MongoDB Inc (MDB) 2025 Q4 法說會逐字稿

Good day and welcome to MongoDB's Q4 fiscal year 2025 earnings call. (Operator Instructions) Please be advised that today's conference is being recorded.

大家好，歡迎參加 MongoDB 2025 財年第四季財報電話會議。（操作員指示）請注意，今天的會議正在錄音。

I would now like to hand the conference over to your speaker, Brian Denyeau from ICR. Please go ahead.

現在，我想將會議交給您的發言人，來自 ICR 的 Brian Denyeau。請繼續。

Thank you, Cheri. Good afternoon and thank you for joining us today to review MongoDB's fourth-quarter, full-year 2025 financial results, which we announced in our press release issued after the close of the market today. Joining me on the call today are Dev Ittycheria, President and CEO of MongoDB; and Serge Tanjga, MongoDB's interim CFO.

謝謝你，Cheri。下午好，感謝您今天加入我們，共同回顧 MongoDB 第四季、2025 年全年財務業績，我們在今天收盤後發布的新聞稿中公佈了該業績。今天與我一起參加電話會議的還有 MongoDB 總裁兼執行長 Dev Ittycheria；以及 MongoDB 的臨時財務長 Serge Tanjga。

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 our non-Alas business, the long-term opportunity of AI, the opportunity of application monetization, our expectations regarding our win rates and salesforce productivity, our financial guidance and underlying assumptions, and our planned investments and growth opportunities in AI.

在本次電話會議中，我們將做出前瞻性陳述，包括與我們的市場和未來成長機會、我們贏得新業務的機會、我們對 Atlas 消費成長的期望、我們的非 Alas 業務的影響、人工智慧的長期機會、應用程式貨幣化的機會、我們對我們的成功率和銷售人員生產力的期望、我們的財務指導和基本假設以及我們在人工智慧和成長機會方面的投資關係的期望

These statements are subject to a variety of risks and uncertainties, including the results of operations and financial condition, because actual results to differ materially from our expectations. For discussion, material risks and uncertainties could affect our actual results. Please refer to the risks described in our quarterly report on Form 10-Q for the quarter ended October 31, 2024, filed with the SEC on December 10, 2024.

這些聲明受各種風險和不確定因素的影響，包括經營結果和財務狀況，因為實際結果可能與我們的預期有重大差異。值得討論的是，重大風險和不確定性可能會影響我們的實際結果。請參閱我們於 2024 年 12 月 10 日向美國證券交易委員會提交的截至 2024 年 10 月 31 日的季度 10-Q 表季度報告中所述的風險。

Any forwarding statements made in this call reflect their 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 the earnings release on the Investor Relations portion of our website for reconciliation of these measures to the most directly comparable GAAP financial measures.

本次電話會議中的任何轉發聲明僅反映他們截至今天的觀點，除非法律要求，否則我們不承擔更新這些聲明的義務。此外，我們將在本次電話會議上討論非公認會計準則財務指標。請參閱我們網站投資者關係部分的收益報告表格，以了解這些指標與最直接可比較的 GAAP 財務指標的對照表。

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

說完這些，我想把電話轉給 Dev。

Thanks, Brian, and thank you to everyone for joining us today. I'm pleased to report that we had a good quarter and executed well against our large market opportunity. Let's begin by reviewing our fourth-quarter results before giving you a broader company update.

謝謝，布萊恩，也謝謝大家今天的參加。我很高興地報告，我們本季表現良好，並充分利用了巨大的市場機會。在向您提供更廣泛的公司更新資訊之前，讓我們先回顧一下我們的第四季度業績。

We generated revenue of 548.4 million, a 20% year-over-year increase and above the high end of our guidance. Atlas revenue grew 24% year over year, representing 71% of revenue. We generated non-GAAP operating income of $112.5 million for a 21% non-GAAP operating margin. We ended the quarter with over 54,500 customers. For the full year, we crossed the $2 billion-revenue mark while growing 19%. And are roughly 20 times the size we were the year before we went public.

我們創造了 5.484 億的收入，比去年同期成長了 20%，並且高於我們預期的高點。Atlas營收年增24%，佔總營收的71%。我們創造了 1.125 億美元的非公認會計準則營業收入，非公認會計準則營業利潤率為 21%。截至本季末，我們擁有超過 54,500 名客戶。全年我們的營收突破了 20 億美元大關，成長了 19%。現在我們的規模大約是我們上市前一年的 20 倍。

Overall, we were pleased with our fourth-quarter performance. We had a healthy new business quarter led by continued strength and new workload acquisition within the existing Atlas customers. In addition, we again benefited from a greater-than-expected contribution from multi-year non-Atlas deals.

總體而言，我們對第四季的表現感到滿意。我們迎來了一個健康的新業務季度，這得益於現有 Atlas 客戶的持續成長和新工作量的取得。此外，我們再次受益於多年期非 Atlas 交易超出預期的貢獻。

Moving on to Atlas consumption, the quarter played out better than our expectations, with consumption growth stable compared to the year-ago period. Serge will discuss consumption trends in more detail. Finally, retention rates remain strong in Q4, demonstrating the quality of our product and the mission criticality of our platform.

談到 Atlas 的消費，本季的表現優於我們的預期，消費成長與去年同期相比保持穩定。Serge 將更詳細地討論消費趨勢。最後，第四季的留存率仍然強勁，證明了我們產品的品質和我們平台的任務關鍵性。

As I look into fiscal '26, let me share with you what I see as the main drivers of our business. First, we expect another strong year of new workload acquisition. As we said many times in the past, in today's economy, companies build competitive advantage through custom built software. In fiscal '26, we expect that customers will continue to gravitate towards building their competitive differentiation on MongoDB. Second, we expect to see stable consumption growth for Atlas in fiscal '26 compared to fiscal '25.

當我展望26財年時，讓我與大家分享我所看到的我們業務的主要動力。首先，我們預計今年將是新工作量取得強勁的一年。正如我們過去多次說過的，在當今的經濟中，公司透過客製化軟體來建立競爭優勢。在 26 財年，我們預期客戶將繼續傾向在 MongoDB 上建立自己的競爭差異化。其次，我們預期與 25 財年相比，26 財年的 Atlas 消費量將穩定成長。

Usage growth to start fiscal '26 is consistent with the environment we have seen in recent quarters. This consistency, coupled with an improved fiscal '25 cohort of workloads, gives us confidence that Atlas will continue to see robust growth as it approaches a $2 billion run rate this year.

26財年開始的使用量成長與我們最近幾季看到的環境一致。這種一致性，加上 25 財年工作量的改善，使我們相信 Atlas 將繼續保持強勁增長，今年的運行率將接近 20 億美元。

Third, a surge will cover more detail, we expect our non-Atlas business will represent a meaningful headwind to our growth in fiscal '26 because we expect fewer multi-year deals and because we see that historically non-Atlas customers are deploying more of their incremental workloads on Atlas. Fourth, we are very excited about a long-term opportunity in AI, as I will explain a bit later.

第三，激增將涵蓋更多細節，我們預計我們的非 Atlas 業務將對我們 26 財年的成長造成重大阻力，因為我們預計多年期交易會減少，而且我們看到歷史上非 Atlas 客戶正在 Atlas 上部署更多的增量工作負載。第四，我們對人工智慧的長期機會感到非常興奮，稍後我會解釋。

In fiscal 206, we expect our customers will continue on their AI journey from experimenting with new techno technology stacks to building prototypes to deploying apps in production. We expect the progress to remain gradual, as most enterprise customers are still developing in-house skills to leverage AI effectively. Consequently, we expect the benefits of AI to be only modestly incremental to revenue growth in fiscal '26.

在 206 財年，我們預計我們的客戶將繼續他們的人工智慧之旅，從嘗試新的技術堆疊到建立原型，再到在生產中部署應用程式。我們預計進展仍將是漸進的，因為大多數企業客戶仍在開發內部技能以有效利用人工智慧。因此，我們預期人工智慧帶來的好處對於 26 財年的營收成長僅有小幅提升。

Fifth, we'll continue scaling our application modernization efforts. Historically, this segment of the market was not widely available to us because of the effort, cost, and risk of modernizing old and complex custom applications. In fiscal '25, our pilots demonstrated that AI tooling combined with services can reduce the cycle time of modernization.

第五，我們將繼續擴大應用程式現代化。從歷史上看，由於對舊的、複雜的客製化應用程式進行現代化改造所需付出的努力、成本和風險，我們並沒有廣泛涉足這一細分市場。在25財年，我們的試點證明，人工智慧工具與服務結合可以縮短現代化的周期。

This year we'll expand our customer engagements so that at modernization can meaningfully contribute to our new business growth in fiscal '27 and beyond. To start with, and based on customer demand, we are specifically targeting Java apps running on Oracle, which often have thousands of complex store procedures that need to be understood, converted, and tested to successfully modernize the application.

今年，我們將擴大與客戶的合作，以便現代化能夠為我們 27 財年及以後的新業務成長做出有意義的貢獻。首先，根據客戶需求，我們專門針對在 Oracle 上運行的 Java 應用程序，這些應用程式通常有數千個複雜的儲存過程，需要理解、轉換和測試這些過程才能成功實現應用程式的現代化。

We address this through a combination of AI tools and agents along with inspection verification by delivery teams. Though the complexity of this work is high, the revenue opting for modernizing these applications is significant. For example, we successfully modernized a financial application for one of the largest ISPs in Europe, and we're now in talks to modernize the majority of the Lexia state.

我們透過結合人工智慧工具和代理以及交付團隊的檢查驗證來解決這個問題。雖然這項工作的複雜性很高，但選擇對這些應用程式進行現代化改造所帶來的收入卻是巨大的。例如，我們成功地為歐洲最大的 ISP 之一實現了金融應用程式的現代化，目前我們正在商談對 Lexia 州大部分地區進行現代化改造。

As I take a step back, I see fiscal '26 as a year of solid Atlas growth enabled by a large market, superior product, and strong go-to-market execution. We expect continued strong win rates as we require incremental workloads across our customer base. We will continue building on our core land expand go-to-market motion to further accelerate workload acquisition.

回顧過去，我認為 26 財年是 Atlas 穩健成長的一年，這得益於龐大的市場、優質的產品以及強大的市場執行力。由於我們需要在整個客戶群中增加工作量，因此我們預計獲勝率將繼續保持強勁。我們將繼續在核心土地上擴大上市舉措，以進一步加快工作量取得。

In fiscal '25, we saw improved salesforce productivity, and we are forecasting continued improvements in fiscal '26. In addition, we will continue investing to become a standard in more of our accounts. We are not marketing constrained in even our largest accounts. For example, we finished the year with 320 customers with over $1 million in ARR, a year-over-year growth rate of 24%. This reinforces our move-up market.

在25財年，我們看到銷售人員的生產效率提高，並且我們預測26財年將繼續提高。此外，我們將繼續投資，使其成為更多帳戶的標準。即使對於我們最大的帳戶，我們的行銷也不會受到限制。例如，今年我們有 320 名客戶，ARR 超過 100 萬美元，年成長率為 24%。這增強了我們向上發展的市場。

To that end, in fiscal '26, we will make significant incremental investments in our strategic accounts program. Looking beyond fiscal '26, I'm incredibly excited about a long-term opportunity, particularly our op opportunity to address the expanded requirements of a database in the AI era.

為此，在26財年，我們將對戰略客戶計畫進行大量增量投資。展望26財年後，我對長期機會感到非常興奮，特別是我們滿足人工智慧時代資料庫擴展需求的營運機會。

Let me tell you what we're seeing in our customer base as they work to adopt AI.

