MongoDB, Inc. (MDB) 2027 Q1 法說會逐字稿

Hello, and welcome to MongoDB's first-quarter fiscal year '27 earnings conference call. (Operator Instructions)

您好，歡迎參加 MongoDB 2027 會計年度第一季財報電話會議。(接線員指示)

I would now like to hand the conference over to Jess Lubert, Mongo's Vice President of Investor Relations. You may begin.

現在我想把會議交給 MongoDB 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 first-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.

謝謝您，接線員。各位下午好，感謝各位今天加入我們，一同回顧 MongoDB 2027 會計年度第一季財務結果；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 consumption growth, the impact of EA, and other business and multi-year license revenue, the long-term opportunity of AI, our financial guidance, and underlying assumptions in our investments in growth opportunities in AI.

在本次電話會議中，我們將作出前瞻性陳述，包括與我們的市場與未來成長機會、我們贏得新業務的機會、我們對 Atlas 消費成長的預期、EA 的影響，以及其他業務與多年期授權收入、AI 的長期機會、我們的財務指引，以及我們在 AI 成長機會投資中的相關假設等陳述。

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. For a discussion of material risks and uncertainties that could affect our actual results, please refer to the risks described in our annual report on Form 10-K for the year ended January 31, 2026, filed with the SEC on March 11, 2026.

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有重大差異。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 1 月 31 日止年度的 Form 10-K 年報中所述風險；該文件已於 2026 年 3 月 11 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. 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.

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。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頁面之財報新聞稿中的表格，以取得這些指標與最直接可比較之 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, Jeff, and thank you all for joining us today. I continue to spend a lot of time working with a wide range of customers, from AI natives and digital natives to large enterprises and public sector organizations. This customer-driven focus is to deliver meaningful outcomes for MongoDB. The process I follow is tightly linked, so each part strengthens the others.

謝謝你，Jeff，也謝謝各位今天加入我們。我持續花大量時間與各式各樣的客戶合作，從 AI 原生與數位原生公司，到大型企業與公部門組織。這種以客戶驅動為核心的聚焦，是為 MongoDB 帶來有意義的成果。我所採取的流程彼此緊密連結，因此每個環節都能相互強化。

Number one, engage directly with C-suite leaders to elevate MongoDB from a technical decision to a strategic platform commitment. Number two, surface new pipeline by helping customers connect their most pressing modernization and AI opportunities to what MongoDB can uniquely solve. Number three, feed what I learned directly into our product and technology teams to accelerate our customer-driven innovation road map.

第一，直接與 C 級高層領導者互動，將 MongoDB 從技術性決策提升為策略性平台承諾。第二，透過協助客戶把其最迫切的現代化與 AI 機會，連結到 MongoDB 能夠獨特解決的問題，以開拓新的銷售管線。第三，將我所學到的內容直接回饋給我們的產品與技術團隊，加速以客戶驅動的創新路線圖。

These conversations reinforce my conviction in both what we have built and the scale of the opportunity ahead. That opportunity has two dimensions. The first is core workloads where large customers run their most demanding, mission-critical workloads on MongoDB across on-prem, public clouds and hybrid environments. The second is AI, where enterprises, digital natives, frontier labs, and AI natives alike are moving agentic applications into production and choosing MongoDB as the data platform to power them.

這些對話強化了我對我們所打造成果以及未來機會規模的信心。這個機會有兩個面向。第一是核心工作負載：大型客戶在 MongoDB 上執行其最具挑戰、最關鍵任務的工作負載，涵蓋地端、公有雲與混合環境。第二是 AI：企業、數位原生公司、前沿實驗室與 AI 原生公司都在將代理式（agentic）應用導入正式環境，並選擇 MongoDB 作為驅動這些應用的資料平台。

As you heard from other software companies, these two opportunities are not distinct and, in fact, reinforce each other. Enterprises are starting to build agentic application on top of the very data already running on MongoDB. This dual opportunity compounding together is what gives us so much optimism about the road ahead.

正如各位從其他軟體公司所聽到的，這兩個機會並非彼此獨立，事實上還會相互強化。企業正開始在既有已於 MongoDB 上運行的資料之上，建置代理式應用。這種雙重機會相互疊加，正是讓我們對前方道路充滿樂觀的原因。

Today, I'm proud to share with you our Q1 results. We generated total revenue of $688 million, up 25% year over year, beating the high end of guidance and accelerating from the 22% growth we reported in fiscal Q1 of the prior two years. Top line strength was driven by Atlas, which grew 29.4% year over year including a record $117 million year-over-year dollar growth. Now at a $2 billion run rate, this is the fourth quarter in a row Atlas delivered year-over-year growth of at least 29%. EA & other previously referred to as non-Atlas grew 13% year over year.

今天，我很自豪與各位分享我們第一季的成果。我們的總營收達 6.88 億美元，年增 25%，不僅超越指引上緣，也較前兩個會計年度第一季所報告的 22% 成長加速。營收成長動能主要來自 Atlas，其年增 29.4%，其中年增金額達到創紀錄的 1.17 億美元。Atlas 目前年化營收運行率達 20 億美元，且已連續第四季實現至少 29% 的年增率。EA 與其他（先前稱為非 Atlas）年增 13%。

We delivered a non-GAAP operating margin of 18%, above the high end of the guidance. We ended the quarter with over 67,700 customers, adding 2,500 customers in Q1, growing year over year and quarter over quarter. AI adoption of MongoDB technologies across our customer base continues to accelerate. MCP server usage is growing significantly. Voyage customers have more than doubled quarter over quarter and vector search adoption is far outpacing overall company growth.

我們的非 GAAP 營業利益率為 18%，高於指引上緣。本季結束時客戶數超過 67,700 家，第一季新增 2,500 家客戶，無論年比年或季比季皆呈成長。在我們的客戶群中，MongoDB 技術的 AI 採用持續加速。MCP 伺服器使用量顯著成長。Voyage 客戶數季比季增加逾一倍，而向量搜尋的採用成長速度遠超過公司整體成長。

Let me walk through each dimension of our opportunity. Across my conversations with customers, one shift stands out. MongoDB is starting to become a strategic platform decision in addition to a workload-by-workload evaluation. This is driven by a powerful combination of our platform technology fundamentals, high performance at scale, the ability to run anywhere and AI capabilities that are fully integrated in a single data platform.

我來逐一說明我們機會的每個面向。在我與客戶的對話中，有一個轉變特別明顯。MongoDB 除了以單一工作負載逐案評估之外，正開始成為策略性平台決策。這是由多項強大因素共同驅動：我們平台技術的扎實基礎、可在大規模下維持高效能、可在任何環境運行的能力，以及在單一資料平台中完整整合的 AI 能力。

Zoom is a clear example of that. Zoom, a global leader in AI-powered workplace collaboration runs MongoDB Enterprise Advance as a unified data platform for Zoom Meetings, Zoom Phone, Zoom Contact Center, and Zoom Virtual Agent deployed across dozens of clusters globally to deliver low latency, highly available communications at scale. By standardizing these workloads on MongoDB, Zoom gains are cloud-agnostic, hybrid deployment model that runs anywhere their business requires.

Zoom 就是一個明確的例子。Zoom 作為全球 AI 驅動的工作場所協作領導者，採用 MongoDB Enterprise Advance 作為統一資料平台，用於 Zoom Meetings、Zoom Phone、Zoom Contact Center 與 Zoom Virtual Agent，並在全球數十個叢集中部署，以在大規模下提供低延遲、高可用的通訊服務。透過將這些工作負載標準化於 MongoDB 之上，Zoom 得以採用雲端無關（cloud-agnostic）的混合部署模型，可在其業務所需的任何地方運行。

This simplifies the previously polyglot data estate, improves our resilience and reduces total cost of ownership across mission-critical services. We look forward to continuing to support Zoom as they deliver the next generation of workplace experiences.

這簡化了先前多語系（polyglot）的資料環境，提升我們的韌性，並降低關鍵任務服務的總持有成本。我們期待持續支持 Zoom，協助其打造下一代的工作場所體驗。

Turning to AI. This opportunity spans three distinct segments. First is the frontier labs. Several of this have selected MongoDB for use cases that are mission-critical to the deployment of their products among the most demanding data workloads in the industry. The depth of engagement varies by lab and by workload, and it is still early. But we feel great about the use cases we are winning and the ability to expand within these customers over time.

接著談 AI。這個機會涵蓋三個不同的區隔。第一是前沿實驗室（frontier labs）。其中有數家已選擇 MongoDB，用於對其產品部署至關重要的使用情境，這些情境也屬於業界最具挑戰性的資料工作負載。各實驗室與各工作負載的合作深度不一，目前仍在早期階段。但我們對正在贏得的使用情境，以及隨時間在這些客戶內擴張的能力感到非常有信心。

Second is AI-native companies. These customers are choosing MongoDB as the foundation for their AI products from day one because the data layer determines if you can scale to support rapid growth. For example, Endor Labs is an AI-native application security platform, protecting over 7 million applications across both human written and AI-generated code.

第二是 AI 原生公司。這些客戶從第一天起就選擇 MongoDB 作為其 AI 產品的基礎，因為資料層決定了你是否能擴展以支援快速成長。例如，Endor Labs 是一個 AI 原生的應用程式安全平台，保護超過 700 萬個應用程式，涵蓋人類撰寫與 AI 生成的程式碼。

Endor selected Atlas as its default database to support 225% year-over-year revenue growth. Endor uses Atlas and Atlas Search to power its mission-critical security workflows, including AURI, its new security intelligence layer for AI coding agents, allowing the company to reduce operational friction and accelerate delivery of its differentiated offerings.

Endor 選擇 Atlas 作為其預設資料庫，以支援 225% 的年比年營收成長。Endor 使用 Atlas 與 Atlas Search 來驅動其關鍵任務的安全工作流程，包括 AURI（其針對 AI 程式碼代理的全新安全情報層），使公司得以降低營運摩擦並加速交付其差異化產品。

Third is enterprise deploying AI. It is still early here, but we are beginning to see customers move from experimentation into production, building AI application on top of the operational data layer already running their business. Zomato is a great example. The world's second largest food delivery company with 25 million monthly active users build Nugget, an AI-native customer support platform, they are now selling to other enterprises on Atlas.

