四季（4Q）Atlas 指引有一些疑問。你能否就該指引提供一些輸入，並說明作為投資人我們應該如何看待它？

Yes. So the first thing, as Mike outlined, our NRR was very strong and high, and that was true across both Atlas and EA. So in terms of retention rate, and what I'm seeing even the dynamics on, hey, a customer may want to optimize the workload with our customer success teams and all that, the trends are very healthy and improving which is a great testimonial to our 8.0 release last year that customers feel very good about price performance and how their consumption is growing.

是的。首先，如 Mike 所概述，我們的 NRR 非常強勁且維持在高位，這在 Atlas 和 EA 兩邊都成立。所以就留存率（retention rate）而言，以及我所看到的動態——例如客戶可能想在我們客戶成功團隊的協助下優化工作負載等等——這些趨勢都非常健康且正在改善；這也很好地印證了我們去年推出的 8.0 版本，客戶對價格/效能（price performance）以及其消費成長的感受都非常正面。

Now there are some customers who have outlined to me the large ones that our consumption is growing faster than we thought it would, and CJ, can we have a conversation on if we continue to grow at this rate should we relook at the contract. But that's not happening a lot. This is like onesies and twosies, maximum single digits but not widespread. So that's encouraging, meaning we are not getting the pushback as the consumption increases, that we want to renegotiate the contract or the commits and so on. So that's how I would answer that.

確實有一些大型客戶跟我提到，他們的消費成長比原先預期更快，並問 CJ，我們是否可以討論：如果我們持續以這個速度成長，是否應該重新檢視合約。但這種情況並不常見。這大概只是零星個案，最多是個位數，但並不普遍。因此這是令人鼓舞的，代表隨著消費增加，我們並沒有遭遇太多「想重新談合約或承諾額度（commits）等等」的反彈。以上就是我對這題的回答。

And in terms of I will state what I stated before, which was Alex's question is I feel very good about Atlas business, the durability of that business the innovation we are driving. Of course, we are not going to guide based on Mike's framework on how we are going to guide for Q3. And we are always going to be prudent about Q4, but we raised, that's why 27% guide for the year on Atlas. Mike?

至於我會重申我先前說過的，也就是 Alex 的問題：我對 Atlas 業務感到非常有信心，對該業務的耐久性以及我們正在推動的創新都很有信心。當然，我們不會依照 Mike 的框架來提供我們對 Q3 的指引。而且我們對 Q4 一向會保持審慎，但我們上調了，這也是為什麼我們對 Atlas 給出全年 27% 的指引。Mike？

Yes. So thank you, CJ. So Ryan, to that point, the guidance methodology and philosophy has stayed consistent all year, and we want to stay with that, which is when we guide for the current quarter, I will call it or the first quarter out, we always want to stay within that, hey, you should look at that 200 to 300 basis point range we provided. Again, hopefully, consumption comes in better, we finish at the upper end of that. And we will always Ryan be prudent on the out quarters. It is a consumption business.

是的。所以謝謝你，CJ。Ryan，針對這點，我們的指引方法論與理念今年一整年都保持一致，我們也希望維持這樣的做法：當我們對當季（我稱之為當前季度，或往外看第一個季度）提供指引時，我們一向希望落在我們提供的 200 到 300 個基點區間內。同樣地，希望用量表現更好，我們就能落在區間上緣。而對於更往後的季度，我們一向會保持審慎，Ryan。這是一門以用量為基礎的業務。

I know it only seems like not that long one more quarter out, but we want to be prudent. Hopefully, then we execute well in Q3, and we're able to increase that guide when we get to Q4.

我知道看起來只是再往外一季而已，似乎不算久，但我們希望保持審慎。希望接著我們在 Q3 執行得很好，等到 Q4 時我們就能上調那個指引。

Kirk Materne with Evercore ISI.

Evercore ISI 的 Kirk Materne。

CJ, the question for you is really about sort of attach rates on voyage and vector. And I'm kind of curious, when you land a new customer with these products, are they coming in and experimenting first and then scaling quickly. I'm just kind of curious, obviously, landing a new customer does great, but getting them to scale and getting the ARR to be more meaningful from a total company perspective is where you want to go. I'm just kind of curious how fast those products can go from something that's maybe piloted in the department to being thought of as strategic or company-wide as something like Atlas or EA.