讓我告訴你我們在客戶群採用人工智慧時看到的情況。

AI is ushering in a new era of accelerated change, and every company will have to adapt. We're witnessing a once-in-a-generation shift that will fundamentally reshape industries, accelerate the pace of innovation, and redefine competitive dynamics in ways we've never seen before. We joke that the world will move so fast that tomorrow's plans will happen yesterday. The winners will be those companies that can transform adapt quickly to this new pace of change. Those cannot will fall rapidly behind.

人工智慧正在開啟一個加速變革的新時代，每家公司都必須適應。我們正在見證百年不遇的轉變，它將從根本上重塑產業，加快創新步伐，並以前所未有的方式重新定義競爭態勢。我們開玩笑說，世界變化得太快了，明天的計畫昨天就實現了。獲勝者將是那些能夠迅速轉型以適應新的變化步伐的公司。那些不能這樣做的人將會迅速落後。

AI is transforming software from a static tool into a dynamic decision-making partner. No longer limited to pre-defined tasks, AI powered applications will continuously learn from real-time data, but this software can only adapt as fast as the data infrastructure is built on, and legacy symptoms -- systems simply cannot keep up.

人工智慧正在將軟體從靜態工具轉變為動態的決策夥伴。人工智慧應用程式不再局限於預先定義的任務，它將不斷從即時數據中學習，但這種軟體的適應速度只能與數據基礎設施的構建速度一樣快，而遺留的症狀——系統根本無法跟上。

Legacy technology stacks were not designed for continuous adaptation. Complex architectures, batch processing, and rigid data models create friction at every step, slowing development, limiting organization's ability to act quickly, and making even small updates time consuming and risky. AI will only magnify these challenges.

遺留的技術堆疊不是為持續適應而設計的。複雜的架構、批次和僵化的資料模型會在每一步產生摩擦，從而減慢開發速度、限制組織快速採取行動的能力，甚至會使小的更新變得耗時且充滿風險。人工智慧只會加劇這些挑戰。

MongoDB was built for change. MongoDB was designed from the outset to remove the constraints of Lexy databases, enabling businesses to scale, adapt, and innovate at AI speed. Our flexible document model handles all types of data, while seamlessly scalability ensures high performance for unpredictable workloads.

MongoDB 是為了變革而建立的。MongoDB 從一開始就被設計為消除 Lexy 資料庫的限制，使企業能夠以 AI 速度進行擴展、調整和創新。我們靈活的文件模型可以處理所有類型的數據，同時無縫可擴展性可確保在不可預測的工作負載下實現高效能。

With the Voyage AI acquisition, MongoDB makes AI applications more trustworthy by pairing real-time data and sophisticated embedding and retrieval models that ensure accurate and relevant results. We also simplify AI development by natively including vector and tech search directly in the database, providing a seamless developer experience that reduces cognitive load, system complexity, risk, and operational overhead, all with the transactional, operational, and security benefits intrinsic to MongoDB.

透過收購 Voyage AI，MongoDB 將即時數據與複雜的嵌入和檢索模型相結合，以確保準確和相關的結果，從而使 AI 應用程式更加值得信賴。我們還透過在資料庫中直接包含向量和技術搜尋來簡化 AI 開發，提供無縫的開發人員體驗，減少認知負荷、系統複雜性、風險和營運開銷，所有這些都具有 MongoDB 固有的事務、操作和安全優勢。

But technology alone isn't enough. MongoDB provides a structured, solution-oriented approach that addresses the challenges customers have with the rapid evolution of AI technology, high complexity, and a lack of in-house skills. We are focused on helping customers move from AI experimentation to production faster with best practices that reduce risk and maximize impact.

但僅有技術還不夠。MongoDB 提供一種結構化的、解決方案導向的方法，解決客戶在 AI 技術快速發展、高複雜性以及缺乏內部技能方面所面臨的挑戰。我們專注於透過降低風險和最大化影響力的最佳實踐來幫助客戶更快地從人工智慧實驗轉向生產。

Our decision to acquire Voyage AI addresses one of the biggest problems customers have when building and deploying AI applications, the risk of hallucinations. AI powered applications excel where traditional software often falls short, particularly in scenarios that require nuanced understanding, sophisticated reasoning, and interaction in natural language.

我們收購 Voyage AI 的決定解決了客戶在建置和部署 AI 應用程式時遇到的最大問題之一，即幻覺風險。人工智慧應用程式在傳統軟體常常不足的地方表現出色，特別是在需要細緻理解、複雜推理和自然語言互動的場景中。

This means they are uniquely capable of handling tasks that are more complex and open-ended. But because models are probabilistic and not deterministic, they can hallucinate or generate false or misleading information. This creates serious risks.

這意味著他們具有獨特的能力來處理更複雜和開放的任務。但由於模型是機率性的而非確定性的，它們可能會產生幻覺或產生虛假或誤導性的信息。這會帶來嚴重的風險。

Imagine a financial services agent that autonomously allocates capital on behalf of its customers or a cancer screening application in the hospital that analyzes scans to detect early signs of pancreatic cancer. For any mission-critical application, inaccurate or low-quality results are simply not acceptable.

想像一下，一個金融服務代理可以代表其客戶自主分配資金，或者醫院裡有一個癌症篩檢應用程序，可以分析掃描結果以檢測胰腺癌的早期跡象。對於任何關鍵任務應用程式來說，不準確或低品質的結果都是不可接受的。

The best way to ensure accurate results is through high-quality data retrieval, which ensures that not only the most relevant information is extracted from an organization's data with precision, high-quality retrieval is enabled by vector embedding and re-ranking models. Voyage's embedding and re-ranking models are among the highest rated in the Hugging Face community for retrieval, classification, clustering, and re-ranking and are used by AI leaders like Anthropic, LangChain, Harvey, and Replit.

確保準確結果的最佳方法是透過高品質的資料檢索，這不僅可以確保從組織資料中精確提取最相關的信息，還可以透過向量嵌入和重新排名模型實現高品質檢索。Voyage 的嵌入和重新排名模型在 Hugging Face 社群的檢索、分類、聚類和重新排名模型中評價最高，並被 Anthropic、LangChain、Harvey 和 Replit 等 AI 領導者所採用。

Voyage AI is led by Stanford professor Tengyu Ma, who has assembled a world-class AI research team from AI Labs at Stanford, MIT, Berkeley, and Princeton. With this acquisition, MongoDB will offer best-in-class embedding and re-ranking models to power native AI retrieval.

Voyage AI 由史丹佛大學教授馬騰宇領導，他組建了一支來自史丹佛大學、麻省理工學院、柏克萊大學和普林斯頓大學人工智慧實驗室的世界級人工智慧研究團隊。透過此次收購，MongoDB 將提供一流的嵌入和重新排名模型，為原生 AI 檢索提供支援。

Put simply, MongoDB democratizes the process of building trustworthy applications right out of the box. Instead of cobbling together all the necessary piece parts and operational data store, a vector database, and embedding and re-ranking models, MongoDB delivers all of it with a compelling developer experience. As a result, MongoDB has redefined the database for the AI era.

簡而言之，MongoDB 使建立可信任應用程式的過程變得民主化。MongoDB 無需將所有必要的部件和操作資料儲存、向量資料庫以及嵌入和重新排序模型拼湊在一起，而是透過引人注目的開發人員體驗來提供所有這些。因此，MongoDB重新定義了AI時代的資料庫。

Now, I'd like to spend a few minutes reviewing the adoption trends of MongoDB across our customer base. Customers across industries and around the world are running mission critical projects and atlas, leveraging the full power of our platform, including Informatica, Sonos, Zebra Technologies, and Grab.

現在，我想花幾分鐘回顧我們客戶群中 MongoDB 的採用趨勢。各行各業、遍佈全球的客戶都在運行關鍵任務項目和地圖集，充分利用我們平台的全部功能，包括 Informatica、Sonos、Zebra Technologies 和 Grab。

Grab, Southeast Asia's leading super app, which provides everyday services like delivery, mobility, digital financial services, and serves over 700 cities in 8 Southeastern Asian countries, successfully migrated its key app, GrabKios to Atlas.

Grab 是東南亞領先的超級應用程序，提供配送、出行、數位金融服務等日常服務，服務範圍涵蓋東南亞 8 個國家的 700 多個城市，現已成功將其主要應用程式 GrabKios 遷移至 Atlas。

Atlas provides Grab with an automated, scalable, and secure platform which empowers engineering teams to focus on product development to accommodate Grab's rapid growth. By leveraging Atlas, Grab achieved significant efficiency gains, saving around 50% of the time previously spent on database maintenance.

Atlas 為 Grab 提供了一個自動化、可擴展且安全的平台，使工程團隊能夠專注於產品開發，以適應 Grab 的快速成長。透過利用 Atlas，Grab 實現了顯著的效率提升，節省了約 50% 的資料庫維護時間。

The Associated Press, the Catalan Department of Health, Urban Outfitters, and Lombard Odier are turning to MongoDB to modernize applications. Urban Outfitters chose MongoDB as a database platform to provide a flexible, scalable foundation for its infrastructure. With a vision of integrating data across systems for elevated and consistent customer experiences, the retailer found legacy database is inadequate.

美聯社、加泰隆尼亞衛生部、Urban Outfitters 和 Lombard Odier 正在採用 MongoDB 來實現應用程式的現代化。Urban Outfitters 選擇 MongoDB 作為資料庫平台，為其基礎架構提供靈活、可擴展的基礎。該零售商希望透過跨系統整合資料來提升和實現一致的客戶體驗，但發現傳統資料庫存在不足。

By adopting Atlas and its flexible document model, Urban Outfitters accelerated development, boosted scalability, while seamlessly integrating data. This transformation facilitated the introduction of AI-driven personalization and cutting-edge search features, enriching the shopping experience across both digital and physical spaces.

透過採用 Atlas 及其靈活的文檔模型，Urban Outfitters 加速了開發速度、提高了可擴展性，同時無縫整合資料。這種轉變促進了人工智慧驅動的個人化和尖端搜尋功能的引入，豐富了數位和實體空間的購物體驗。

Mature companies and startups alike are using MongoDB to help deliver the next wave of AI-powered applications to their customers, including Swisscom, NTT Communications, and Paychex. Swisscom, Switzerland's leading provider of mobile, internet and TV services, deployed a new GenAI app in just 12 weeks using Atlas.

成熟公司和新創公司都在使用 MongoDB 幫助為其客戶提供下一波人工智慧應用程序，其中包括 Swisscom、NTT Communications 和 Paychex。瑞士領先的行動、網路和電視服務供應商 Swisscom 利用 Atlas 僅用 12 週就部署了一款新的 GenAI 應用程式。

Swisscom implemented Atlas to power a RAG application for the East Forsyth Library, transforming unstructured data such as reports, recordings, and graphics into vector embeddings that large language models can interpret. This enables vector search to find any relevant contexts resulting in more accurate and tailored responses for users.

Swisscom 實施了 Atlas 來為 East Forsyth 圖書館的 RAG 應用程式提供支持，將報告、記錄和圖形等非結構化資料轉換為大型語言模型可以解釋的向量嵌入。這使得向量搜尋能夠找到任何相關的上下文，從而為使用者提供更準確、更有針對性的回應。

In summary, we had a healthy Q4. We saw stabilizing Atlas consumption growth along with a few new business, a strong new business quarter, and we remain confident in our ability to execute on a long-term opportunity. Fiscal '26 is a transition year as we execute on our go-to-market motion while investing to prepare to capture the AI opportunity both through GreenField AI applications and AI-assisted modernization of legacy applications. We want to capitalize on a once in a generation opportunity.