第三是企業部署 AI。這方面仍處於早期階段，但我們已開始看到客戶從實驗走向正式上線（production），在已支撐其業務運作的營運資料層之上建置 AI 應用。Zomato 就是一個很好的例子。這家擁有 2,500 萬月活躍用戶、全球第二大的外送公司打造了 Nugget——一個 AI 原生的客戶支援平台；他們現在也在 Atlas 上將其銷售給其他企業。

After evaluating DynamoDB and DocumentDB, they chose Atlas for its aggregation pipeline, right consistency and flexible schema. Nugget now orchestrates 15 million conversations per month on MongoDB's platform, reducing support cost by 55% and improving human agent productivity by 40%.

在評估 DynamoDB 與 DocumentDB 之後，他們選擇 Atlas，原因在於其聚合管線、正確的一致性與彈性的綱要（schema）。Nugget 目前在 MongoDB 平台上每月協調 1,500 萬次對話，將支援成本降低 55%，並把人工客服專員的生產力提升 40%。

Another exciting pattern is also emerging across these segments, something I'm really excited about. Customers choosing MongoDB as the memory layer for AI agents themselves. Agent Workloads need memory, that's transactional, high velocity, and able to retrieve the right context at the right time. Adobe's Journey agent is a clear example. A composite multimodal AI agent that unifies Adobe's marketing suite and orchestrates end-to-end customer journeys for their global B2C user base with MongoDB as the agent's long-term memory and reasoning layer.

另一個令人振奮的模式也正在這些區隔中浮現，這也是我非常興奮的一點。客戶選擇 MongoDB 作為 AI 代理（agents）本身的記憶層。代理工作負載需要記憶：具交易性、高速率，並能在正確的時間取回正確的上下文。Adobe 的 Journey 代理就是一個明確的例子。這是一個複合式多模態 AI 代理，整合 Adobe 的行銷套件，並為其全球 B2C 用戶群編排端到端的客戶旅程；MongoDB 作為該代理的長期記憶與推理層。

Adobe leverages the MongoDB platform, Atlas Search and Atlas Vector Search together to power the sub-100 millisecond hybrid search the agent needs to act in real time. To be clear, our results today are driven primarily by core workloads, but we are seeing real and growing momentum from AI and agenetic workloads and believe MongoDB is purpose-built to be generational data platform for the agentic era.

Adobe 運用 MongoDB 平台，並將 Atlas Search 與 Atlas Vector Search 結合，以支援該代理在即時行動所需的 100 毫秒以內混合式搜尋。需要說明的是，我們今天的成果主要仍由核心工作負載驅動，但我們也看到來自 AI 與代理型（agentic）工作負載的真實且持續增長的動能，並相信 MongoDB 是為代理時代打造的世代級資料平台。

Built natively into the platform, MongoDB's innovations in the core database, embeddings and vector capabilities are moving us beyond a system of record to becoming the real-time system of intelligence. That just comes down to five core strengths.

MongoDB 在核心資料庫、嵌入（embeddings）與向量能力上的創新原生內建於平台之中，正推動我們從「記錄系統」邁向「即時智慧系統」。這歸結為五項核心優勢。

Number one, MongoDB is architecturally built for AI in two key ways. First, our flexible schema is uniquely suited to how applications get built in the agentic era. A growing share of software is now created through pro-driven development, natural language iteration rather than line-by-line authorship. Whether the prompt comes from a developer or an agent, the shape of the application shifts with prompt and a rigid relational schema becomes a tax on every iteration compromising agility. In addition, LLMs are the lingua franca for AI, and they speak in unstructured documented shape data, the exact form MongoDB was built around. We have been compounding both advantages for 15 years, well before the current AI wave gave them a tailwind.

第一，MongoDB 在架構上以兩個關鍵面向為 AI 而建。首先，我們的彈性綱要特別適合代理時代的應用建置方式。如今越來越多的軟體是透過提示驅動（prompt-driven）的開發、以自然語言反覆迭代，而非逐行撰寫而成。無論提示來自開發者或代理，應用的形態都會隨提示而改變；僵硬的關聯式綱要會對每次迭代形成負擔，犧牲敏捷性。此外，大型語言模型（LLM）是 AI 的通用語言，而它們使用的是非結構化、文件形態的資料——這正是 MongoDB 所圍繞打造的資料形式。我們在這兩項優勢上已累積強化了 15 年，遠早於當前 AI 浪潮為其帶來順風。

Second, MongoDB is a transactional, high-performance data platform built for how agents actually work. Agents don't behave like traditional applications. They read, write, and act continuously across multiple simultaneous threats with a single agent responding subagents that each make independent reads and writes in real time. Analytical systems built for off-line processing weren't designed for this, and it shows in the performance when you run agents on top of them. MongoDB 8.3 released this month takes that step one further, delivering up to 45% more reads, 35% more writes and 15% more (inaudible) transactions over 8.0 without changing a line of application code.

第二，MongoDB 是一個具交易性、高效能的資料平台，專為代理實際運作方式而建。代理的行為不同於傳統應用。它們會在多個同時進行的執行緒（threads）上持續讀取、寫入與採取行動；單一代理會回應並驅動子代理，而每個子代理都會即時進行獨立的讀寫。為離線處理而設計的分析系統並非為此而生，當你在其上運行代理時，效能差異就會顯現。本月發布的 MongoDB 8.3 更進一步：在不需改動任何一行應用程式碼的情況下，相較 8.0 可提供最高 45% 更多讀取、35% 更多寫入，以及 15% 更多（聽不清）交易。

Third, MongoDB is a data platform that delivers the retrieval accuracy agents need to be trusted while optimizing tokens and cost in production. For internal tools, occasional errors may be tolerable. But for customer-facing application such as clinical decision support, fraud detection, financial transaction, insurance transaction, accuracy is non-negotiable. MongoDB delivers best-in-class retrieval through integrated Vector Search and Voyage embeddings and reranked models, purpose built to surface the most relevant context when agent needs it. This quarter, automated Voyage AI embeddings entered public preview, removing weeks of infrastructure work and enabling developers to deliver semantic search in minutes.

第三，MongoDB 是一個資料平台，能提供代理所需的檢索準確度，使其值得被信任，同時在正式環境中最佳化 token 與成本。對內部工具而言，偶發錯誤或許可以容忍。但對面向客戶的應用（例如臨床決策支援、詐欺偵測、金融交易、保險交易），準確性不容妥協。MongoDB 透過整合式 Vector Search、Voyage embeddings 與重排序（reranked）模型，提供同級最佳的檢索能力，專為在代理需要時呈現最相關的上下文而打造。本季，Voyage AI 自動化 embeddings 進入公開預覽，省去數週的基礎設施工作，讓開發者能在數分鐘內交付語意搜尋。

Fourth, MongoDB runs wherever the agent needs to run across all three major clouds, on-prem and in hybrid environments. The assumption that every workload eventually migrates to the public cloud is being challenged by real factors: cost at scale, capacity challenges, latency requirements, and regulatory mandates on data residency. Many customers run Atlas and EA simultaneously, and they need a platform that doesn't force a choice.

第四，MongoDB 可在代理需要運行的任何地方運行：三大公有雲、地端（on-prem）以及混合環境。「所有工作負載最終都會遷移到公有雲」的假設正受到現實因素挑戰：規模化成本、容量挑戰、延遲要求，以及對資料駐留（data residency）的法規要求。許多客戶同時運行 Atlas 與 EA，並需要一個不會迫使他們二選一的平台。

Fifth, MongoDB is embedded in the tools, developers and agents actually use to build agentic applications. LangChain is the world's most widely adopted agent framework with over 1 billion downloads. We delivered 10-plus native integrations with LangChain for vector search, hybrid retrieval, semantic cashing, and agent memory. We recently announced that MongoDB Checkpointer for LangChain deployment, which collapses what used to be a dedicated Postgres instance per agent into a single, shared Atlas cluster, state memory and operational data unified in one place.

第五，MongoDB 已嵌入開發者與代理實際用來建置代理型應用的工具之中。LangChain 是全球採用最廣的代理框架，下載量超過 10 億次。我們為 LangChain 提供了 10 多項原生整合，涵蓋向量搜尋、混合式檢索、語意快取（semantic caching）與代理記憶。我們近期宣布推出用於 LangChain 部署的 MongoDB Checkpointer，將過去每個代理都需要一個專用 Postgres 執行個體的做法，整合為單一共享的 Atlas 叢集，讓狀態記憶與營運資料在同一處統一管理。

Last month, we also launched the MongoDB plug-in and agent skills on the Claude core marketplace, where we are already seeing strong early traction with developers. Whenever agents are built, MongoDB is already there. Executing on this opportunity requires a world-class team. On the product side, we recently announced two CPO appointments.

上個月，我們也在 Claude 核心市集推出 MongoDB 外掛與代理技能，目前已看到開發者端強勁的早期採用動能。無論代理在哪裡被建置，MongoDB 都已經在那裡。要把握這個機會，需要世界級的團隊。在產品端，我們近期宣布兩項 CPO 任命。

Ben Cefalo, a long-time MongoDB leader, is now Chief Product Officer for core products overseeing Atlas and Enterprise Advanced. Pablo Stern-Plaza, who is based in San Francisco joined as Chief Product Officer for AI and Emerging Products with responsibility for our AI product portfolio and our strategic relationships with top AI native and frontier customers. Over the years, Pablo has worked for many software companies in technical roles, helping scale their product lines into meaningful thriving businesses.

Ben Cefalo 是長期的 MongoDB 領導者，現任核心產品首席產品長（Chief Product Officer），負責監督 Atlas 與 Enterprise Advanced。常駐舊金山的 Pablo Stern-Plaza 加入擔任 AI 與新興產品首席產品長，負責我們的 AI 產品組合，以及與頂尖 AI 原生與前沿客戶的策略合作關係。多年來，Pablo 曾在多家軟體公司擔任技術職務，協助將其產品線擴展為具規模且蓬勃發展的業務。

Anchoring our technology organization is Jim Scharf, our Chief Technology Officer, who continues to focus on the enterprise requirements that matter most: security, durability, availability, and performance.