CJ，我想問的是關於 Voyage 和 Vector 的附掛率（attach rates）。我有點好奇，當你們用這些產品拿下新客戶時，他們是先進來試驗，然後很快擴大規模嗎？我只是想了解，顯然拿下新客戶很棒，但讓他們擴大使用、讓 ARR 從整體公司角度變得更有意義，才是你們想達到的方向。我想問的是，這些產品從可能只是在部門內試點，到被視為像 Atlas 或 EA 那樣具策略性或公司層級的方案，速度能有多快？

Yes, of course. So I'll touch on both. Atlas is completely consumption driven, as you are fully well aware. So when I see some of the large customers like these are a few Fortune 100 customers, the ask, they did all their testing. They're like, okay, we do search in this other siloed system or we are trying to do Vector Search from some early-stage start-up that provides a Vector functionality.

是的，當然。我會兩個都談。Atlas 完全由用量驅動，這點你非常清楚。所以當我看到一些大型客戶——其中有幾家是《財富》100 大客戶——他們的需求是：他們已經做完所有測試。他們會說，好，我們在另一個孤島式系統裡做搜尋，或是我們正嘗試用某家早期新創提供的向量功能來做向量搜尋。

This large bank told me that based on their testing they believe that Vector should be integrated fully in the operational data layer and MongoDB doing that was seen as a huge advantage. And the time to value there was few weeks, and then we would have a dedicated search node and so on, which drives the consumption.

有一家大型銀行告訴我，根據他們的測試，他們相信向量應該要完整整合到營運資料層（operational data layer）中，而 MongoDB 能做到這點被視為一個巨大優勢。那裡的價值實現時間（time to value）是幾週，接著我們會有專用的搜尋節點等等，這些都會帶動用量。

Even a large media company, which became one of our biggest Vector Search customer, that was driven by an agent trying to do the cementer query and figuring it out, okay, if this is an operational data layer, then it just works. And we are seeing that even in AI-native cohort, that vector being part of the database is received really, really well. And one of the examples I shared last quarter, 11 labs, which continues to scale nicely with MongoDB, they see that as a huge advantage of vector being embedded. And we are doing the same thing now in EA.

甚至有一家大型媒體公司，後來成為我們最大的 Vector Search 客戶之一，這是由一位代理人（agent）嘗試做語意查詢（semantic query）並摸索出來所驅動的：好，如果這是一個營運資料層，那它就能直接運作。我們也看到，即使在 AI 原生（AI-native）族群中，把向量作為資料庫的一部分也獲得非常、非常好的反饋。我上季分享的其中一個例子是 11 labs，他們持續在 MongoDB 上良好擴張，他們認為向量內嵌是一個巨大優勢。而我們現在也在 EA 做同樣的事情。

Now on embedding, we are making it easier, like I shared in my remarks, to make sure that we have auto embeddings in Atlas how it works, how does it work in the cloud. And that time, what I am right now in all the customer conversations seeing is that still the awareness is low that Voyage is actually coming from MongoDB. And this customer told me, oh, we love Voyage. We are using Voyage. And I said, that is a MongoDB product. And they're like, oh, we did not know that. Okay, then we should now look at Atlas because you have Atlas Auto embeddings. So it varies.

至於 embedding（嵌入），我們正在讓它更容易——就像我在發言中分享的——確保我們在 Atlas 裡有自動嵌入（auto embeddings），它如何運作、在雲端如何運作。而目前我在所有客戶對話中看到的是，大家對於 Voyage 其實是來自 MongoDB 的認知仍然偏低。有位客戶跟我說，喔，我們很喜歡 Voyage。我們正在用 Voyage。我說，那是 MongoDB 的產品。他們說，喔，我們不知道。好，那我們現在應該看看 Atlas，因為你們有 Atlas Auto embeddings。所以情況不一。

But Search, Vector Search, I would say the time to value is not that long because the moving pieces as in the moving systems are fewer, and that's why it works.

但就 Search、Vector Search 而言，我會說價值實現時間不會太長，因為需要移動的環節——也就是需要更動的系統——比較少，所以它才行得通。

Tyler Radke with Citi.

Citi 的 Tyler Radke。

CJ, you talked about some inference workloads at AI Labs. Can you just elaborate Were those new this quarter, how do you see sort of the sizing of those workloads compared to some other kind of large workloads across traditional companies? And then Mike, just on EA clearly, a big outperformance this quarter. I think you had some of the new capabilities released from a GA perspective in July. So I guess, what gives you the confidence that there's not even more upside in the second half given that most of the raise in the second half was Atlas for CA?