總而言之，我們的第四季表現健康。我們看到 Atlas 消費成長趨於穩定，同時出現了一些新業務，這是一個強勁的新業務季度，我們對自己抓住長期機會的能力仍然充滿信心。26 財年是一個過渡年，我們執行市場進入計劃，同時進行投資，準備透過 GreenField AI 應用程式和 AI 輔助的遺留應用程式現代化來抓住 AI 機會。我們希望抓住千載難逢的機會。

With that, here's Serge.

以下是 Serge 的發言。

Thanks, Dev. I'll begin with a detailed review of our fourth-quarter results and then finish with our outlook for the first-quarter and full fiscal year 2026.

謝謝，Dev。我將首先詳細回顧我們第四季的業績，然後介紹我們對 2026 年第一季和全年財年的展望。

First, I will start with our fourth-quarter results. Total revenue in the quarter was $548.4 million, up 20% year over year and above the high end of our guidance. Shifting to our product mix, let's start with Atlas. Atlas grew 24% in the quarter compared to the previous year and now represents 71% of total revenue compared to 68% in the fourth quarter of fiscal 2024 and 68% last quarter. Atlas revenue is recognized primarily based on customer consumption of our platform, and that consumption is closely related to end user activity of their applications.

首先，我先介紹一下我們第四季的業績。本季總營收為 5.484 億美元，年增 20%，高於我們預期的最高值。轉向我們的產品組合，讓我們從 Atlas 開始。Atlas 本季較上年同期成長 24%，目前佔總營收的 71%，而 2024 財年第四季為 68%，上季為 68%。Atlas 收入主要基於客戶對我們平台的消費而確認，而該消費與最終用戶對其應用程式的活動密切相關。

Let me provide some context on analyst consumption in the quarter. In Q4, consumption was ahead of our expectations. If we compare this year's Q4 with Q4 fiscal year '24, both usage and consumption growth were stable on a year-over-year basis. While this is only one quarter and consumption trends around the holidays can be particularly volatile, we are encouraged to see signs of stability and consumption growth.

讓我提供一些本季分析師消費的背景資訊。第四季的消費超出了我們的預期。如果我們將今年第四季與24財年第四季進行比較，使用量和消費量的成長較去年同期都保持穩定。雖然這只是一個季度，而且假期期間的消費趨勢可能特別不穩定，但我們很高興看到穩定和消費成長的跡象。

Turning to non-Atlas revenue, non-Atlas came in ahead of our expectations in part due to greater-than-expected contribution from multi-year deals, as Dev mentioned. As you know, due to ASC 606, recognized the entire term license component of a multi-year contract at the start of that contract. This multi-year license revenue benefit was over $10 million more than was contemplated in our Q4 guidance.

談到非 Atlas 收入，正如 Dev 所提到的，非 Atlas 收入超出了我們的預期，部分原因是多年期交易的貢獻超出預期。如您所知，根據 ASC 606，在合約開始時承認了多年期合約的整個期限許可部分。這項多年授權收入收益比我們第四季預期高出 1,000 多萬美元。

We realized that ASC 606 introduces increased variability into our non-Atlas revenue, making it harder to understand underlying trends. To address that, we wanted to provide some incremental color. If we look at non-Atlas ARR growth rather than revenue, in Q4 of fiscal year '25, the growth was in the mid-single digits year over year compared to low-double digit growth in the year ago period.

我們意識到 ASC 606 為我們的非 Atlas 收入帶來了更大的可變性，使得我們更難理解潛在的趨勢。為了解決這個問題，我們希望提供一些增量顏色。如果我們看非 Atlas ARR 成長而不是收入，那麼在 25 財年第四季度，成長率為中等個位數，而去年同期的成長率為低兩位數。

We observed that customers who were predominantly non-Atlas historically are deploying a growing share of incremental workloads on Atlas. In other words, the revenue growth of those customers is increasingly showing up in Atlas.

我們觀察到，歷史上主要使用非 Atlas 的客戶正在 Atlas 上部署越來越多的增量工作負載。換句話說，這些客戶的收入成長越來越多地體現在 Atlas 中。

Turning to customer growth during the fourth quarter, we grew our customer base by approximately 1,900 sequentially, bringing our total customer count to over 54,500, which is up from over 47,800 in the year-ago period. Of our total customer count, over 7,500 are direct sales customers, which compares to over 7,000 in the year-ago period.

談到第四季度的客戶成長，我們的客戶群環比增長了約 1,900 個，使我們的總客戶數量超過 54,500 個，高於去年同期的 47,800 個。在我們的總客戶數中，超過 7,500 名是直銷客戶，而去年同期數字超過 7,000 名。

The growth in our total customer count is being driven primarily by Atlas, which had over 53,100 customers at the end of the quarter compared to over 46,300 in the year-ago period. It's important to keep in mind the growth in our Atlas customer count reflects new customers to MongoDB in addition to existing EA customers adding incremental Atlas workloads.

我們客戶總數的成長主要得益於 Atlas，本季末有超過 53,100 名客戶，而去年同期的客戶數量超過 46,300 名。重要的是要記住，我們的 Atlas 客戶數量的成長反映了 MongoDB 的新客戶以及現有 EA 客戶增加了增量 Atlas 工作負載。

In Q4, our net ARR expansion rate was approximately 118%. The decline versus historical period is attributable to a smaller contribution from expanding customers. We ended the quarter with 2,396 customers with at least $100,000 in ARR and annualized MRR, up from 2,052 in the year-ago period. As Dev mentioned, we also finished the year with 320 customers spending $1 million or more annualized on our platform compared to 59 a year ago.

第四季度，我們的淨ARR擴張率約為118%。與歷史時期相比，下降是由於不斷擴大的客戶貢獻較小。截至本季末，我們擁有 2,396 名 ARR 和年化 MRR 至少 100,000 美元的客戶，高於去年同期的 2,052 名。正如 Dev 所提到的，今年我們平台上年消費 100 萬美元或以上的客戶數量達到 320 位，而一年前只有 59 位。

Moving down the income statement, I will be discussing our results on a non-GAAP basis unless otherwise noted. Gross profit in the quarter was $411.7 million, representing a gross margin of 75%, which is down from 77% in the year-ago period. Our year-over-year gross margin decline is driven in part by Atlas growing as a percent of the overall business. Our income from operations was $112.5 million, or a 21% operating margin for the fourth quarter, compared to a 15% margin in the year-ago period.

在損益表中，除非另有說明，我將根據非 GAAP 基礎討論我們的結果。本季毛利為4.117億美元，毛利率為75%，低於去年同期的77%。我們的毛利率年減部分是由於 Atlas 在整體業務中所佔的百分比成長。我們第四季的營業收入為 1.125 億美元，營業利益率為 21%，而去年同期的利潤率為 15%。

Our operating income results versus guidance benefited from our revenue of performance. In addition, we benefited from timing of hiring around year end. Net income in the fourth quarter was $108.4 million or $1.28 per share based on 84.6 million diluted weighted average shares outstanding. This compares to a net income of $71.1 million or $0.80 cents per share on 82.9 million diluted weighted average shares outstanding in the year ago period.

我們的營業收入結果與預期相比受益於我們的業績收入。此外，我們也受益於年底左右的招募時機。根據 8,460 萬股稀釋加權平均流通股計算，第四季淨收入為 1.084 億美元，即每股 1.28 美元。相較之下，去年同期的淨利潤為 7,110 萬美元，即每股 0.80 美分，攤薄加權平均流通股數為 8,290 萬股。

Turning to the balance sheet and cash flow, we ended the fourth quarter with $2.3 billion in cash, cash equivalents, short-term investments, and restricted cash. During Q4, we also completed the redemption of our 2026 convertible notes, and as a result, our balance sheet is debt-free.

談到資產負債表和現金流，我們在第四季結束時擁有 23 億美元的現金、現金等價物、短期投資和受限現金。在第四季度，我們也完成了 2026 年可轉換票據的贖回，因此我們的資產負債表上沒有債務。

Operating cash flow in the fourth quarter was $50.5 million. After taking into consideration approximately $27.6 million in capital expenditures and principal repayments of finance lease liabilities, free cash flow was $22.9 million in the quarter. This compares to free cash flow of $50.5 million in the year-ago period. Our Q4 CapEx included approximately $24 million for purchase of IPv4 addresses, as we discussed previously. This concludes our IPv4 address purchases.

第四季營運現金流為5050萬美元。考慮到約 2,760 萬美元的資本支出和融資租賃負債的本金償還後，本季的自由現金流為 2,290 萬美元。相比之下，去年同期的自由現金流為 5,050 萬美元。正如我們之前所討論的，我們的第四季度資本支出包括約 2,400 萬美元用於購買 IPv4 位址。我們的 IPv4 位址購買到此結束。

I'd now like to turn to our outlook for the first-quarter and full fiscal year 2026. For the first quarter, we expect revenue to be in the range of $524 million to $529 million. We expect non-GAAP income from operations to be in the range of $54 million to 58 million, and non-GAAP net income per share to be in the range of $0.63 to $0.67 based on 86 million estimated diluted weighted average shares outstanding.

現在，我想談談我們對 2026 年第一季和全年財年的展望。我們預計第一季的營收將在 5.24 億美元至 5.29 億美元之間。我們預計非公認會計準則下的營業利潤將在 5,400 萬美元至 5,800 萬美元之間，基於預計的 8,600 萬股稀釋加權平均流通股，非公認會計準則下的每股淨利潤將在 0.63 美元至 0.67 美元之間。

For the full fiscal year 2026, we expect revenue to be in the range of $2.24 billion to $2.28 billion non-GAAP income from operations to be in the range of $210 million to $230 million and non-GAAP net income per share to be in the range of $2.44 and $2.62 based on 87.3 million estimated diluted weighted average shares outstanding.

對於 2026 財年全年，我們預計收入在 22.4 億美元至 22.8 億美元之間，非 GAAP 營業收入在 2.1 億美元至 2.3 億美元之間，非 GAAP 每股淨利潤在 2.44 美元至 2.62 美元之間（基於 8,730 萬每股淨利潤在 2.44 美元至 2.62 美元之間（基於 8,730 萬股每股淨利潤在平均流通股）。

Note that the non-GAAP net income per share guidance for the first quarter and full fiscal year 2026 include a non-GAAP tax provision of approximately 20%.

請注意，2026 財年第一季和全年非 GAAP 每股淨收入指引包括約 20% 的非 GAAP 稅務準備金。

I'll now provide some more context on our guidance, starting with the full year.

我現在將提供一些有關我們指導的背景信息，從全年開始。

First, as Dev mentioned, we expect a roughly stable Atlas consumption growth compared to fiscal year '25. Atlas consumption will benefit from stronger contributions from workloads acquired in fiscal year '25 compared to the contribution that fiscal year '24 workloads had last year. As you know, we made changes to sales compensation plans at the start of last year to focus more on the size of new workloads acquired, and we believe that those changes are having the desired impact.

首先，如 Dev 所提到的，我們預期與 25 財年相比，Atlas 的消費成長大致穩定。與去年 24 財年的工作量貢獻相比，Atlas 的消費將受益於 25 財年獲得的工作量帶來的更強勁貢獻。如您所知，我們在去年年初對銷售薪酬計劃進行了更改，以便更加關注獲得的新工作量規模，我們相信這些變化正在產生預期的影響。

Second, we expect our non-Atlas subscription revenue will be down in the high-single digits for the year. The primary reason is that we expect an approximately $50 million headwind for multi-year license revenue in fiscal year '26, an estimate that is based on a bottom- up analysis of our non-Atlas renewal base.