作為我們技術組織的核心支柱，是首席技術長（CTO）Jim Scharf；他持續聚焦於企業最重視的需求：安全性、耐久性、可用性與效能。

On the go-to-market side, Erica Volini joined as Chief Customer Officer earlier in Q1, bringing two decades of enterprise growth experience, most recently architecting the partner-led motion that drove ServiceNow from $5 billion in revenues to more than $10 billion.

在市場推進（go-to-market）方面，Erica Volini 於第一季稍早加入擔任首席客戶長（Chief Customer Officer），帶來二十年的企業成長經驗；最近一次的代表作是設計以合作夥伴為主導的推進模式，推動 ServiceNow 營收從 50 億美元成長至超過 100 億美元。

Ryan Mac Ban joined us as Chief Revenue Officer, bringing 20-plus years scaling global go-to-market organization, most recently as CRO of Confluent, where he led a cloud-native consumption-oriented platform business with strong parallels to our own and previously in senior roles serving large enterprise customers at VMware and Cisco.

Ryan Mac Ban 加入擔任首席營收長（Chief Revenue Officer），帶來 20 多年擴展全球市場推進組織的經驗；最近一次任職為 Confluent 的 CRO，在那裡他領導一個雲原生、以用量消費為導向的平台型業務，與我們自身業務有許多相似之處；此前也曾在 VMware 與 Cisco 擔任資深職務，服務大型企業客戶。

Erica and Ryan are partnering as a unified go-to-market team jointly responsible for the full customer life cycle. With this team in place, I'm confident in our ability to capture the opportunity ahead. I also want to extend my deepest thanks to the entire MongoDB team and especially our go-to-market organization whose hard work and sharp execution delivered a stellar Q1.

Erica 與 Ryan 正以一個整合的市場推進團隊合作，共同負責完整的客戶生命週期。有了這支團隊到位，我對我們把握前方機會的能力充滿信心。我也要向整個 MongoDB 團隊致上最深的感謝，特別是我們的市場推進組織；他們的辛勤付出與精準執行，帶來了出色的第一季表現。

One last note before I hand it over to Mike, I would like to personally invite you to our Investor Day, which will be in New York City on September 29. Please e-mail ir@mongodb.com, if you would like to attend. We hope to see many of you there.

在我把時間交給 Mike 之前，最後補充一點：我想親自邀請各位參加我們的投資人日（Investor Day），活動將於 9 月 29 日在紐約市舉行。若您希望出席，請寄電子郵件至 ir@mongodb.com。我們希望在現場見到各位中的許多人。

With that, Mike, please take it away.

那麼，Mike，請你接著說。

Great. Thank you, CJ, and good afternoon to everyone on the call. I will start by reviewing our first quarter fiscal '27 financial performance before moving on to our outlook for the second quarter and the remainder of the fiscal year. I will be discussing both GAAP and non-GAAP results. As CJ highlighted, we delivered a strong quarter that exceeded all of our guidance ranges, and we are raising our outlook across the board for fiscal '27.

好的。謝謝你，CJ，也向所有參與電話會議的各位致上下午好。我將先回顧我們 2027 會計年度第一季的財務表現，接著說明我們對第二季以及本會計年度剩餘期間的展望。我會同時討論 GAAP 與非 GAAP 的結果。如 CJ 所強調，我們交出了一個強勁的季度，表現超出所有指引區間，並且我們正全面上調 2027 會計年度的展望。

Before diving into details, I want to highlight three key takeaways from the quarter. First, Atlas growth remained strong, with the fourth straight quarter of year-over-year growth above 29%. Second, EA growth remains durable as we continue to grow both Atlas and EA. And third, our business model continues to deliver operating margin and cash flow expansion.

在深入細節之前，我想先強調本季三個關鍵重點。第一，Atlas 成長依然強勁，已連續第四個季度年增率高於 29%。第二，EA 成長仍具韌性，因為我們持續同時推動 Atlas 與 EA 的成長。第三，我們的商業模式持續帶來營業利益率與現金流的擴張。

Looking at the top line in more detail. Total revenue in the first quarter reached $688 million, representing 25% year-over-year growth compared to 22% growth in the year ago quarter.

更詳細來看營收表現。第一季總營收達 6.88 億美元，年增 25%，相較於去年同期的年增 22%。

Turning to our product breakdown. Atlas consumption was stronger than expected in the quarter, and revenue grew by more than 29% year over year and exceeded our guidance. This is the fifth straight quarter of year-over-year dollar growth in Atlas, adding a record $117 million in the quarter. Atlas now accounts for approximately 75% of total Q1 revenue, up from 72% in the year-ago quarter.

接著看產品組合。本季 Atlas 的使用量（consumption）強於預期，營收年增超過 29%，並且高於我們的指引。這是 Atlas 連續第五個季度的年增金額（dollar growth）成長，本季新增金額創紀錄達 1.17 億美元。Atlas 目前約占第一季總營收的 75%，高於去年同期的 72%。

Our main growth driver continued to be the strength in use cases at established enterprise customers with momentum across the financial services, technology and media industries in Q1. Smaller but accelerating growth drivers included early AI deployments with many of these same enterprise customers and momentum with Frontier Labs and AI native companies. We experienced particular strength in North America that was driven by our larger customers, although our self-serve business also performed well in the period. This ongoing momentum across our customer base is reflected in our total company net ARR expansion rate, which was 121% for the quarter compared to 119% a year ago.

我們主要的成長動能，仍來自既有企業客戶在既定使用情境上的強勁需求；第一季在金融服務、科技與媒體產業皆呈現動能。規模較小但正在加速的成長動能，包括許多相同企業客戶的早期 AI 部署，以及 Frontier Labs 與 AI 原生（AI native）公司的動能。我們在北美表現特別強勁，主要由大型客戶帶動，儘管如此，我們的自助式（self-serve）業務在本期也表現良好。這股在客戶基礎上的持續動能，反映在公司整體淨 ARR 擴張率上：本季為 121%，高於一年前的 119%。

Turning to EA & Other revenue, which encompasses the metrics we previously referred to as non-Atlas, we saw solid results with revenue growing 13% year over year. This strength was driven by existing customers across all types of industries, particularly in the finance and technology verticals, where customers continue to expand their on-prem footprint to support both traditional and AI applications.

再看 EA 與其他（Other）營收（涵蓋我們先前稱為非 Atlas 的指標），我們看到穩健的結果，營收年增 13%。這項強勁表現主要由各產業的既有客戶所帶動，尤其是金融與科技垂直領域；客戶持續擴大其地端（on-prem）部署規模，以支援傳統與 AI 應用。

EA & Other ARR, which normalizes for duration impacts grew approximately 11% year over year.

EA 與其他 ARR（已將合約期間長短的影響標準化）約年增 11%。

Moving down to P&L. Total non-GAAP gross margins of 74.5% expanded by approximately 40 basis points year over year and were approximately 100 basis points below the fourth quarter. Subscription gross margin finished at 77.1%, approximately 60 basis points below the first quarter fiscal '26 and 170 basis points lower than the fourth quarter.

接著往下看損益表（P&L）。非 GAAP 總毛利率為 74.5%，年增約 40 個基點，但較第四季低約 100 個基點。訂閱毛利率為 77.1%，較 2026 會計年度第一季低約 60 個基點，且較第四季低 170 個基點。

The quarter-over-quarter variances were driven mainly by product mix between Atlas and EA as well as the normal seasonality impact to margins in the first quarter of the fiscal year. Moving to profitability. I'd like to start by noting that we had our second quarter in a row of GAAP profitability, which is a great trend. Non-GAAP income from operations came in at $123 million, yielding an operating margin of 18% compared to 16% in the year ago period. We are very pleased with our operating margin results which benefited primarily from strength in revenue, driven mainly by Atlas.

季對季的差異主要由 Atlas 與 EA 之間的產品組合變化所致，此外也受到會計年度第一季毛利率的正常季節性影響。接著談獲利能力。我想先指出，我們已連續第二個季度達成 GAAP 獲利，這是一個很好的趨勢。非 GAAP 營業利益為 1.23 億美元，營業利益率為 18%，相較去年同期為 16%。我們對營業利益率的表現非常滿意，主要受惠於營收的強勁表現，而這主要由 Atlas 所帶動。

First quarter non-GAAP net income was $112 million, which translates to $1.32 per share based on 85.3 million diluted shares outstanding. This compares to net income of $86 million or $1 per share on 86.3 million diluted shares outstanding in the year ago period. Our remaining performance obligations, which we define specifically as obligations for contracts with a duration greater than 12 months stayed relatively consistent quarter over quarter and ended the period at $1.46 billion.

第一季非 GAAP 淨利為 1.12 億美元，按 8,530 萬股稀釋後流通股數計算，每股盈餘為 1.32 美元。相較之下，去年同期淨利為 8,600 萬美元，按 8,630 萬股稀釋後流通股數計算，每股盈餘為 1 美元。我們的剩餘履約義務（remaining performance obligations；我們特別定義為合約期間超過 12 個月的合約義務）季對季大致持平，期末為 14.6 億美元。

This represents year-over-year growth of 88% with the current portion growing at 69%. Customer adds grew by 2,500 sequentially, bringing the total customer count to 67,700, which is up from 57,100 in the year ago period. The growth in our total customer count is being driven primarily by Atlas, which had 66,400 customers at the end of the first quarter compared to 55,800 in the year ago period.

這代表年增 88%，其中當期部分年增 69%。客戶新增數較前一季增加 2,500 家，使客戶總數達到 67,700 家，高於去年同期的 57,100 家。客戶總數的成長主要由 Atlas 帶動；第一季末 Atlas 客戶數為 66,400 家，相較去年同期為 55,800 家。

Within Atlas, we saw a strong quarter of Voyage customer additions, reflecting early but encouraging demand for our AI embedding capabilities. We feel good about the momentum we are seeing with new customers and please keep in mind, this metric will fluctuate from quarter to quarter. We closed out Q1 with 2,895 customers with at least $100,000 in ARR, representing 16% year-over-year growth. Revenue growth from this cohort was strong and outpaced total company revenue growth, consistent with our move upmarket.