CJ，你提到在 AI Labs 有一些推論（inference）工作負載。你能否再多說一些：那些是本季新增的嗎？你怎麼看這些工作負載的規模，相較於傳統公司裡其他一些大型工作負載？另外 Mike，關於 EA，本季顯然大幅超出預期。我記得你們在 7 月從 GA（正式可用）角度發布了一些新能力。所以我想問，你們為什麼有信心下半年不會有更多上行空間？尤其是下半年上調的大部分似乎是 Atlas（CA）？

Yes. So Tyler, we were very specific on comments because we want to be extremely transparent with you. So with one of the labs, they started towards the later half of last calendar year with one of the workloads that was running in France on MongoDB and used us as a memory layer. With that lab, our team, what they saw on the Atlas performance for that specific inference, then they said, wow, Atlas is performing really well across reads and rights compared to PostgreSQL, that's what they were using originally. They started then moving just recently in Q2, a few other workloads for inference on Atlas.

是的。Tyler，我們在評論上非常具體，因為我們希望對你們極度透明。以其中一家實驗室為例，他們從上一個曆年的後半段開始，將其中一個工作負載在法國用 MongoDB 跑，並把我們當作記憶體層（memory layer）。在那家實驗室，我們團隊看到 Atlas 在那個特定推論上的效能後，他們說，哇，Atlas 在讀寫方面的表現相較於 PostgreSQL 真的很好——那是他們原本使用的。接著他們就在最近的 Q2 開始把另外幾個用於推論的工作負載搬到 Atlas 上。

So we had one inference workload that started last year in November, December time frame. And then they moved another couple of workloads for inference or there's some other products that they have created. And I want to say this is August or around June, July time frame. So we are seeing, they told me like straight up. This is the technology team that Atlas has taken all the pain away from an uptime perspective, performance perspective, we don't even think about it.

所以我們有一個推論工作負載是在去年 11、12 月左右開始的。然後他們又把另外幾個用於推論的工作負載搬過來，或是他們建立的一些其他產品。我想這是在 8 月或大約 6、7 月的時間點。所以我們看到——他們很直接地告訴我。他們的技術團隊說，Atlas 在可用性（uptime）與效能方面把所有痛點都拿掉了，我們甚至不需要去想它。

And we are now, as we create new products, we want to run inference on it. So that's what I would say that we started with one in France, some of the other workloads, and then we got some additional just in Q2.

而且現在，隨著我們打造新產品，我們希望在其上跑推論。所以我會說，我們從法國的一個開始，接著是一些其他工作負載，然後在 Q2 又新增了一些。

And Tyler, this is Mike. On your question on EA, great question. Thank you for that. So the last three quarters, we've seen ARR growth, as CJ talked about, in double digit. We did increase the full year guide from mid-single digit to 11% for the full year.

Tyler，我是 Mike。關於你對 EA 的問題，問得很好。謝謝你。過去三個季度，我們看到 ARR 成長——如 CJ 所說——達到雙位數。我們也把全年指引從中個位數上調到全年 11%。

Just like us, the hard part here is estimating the multiyear deals. We will always be prudent for all of our sake, to make sure that we don't lean over until we see those deals land. If the last four quarters are any history, hopefully, some of those do come in as multiyear yields or larger than we expected. So certainly, we want to make sure that especially for EA we're prudent on the guide. But as CJ talked about, we feel really good about the progress there.

就像我們一樣，這裡最困難的部分是估算多年期合約。為了大家的利益，我們一向會保持審慎，確保在看到這些合約真正落地之前，我們不會過度倚重。如果過去四個季度可作為參考，希望其中一些會以多年期收益的形式落地，或規模比我們預期更大。因此，我們當然要確保，特別是在 EA 方面，我們在指引上保持審慎。但正如 CJ 所談到的，我們對那裡的進展感到非常好。

We think it can be and now it can be a durable growth driver. Hopefully, we can do better than we guided.

我們認為它可以，而且現在也可以成為一個可持續的成長驅動因素。希望我們能做得比指引更好。

Koji Ikeda with Bank of America.

美國銀行的 Koji Ikeda。

I wanted to ask about EA and really around Atlas in the total business, too. And so clearly, in the prepared remarks and your answers to all these questions, AI is definitely sounds like it's becoming a driver for the total business. And EA sounds really, really good, too. And CJ, I think you mentioned that EA is not coming at the expense of Atlas, but how should we be thinking about just Atlas and EA? And any sort of change ultimately to how Atlas could become, or the revenue mix from Atlas to total revenue over the next three to five years?