其次，我們預計今年我們的非 Atlas 訂閱收入將下降高個位數。主要原因是我們預計 26 財年的多年許可收入將面臨約 5000 萬美元的逆風，這一估計是基於我們對非 Atlas 續約基礎的自下而上的分析得出的。

Simply put, after two years of very strong multi-year performance, we expect the mix of multi-year non-Atlas revenue to not only be lower than the last two years, but also below the historical trends. This is due to the fact that in fiscal year '26, we have a more limited set of large non-Atlas accounts that can sign multi-year deals.

簡而言之，在經歷了兩年非常強勁的多年期表現之後，我們預計多年期非 Atlas 收入組合不僅會低於過去兩年，而且會低於歷史趨勢。這是因為在 26 財年，我們能夠簽署多年期協議的大型非 Atlas 帳戶數量更加有限。

Finally, I wanted to provide some context to better understand the operating margin guidance. We expect operating margin of 10% at the midpoint of the range, down from 15% that we reported in fiscal year '25. There are three primary reasons for the margin decline. First, the $50 million of fiscal year '25 multi-year license revenue that won't repeat in fiscal year '26 is very high margin, making for a difficult margin compare. This will primarily impact the second half of the year.

最後，我想提供一些背景資訊以便更好地理解營業利潤率指引。我們預計營業利潤率將處於該範圍的中間值 10%，低於 25 財年報告的 15%。利潤率下降主要有三個原因。首先，25 財年的 5,000 萬美元多年期許可收入不會在 26 財年重複，這是一個非常高的利潤率，因此很難進行利潤率比較。這將主要影響今年下半年。

Second, we are investing aggressively in R&D inclusive of the recently announced acquisition of Voyage AI. We see an opportunity to further distance ourselves from the competition in terms of performance and scalability, and to redefine what it means to be a database in the age of AI.

其次，我們正在積極投資研發，包括最近宣布的收購 Voyage AI。我們看到了在效能和可擴展性方面進一步拉開與競爭對手距離的機會，並重新定義了人工智慧時代的資料庫的含義。

Third, we are increasing our marketing investments, specifically to drive improved awareness and understanding of MongoDB's capability. Our goal is to better educate new and existing customers on the full power of our platform and highlight the widening gap between us and the legacy competitors.

第三，我們正在增加行銷投入，特別是為了提高人們對 MongoDB 功能的認識和理解。我們的目標是更好地讓新舊客戶了解我們平台的全部功能，並強調我們與傳統競爭對手之間日益擴大的差距。

Moving on to our Q1 guidance, a few things to keep in mind. First, we expect Atlas revenue to be flat to slightly up sequentially. Please keep in mind that Q1 has 3 fewer days than Q4. Also, the typical seasonally slower Atlas consumption growth during the holidays has a bigger impact on incremental Q1 revenue than it did in Q4, thereby negatively impacting sequential revenue growth.

繼續討論我們的第一季指引，有幾點需要記住。首先，我們預計 Atlas 的營收將與上一季持平或略有成長。請記住 Q1 比 Q4 少 3 天。此外，假期期間 Atlas 消費成長通常會比較慢，這對第一季的增量收入的影響比第四季度更大，從而對連續的收入成長產生負面影響。

Second, we expect to see a meaningful sequential decline in EA revenue. As discussed in the past, Q4 is our seasonally highest quarter in terms of our EA renewal base, which is a strong indicator of our ability to win new EA business. In Q1, the EA renewal base is sequentially much lower. Third, we expect operating income to decline sequentially due to the lower revenue as well as our increased base of hiring.

其次，我們預期 EA 收入將持續大幅下降。正如過去所討論的，就我們的 EA 續約基數而言，第四季度是我們季節性最高的季度，這有力地表明了我們贏得新 EA 業務的能力。第一季度，EA 更新基數季減。第三，由於收入下降以及招聘人數增加，我們預計營業收入將持續下降。

Finally, let me address how the acquisition of Voyage AI will impact our financials. We disclosed last week that the total consideration was $220 million. Most Voyage shareholders received the consideration in MongoDB stock, with only $20 million being paid out in cash. To offset the dilutive impact of the acquisition, today we are announcing that our Board has authorized a $200 million stock buyback.

最後，讓我來談談收購 Voyage AI 將如何影響我們的財務狀況。我們上週披露，總對價為 2.2 億美元。大多數 Voyage 股東都獲得了 MongoDB 股票的對價，只有 2000 萬美元以現金支付。為了抵​​銷收購帶來的稀釋影響，今天我們宣布董事會已批准 2 億美元的股票回購。

In fiscal year '26, we expect an immaterial amount of revenue from the acquisition as we work to expand the reach of the technology and integrate it into the MongoDB database. On the expense side, we attempt to grow the voyage team to accelerate innovation and help with integration. These expenses will show up in the R&D line in our income statement and will be modestly diluted to operating margin for the year.

在 26 財年，我們預計此次收購將帶來大量收入，因為我們致力於擴大該技術的覆蓋範圍並將其整合到 MongoDB 資料庫中。在成本方面，我們嘗試擴大航行團隊以加速創新並幫助整合。這些費用將出現在我們的損益表的研發項目中，並將適度稀釋當年的營業利潤率。

To summarize, MongoDB delivered strong fourth-quarter results. We are pleased with our ability to win new business and see stable consumption trends and outs. We remain incredibly excited about the opportunity ahead and we'll continue to invest responsibly to maximize our long-term value.

總而言之，MongoDB 第四季業績強勁。我們很高興能夠贏得新業務並看到穩定的消費趨勢和變化。我們對未來的機會仍然感到無比興奮，我們將繼續負責任地投資，以最大化我們的長期價值。

With that, we'd like to open it up for questions, operator.

現在，我們想開始回答問題，接線員。

(Operator Instructions)

（操作員指令）

Raimo Lenschow, Barclays.

巴克萊銀行的 Raimo Lenschow。

Perfect. Thank you. Two quick questions for me. One on the multi-year guidance or the multi-year situation, like, if you look, like this quarter and the quarters before, you overperformed there. The guidance is obviously your expense for a slightly weaker because you have a lower renewal portfolio. Is it just a portfolio or do you see a change in trend that they -- so is it just a mechanical problem or like, or a mechanical situation, or is there also a change in behavior? And then I had one follow up.

完美的。謝謝。我有兩個快速問題。一個是關於多年指導或多年情況，例如，如果你看一下這個季度和之前的幾個季度，你會發現你的表現都超出預期。由於您的續約組合較低，因此該指導顯然會使您的費用略微減弱。這只是一個投資組合，還是你看到趨勢的變化——那麼這只是一個機械問題，或者像機械情況，還是行為也改變了？然後我進行了一次跟進。

Yeah, so Raimo, why don't I take that one? Thank you for the question.

是啊，那麼 Raimo，我為什麼不選擇那個呢？感謝您的提問。

So let me just make sure that I repeat and level set. So in fiscal year '24, we had exceptionally strong multi-year performance led by our Alibaba deal. And going into fiscal year '25, we expected a $40 million headwind based on the assumption that fiscal year '25 would be in line with long-term trends.

因此，我只需確保我重複並設定水平。因此，在 24 財年，在阿里巴巴交易的帶動下，我們取得了異常強勁的多年業績。進入25財年，我們預計會出現4000萬美元的逆風，因為我們認為25財年將與長期趨勢一致。

Instead, after strong Q3 and Q4, the ultimate headwind was significantly lower than that $40 million. And that creates the renewal base effect that sets us up for fiscal year '26. So what I mean by that is because we've done so many more multi-year deals with fiscal year '24 and 25, the renewal base and the opportunity is just much lower to begin with. So it's not a change in trends. In fact, we assume same conversion rates as historically, it's just the opportunity set is lower in fiscal year '26.

相反，在第三季和第四季表現強勁之後，最終的逆風明顯低於 4000 萬美元。這會產生更新基數效應，為我們 26 財年做好準備。我的意思是，由於我們在 24 和 25 財年已經簽訂了太多多年期協議，所以續約基礎和機會一開始就低得多。所以這不是趨勢的改變。事實上，我們假設轉換率與歷史相同，只是 26 財年的機會集較低。

Okay, perfect. And then if you think about the Voyage acquisition, like how do we need to think about that in terms of getting that into the organization and into the market? Is that kind of going to be like an attachment to what you were doing, or do you think it's going to be sold broader than just into the MongoDB installed based? Thank you.

好的，完美。然後，如果您考慮 Voyage 收購，那麼我們需要如何考慮將其納入組織並推向市場？這會成為您所做工作的附屬品，或者您認為它的銷售範圍是否會比只在 MongoDB 安裝基礎上擴大？謝謝。

Thanks, Raimo. I'll take that question.

謝謝，Raimo。我來回答這個問題。

Today, Voyage AI, it does offer its models to other third parties, and we will continue to do that. We think it's important for people to have access to the best-in-class models. We also believe that that'll be a great way to bring people new to MongoDB into the MongoDB sphere.

今天，Voyage AI 確實向其他第三方提供其模型，我們也會繼續這樣做。我們認為讓人們接觸一流的模型非常重要。我們也相信，這將是將新接觸 MongoDB 的人帶入 MongoDB 領域的絕佳方式。

And that's a way for us -- that will be in the short to medium term a better together story where we will basically integrate Voyage AI into the MongoDB platform and do things like auto embeddings. Where data will be embedded as soon as it's basically entered into the MongoDB platform which will make a developer's life that much easier versus having to go to some third party to get the data embedded and then to use our vector store.

這對我們來說是一種方式——在短期到中期內，這將是一個更好的共同故事，我們基本上將 Voyage AI 整合到 MongoDB 平台中並執行諸如自動嵌入之類的操作。一旦數據輸入 MongoDB 平台，它就會被嵌入到其中，這將使開發人員的生活變得更加輕鬆，而不必去第三方獲取嵌入數據，然後使用我們的向量存儲。

And then we have a bunch of other things that we plan to do in terms of the product roadmap, in terms of more sophisticated models, more domain specific models, etc. And we'll talk about that in future calls, but we do want to make this available to all customers, including people who are not MongoDB customers today, and we think that's good for the business long term.

然後，我們還計劃在產品路線圖方面做許多其他事情，包括更複雜的模型、更多領域特定模型等。我們將在未來的電話會議中討論這個問題，但我們確實希望將其提供給所有客戶，包括目前不是 MongoDB 客戶的人，我們認為這對長期業務有利。

Sanjit Singh, Morgan Stanley.

摩根士丹利的 Sanjit Singh。

Great, thank you. (inaudible) for Sanjit. Maybe with my first question building on timeless -- on Voyage AI, when you think about the decision to acquire Voyage AI, could you sort of double click again on the reasons and particularly with a look on your portfolio, what was the aspects that either existing or new customers couldn't do with your portfolio that they can now pursue with the technology that you acquired to Voyage AI?

太好了，謝謝。 （聽不清楚）對於 Sanjit 來說。也許我的第一個問題是基於永恆的——關於 Voyage AI，當您考慮收購 Voyage AI 的決定時，您能否再次仔細考慮一下原因，特別是看一下您的投資組合，現有客戶或新客戶無法使用您的投資組合實現的方面是什麼，現在他們可以使用您收購的 Voyage AI 技術來實現？

And then as the second question as it relates to your operating expense guidance that's implied in your investments there. Last quarter, you talked a lot about reallocating investments, but I'd be curious just if you can double click on what's changed over the last 90 days that really pushed you to make those incremental investments.