在 Atlas 方面，我們看到 Voyage 客戶新增表現強勁，反映市場對我們 AI 向量嵌入（embedding）能力的早期但令人鼓舞的需求。我們對新客戶動能感到樂觀，但也請記得，這項指標會隨季度而波動。第一季結束時，ARR 至少 10 萬美元的客戶數為 2,895 家，年增 16%。此客群帶來的營收成長強勁，且增速超過公司整體營收成長，與我們持續往中大型客戶市場（move upmarket）的策略一致。

Furthermore, we continue to see strong Atlas platform adoption. Of our Atlas customers generating at least $100,000 in ARR, 45% are leveraging two-or-more features of our platform, which is up from 37% in the year-ago quarter driven largely by Vector and tech search adoption.

此外，我們持續看到 Atlas 平台的強勁採用。在 ARR 至少 10 萬美元的 Atlas 客戶中，有 45% 使用我們平台的兩項或以上功能，高於去年同期的 37%，主要由向量（Vector）與技術搜尋（tech search）的採用所帶動。

Moving on to the balance sheet and cash flow. We ended the first quarter with $2.4 billion in cash, cash equivalents and short-term investments. During Q1, we allocated $100 million towards share repurchases and $58 million to settle taxes on employee RSUs. Operating cash flow for the quarter was $202 million versus $110 million last year, and free cash flow was $198 million versus $106 million last year. Our cash flow results were driven primarily by strong operating profit and seasonally higher cash collections.

接著談資產負債表與現金流。第一季末，我們持有 24 億美元的現金、約當現金與短期投資。第一季期間，我們投入 1 億美元進行庫藏股回購，並支付 5,800 萬美元用以結清員工 RSU 的稅款。本季營運現金流為 2.02 億美元，去年同期為 1.10 億美元；自由現金流為 1.98 億美元，去年同期為 1.06 億美元。我們的現金流表現主要由強勁的營業獲利以及季節性較高的現金收款所帶動。

Before moving on to guidance, I am pleased to share that we have acquired Clarity Business Solutions. As we have discussed previously, we are strategically increasing our investment in the US federal vertical and this acquisition is a key component of that strategy. Clarity has been a trusted partner of ours since 2021, providing specialized support and professional services for highly classified workloads within the US government.

在進入財測指引之前，我很高興分享我們已收購 Clarity Business Solutions。如同我們先前所討論，我們正策略性地增加對美國聯邦政府垂直領域的投資，而這項收購是該策略的關鍵組成。Clarity 自 2021 年起即為我們值得信賴的合作夥伴，為美國政府高度機密的工作負載提供專業支援與專業服務。

We have held a small equity stake in Clarity for some time. And this acquisition brings in MongoDB, the deep domain expertise and high-level security clearances required to further accelerate our US federal vertical. Financially, this transaction represents approximately $10 million in services revenue annually at roughly breakeven profitability, and these impacts are already reflected in our updated guidance.

我們持有 Clarity 的小部分股權已經有一段時間了。而這項收購帶來了 MongoDB，以及進一步加速我們美國聯邦垂直市場所需的深厚領域專業能力與高階安全許可。從財務角度來看，這筆交易每年約帶來 1,000 萬美元的服務營收，獲利能力大致損益兩平，而這些影響已反映在我們更新後的財測指引中。

Now I'd like to share some of the assumptions driving our Q2 outlook and provide some additional detail into how we're thinking about the rest of fiscal '27. To begin, as I mentioned earlier, we continue to see strong and consistent Atlas growth. This performance is driven primarily by strength in core workloads as well as early AI tailwinds from both enterprise and AI native customers. We are encouraged by the continued strength in Atlas and feel good about the business entering the second quarter where we expect Atlas revenue growth of approximately 26%.

接下來我想分享一些推動我們第二季展望的假設，並補充我們對 2027 會計年度其餘期間的思考細節。首先，如我先前提到的，我們持續看到 Atlas 強勁且一致的成長。這項表現主要由核心工作負載的強勢所帶動，同時也受惠於來自企業客戶與 AI 原生客戶的早期 AI 順風。我們對 Atlas 持續的強勁表現感到鼓舞，並對業務進入第二季感到樂觀；我們預期 Atlas 營收成長約為 26%。

This strength is not only driving our second quarter fiscal '27 outlook, but is also giving us confidence to raise our full year growth expectation to a range of 23% to 25%, an increase of 200 basis points. As we said last quarter, we would like to remind you that as Atlas has gotten larger, it has become more predictable and less sensitive to revenue movements with any individual customer or cohort. With this in mind, we would encourage you to not expect large swings versus guidance for the current quarter as changes in consumption in inter-quarter only have a modest impact on revenue within the period.

這股強勁動能不僅推動我們 2027 會計年度第二季的展望，也讓我們有信心將全年成長預期上調至 23% 至 25% 的區間，提升 200 個基點。正如我們上季所說，我們想提醒各位，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波動敏感度更低。基於此，我們建議各位不要預期本季實際表現相對於指引會出現大幅擺動，因為跨季度的用量變化對當期營收的影響僅屬溫和。

Given Atlas as a consumption-based product, there is more room for variability as we go further out in the year. For EA & Other, we have line of sight into a very strong Q2 and expect to see revenue growth of approximately 20%. This reflects our expectations for continued ARR momentum as well as the timing of several large multiyear deals with existing customers. The continued momentum highlights the strategic importance of EA to some of our largest customers.

鑑於 Atlas 是以用量計費（consumption-based）的產品，隨著時間拉長到年度後段，變動空間會更大。至於 EA 與其他（EA & Other），我們對第二季有非常清晰的能見度，並預期營收成長約 20%。這反映了我們對 ARR 動能持續的預期，以及與既有客戶簽訂數筆大型多年期合約的時點因素。這股持續動能凸顯 EA 對我們部分最大客戶的策略重要性。

Given our current momentum, balanced against the timing of certain deals and a more difficult Q4 compare, we are raising our full year expectations for EA and other revenue to mid-single-digit growth in fiscal '27. This implies that EA and other revenue will be approximately flat during the second half of the year, again, due to the tougher compares from the second half of fiscal '26. While we remain optimistic regarding our ability to grow our EA and other revenue over the long term, it remains difficult to predict the duration of our EA deals. So we only include deals in our forecast that have either closed or have a high probability of closing to limit the risk of a negative surprise.

在目前的動能基礎上，並考量部分交易的時點以及第四季較具挑戰的比較基期，我們將 2027 會計年度 EA 與其他營收的全年預期上調至中個位數成長。這意味著下半年 EA 與其他營收將大致持平，同樣是因為 2026 會計年度下半年的比較基期較高。雖然我們對長期提升 EA 與其他營收的能力仍保持樂觀，但 EA 合約的持續期間仍難以預測。因此，為降低出現負面意外的風險，我們僅將已完成簽約或極高機率完成簽約的交易納入預測。

Turning to profitability. We remain committed to driving both revenue growth and operating margin expansion, and we now expect to expand operating margin by 100 to 150 basis points in fiscal '27. We will achieve this expansion while investing in key growth initiatives across both products and go-to-market. Our product investment is focused around enhancing our AI capabilities, which includes Vector Search and Voyage and expanding EA's product value with new and advanced features, including native AI functionality.

接著談獲利能力。我們仍致力於同時推動營收成長與營業利益率擴張，且目前預期在 2027 會計年度將營業利益率擴張 100 至 150 個基點。我們將在投資產品與市場拓展（go-to-market）兩大關鍵成長計畫的同時，達成這項擴張。我們的產品投資聚焦於強化 AI 能力，包括 Vector Search 與 Voyage，並透過新增與進階功能（包含原生 AI 功能）擴大 EA 的產品價值。

Our go-to-market investments include building out our presence in Japan as well as strengthening our US federal vertical, highlighted by our acquisition of Clarity Business Solutions. We will also continue to invest in quota-carrying headcount, marketing programs and developer awareness. Now let's shift to how that translates to guidance for Q2 and fiscal '27.

我們的市場拓展投資包括在日本擴大布局，以及強化我們的美國聯邦垂直市場，而收購 Clarity Business Solutions 即為其中亮點。我們也將持續投資於具配額責任（quota-carrying）的員額、行銷方案與開發者認知。現在讓我們轉到這些內容如何反映在第二季與 2027 會計年度的指引上。

For Q2, we expect revenue of $729 million to $734 million, which equates to 23% to 24% year-over-year growth. We expect non-GAAP income from operations to be in the range of $152 million to $156 million for an operating margin of approximately 21% at the high end of guidance. We expect non-GAAP net income per share to be in the range of $1.58 to $1.61 based on 86.3 million diluted shares outstanding. For fiscal '27, we expect revenue to be in the range of $2.92 billion to $2.96 billion, representing full year revenue growth of 19% to 20%.

就第二季而言，我們預期營收為 7.29 億至 7.34 億美元，相當於年增 23% 至 24%。我們預期非 GAAP 營業利益為 1.52 億至 1.56 億美元；在指引高端時，營業利益率約為 21%。我們預期非 GAAP 每股淨利為 1.58 至 1.61 美元，係以 8,630 萬股稀釋後流通股數為基礎。就 2027 會計年度而言，我們預期營收為 29.2 億至 29.6 億美元，代表全年營收成長 19% 至 20%。

We expect non-GAAP income from operations of $571 million to $591 million for an operating margin of approximately 20% at the high end of guidance. With a combination of 20% revenue growth, the 20% operating margin, we are targeting a Rule of 40 performance at the high end of our outlook. We expect non-GAAP net income per share to be in the range of $5.95 to $6.14 based on 86.7 million diluted shares outstanding. Note that the non-GAAP net income per share guidance for the second quarter and fiscal '27 assumes a non-GAAP tax provision of 20%.