我想詢問關於 EA，以及 Atlas 在整體業務中的情況。很明顯，從事先準備的發言以及你們對這些問題的回答來看，AI 聽起來確實正在成為整體業務的驅動因素。而 EA 聽起來也非常、非常好。CJ，我想你提到 EA 並不是以犧牲 Atlas 為代價，但我們應該如何看待 Atlas 與 EA 之間的關係？以及未來三到五年，Atlas 可能會如何演變，或 Atlas 收入占比相對於總收入的組合是否會有任何變化？

Koji, it's Mike. So let me take that. So we will talk more obviously as we guide next year, we'll have a financial session at Investor Day. If you take a look at this year's full year guidance with Atlas at 27% and EA now at 11%. If Atlas is around 74% now, that certainly should continue to increase as a percent but we do expect EA to be a more durable growth driver.

Koji，我是 Mike。我來回答這個問題。我們在提供明年指引時會更進一步說明，並且在投資人日會有一場財務專場。如果你看今年的全年指引，Atlas 為 27%，而 EA 現在為 11%。如果 Atlas 現在約為 74%，那麼它作為占比當然應該會持續提高，但我們確實預期 EA 會成為更具持續性的成長驅動因素。

So to that extent, it should continue to increase, probably not at the rate we thought before because as you talked about the AI push is both in Atlas and in EA, and we feel very good that it is an and, not an or. So we expect Atlas to continue as a percent of total revenue but certainly, EA also contributing much more than we thought when we started the year.

因此在這個程度上，它應該會持續增加，只是可能不會像我們先前想的那樣快，因為正如你所提到的，AI 的推動同時存在於 Atlas 與 EA 之中，而我們非常有信心這是「兩者皆是」，而不是「二選一」。所以我們預期 Atlas 作為總收入占比會持續提升；但當然，EA 的貢獻也會比我們在年初開始時所想的多得多。

And Koji, I would say from a technical perspective, this run anywhere of resilience, hybrid multi-cloud, these are different terms that customers use with us. And what we are seeing is like specifically, there was a question on Neo Clouds and others, yes, what happens is when somebody wants to run in Neo Cloud because of the capacity issues in a public cloud, that they may have, they are saying, hey, can we run EA in that Neo Cloud, which goes to our run anywhere and driving demand. We offer database as service in some of the other Neo Clouds, which also goes to EA bucket line. And that's why we are very clear that EA doesn't come at expense of Atlas and just seeing that broad-based strength versus just one particular customer, all three or four customers is what very encouraging for these to be durable growth drivers.

Koji，從技術角度來看，這種「可在任何地方運行」的韌性、混合多雲，這些是客戶與我們互動時使用的不同術語。我們看到的情況是，例如先前有人問到 Neo Clouds 等，是的，當有人因為公有雲的容量問題而想在 Neo Cloud 上運行時，他們會說：嘿，我們能否在那個 Neo Cloud 上運行 EA？這就呼應了我們的「可在任何地方運行」並帶動需求。我們也在其他一些 Neo Clouds 上提供資料庫即服務（DBaaS），這同樣會歸入 EA 的範疇。這也是為什麼我們非常明確地說，EA 並不是以犧牲 Atlas 為代價；而且我們看到的是廣泛的強勁動能，而不是只來自某一個特定客戶、三四個客戶，這讓我們非常受到鼓舞，認為這些將成為可持續的成長驅動因素。

Ladies and gentlemen, at this time, I would like to turn the call back to management for closing remarks.

各位女士、先生，現在我想把電話交回管理層作結語。

Thank you very much, operator. So in summary, we delivered a strong second quarter with broad-based strength across Atlas, EA and AI workloads. What's notable is the breadth of the demand, Frontier labs, global banks, public sector, fast scaling start-ups, some expanding what they already run with us, others coming to us new.

非常感謝，接線員。總結來說，我們第二季表現強勁，Atlas、EA 與 AI 工作負載皆呈現廣泛的強勢。值得注意的是需求的廣度：Frontier labs、全球銀行、公部門、快速擴張的新創公司；有些在擴大他們已經在我們這裡運行的內容，另一些則是首次採用我們。

We are seeing AI workloads land on MongoDB across all of them. That's why we raised our outlook for the second half and why we are confident we can keep expanding operating margin while we invest.

我們看到 AI 工作負載在所有這些客戶上都落地於 MongoDB。這也是為什麼我們上調了下半年的展望，以及為什麼我們有信心在持續投資的同時，仍能繼續擴大營業利潤率。

MongoDB is emerging as the real-time intelligent data platform of choice and I have never felt better about our position with our customers. Thank you very much.

MongoDB 正在成為首選的即時智慧資料平台，而我從未對我們在客戶中的定位感到如此有信心。非常感謝。

Ladies and gentlemen, that concludes today's conference call. Thank you for your participation. You may now disconnect.

各位女士、先生，今天的電話會議到此結束。感謝各位的參與。您現在可以掛線。