第二個問題與你在投資中隱含的營運費用指引有關。上個季度，您談了很多關於重新分配投資的事情，但我很好奇，您是否可以仔細看看過去 90 天裡發生了什麼變化，真正促使您進行這些增量投資。

I appreciate the reasons that you gave some of those like the $50 million multi-year. Dev, you probably had some visibility on that 90-days ago. So sort of what has changed in those 90 days that pushed you to make those incremental investments?

我很欣賞你給出的一些理由，例如 5000 萬美元的多年合約。Dev，你可能在 90 天前就對此有所了解。那麼在這 90 天裡發生了什麼變化促使您進行這些增量投資？

So great, I'll start. I'll address the Voyage AI question and I'll hand it off to Serge, talk about OpEx. So stepping back and just thinking from a customer's perspective, one of the main reasons what gives customers pause in terms of deploying mission-critical AI use cases is the risk of hallucinations.

太好了，我要開始了。我將解決 Voyage AI 問題，並將其交給 Serge，討論 OpEx。因此，退一步來說，從客戶的角度思考，客戶在部署關鍵任務 AI 用例時猶豫不決的主要原因之一就是出現幻覺的風險。

Because AI systems are probabilistic, you can't always guarantee what the output will be. And if you're in a regulated industry, whether it's financial services or health care or other industries where precision of the response of the quality of the response really matters, it really prevents people from deploying these production-level AI use cases.

由於人工智慧系統是機率性的，所以你無法總是保證輸出結果是什麼。如果你身處受監管的行業，無論是金融服務、醫療保健還是其他行業，響應的精確度和回應的品質都非常重要，這確實會阻止人們部署這些生產級 AI 用例。

So the reason we acquired Voyage AI is to provide what's called embedding and re-ranking models. And let me just explain, how this all works. So think about the LLM as the brain. Think about the database is about your memory and the state of how things are. And so -- and then think about embeddings as an ability to find the right information for the right question.

因此，我們收購 Voyage AI 的原因是為了提供所謂的嵌入和重新排名模型。讓我解釋一下這一切是如何運作的。所以，可以把 LLM 想像成大腦。想想資料庫是關於你的記憶和事物的狀態。所以 — — 然後將嵌入視為一種為正確問題找到正確資訊的能力。

So imagine you have a very smart person, say like Albert Einstein on your staff and you're asking him, in this case the LLM, a particular question. Well, Einstein still needs to go do some homework based on what the question is about finding some information before he can formulate an answer.

想像一下，你的員工中有一個非常聰明的人，例如阿爾伯特·愛因斯坦，而你正在問他（在本例中是法學碩士）一個特定的問題。那麼，愛因斯坦仍然需要根據問題的內容去做一些功課，找到一些訊息，然後才能得出答案。

Rather than reading every book in a library, what the embedding models do is essentially act like a librarian pointing Einstein to the right section, the right aisle, the right shelf, the right book, and the right chapter, the right page to get the exact information to formulate an accurate and high-quality response.

嵌入模型所做的不是閱讀圖書館中的每一本書，而是本質上像圖書館員一樣，引導愛因斯坦到正確的區域、正確的過道、正確的書架、正確的書籍、正確的章節、正確的頁面，以獲取準確的信息，從而製定準確而高質量的回應。

So the performance gains you get by leveraging embedding models is significant, and even embedded models themselves have different qualities, and Voyage AI's embedding models are typically the top-ranked models on Hugging Face, so. We know a lot of ISPs have already reached out to us since the acquisition saying they switched to Voyage from other model providers and they got far better performance.

因此，利用嵌入模型所獲得的效能提升是顯著的，甚至嵌入模型本身也有不同的品質，而 Voyage AI 的嵌入模型通常是 Hugging Face 上排名靠前的模型，所以。我們知道，自收購以來，許多 ISP 已經與我們聯繫，表示他們從其他模型提供者轉向 Voyage，並且獲得了更好的效能。

So the value of Voyage is being able to increase the quality and, hence, the trustworthiness of these AI applications that people are building in order to serve the most demanding and mission-critical use cases.

因此，Voyage 的價值在於能夠提高人們為滿足最苛刻和最關鍵的任務用例而構建的 AI 應用程式的質量，從而提高其可信度。

Yeah, and I'll take the OpEx question. So you're absolutely right, we are both reallocating and reinvesting at the same time. And so 90-days ago we talked about some reallocations in our sales and marketing line where we're reducing our investments in the mid-market in order to deploy those resources in the upmarket.

是的，我將回答有關 OpEx 的問題。所以你完全正確，我們同時在重新分配和再投資。因此，90 天前，我們談到了銷售和行銷線的一些重新分配，我們正在減少對中端市場的投資，以便將這些資源部署到高端市場。

And then we also talked about discontinuing or de-emphasizing a few products so that we can focus more on the remaining portfolio where we see traction and the opportunity. We are investing over and above what we're reallocating, and that was the plan all along. And the reason for that is because of the opportunity that we see.

然後我們還討論了停止或不再強調一些產品，以便我們能夠更加專注於我們看到的具有吸引力和機會的剩餘產品組合。我們的投資超出了我們重新分配的金額，這也是一直以來的計劃。原因在於我們看到了機會。

You've heard Dev talked in his prepared remarks about the unique opportunity that AI will present for people to revisit their infrastructure stack, and we see that as a unique once in a lifetime opportunity that we want to capitalize on. And what we don't want to do as a management team is sit here five years from now and wonder whether we invested enough to fully maximize our long-term opportunities. And what gives us comfort in this investment is frankly our margins track record.

您已經聽到 Dev 在他的準備好的演講中談到人工智慧將為人們重新審視其基礎設施堆疊提供的獨特機會，我們認為這是我們希望利用的千載難逢的獨特機會。作為管理團隊，我們不想做的是五年後坐在這裡思考我們是否投入了足夠的資金來充分發揮我們的長期機會。坦白說，讓我們對這項投資感到放心的是我們的利潤記錄。

So if you go back and take a step back and go back as far as the IPO, our margins were in the negative 30% and, obviously, we've come a long way in terms of growth and margin expansion at the same time. And even if you look more recently over the last couple of years, at the moments when we slowed investments, we saw margins spike, including, frankly in Q4, where we printed 21% operating margin, which is in line with our long-term guidance.

因此，如果回過頭來回顧，回溯到 IPO 時，我們的利潤率為負 30%，顯然，我們在成長和利潤率擴張方面同時取得了長足的進步。即使你回顧過去幾年，在我們放緩投資的時候，我們也看到利潤率飆升，其中坦率地說在第四季度，我們的營業利潤率達到了 21%，這符合我們的長期指導。

So we have confidence that the economics of the business are strong and that the business scales, but it's about investing at the right moment in time and the conviction that we have to really play offense and optimize on our opportunities.

因此，我們有信心，該業務的經濟實力雄厚，而且可以擴大規模，但關鍵在於在正確的時機進行投資，並堅信我們必須真正發揮進攻作用並充分利用我們的機會。

Mike Cikos, Needham.

麥克·西科斯，尼德姆。

Hey guys, thanks for taking the questions here. I wanted to come back. I know we're talking about the Atlas consumption trends and how they're shaking out where we sit today. Just curious when we think about the full year you guys are talking about taking most recent couple of quarters, as far as how that consumption is played out.

嘿夥計們，感謝你們在這裡回答問題。我想回來。我知道我們正在談論 Atlas 消費趨勢以及它們如何影響我們今天的狀況。只是好奇，當我們考慮你們談論的最近幾季的全年情況時，這種消費是如何發揮作用的。

And is there anything specific to Q4 consumption that started to drive this dynamic on the growth? I'm interested in what you would point to was it the improving sales initiatives in the cohorts you guys are capturing or is there something else you could point us to on that?

第四季消費有何特殊因素開始推動這種成長動能？我感興趣的是，您要指出的是您所捕獲的群體中銷售計劃的改善，還是您能指出其他一些問題？

Yeah, I'll take a first at Mike. So when it comes to (technical difficulty), first of all, as we've discussed in the past, it's sort of the seasonally lowest quarter of the year in terms of consumption growth because we do see both usage and consumption slow down around the holidays, and we saw that happen. Q4 consumption was slower than Q3, this parent volatility when it comes to the holiday season.

是的，我會在 Mike 那裡獲得第一名。因此，說到（技術難度），首先，正如我們過去討論過的，就消費增長而言，這是一年中季節性最低的季度，因為我們確實看到假期期間使用量和消費量都會放緩，我們看到了這種情況。第四季的消費速度低於第三季度，這是節慶季波動的主要原因。

When it comes to stable consumption growth and fiscal year '26 though, I would consumption in three components. So the first component is the base itself, which is obviously the largest, but it grows at the percentage rate because you know it consists of the oldest workload. That's layer one.

當談到穩定的消費成長和26財年時，我會將消費分成三個部分。因此，第一個部分是基數本身，它顯然是最大的，但它以百分比增長，因為你知道它包含了最古老的工作量。這是第一層。

And layer two is workload the prior year. Those workloads the current year are meaningful size and are still growing. They're (technical difficulty) in any given year, you need the workloads of the prior year and the workloads of the current year to basically offset the growing base effect.

第二層是前一年的工作量。今年的工作量相當大，而且還在成長。在任何一年中，它們（技術難度）都需要前一年的工作量和當年的工作量來基本上抵消不斷增長的基數效應。

And so last year we were not able to do that, for three reasons. Number one is the base itself slowed down, which was a macro phenomenon that we call that in Q1. Secondly, fiscal year '24 workloads didn't meet our expectations going into fiscal year '25. And then new workloads in fiscal year '25., we're off to a slow start, slightly for operational reasons and that kind of was it was the headwind that we face for the rest of the year.

因此，去年我們未能做到這一點，原因有三。第一是基數本身放緩了，這是我們在第一季稱之為的宏觀現象。其次，24 財年的工作量沒有達到我們對 25 財年的預期。然後是 25 財年的新工作量，我們起步緩慢，稍微是由於營運原因，而這是我們在今年剩餘時間面臨的阻力。

So now turning the calendar forward for this year, we are calling for stable macro environment and usage growth in the base. Obviously, we don't have a crystal ball, but that's what we see currently. Then we're expecting more from fiscal year '25 workloads. We're optimistic that those are doing better based on the data that we have.

因此，現在回顧今年的日曆，我們預計宏觀環境將保持穩定，使用量將成長。顯然，我們沒有水晶球，但這就是我們目前看到的。我們預計 25 財年的工作量會更大。根據我們掌握的數據，我們樂觀地認為這些情況會好轉。

And because of our move up market, we expect to get more new workload ARR and sales productivity in fiscal year '26 cohort, and those two things ought to offset the fact that the base is again larger than it was a year ago and result in stable consumption growth.

而且由於我們的市場不斷上升，我們預計在 26 財年將獲得更多的新工作量 ARR 和銷售生產力，這兩件事應該可以抵消基數比一年前更大的事實，並帶來穩定的消費成長。

Thank you for that, Serge. It's really appreciated and for a follow up here I know that we're citing this $50 million multi-year headwind as it relates to the non-Atlas business. Can you kind of chunk that up? Is like if I'm thinking about a Q1, Q2, is there any way you can help us get a sense of what those headwinds are on a quarter-by-quarter basis?

謝謝你，Serge。我們真的很感激，為了跟進，我知道我們引用了這個 5000 萬美元的多年期逆風，因為它與非 Atlas 業務有關。你能把它分成幾個部分嗎？就好像我在考慮第一季和第二季的情況，您有什麼辦法可以幫助我們了解每季面臨的阻力是什麼嗎？

It's sort of the mirror image of the outperformance that we've seen here in Q3 and Q4 that we called out, and so I would think of it as a back-half-weighted phenomenon.