我們預期非 GAAP 營業利益為 5.71 億至 5.91 億美元；在指引高端時，營業利益率約為 20%。在 20% 的營收成長與 20% 的營業利益率組合下，我們的目標是在展望高端達成「40 法則」（Rule of 40）的表現。我們預期非 GAAP 每股淨利為 5.95 至 6.14 美元，係以 8,670 萬股稀釋後流通股數為基礎。請注意，第二季與 2027 會計年度的非 GAAP 每股淨利指引假設非 GAAP 稅負提列為 20%。

In closing, I also want to thank all of the MongoDB employees for staying focused and executing very well in Q1. We are very pleased with our Q1 results and remain highly confident in the long-term opportunity ahead for MongoDB. We are optimistic regarding our growth prospects, and we'll continue to invest responsibly to drive long-term shareholder value.

最後，我也要感謝所有 MongoDB 員工在第一季保持專注並且執行得非常出色。我們對第一季的結果非常滿意，並對 MongoDB 長期的機會仍然高度有信心。我們對成長前景保持樂觀，並將持續以審慎負責的方式投資，以驅動長期股東價值。

With that, operator, we're now ready to take questions.

接下來，接線員，我們準備開始回答提問。

(Operator Instructions) Matt Martino, Goldman Sachs.

（接線員指示）高盛（Goldman Sachs）的 Matt Martino。

Hi, thanks for taking the questions, guys. CJ, maybe to start with you. The agentic conversation seems to have really shifted even over the past three months from proof of concept into real production deployments. And Mongo has put a lot of work into the platform to meet that moment with the LangChain partnership and the performance upgrades to the core database.

嗨，謝謝各位接受提問。CJ，我想先從你開始。在過去三個月裡，關於代理式（agentic）對話的討論似乎已經明顯從概念驗證轉向真正的生產環境部署。Mongo 也在平台上投入了大量工作來把握這個時刻，包括與 LangChain 的合作夥伴關係，以及對核心資料庫的效能升級。

I think as those pieces come together, do you feel like we're approaching the point where agentic workloads start to genuinely move the needle on consumption? Or is the bigger inflection still ahead of us? I'd love to get your thoughts there.

我想，當這些要素逐步到位時，你是否覺得我們正接近一個拐點：代理式工作負載開始真正對用量帶來顯著影響？還是更大的拐點仍在後面？很想聽聽你的看法。

Thank you, Matt. We wanted to make sure on behalf of our products and technology organization that we are ready to scale when somebody wants to create an agentic workload in production that is customer-facing, which is typically where the scale is much higher and have all the capabilities in a single platform, so you are not doing search somewhere else.

謝謝你，Matt。我們希望代表產品與技術團隊，確保當有人想在生產環境中建立面向客戶的代理式工作負載時，我們已準備好進行規模化；而這通常是規模顯著更高的情境。同時，我們也希望在單一平台內具備所有能力，讓你不需要在其他地方做搜尋。

You are not doing vectorization somewhere else and embeddings which I was still trying to understand the power of embedding and what would that do for agentic workloads. But now seeing that with some of the large financial services and health care companies gives me a lot of confidence that our data platform can truly act as a real-time system of intelligence.

你也不需要在其他地方做向量化與嵌入（embeddings）；我之前仍在試著理解嵌入的威力，以及它能為代理式工作負載帶來什麼。但現在看到一些大型金融服務與醫療保健公司採用後，讓我對我們的資料平台能真正作為即時的智慧系統（real-time system of intelligence）充滿信心。

So the answer is, I'm seeing it's still early math, just to be clear, because the security governance, observability, there are many, many aspects to the agents and what kind of outcomes they deliver if it is agents at scale.

所以答案是，我看到這仍然是早期階段的推算（early math），先說清楚，因為在安全治理、可觀測性等方面，對於代理（agents）以及它們在大規模部署時能交付什麼樣的成果，牽涉到非常非常多的面向。

But we feel that we are ready and just yesterday, Matt, I was with a Fortune 25 firm. And when we outlined what we already have, where MongoDB can not only act as an operational data layer, but can also act as a long-term memory and some of the things that we are building right now they got really, really excited as they think about rolling out production agents at scale. So early but I'm seeing very encouraging signs, and we are ready.

但我們覺得我們已經準備好了，就在昨天，Matt，我和一家《財富》前25大企業在一起。當我們闡述我們已經具備的能力時，MongoDB 不僅可以作為營運資料層（operational data layer），也可以作為長期記憶（long-term memory），再加上我們現在正在打造的一些東西，他們在思考要把生產環境代理（production agents）大規模推廣時，真的非常、非常興奮。所以雖然還早，但我看到非常令人鼓舞的跡象，而且我們已經準備就緒。

And then, Mike, for you, you made a comment, I think, not to expect huge swings on Atlas revenue for the quarter ahead. Can you unpack that comment a bit? Should we take that as (inaudible) magnitude similar to what we saw this quarter or something different?

接著，Mike，想請教你，你提到我想是說，不要預期下一季 Atlas 營收會有很大的波動。你能把這個評論再拆解說明一下嗎？我們應該把它理解為（聽不清）幅度會和本季看到的差不多，還是會不一樣？

Yeah. Thank you for the question, Matt. So as it relates to guidance, we think it's important that our guidance reflects the true strength of the underlying business and feel there's room to do that while still being imprudent. As Atlas has gotten bigger, it has become more predictable and has become less sensitive to movements from individual customers or cohorts.

是的。謝謝你的問題，Matt。就財測指引而言，我們認為很重要的一點是，我們的指引要反映出基礎業務的真實強勁程度，同時我們也覺得在不至於不審慎的前提下，仍有空間做到這點。隨著 Atlas 規模變大，它變得更可預測，也變得較不受單一客戶或單一客群變動的影響。

Coming off a strong Q1 where consumption came in better than expected, we're guiding Q2 consistent with the framework of how we've guided the past two quarters. To put that in context, in Q4, consumption came largely in line with our expectations. And in Q1, it came in a little better, which you can see reflected in our results versus guidance. The strength in Atlas this quarter allowed us to roll the beat and raise guidance for the full year.

在一個強勁的 Q1 之後，使用量（consumption）表現優於預期，我們對 Q2 的指引仍延續過去兩季的指引框架。提供一些背景：在 Q4，使用量大致符合我們的預期。而在 Q1，使用量略優一些，你可以從我們實際結果相對於指引的表現看出來。本季 Atlas 的強勁表現，讓我們得以把超預期的部分延續下去，並上調全年指引。

And then, of course, that revenue drove higher profitability and EPS. For the full year, given Atlas is a consumption-based product, there's a little more room for variability as we go further out from the year -- in the year. So we've not changed our philosophy on EA, where we'll always guide conservatively due to the uncertainty around the timing of the deals. So hopefully, that gives you the context of the framework in terms of how we guided Q2.

當然，這些營收也帶動了更高的獲利能力與 EPS。就全年來看，由於 Atlas 是以使用量計費（consumption-based）的產品，隨著我們把視野拉得更遠——在一年之內往後看——變動空間會稍微大一些。因此我們沒有改變對 EA 的理念：由於交易時點存在不確定性，我們會一向採取保守指引。希望這能讓你理解我們在 Q2 指引上所採用的框架與脈絡。

Ryan MacWilliams, Wells Fargo.

Ryan MacWilliams，富國銀行（Wells Fargo）。

Thanks for tkaing the question. Mike, you're guiding to another strong 2Q for Atlas against the strong performance you had last year. Is this how we should think about the seasonality for the Atlas is going forward? Or is this Atlas guide being impacted by other factors we should keep in mind?

謝謝讓我提問。Mike，你對 Atlas 的 2Q 指引仍然很強，且是在去年表現很強的基期之上。我們是否應該把這視為 Atlas 未來的季節性走勢？或者這次 Atlas 的指引還受到其他我們需要留意的因素影響？

Yeah. Thanks for the question, Ryan. So as we guided Q2, a lot of that was coming off of a strong Q1 in terms of consumption. And as we've talked about, Ryan, as the business gets a little bit bigger, there's always some small seasonal changes, but on a year-over-year basis, I wouldn't expect significant changes. Now quarter on quarter, certainly, it does change a little bit. But year over year, I wouldn't expect much change in the seasonality.

是的。謝謝你的問題，Ryan。我們在給出 Q2 指引時，很大一部分是承接 Q1 在使用量方面的強勁表現。而且如同我們談過的，Ryan，當業務規模變得更大時，總會有一些小幅的季節性變化，但以年對年（year-over-year）來看，我不會預期有顯著變化。當然，以季對季（quarter on quarter）來看，確實會有些微變動。但以年對年來看，我不預期季節性會有太大改變。

Excellent. And then for CJ, I'd like to hear about the opportunity for AI native with Mongo as those customers really start to scale their own businesses are there use cases for large AI native that they may make more sense for Mongo? And I guess for the quarter itself, like how can we think about the contribution from AI natives to Atlas?

很好。接著想請教 CJ，我想聽聽 Mongo 在 AI 原生（AI native）方面的機會：當這些客戶開始真正擴大他們自己的業務規模時，是否存在一些大型 AI 原生的使用案例，可能更適合用 Mongo？另外就本季而言，我們該如何看待 AI 原生客戶對 Atlas 的貢獻？

So Brian, first is that AI natives what we are finding, and I shared the example of somebody like ElevenLabs and .local London a few weeks ago, they were using first-party database for operational data. They were using another software for search. And basically, most of those product lines were really choking as ElevenLabs was growing significantly, right? They are now at a $500 million ARR.

所以 Brian，首先是我們在 AI 原生客戶身上看到的情況——我幾週前分享過像 ElevenLabs 和 .local London 這樣的例子——他們用第一方資料庫來承載營運資料（operational data）。他們用另一套軟體來做搜尋。基本上，隨著 ElevenLabs 顯著成長，這些產品線大多都真的被「卡住」了，對吧？他們現在的 ARR 已經達到 5 億美元。

So when I asked the team, technically the engineer who made that decision saw that the growth of the company as in that AI native company, ElevenLabs was being held up by the data layer. And us having search, vector search and operational data in a single platform, they are -- they made the decision to move to MongoDB not too long ago.