這有點像我們在第三季和第四季看到的優異表現的鏡像，因此我認為這是一種後半加權現象。

Got it thank you so much, guys.

明白了，非常感謝你們。

Brent Bracelin, Piper Sandler.

布倫特·布雷斯林，派珀·桑德勒。

Thank you. Good afternoon. I wanted to double click into Atlas. If we normalize for the unused credits last year, the implied Atlas growth here in the low-20s is actually a bit of a bigger step down in growth. So could you just revisit the growth levers as we think about Atlas here?

謝謝。午安.我想雙擊進入 Atlas。如果我們根據去年未使用的信用額度進行標準化，那麼這裡隱含的 Atlas 成長率將略低於 20%，這實際上是一個更大的成長下降。那麼，當我們思考 Atlas 時，您能否重新審視成長槓桿？

I know it's a larger business going into this year than last. But it does look like normalized growth after accounting for these unused credits last year is decelerating a little bit more. Just curious why.

我知道今年的業務規模比去年更大。但考慮到去年這些未使用的信貸後，正常化成長速度似乎確實有所放緩。只是好奇為什麼。

Yeah, so I'm going to go through the puts and takes a little bit, but I think it will help with the math. So the first thing I would reiterate is that on average we expect consumption growth in fiscal year '26, to be stable with what we've seen in fiscal year '25. So that's point number one.

是的，所以我將稍微討論一下投入和產出，但我認為這對數學有幫助。因此，我要重申的第一件事是，我們平均預期26財年的消費成長將與25財年的水準保持穩定。這是第一點。

Point number two is you have the total guidance, and then you can take the high-single digit rate of decline in the non-Atlas business, and that will give you a sense of what is -- what's left in Atlas and that foots with the with the roughly stable Atlas consumption growth in fiscal year '26.

第二點是，你有整體指導，然後你可以採取非 Atlas 業務的高個位數下降率，這將讓你了解 Atlas 還剩下什麼，並且這與 26 財年大致穩定的 Atlas 消費增長相符。

The final thing I would say is, as you think about the growth in fiscal year '25, just a reminder that what matters is the exit rate as opposed to the average throughout the year, because we have seen revenue slow down on a year-over-year basis because we have had slower consumption in all quarters, consumption growth in all quarters except Q4 of last year.

我最後要說的是，當你考慮 25 財年的成長時，需要提醒的是，重要的是退出率，而不是全年的平均水平，因為我們看到收入同比放緩，因為我們所有季度的消費都在放緩，除去年第四季度外，所有季度的消費都在增長。

Got it. And then Dev, as a follow up for you. We've seen AI workloads; I would argue in the experimental phase for the last two years. We're now seeing AI go into production, starting to see early signs of some of these agentic functions, show up in revenue renew. What's your expectation as you think about customer conversations, customers that are in experimentation going to production? When do you expect to see a bit of a lift there on your business?

知道了。然後是 Dev，作為對您的後續。我們已經看到了人工智慧工作負載；我認為過去兩年處於實驗階段。我們現在看到人工智慧投入生產，開始看到一些代理功能的早期跡象，並體現在收入更新中。當您考慮客戶對話、處於試驗階段並準備投入生產的客戶時，您的期望是什麼？您預計什麼時候您的業務會有所提升？

Yeah, so again we do see, I mean we have some high-profile AI companies who are building on top of Atlas. I'm not at liberty to name who they are, but in general I would say that the AI journey for customers is going to be gradual. I would say one is a lack of AI skills in their organizations. They really don't have a lot of experience and it's compounded by the rapid evolution of AI technology that they feel like it's very hard for them to kind of think about like what stack to use and so on and so forth.

是的，我們再次看到，我們有一些知名的人工智慧公司正在 Atlas 的基礎上進行建設。我不能透露他們的名字，但總的來說，我認為客戶的人工智慧之旅將是漸進的。我想說的是，其中之一就是他們的組織缺乏人工智慧技能。他們確實沒有很多經驗，再加上人工智慧技術的快速發展，他們覺得很難思考該使用什麼堆疊等等。

The second, as I mentioned earlier on the Voyage question, there's also a real worry about the trustworthiness of a lot of these applications. So I would say the use cases you're seeing are fairly simplistic, customer chatbots, maybe document summarization, maybe some very simple agentic workflows. But I do think that that is -- we're in the early innings and I expect that sophistication to increase as people get more and more comfortable. We think, architecturally, we have a huge advantage of the competition.

第二，正如我之前在 Voyage 問題上提到的，人們對許多此類應用程式的可信度也存在擔憂。所以我想說，您看到的用例相當簡單，客戶聊天機器人，也許是文件摘要，也許是一些非常簡單的代理工作流程。但我確實認為，我們正處於早期階段，我預計，隨著人們越來越適應，這種複雜程度將會增加。我們認為，從架構上講，我們在競爭中具有巨大的優勢。

One, the document model really supports different types of data, structured, semi-structured, and unstructured. We embed a search and vector search onto a platform. No one else does that. Then we now with Voyage AI, we have the most accurate embedding and re-ranking models to really address the quality and trust issue. And all this is going to be put together in a very, elegant developer experience that reduces friction and enables them to move fast.

首先，文檔模型確實支援不同類型的數據，結構化、半結構化和非結構化。我們將搜尋和向量搜尋嵌入到平台上。沒人會這麼做。然後，我們現在有了 Voyage AI，我們擁有最準確的嵌入和重新排名模型，可以真正解決品質和信任問題。所有這些都將結合在一起，為開發人員提供非常優雅的體驗，從而減少摩擦並使他們能夠快速行動。

So we feel we are really well positioned for this for this opportunity, and we're really excited. We are already obviously excited about what Voyage brings to us and excited by what customers are telling us. But we do think it's going to take some time because customers again are naturally getting their arms around this technology and starting slowly.

因此，我們覺得我們確實已經準備好迎接這個機會，我們真的非常興奮。我們顯然已經對 Voyage 為我們帶來的好處以及客戶告訴我們的事情感到興奮。但我們確實認為這將需要一些時間，因為客戶再次自然地掌握這項技術並起步緩慢。

Karl Keirstead, UBS.

瑞銀的卡爾·科爾斯特德（Karl Keirstead）

Well, thanks. Dev, on the last call and even the prior one you talked quite a bit about this go-to-market pivot where you were pushing MongoDB to go after more opportunities upmarket. To what extent is that go-to-market transition reflected in your guidance?

好吧，謝謝。Dev，在上次電話會議甚至更早之前的一次電話會議上，您談了很多關於進入市場轉型的問題，您正在推動 MongoDB 在高端市場上尋求更多的機會。您的指導在多大程度上反映了這種市場轉型？

Are you assuming some upside from that effort in your revenue guidance and, conversely, does that require a decent amount of sales investments that might be a factor in your margin guidance. And how is that effort going at a high level? Thank you.

您是否認為這項努力會為您的收入指引帶來一些好處？相反，這是否需要大量的銷售投資，而這也許是影響利潤率指引的因素。高層的努力進度如何？謝謝。

Yeah, thanks for the question, Karl. We're really pleased with the progress we're making, as evidenced by just even the data point we shared on the million-dollar customers. I mean that that customer count is growing faster than the rest of our customer base.

是的，謝謝你的提問，卡爾。我們對所取得的進展感到非常高興，這一點從我們與百萬美元客戶分享的數據點就可以看出。我的意思是，該客戶數量的成長速度比我們其他客戶群的速度更快。

So we're already seeing dividends from the investors are making up market. And we did see sales productivity gains last year from the move-up market and so we expect even further sales productivity gains this year.

因此，我們已經看到投資者的股息正在彌補市場缺口。去年我們確實看到了銷售生產力因升級市場而提高，因此我們預計今年的銷售生產力將進一步提高。

In terms of the margin, we actually reallocated investments and sales, moving resources from the mid-markets to the upmarket. So I wouldn't say that there was a demonstrable increase in sales investments. The investments are really more in R&D and driving more awareness of our platform and educating spending more time educating customers because you still find that a lot of people are still not completely aware of MongDB's full capabilities and also don't necessarily have the skills to use all of our capabilities.

在利潤方面，我們實際上重新分配了投資和銷售，將資源從中端市場轉移到高端市場。因此我不會說銷售投資有明顯的增加。我們的投資實際上更集中在研發方面，以及提高我們平台的知名度和教育方面，我們花了更多的時間來教育客戶，因為你仍然發現很多人仍然沒有完全意識到 MongDB 的全部功能，也不一定具備使用我們所有功能的技能。

So you're seeing a lot of investments going there and that's what we're really focused on. But in terms of the move-up market we're really happy with the results we're seeing.

所以你會看到很多投資流向那裡，而這正是我們真正關注的。但就升級市場而言，我們對所看到的結果感到非常滿意。

And Karl, the only thing I would add is that increased productivity is definitely a part of the guidance.

卡爾，我唯一想補充的是，提高生產力絕對是指導的一部分。

Okay, terrific. Thank you, both.

好的，太棒了。謝謝你們兩位。

Kingsley Crane, Canaccord Genuity.

金斯利‧克蘭 (Kingsley Crane)，Canaccord Genuity。

Hi, thanks for taking the question. So again on Voyage AI, you mentioned that technology is not enough in the prepared remarks. So to what extent do you think feature sets like that of Voyage can drive workload creation within AI apps, or is that more market oriented? And then is Voyage additive in its ability to reduce specter storage costs similar to your efforts in quantization?

你好，謝謝你回答這個問題。所以再次提到 Voyage AI，您在準備好的發言中提到技術還不夠。那麼您認為像 Voyage 這樣的功能集在多大程度上可以推動 AI 應用程式內的工作負載創建，或者是否更以市場為導向？那麼，Voyage 在降低幽靈儲存成本方面的能力是否與您在量化方面的努力相似？

Yeah, so a couple of points. I'm just stepping back. We see two big challenges that customers have. They have a skills gap and then they have a trust gap with AI. Voyage AI addresses the trust gap, enabling to build high-quality AI applications where the results, they have a high degree of confidence in.

是的，有幾點。我只是退後一步。我們發現客戶面臨兩大挑戰。他們之間存在技能差距，並且他們與人工智慧之間存在信任差距。Voyage AI 解決了信任鴻溝，使得能夠建立高品質的 AI 應用程序，並且對結果具有高度信心。

But the skills gap is still inherent to these organizations. So what we are doing is not just bringing technology, but we're really taking a solutions approach where we're coming together with a combination of technology, best practices, experience, so that we can really help customers deal with their business problems, not just throw technology at them.

但這些組織仍存在著技能差距。因此，我們所做的不僅僅是帶來技術，而且我們真正採取了一種解決方案，將技術、最佳實踐和經驗結合在一起，這樣我們才能真正幫助客戶解決他們的業務問題，而不僅僅是向他們提供技術。

And customers really appreciate this approach and so you'll see us really take more of a solution approach to help, for example, in modernizing their existing legacy applications as well as helping them build new AI applications.

客戶非常欣賞這種方法，因此您會看到我們真正採取更多的解決方案來幫助他們，例如，使他們現有的遺留應用程式現代化，以及幫助他們建立新的人工智慧應用程式。

In terms of storage, yes, the advances we made in quantization really reduce the storage costs and improves the performance of our [vector] store. I would say with these embedding models are slightly different. What that's doing is essentially helping, really quickly find the precise information needed based on the query being posed to the to the application to the underlying LLM to get the best quality answers.

就儲存而言，是的，我們在量化方面取得的進步確實降低了儲存成本並提高了我們的[向量]儲存的效能。我想說這些嵌入模型略有不同。這樣做本質上是為了幫助根據向底層 LLM 應用程式提出的查詢快速找到所需的精確信息，從而獲得最優質的答案。

So we really feel like this is all about like increasing the trustworthiness and the accuracy -- through the accuracy of the results that are generated from these AI applications.

因此，我們真的覺得這一切都是為了提高可信度和準確性——透過這些人工智慧應用程式產生的結果的準確性。

Great, really helpful. And a quick follow up, how did GCP partner influence deals fare in the quarter? You called out strength last quarter and that you were looking to do more with them in Q4. Also saw that they made some cuts more recently. Thanks.

太棒了，真的很有幫助。簡單問一下，GCP 合作夥伴如何影響本季的交易？您在上個季度強調了實力，並表示希望在第四季度與他們取得更多進展。還看到他們最近做了一些削減。謝謝。

No, our relationship with Google Cloud is still very constructive and productive. I mean, generally I would say our relationship with all the hyperscalers is very positive, and so we're working with them depending on the customers. I see some customers have relations with only one hyperscaler, sometimes they have relations with multiple hyperscalers, and we work closely with GCP as well as AWS and Azure. And I would say all three are actually quite productive.

不，我們與 Google Cloud 的關係仍然非常建設性和富有成效。我的意思是，總的來說，我們與所有超大規模企業的關係都非常積極，因此我們根據客戶情況與他們合作。我發現有些客戶只與一個超大規模供應商有關係，有時他們與多個超大規模提供者有關係，並且我們與 GCP 以及 AWS 和 Azure 密切合作。我想說這三種做法其實都是非常有成效的。

Patrick Walravens, Citizens Bank.

派崔克‧沃爾拉文斯 (Patrick Walravens)，公民銀行。

Oh great, thank you. I was wondering if you could just step back for us and give us sort of the five-year trajectory on non-Alas as I look at the growth rates going back, '21 was 23%, '22 19%, '23 25%, '24 [25%]. So was there a period, Dev, where it exceeded your expectations and then where it came in below what you thought? What sort of -- what was the ebb and flow of non-Atlas?

噢，太好了，謝謝你。我想知道您是否可以為我們回顧非 Alas 的五年發展軌跡，回顧一下成長率，'21 年為 23%，'22 年為 19%，'23 年為 25%，'24 年為 [25%]。那麼，Dev，是否存在一個時期，它超出了你的預期，然後又低於你的預期？什麼樣的－非 Atlas 的興衰情況是怎麼樣的？

Yeah, I wanted to say we don't really manage the business necessarily by product. We manage it by channel and really work from a customer orientation working backwards. What we do see at the high end of the customer segment is that customers do like choice. They don't believe that every workload will go to the cloud.

是的，我想說我們實際上並不一定透過產品來管理業務。我們透過通路進行管理，真正從客戶導向出發，逆向展開工作。在高端客戶群中，我們確實看到客戶確實喜歡選擇。他們不相信所有的工作負載都會轉移到雲端。

And in many cases some customers are still very much, focused on building their technology stack on prem. In fact, a lot of large banks in Europe and actually a number of banks here in the US have a predisposition to workloads on prem. So it's really about serving the customers' needs.

在很多情況下，一些客戶仍然非常專注於在本地建立他們的技術堆疊。事實上，歐洲的許多大型銀行以及美國的許多銀行都傾向於在本地部署工作負載。因此這其實是為了滿足客戶的需求。

The customers do like the ability to have choice on how they run their workloads. And but we do also see, and this is what's called out in the prepared remarks, for those customers who start with the EA who have a significant amount of EA, we are seeing them -- seeing more of those incremental workload to move to Atlas.

客戶確實喜歡能夠選擇如何運行他們的工作負載。但是我們也看到，這就是在準備好的評論中提到的，對於那些從 EA 開始並且擁有大量 EA 的客戶，我們看到他們 - 看到更多的增量工作負載轉移到 Atlas。

And some of these new capabilities like Voyage and all that will be available only on Atlas. And so there are some things that we're doing that you'll see customers probably inherently do more on Atlas and I'll hand the -- let Serge comment on the question on the guide.

其中一些新功能（例如 Voyage 等）僅在 Atlas 上可用。因此，我們正在做的一些事情，您會看到客戶可能天生就在 Atlas 上做更多的事情，我會把 - 讓 Serge 對指南上的問題發表評論。

Yeah, so I think that I would just stress a couple of things, Pat. One is we talked in prepared remarks about non-Atlas ARR growth being in the mid-single digits year over year in Q4 and that being a slowdown from double-digits growth in Q4 fiscal year '24.

是的，所以我想我只想強調幾件事，帕特。一是我們在準備好的評論中談到，第四季度非 Atlas ARR 的同比增幅處於中等個位數，與 2024 財年第四季度的兩位數增幅相比有所放緩。

And that's the phenomenon that day is talking about which is that we are seeing increasingly customers who are historically non-Atlas deploy incremental workloads on Atlas. They do deploy incremental workloads on EA. That's why that line item continues growing, and we expect it to continue growing.

這就是那天談論的現象，即我們看到越來越多的傳統非 Atlas 客戶在 Atlas 上部署增量工作負載。他們確實在 EA 上部署了增量工作負載。這就是該項目持續成長的原因，而且我們預計它還會繼續成長。

But we see more and more of it actually coming on Atlas. So if you look at the ARR of those customers, it's actually growing more than what the just the non-Atlas component would suggest, and that's baked into the both the Atlas and the non-Atlas portion of the guy.

但我們發現越來越多的內容實際上出現在 Atlas 上。因此，如果您查看這些客戶的 ARR，它實際上的成長速度超過了非 Atlas 部分所顯示的水平，並且這是 Atlas 和非 Atlas 部分共同作用的結果。

Okay, and then just as a follow up and you might not be able to comment on this, but as if as we look out past '26, I know you don't run the business this way, but we do model it this way. So as we look out past '26, should we expect the growth to stay really muted?

好的，然後作為後續問題，您可能無法對此發表評論，但就好像我們展望26年之後，我知道您不會以這種方式經營業務，但我們確實以這種方式進行建模。那麼，當我們展望26年後時，我們是否應該預期成長將保持緩慢？

Yes, so I would say two things. One is the puts and takes of the following. We do still have low market share even in those customers who are predominantly non-Atlas. So we do expect to continue growing workloads that we can acquire in EA, number one.

是的，所以我想說兩件事。一是以下的利弊。即使在那些主要非 Atlas 客戶中，我們的市場份額仍然很低。因此，我們確實期望繼續增加我們在 EA 中可以獲得的工作量，這是第一名。

Number two, we do expect to move to the cloud to continue. So we do expect those customers for us to gain even more share in those customers by getting incremental Atlas workloads. And then a bit of a question mark at this point is sort of how the app modernization initiative is going to play out. We think that will benefit both Atlas and non-Atlas, or EA rather, and so obviously that's still nascent and it's sort of hard to ascertain, but that actually ought to be helpful across the board.

第二，我們確實希望繼續轉向雲端。因此，我們確實期望透過增加 Atlas 工作負載，我們可以在這些客戶中獲得更多份額。現在，有點疑問的是應用程式現代化計劃將如何發揮作用。我們認為這將使 Atlas 和非 Atlas 公司（或更確切地說是 EA）都受益，顯然這仍處於起步階段，很難確定，但實際上這應該對所有人都有幫助。

Brad Sills, Bank of America.

美國銀行的布拉德·西爾斯。

Oh great, thank you so much. A question for you, Dev. You mentioned fiscal '26 kind of being a year of transition.

噢，太好了，非常感謝。問你一個問題，Dev。您提到 26 財年是過渡年。

I wanted to get your thoughts on what that means exactly. It seems to me that, the consumption patterns are stabilizing. You're seeing some traction with new workloads. Last year was a year of kind of go-to-market changes. It feels like this would be the year where you would start to see some progress on some of the transition items you saw last year. So maybe transition is a little bit strong as a way to describe this year, but I just wanted to kind of double click on your thoughts on that.

我想要聽聽你對這到底意味著什麼的看法。在我看來，消費模式正在趨於穩定。您會看到新的工作負載的一些變化。去年是市場變革的一年。感覺今年您將開始看到去年看到的一些過渡項目取得進展。因此，也許用「轉變」來描述今年有點太強烈了，但我只是想仔細了解你對此的想法。

Yeah, so let me be clear, I feel very bullish about the future of this business. I have not been excited like this for a long time. I think, the way people are building these new AI applications are ready made for a platform like MongoDB in terms of the ability to handle different data types, the scalability of the platform to be able to natively support lexical and semantic search. And obviously now to be able to also give a very elegant experience to be able to leverage embedding and re-ranking models.

是的，讓我說清楚一點，我對這個業務的未來非常樂觀。我已經很久沒有這麼興奮過了。我認為，人們建立這些新 AI 應用程式的方式已經為 MongoDB 這樣的平台做好了準備，包括處理不同資料類型的能力、平台的可擴展性，能夠原生支援詞彙和語義搜尋。顯然現在還能夠提供非常優雅的體驗，以便能夠利用嵌入和重新排名模型。

So that's I feel very bullish, and I think this business can grow meaningfully faster than it is today. But we are obviously making the right investments. We believe the right investments to kind of position the company for that growth. We're pleased to see that the Atlas business is stabilizing and that that was also mentioned by both myself and Serge to the prepared remarks.

所以我感到非常樂觀，我認為這項業務可以比現在更快成長。但我們顯然做了正確的投資。我們相信正確的投資可以幫助公司成長。我們很高興看到 Atlas 業務正在穩定，我和 Serge 在準備好的演講中也提到了這一點。

I know that's been a question for a lot of investors. I think obviously with the EA business there puts and takes with this multi-year, but the long-term trends are in our favor. It's a big market. We're going after, essentially, a big opportunity because customers are essentially looking to run their business through custom software. And I believe that when you think about like a competitive differentiation against the other players in space, I think we are very, very differentiated, and that will show up in the numbers over time.

我知道這是很多投資者的疑問。我認為，很明顯，EA 業務在這幾年中存在著得失，但長期趨勢對我們有利。這是一個很大的市場。從本質上說，我們正在追尋一個巨大的機遇，因為客戶本質上希望透過客製化軟體來經營他們的業務。我相信，當你考慮到與該領域其他參與者的競爭差異時，我認為我們的差異化非常大，而且隨著時間的推移，差異化會反映在數字上。

The only thing I would add, Brad, is that it's a transition year in the sense that some of our largest initiatives and focus areas, those being application modernization and then generally winning the AI stack, are going to only incrementally be beneficial to our revenue this year, but we expect them to be meaningful growth drivers in the years beyond.

我唯一想補充的是，布拉德，今年是一個過渡年，我們的一些最大舉措和重點領域，即應用程序現代化以及總體上贏得人工智能堆棧，今年對我們的收入只會逐步有利，但我們預計，它們將成為未來幾年有意義的增長動力。

Wonderful, thanks for that. And then would love to get your thoughts on where you're seeing new workload strength that you called out any categories in particular. Thank you.

太好了，謝謝。然後，我很想聽聽您對新工作量強度的看法，以及您特別提到的類別。謝謝。

Yeah, actually, the short answer is we're seeing it everywhere. We're seeing it both at the high end of the market as well as at the low end of the market. If you see our customer accounts, our customer, new customer account for this quarter, this past quarter was quite strong. And so and as we called out our move up market is generating, our million-dollar customer count has grown faster than our overall customer base, so we're seeing results at both the top end and the bottom end of the market.