所以當我問團隊時，從技術面來看，做出那個決策的工程師發現，作為一家 AI 原生公司，ElevenLabs 的成長其實被資料層拖住了。而我們把搜尋、向量搜尋（vector search）與營運資料整合在同一個平台上，他們——他們不久前就決定遷移到 MongoDB。

And two things they said that really resonated with me, Ryan. Number one, they are like, gee, we should have done this a lot sooner. Otherwise, we would have not to deal with all these outages and other things they dealt with the previous platform.

他們提到兩件事，讓我很有共鳴，Ryan。第一，他們說，天啊，我們早就該這麼做了。不然我們就不必在先前的平台上，去面對所有那些當機（outages）和其他他們遇到的問題。

And number two, now choosing MongoDB even though they have scaled significantly on their ARR as an AI native company gives them peace of mind. I'm hearing them from other AI native companies who also chose maybe a Postgres or something and Postgres completely choked on the performance.

第二，即使作為一家 AI 原生公司，他們的 ARR 已經大幅擴張，但現在選擇 MongoDB 讓他們更安心（peace of mind）。我也從其他 AI 原生公司聽到類似的說法：他們可能選了 Postgres 或其他方案，但 Postgres 在效能上完全撐不住（completely choked）。

So that just gives me a lot of confidence that if AI native company where AI is the business or agentic layer is the business and they feel that they can scale with MongoDB. When that moves over to the enterprises, whether banks, health care and other firms, they will also realize the same thing a little bit later. And as Mike shared and I shared earlier, the contribution is there. We are seeing very encouraging signs right now. But a lot of growth was still driven by core enterprise workloads, which I would argue are also getting ready for AI world.

所以這讓我非常有信心：如果 AI 原生公司——也就是 AI 就是其業務本體，或代理式（agentic）層就是其業務本體——都覺得能用 MongoDB 來擴展規模，那當這種需求延伸到企業端，不論是銀行、醫療保健或其他公司，他們也會在稍晚一些時候意識到同樣的事情。而且如同 Mike 以及我先前分享的，貢獻是存在的。我們現在看到非常令人鼓舞的跡象。但成長的很大一部分仍是由核心企業工作負載所驅動；我會主張，這些工作負載其實也正在為 AI 世界做準備。

Raimo Lenschow, Barclays.

Raimo Lenschow，巴克萊（Barclays）。

Thank you. Congrats from me as well. CJ, on that note, you're meeting a lot of customers at the moment. The one theme that comes up in the industry around data is that people realize with AI, should data needs to be consolidated and cleaner. So what are you seeing there in terms of that kind of consolidation move towards Mongo. And maybe just talk to how that's kind of impacting Atlas and EA. And then I had one follow-up for Mike.

謝謝。我也要恭喜你們。CJ，延續這個話題，你最近正在見很多客戶。產業裡關於資料的一個主題是：大家意識到在 AI 時代，資料需要被整合得更集中、也更乾淨。所以你在這方面看到什麼——例如這種整合趨勢是否會推動客戶轉向 Mongo？也請談談這對 Atlas 和 EA 的影響。然後我還有一個問題要追問 Mike。

Raimo, great question. So we definitely see I would say, and Raimo, thanks for acknowledging. But in Q1, just in Q1, I individually met 200 customers, okay? So I have lots of data points. And what we actually see is that a lot more modernization acceleration where somebody is moving to Atlas so that they are ready on scaling out for AI workloads rather than a consolidation play. What I see.

Raimo，問得很好。我們確實看到——我會這麼說，而且 Raimo，謝謝你的提及。但在 Q1，就只是在 Q1，我個人就見了 200 位客戶，好嗎？所以我有很多資料點。而我們實際看到的是：更多的是現代化（modernization）的加速——也就是有人遷移到 Atlas，讓他們能為 AI 工作負載的擴展做好準備——而不是一個資料整併（consolidation）的策略。這是我所看到的。

Yes, there are some examples where they are saying, look CJ, now you have Search and Vector Search in the database that improves our data pipelines. We don't need to ETL now to some other search provider. We try to use open source that didn't work. So we are seeing some movement of data. And we are also seeing some migration from Postgres and others into MongoDB given that we do unstructured data really, really well.

是的，有一些例子是他們在說，你看 CJ，現在你們在資料庫裡有 Search 和 Vector Search，這改善了我們的資料管線。我們現在不需要再 ETL 到其他搜尋供應商了。我們嘗試使用開源方案但沒有成功。所以我們看到一些資料的流動。而且我們也看到，鑑於我們在非結構化資料方面做得非常、非常好，一些客戶正從 Postgres 等遷移到 MongoDB。

And LLM speak the language of Json or low Jason. So that's how I would describe it more than data consolidation, modernization and also getting ready where you're not ETLing out data and just use MongoDB as the layer for AI.

而 LLM 使用的是 Json（或「low Jason」）這種語言。所以我會把它描述為：不只是資料整併，而是現代化，同時也為未來做好準備——你不再把資料 ETL 出去，而是直接把 MongoDB 當作 AI 的資料層。

Okay. Perfect. Make sense. Sounds exciting. And then, Mike, one for you, like with the two new hires on the go-to-market side, I know it's now we are now in Q2, but any changes we need to be aware of there? Or what are you thinking there in terms of impact on the organization this year?

好的。完美。明白了。聽起來很令人興奮。接著，Mike，問你一個：在 go-to-market 端新增了兩位新任用人員，我知道現在已經是 Q2 了，但那邊有沒有我們需要注意的任何變化？或者你怎麼看今年對組織的影響？

Yes. So thanks for the question. As we talked about going into Q1, we felt very confident in terms of making sure that there was not going to be any disruption. So from a territory plan in quota, all of that stuff, those are all out. We don't expect there to be any changes in the year.

是的。謝謝你的問題。正如我們在進入 Q1 時談到的，我們對於確保不會有任何干擾感到非常有信心。所以從區域規劃、配額等各方面來看，這些都已經定案。我們不預期今年會有任何變動。

As you know, making changes to comp plans during the year is always fraught with issues. Ryan has done a great job so far. He'll get his arms around the organization, maybe some tweaks next year. We'll see what he wants to do. But I wouldn't expect any significant changes for the remainder of fiscal '27.

如你所知，在年度中途調整獎酬方案（comp plans）總是問題重重。Ryan 目前做得非常好。他會逐步全面掌握組織運作，也許明年會做一些微調。我們會看他想怎麼做。但在 27 財年剩餘期間，我不預期會有任何重大變更。

Ittai Kidron, Oppenheimer & Co.

Ittai Kidron，Oppenheimer & Co.（奧本海默公司）

Thank you, guys. Congrats on a good quarter. CJ, I wanted to get your perspective on the AI natives. In what way do you think your go-to-market needs to evolve to address them differently? Is there a need to address them differently in the go-to-market effort?

謝謝各位。恭喜交出一個不錯的季度成績。CJ，我想聽聽你對 AI 原生（AI natives）公司的看法。你認為你們的 go-to-market 需要如何演進，才能以不同方式來應對他們？在 go-to-market 的推進上，是否需要用不同方式來面對他們？

Yeah. Ittai, I'll give you a straightforward answer. This is work in progress. So what we find is that some of these AI-native companies come through our self-serve motion. We constantly watch -- we had so many customers through our self-serve motion, and that motion has been working really, really well as a lot of venture investments have gone into AI native companies.

是的。Ittai，我給你一個直接的答案。這還在進行中。我們發現其中一些 AI 原生公司是透過我們的自助式（self-serve）模式進來的。我們持續觀察——我們透過自助式模式獲得了非常多客戶，而這個模式運作得非常、非常好，因為大量創投資金流入了 AI 原生公司。

So post 2023, first, I want to acknowledge through our self-serve motion, we are getting some of these iconic logos that have now become a truly company with $100 million ARR plus. With Ryan now in place, we are figuring it out what is the right point to intervene and that is a work in progress, okay, what are the characteristics? It's a Tier 1 VC company? Maybe it's not.

所以在 2023 年之後，首先我想先肯定一點：透過我們的自助式模式，我們拿到了一些具代表性的標誌性客戶（iconic logos），而這些客戶如今已經真正成長為 ARR 超過 1 億美元的公司。Ryan 現在到位後，我們正在釐清：介入的正確時點是什麼——這也仍在摸索中，好嗎？要看哪些特徵？是 Tier 1 的 VC 投資公司？也許不是。

Like, for example, a customer that grew in Q1, we found out that there was a AI/robotics company and they were growing a lot on Atlas, and then our team reached out to them right away. So this -- we see that some of these companies are coming via our self-serve motion.

例如，有一個在 Q1 成長的客戶，我們發現那是一家 AI／機器人公司，他們在 Atlas 上成長很快，然後我們的團隊立刻就去聯繫他們。所以——我們看到其中一些公司是透過我們的自助式模式進來的。

And then one, when do we intercept and put a field wrap on it. And number two is that how do we scale and focus on that motion because we have a great database for those kind of companies. So work in progress, but we are making definitely improvements as we learn.

接著第一個問題是：我們何時要攔截並配置現場銷售支援（field wrap）。第二個問題是：我們要如何擴大規模並聚焦在這個模式上，因為我們對這類公司有很棒的資料庫產品。所以仍在進行中，但我們確實在學習的過程中持續改進。

Fantastic. And then for you, Mike, great numbers again. Two small things. First on the EA comments on your second half when you talked about flat year over year in the second half. I'm just wondering, is there -- were there any large deals?

太好了。接著 Mike，你的數字再次很漂亮。兩個小問題。第一個是關於你在談到下半年時對 EA 的評論，你提到下半年年對年持平。我想問的是——是否有任何大型交易？

I talked about large multiyear deals in the quarter. Was there any movement from future quarters into 2Q that have made that, that could also explain the flat second half or kind of things kind of fall where they would you expect them to fall?

你提到本季有大型多年期（multiyear）交易。是否有任何原本在未來季度的案子被提前到 2Q，導致這種情況、也可能解釋下半年持平？還是說整體就是如你預期地落在該落的位置？

Yeah. Thanks, Ittai. They largely felt where we expected. The biggest impact in the second half is really not this year, fiscal '27, it's '2%. As you remember, we had a very strong Q4, especially in '26. So that's really what's driving that guidance. I would say, and I've said it the whole time, hey, this is an area where we're going to be prudent. We're not going to go over our skiis in terms of multiyear deals. Hopefully, those build as we go through the year. You saw that last year. But we need to guide what we see today.

是的。謝謝，Ittai。整體上都如我們預期地落點。下半年最大的影響其實不是今年、也就是 27 財年，而是去年同期的比較基期。如你所記得的，我們在 26 財年的 Q4 非常強勁。所以真正推動這個指引的就是那一點。我會說——而且我一直都這麼說——嘿，這是我們會保持審慎的一個領域。在多年期交易方面，我們不會做得太激進（不會「衝過頭」）。希望這些交易會在一年當中逐步累積。你去年也看到了。但我們必須依據今天看到的情況來給指引。

Jason Ader, William Blair.

Jason Ader，William Blair（威廉布萊爾）

Yeah, thank you. I wanted to ask CJ about the federal business. I think it's interesting what you're doing there. And historically, has that not been a big part of the business and that's what drove this. Maybe just talk about the catalyst for the acquisition of Clarity.

是的，謝謝。我想問 CJ 關於聯邦政府業務。我覺得你們在那邊做的事情很有意思。從歷史上看，那是否一直不是業務的大宗，因此才促成這些動作？也請談談收購 Clarity 的催化因素。

Yes, I'll touch on it, and then Mike will add. First is we see tremendous opportunity in federal business, not only just United States, but in Europe and other places as well. Federal business, when you think about whether it's tax agencies, whether you think about other types of agency, for example, administrations of various kinds, there is a lot of unstructured data. And there is a lot of unstructured data that needs to be stored properly or documents for a lack of better term, and that needs to be retrieved, performance has to be high and the cost has to be lower.

是的，我先簡單談一下，然後 Mike 會補充。第一，我們在聯邦政府業務上看到巨大的機會，不僅在美國，也包括歐洲及其他地區。聯邦業務方面，當你想到不論是稅務機關，或其他類型的機構，例如各式各樣的行政機關，都有大量的非結構化資料。而且有大量非結構化資料需要被妥善儲存——更通俗地說就是文件——並且需要能被檢索，效能必須很高、成本必須更低。

So I am 100% believer that this is a large TAM for us. We have not invested significantly both from a go-to-market perspective as well as product perspective in the past. But the good news is we will have FedRamp high certification for US federal this year. That comes with other set of requirements on how we support these federal customers.

所以我百分之百相信，這對我們而言是一個很大的 TAM（總可服務市場）。過去我們無論在 go-to-market 或產品面，都沒有投入太多。但好消息是，今年我們將取得美國聯邦的 FedRAMP High 認證。而這也會帶來另一套要求，關於我們要如何支援這些聯邦客戶。

And one of the things that I have observed after being here is that a lot of these customers are still using our community version, and they would love to understand as we get FedRAMP High certification can we sell to them properly and serve them properly and have enough coverage. So massive potential, and that's why the acquisition, and I'll ask Mike to add.

我在這裡之後觀察到的一件事是，很多這些客戶仍在使用我們的社群版，而他們很希望了解，隨著我們取得 FedRAMP High 認證後，我們是否能夠以合規的方式向他們銷售、妥善地服務他們，並且具備足夠的涵蓋與支援。所以潛力非常巨大，這也是我們進行這項收購的原因；我也請 Mike 補充一下。

Yes. So great answer. Thank you, CJ. Just to add on to that, Jason. One of the things that when we looked at the business, it has grown nicely, but it is a pretty small piece of our business today.

是的。這個回答很棒。謝謝你，CJ。Jason，我再補充一點。我們在檢視這項業務時發現，它成長得不錯，但就目前而言，它在我們整體業務中仍只占相當小的一部分。

We would like to make sure that we can play in all areas of the federal government, civilian intel, defense, all those areas. And we've partnered with Clarity, they've been a wonderful partner for several years. But when we have services and other engagements, we've typically had to use them. We would like that to be a MongoDB capability going forward. And then you marry that with getting FedRAMP High later in the year, we feel really good about our momentum going into next year.

我們希望確保我們能在聯邦政府的所有領域參與，包括文職、情報、國防等各個領域。我們與 Clarity 合作多年，他們一直是非常出色的合作夥伴。但當我們有服務與其他專案合作時，通常都必須使用他們。我們希望未來這能成為 MongoDB 自身的能力。再加上我們預計在今年稍晚取得 FedRAMP High，我們對於進入明年的動能感到非常有信心。

Then a quick follow-up for you, Mike. NRR up by a point sequentially. What's the right way to think about the drivers there? Is it the 45% of customers that are adding additional capabilities on the platform? Or is there something else going on?

接著我想對你做個簡短追問，Mike。NRR 連續較上一季提升了 1 個百分點。我們應該如何理解背後的驅動因素？是因為有 45% 的客戶在平台上加購更多功能？還是有其他因素在發生？

Yes. So thanks for the question. I would say it's all of the above. Keep in mind that, that's a total company number. Atlas is higher than the company average EA is a little bit lower, and it's really Atlas that can drive that growth.

是的。謝謝你的問題。我會說以上皆是。請記得，那是全公司層級的數字。Atlas 高於公司平均，而 EA 略低一些；真正能推動那個成長的主要是 Atlas。

And a lot of that is due to the platform adoption as well as really the big driver there with the adoption to is the move-up market and our focus on the large enterprises.

其中很大一部分來自平台採用率的提升；而採用率背後真正的主要驅動因素，是我們往中高端市場（move-up market）的推進，以及我們對大型企業客戶的聚焦。

(Operator Instructions) Patrick Colville, Scotiabank.

(接線員指示) Patrick Colville，豐業銀行（Scotiabank）。

Thank you for taking the question. And congrats on a healthy print. I guess, CJ, I want to ask you this question, please. In your prepared remarks, you mentioned Frontier labs and it sounded like it was labs plural. I know you choose the words very carefully in the prepared remarks. I guess, did I pick that up correctly, that Mongo might not be working with multiple Frontier Labs? And then of course can you just unpack the statement around kind of mission-critical workloads and use cases because that sounded really interesting.

謝謝讓我提問。也恭喜你們交出一份健康的成績。CJ，我想請教你這個問題。在你準備好的發言中，你提到了 Frontier labs，聽起來像是複數的 labs。我知道你在準備稿裡用詞非常謹慎。所以我想確認一下，我是否理解正確：Mongo 可能不只是在與一家 Frontier Labs 合作？另外，你能否再多解釋一下你提到的那段關於「關鍵任務（mission-critical）」工作負載與使用案例的說法？那聽起來非常有意思。

So short answer to your first question, yes, it is plural, and it was chosen carefully. Thank you for noticing, Patrick. Number two, as we work with them, and as they have tried, whether it's Postgres alternative or others, they have come to realize that. And these are truly at the forefront of innovation in AI space or driving innovation that MongoDB is just a great data platform for some of the workloads.

先簡短回答你第一個問題：是的，確實是複數，而且是刻意這樣選字的。謝謝你注意到，Patrick。第二點，當我們與他們合作、他們也嘗試過不論是 Postgres 的替代方案或其他方案之後，他們逐漸意識到——而這些客戶確實站在 AI 領域創新的最前沿、推動創新——MongoDB 對於其中一些工作負載而言，就是一個非常出色的資料平台。

And the point around -- of course, we cannot go into specific details with our agreements with them on type of use cases, but they vary and there are multiple use cases depending on the lab, that we're working with them, and it's certainly, but we will continue to expand.

至於你提到的那一點——當然，基於我們與他們的協議，我們無法就使用案例的類型透露具體細節；但使用案例確實是多樣的，而且會因我們合作的不同實驗室而有多種使用案例。我們也一定會持續擴大合作。

Siti, Mizuho.

Siti，瑞穗（Mizuho）。

Thanks for taking my question. CJ, you talk about AI opportunity early at this point, but some of the moves like your partnership with LangChain, now you extended that to more strategic there. So can you talk about how that's going to help? And specifically, you talked about expanding platform now that you have two CPOs there. Can you help us on your road map? How should we think about the expansion of platform to further capture this AI opportunity?

謝謝讓我提問。CJ，你提到目前 AI 機會仍在早期，但你們的一些動作，例如與 LangChain 的合作，現在又把它延伸得更具策略性。你能談談這將如何帶來幫助嗎？另外，你也提到在你們有兩位 CPO 之後，正在擴展平台。你能否分享一下你們的產品路線圖？我們應該如何看待平台的擴張，以便進一步掌握這個 AI 機會？

Absolutely. So I'll answer your first question. LangChan, great partner. I'm really proud of what Harrison and the team are doing. And the simplicity when we talk to customers is three Legs of the stool for any agentic workload is harness, LLM and data layer. And if they are being used as in LangChain, they have significant traction. Even when I talk to some of the large banks, whether it's on-prem or in the cloud, there's significant traction, the harness layer.

當然可以。我先回答你第一個問題。LangChan 是很棒的合作夥伴。我對 Harrison 和團隊正在做的事情感到非常自豪。我們在與客戶溝通時，會把任何代理式（agentic）工作負載比喻成「三腳凳」：編排/框架層（harness）、LLM，以及資料層。而如果他們像在 LangChain 那樣使用這個框架層，他們已經有非常強的市場牽引力。即使我和一些大型銀行交流，不論是地端或雲端，這個框架層（harness layer）都有很強的牽引力。

And then they say, okay, what about the data layer and data layer, MongoDB being a choice for the data layer just makes sense. So we have done many integrations with them, and we are seeing this being played out at some of the large enterprise customers who say, I'm glad that the data layer as in MongoDB really works with the harness layer. And of course, we can choose whichever LLM we want. So that is actually being played out right now in some large customers who are trying to create agentic applications at scale.

接著他們會問：那資料層呢？而在資料層上，MongoDB 作為資料層的選擇其實非常合理。所以我們已經與他們做了許多整合，也看到這件事正在一些大型企業客戶中落地：他們會說，很高興資料層（也就是 MongoDB）確實能與框架層很好地協作。當然，LLM 我們可以選擇任何想要的。因此，這件事現在確實正在一些大型客戶中上演，他們正嘗試以規模化方式打造代理式應用。

Number two, in terms of the CPOs. I really, really proud of Ben and his long tenure here and focus on somebody wakes up every day focused on our foundational layer, whether it's Atlas and EA. And he will continue to do that.

第二點，關於兩位 CPO。我對 Ben 感到非常、非常自豪；他在這裡任職很久，而且專注於每天醒來都把心力放在我們的基礎層上，不論是 Atlas 或 EA。他也會持續這樣做。

And with Pablo, who is based in San Francisco, he will look at emerging products and because the AI ecosystem right now is very concentrated actually in San Francisco City working not only with just the Frontier labs, but also with a lot of our AI native customers who tend to be in Silicon Valley, he wakes up every day to make sure how we are relevant in that ecosystem, and he is a product and technology guy who has scaled many, many product lines over time. So that really gives me one person focused on foundation, second person focused on emerging products as well as AI workloads.

而 Pablo 常駐舊金山，他會著眼於新興產品；而且由於目前 AI 生態系其實高度集中在舊金山市，他不僅與 Frontier labs 合作，也與許多 AI 原生客戶合作——這些客戶往往位於矽谷——他每天醒來都在確保我們在那個生態系中保持相關性。他是產品與技術背景的人才，長期以來也成功擴展過許多產品線。因此，這讓我有一位專注於基礎層，另一位專注於新興產品以及 AI 工作負載。

And what I just wanted to share with you briefly, I am really fired up about our innovation road map that is accelerating, and you will continue to hear new potential products as we move through this year at various local conferences.

我還想簡短跟各位分享的是：我對我們正在加速推進的創新路線圖感到非常振奮；隨著今年的推進，你們會在各地的不同會議上持續聽到我們推出一些新的潛在產品。

Karl Keirstead, UBS.

Karl Keirstead，瑞銀（UBS）。

Okay. Great. Thanks for taking the question. CJ, three months ago on the call, you announced pretty blockbuster deals. I think one was a $90 million tech deal. The other was a $100 million financial deal. Did the incremental portion of those deals ramp during the April quarter? Or is that still really sitting in front of us?

好的。很好。謝謝讓我提問。CJ，三個月前在電話會議上，你宣布了幾筆相當重磅的交易。我記得其中一筆是 9,000 萬美元的科技類交易。另一筆是 1 億美元的金融類交易。這些交易的增量部分在 4 月季度期間有開始放量（ramp）嗎？還是說那仍然主要還在我們前方、尚未反映？

I would have Mike answer that on how that plays out, given those were long-term deals and how we think about it.

我會請 Mike 來回答這個問題，談談在這些屬於長期合約的情況下，實際上會如何發展，以及我們如何看待這件事。

Yeah. So thanks for the question, Karl. So those are multiyear deals. We talked about -- some of those were a combination of Atlas and EA. So there is almost always future growth in Atlas as we grow.

是的。謝謝你的提問，Karl。這些都是多年期合約。我們也提到過——其中一些是 Atlas 與 EA 的組合。因此，隨著我們成長，Atlas 幾乎總是會有未來的成長空間。

They were not part of the original transaction. But that's certainly part of our go-to-market motion is to expand those relationships. So what we booked in the last quarter is largely what you saw in this quarter.

它們不在原始交易之內。但這當然也是我們市場推進（go-to-market）策略的一部分，也就是擴大這些合作關係。所以我們在上一季入帳（booked）的內容，大致上就是你在本季看到的那些。

Yeah. And I would say a, that you also see some of that as we continue to move forward from Q4 to Q1, more than the CRPO number that Mike outlined and how -- whether it's long-term commitments across EA or Atlas is really, really encouraging for us.

是的。我也想說，第一，你也會看到當我們從 Q4 持續往 Q1 推進時，其中一些表現會更明顯，甚至超過 Mike 所概述的 CRPO 數字；而且——無論是跨 EA 或 Atlas 的長期承諾，這對我們來說都非常、非常令人振奮。

Sanjit Singh, Morgan Stanley.

Sanjit Singh，摩根士丹利。

Yeah, thanks for taking me. Congrats on the quarter. CJ, in terms of the opportunity around AI and agents, which sort of part of the stack. Do you think is going to create the most value or the value capture opportunity? Was it sort of being at the embedding model layer? Is it being that long-term memory that you referenced multiple times in your script? Is it that core operational database?

是的，謝謝讓我提問。恭喜本季表現。CJ，就 AI 與代理（agents）相關的機會而言，從技術堆疊的哪一部分來看，你認為會創造最大的價值或最有價值的價值擷取機會？是處在嵌入模型（embedding model）那一層嗎？還是你在講稿中多次提到的長期記憶（long-term memory）？或是那個核心營運資料庫？

And maybe you can sort of stack rank if there's a sequence of that opportunity that should unfold over time? And then for Mike, just a quick follow-up on the RPO CRPO performance the second quarter of really phenomenal bookings performance. My question is to what extent that represents sort of new business expansions, landing new logos versus maybe catching up to the existing consumption rate of your existing customers? If you can give us some color there.

也許你可以把這些機會做個排序（stack rank），如果這些機會會隨時間展開，是否有一個先後順序？另外給 Mike，一個關於 RPO/CRPO 表現的快速追問：第二季的訂單入帳表現非常亮眼。我的問題是，這在多大程度上代表新業務擴張、拿下新客戶（new logos），相較於只是追上既有客戶目前的既有用量（consumption rate）？如果可以的話，請提供一些說明。

Sanjit, I can't believe you asked me to stack rank, but here is how I would say. What I'm seeing today is that our ability to be that because AI workloads fundamentally will the requirements keep on changing the tech stack that these large enterprises are building AI workloads on, whether it's LLMs, they want to use multiple LLMs or SLMs, they want to use continues to change. And as people are building these agents as in developers are building these agents, us being super flexible with a no schema rather than rigidity of relational that you understand well, definitely helps us.

Sanjit，我真不敢相信你要我做排序，但我會這樣說。我今天看到的是：由於 AI 工作負載的需求本質上會持續改變，大型企業用來建置 AI 工作負載的技術堆疊也會一直變動——不管是 LLM，他們想用多個 LLM 或 SLM，他們想用的東西都在持續改變。而當人們在打造這些代理（也就是開發者在打造這些代理）時，我們採用無綱要（no schema）的高度彈性，而不是你很熟悉的關聯式資料庫那種僵硬結構，確實對我們很有幫助。

So I would say that architecture of MongoDB on native JSON even the chat conversations that you want to store could become a long-term memory. So next time you come in and ask a question, it knows the context. But I would say that architecture, it is almost our founder calls it really well that. We would rather be lucky than smart. And when we created MongoDB, this is from (inaudible).

所以我會說，MongoDB 以原生 JSON 為基礎的架構——甚至你想要儲存的聊天對話——都可能成為長期記憶。所以下次你進來提問時，它就能理解上下文。但我會說，這個架構——我們創辦人把它形容得非常貼切：我們寧可幸運，也不要自以為聰明。當我們創造 MongoDB 時，這是從(聽不清楚)。

We didn't have AI workloads in mind, but this architecture is perfectly suited for AI workloads.

我們當時並沒有把 AI 工作負載納入考量，但這個架構非常適合 AI 工作負載。

I would argue that, that's the first part of the stack rank. And then the second part is our ability to do real-time and provide real-time intelligence on operational data and having embeddings so that your token costs are lower and you have right retrieval that is accurate would be the second in the stack rank.

我會主張，這就是排序中的第一部分。第二部分則是：我們能夠做到即時（real-time），並在營運資料上提供即時洞察（real-time intelligence），同時具備 embeddings，讓你的 token 成本更低，並且能做出正確且精準的檢索（retrieval）——這會是排序中的第二位。

And then, Sanjit, it's Mike. So on your question, I would say it's more the latter, the second piece, but I do want to qualify that while we do certainly bring in net new logos, the majority of the RPO is going to be the existing enterprise customers, but with a big caveat, please don't read that to be. It's just the base business we get today. We certainly always want to drive incremental ARR in those relationships. That's going to be through net new workloads, new applications, expansion.

另外，Sanjit，我是 Mike。針對你的問題，我會說比較偏向後者，也就是第二個部分；但我也想補充說明：雖然我們確實會帶來全新的客戶（net new logos），但 RPO 的大多數會來自既有的企業客戶，不過有個重要但書，請不要把這解讀為我們今天只是拿到既有的基本盤業務。我們當然一直希望在這些關係中推動增量 ARR。這會透過全新的工作負載、新的應用程式與擴張來達成。

While it's focused on the existing customer base, we always want to drive incremental revenue with those bookings.

雖然重點放在既有客戶基礎上，但我們始終希望透過這些訂單入帳來推動增量營收。

Yes. And Sanjit, what I'll just add is that what Mike outlined we were really, really pleased that our go-to-market teams globally executed on what we asked them to execute on Q1, which definitely helps that met .

是的。Sanjit，我再補充一點：就像 Mike 所概述的，我們非常、非常滿意全球的 go-to-market 團隊在 Q1 確實執行了我們要求他們執行的事項，這也確實有助於達成。

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

各位女士、先生，我想把電話交回管理團隊作結語。

So thank you, everyone. We delivered a strong first quarter with broad based momentum across Atlas Enterprise Advanced and our AI workloads. We are issuing strong guidance for Q2 and full year fiscal '27 and we remain committed to expanding profitability while investing for growth in line with our long-term financial model. Our results, our customer engagements and the leadership team we have assembled all point to the same conclusion. MongoDB is on its way to becoming the generational data platform of choice for the AI era. Thank you very much for dialing in today.

謝謝各位。我們在第一季交出強勁成績，Atlas Enterprise Advanced 以及我們的 AI 工作負載都呈現廣泛的動能。我們對 Q2 與 2027 會計年度全年提出強勁的財測指引，同時我們仍致力於在依循長期財務模型、為成長投資的同時，擴大利潤率。我們的業績、客戶互動，以及我們所組建的領導團隊，都指向同一個結論。MongoDB 正在邁向成為 AI 時代首選的世代級資料平台。非常感謝各位今天撥冗參與。

Ladies and gentlemen, this 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。