是的，事實上，簡短的回答是我們隨處可見它。我們在高端市場和低階市場都看到了這種現象。如果您查看我們本季的客戶帳戶、客戶和新客戶帳戶，您會發現上個季度的表現相當強勁。正如我們所呼籲的，我們的上升市場正在產生，我們的百萬美元客戶數量增長速度超過了我們的整體客戶群，因此我們在市場的高端和低端都看到了成果。

Patrick Colville, Scotia Bank.

加拿大豐業銀行的派崔克‧科爾維爾。

Oh, thanks so much for taking my question. I guess, Dev, this one's for you, please. So MongoDB is obviously doing a lot of things right. So I guess I just want to ask around the competitive environment as of today, how is MongoDB competing with the hyperscalers, and Postgres as of today, and is that any different to the competitive environment, call it this time of year ago, March 2024. Thank you.

哦，非常感謝您回答我的問題。我想，Dev，這個是給你的。所以 MongoDB 顯然做了很多正確的事。所以我想我只想問一下截至今天的競爭環境，MongoDB 如何與超大規模企業和 Postgres 競爭，這與去年這個時候，即 2024 年 3 月的競爭環境有什麼不同。謝謝。

Yeah, well, I'll say that I'll make two main points and I'll explain what they are. One, a lot of people do compare MongoDB to Postgres and I think that's actually a false comparison. Because Postgres is just an OLTP database, with MongoDB, the right comparison is Postgres plus elastic, plus something like pinecone, plus maybe like an embedding model from like either like OpenAI or Cohere.

是的，好吧，我會說兩個要點並解釋它們是什麼。首先，很多人將 MongoDB 與 Postgres 進行比較，但這實際上是一個錯誤的比較。因為 Postgres 只是一個 OLTP 資料庫，與 MongoDB 相比，正確的比較是 Postgres 加上 elastic，加上像 pinecone 這樣的東西，再加上可能像 OpenAI 或 Cohere 這樣的嵌入模型。

So when you package all those components together, you get a like for like comparison with MongoDB. And I think obviously customers much prefer to have a much more elegant solution than trying to cobble all these pieces together and try and figure out how to how to make it all work.

因此，當您將所有這些元件打包在一起時，您將獲得與 MongoDB 類似的比較。我認為，客戶顯然更喜歡一個更優雅的解決方案，而不是試圖把所有的部分拼湊在一起，並試圖弄清楚如何讓它們全部發揮作用。

The second point I'd say is MongoDB is frankly a much better OLTP database than Postgres. Postgres is based on the relational architecture. It's very rigid. It doesn't handle unstructured data well. It claims to support JSON data, but the performance of anything, north of like two kilobytes of JSON data, the performance of Postgres really suffers, and it's not very scalable.

我想說的第二點是，MongoDB 坦白說是一個比 Postgres 更好的 OLTP 資料庫。Postgres 是基於關係架構。它非常僵硬。它不能很好地處理非結構化資料。它聲稱支援 JSON 數據，但是對於任何超過 2 KB 的 JSON 數據，Postgres 的性能都會受到影響，而且可擴展性較差。

And I think what Postgres is the beneficiary of is the lift and shift of Oracle and Sybase and SQL Server because they're the open-source relational standard, but they're not exactly competing for the same workloads we are. Our win rates against Postgres are very high. The -- when we talk to our salespeople, when we can explain the value proposition of MongoDB against Postgres, our win rates are incredibly high.

我認為 Postgres 受益於 Oracle、Sybase 和 SQL Server 的提升和轉變，因為它們是開源關係標準，但它們並沒有與我們競爭相同的工作負載。我們對抗 Postgres 的勝率非常高。當我們與銷售人員交談時，當我們可以解釋 MongoDB 相對於 Postgres 的價值主張時，我們的成功率非常高。

We just want to get into more battles and what we recognize is who don't know MongoDB may just gravitate to Postgres as their solution because they just don't know how to use MongoDB and that's what we're working on in terms of generating more awareness and infusing more skills into our existing customers as well as the new customers who we want to come to our platform.

我們只是想參與更多的戰鬥，我們認識到那些不知道 MongoDB 的人可能會傾向於使用 Postgres 作為他們的解決方案，因為他們只是不知道如何使用 MongoDB，而這正是我們正在努力提高現有客戶以及我們希望加入我們平台的新客戶的知名度和灌輸更多技能。

Very helpful and I guess it just touch on the hyperscalers just briefly.

非常有幫助，我想它只是簡單地涉及了超大規模器。

The hyperscaler is they have their own variants of Postgres offerings, and they have their clones. I mean, we haven't seen the clones as much lately. Our win rates against the clones are very high. There's something we should talk about for a few years ago, but we seem to our relationship with the hyperscalers, as I mentioned on the previous question, is actually very positive.

超大規模企業擁有自己的 Postgres 產品變體，並且擁有自己的克隆版本。我的意思是，我們最近很少見到克隆人。我們對抗克隆人的勝率非常高。幾年前我們就應該討論一些事情，但正如我在上一個問題中提到的那樣，我們與超大規模企業的關係實際上非常積極。

We, our salespeople, partner with the hyperscalers all around the world. And what we find is that together we win more business and yes, there may be situations where they try and lead with their first-party services, but usually we find that when we partner with them that they're very accommodating and we all can do well working together. So I would say there's no structural issue with the with the hyperscalers.

我們的銷售人員與世界各地的超大規模企業合作。我們發現，透過合作我們可以贏得更多業務，是的，在某些情況下，他們會嘗試以第一方服務為主導，但通常我們發現，當我們與他們合作時，他們非常樂於助人，我們都可以很好地合作。所以我想說超大規模器不存在結構性問題。

Rishi Jaluria, RBC Capital Markets.

Rishi Jaluria，加拿大皇家銀行資本市場。

Oh wonderful thanks so much for taking my question. Dev, I want to follow up on a comment you made which is, a lot of the Postgres successes from lift and shift of existing SQL applications and we're seeing the same thing in our own checks.

哦，太好了，非常感謝您回答我的問題。Dev，我想跟進一下你的評論，即許多 Postgres 的成功來自於現有 SQL 應用程式的提升和轉變，我們在自己的檢查中也看到了同樣的情況。

What needs to happen as companies think about net new generative AI applications for them to think about kind of reconstructing or re-architecting those solutions from scratch, especially when they want to leverage unstructured data. And what role can you play in kind of driving that conversation more towards rebuild rather than just lift and shift.

當公司考慮全新的生成式人工智慧應用時，他們需要考慮從頭開始重建或重新建構這些解決方案，尤其是當他們想要利用非結構化資料時。您可以在推動對話向重建而非簡單的提升和轉變方面發揮什麼作用？

Yeah, that's a good question. I mean, we think AI is fundamentally changing the game. And the reason it's changed the game is that if we thought change was happening fast today, it's going to happen even faster in the world of AI. That means every business will have to make sure that software is incredibly flexible and adaptable as they deal with all these new different modalities, as the use cases change very.

是的，這是個好問題。我的意思是，我們認為人工智慧正在從根本上改變遊戲規則。它改變遊戲規則的原因是，如果我們認為今天變化發生得很快，那麼在人工智慧的世界中變化將發生得更快。這意味著每個企業都必須確保軟體具有極強的靈活性和適應性，因為他們要處理所有這些新的不同模式，因為用例會發生很大的變化。

To change in terms of being able to address use cases that traditional software could not, and what that means is that you need a data infrastructure and a data foundation that's designed to enable change. MongoDB was built for change. The whole notion of our document model was enabled to have a very flexible schema so you can make changes quickly.

要改變這一點，就必須能夠解決傳統軟體無法解決的用例，這意味著您需要一個旨在實現變革的資料基礎架構和資料基礎。MongoDB 是為了變革而建立的。我們的文件模型的整體概念具有非常靈活的模式，因此您可以快速進行更改。

Then obviously over time we added lexical search and then semantic search for a vector database functionality. So all that comes out of the box and now with the Voyage AI acquisition we bring all these pizza parts together and we feel that staying on a relational database will no longer be a viable option just given the rate of change that every business will have to deal with, and they need a very flexible and adaptable data foundation, and that's where we come in.

然後顯然隨著時間的推移，我們添加了詞彙搜尋和語義搜尋以實現向量資料庫功能。因此，所有這些都是現成的，現在隨著 Voyage AI 的收購，我們將所有這些部分整合在一起，我們認為，考慮到每個企業都必須應對的變化率，繼續使用關聯式資料庫將不再是一個可行的選擇，他們需要一個非常靈活和適應性強的資料基礎，這就是我們的用武之地。

Obviously, our job is to educate those customers on our advantages, but we feel -- and that's why I feel so bullish about our futures, because I think the puck is essentially coming to us just given our architectural advantages in this space.

顯然，我們的工作是讓這些客戶了解我們的優勢，但我們感覺——這也是為什麼我對我們的未來如此樂觀，因為我認為，鑑於我們在這個領域的架構優勢，勝利之星基本上會降臨到我們這邊。

Yeah, that's really helpful, Dev. And then maybe just related to that you've talked about the opportunity to do with relational migrator in the past and really how AI can help in accelerating that remapping the data schema, etc.

是的，這真的很有幫助，Dev。然後也許與您談論過去與關係遷移器有關的機會以及人工智慧如何真正幫助加速重新映射資料模式等相關。

What sort of momentum have you seen with relational migrator and maybe how should we be thinking about that as a growth driver going forward? Thank you.

您看到關係遷移者出現了什麼樣的勢頭？我們應該如何將其視為未來的成長動力？謝謝。

Yeah, our confidence and bullish the space is even higher today than it was before. I do want to say that what we're going after is a very hard problem, and I should say we knew this from the start, right? For example, when you're looking at a legacy app that's got hundreds, tens of thousands, if not thousands, not tens of thousands of stored procedures, being able to reason about that code, being able to decipher that code, and ultimately to convert that code takes is a lot of effort.

是的，今天我們的信心和看漲空間比以前更高了。我確實想說，我們正在解決的是一個非常困難的問題，我應該說我們從一開始就知道這一點，對嗎？例如，當您查看一個包含數百、數萬甚至數千個預存程序的遺留應用程式時，推理該程式碼、解密該程式碼並最終轉換該程式碼需要付出很多努力。

And but the good news is that we are seeing a lot of progress in that area. We see a lot of interest from our customers in this area because they're in so much pain with all the technical debt that they've assumed. Second is that when they think about the future and how they enable AI and these applications, there's no way they can do this on their legacy platforms. And so they're motivated to try and modernize as quickly as possible.

但好消息是我們看到該領域取得了巨大進展。我們發現客戶對這一領域非常感興趣，因為他們承擔的所有技術債務都讓他們非常痛苦。其次，當他們思考未來以及如何實現人工智慧和這些應用程式時，他們無法在傳統平台上做到這一點。因此他們積極嘗試盡快現代化。

We are initially focused on Java apps running on Oracle because that seems to be the most pain that customers are seeing, and we feel like, FY26 this year is we're going to scale out this work and then it's really going to start showing up our numbers in FY27.

我們最初專注於在 Oracle 上運行的 Java 應用程序，因為這似乎是客戶看到的最痛苦的問題，我們覺得，今年 26 財年我們將擴大這項工作，然後它將真正開始顯示我們的數字在 27 財年。

Thank you. That is all the time we have for today's question-and-answer session. I would now like to turn the call back over to Dev Ittycheria for any closing remarks.

謝謝。這就是今天問答環節的全部時間。現在我想將電話轉回給 Dev Ittycheria，請他做最後發言。

Thank you for everyone for joining us today. I just want to remind and to summarize that we had a really strong quarter year as we executed on a large opportunity. We expect this coming fiscal year to play out similar to last year with healthy new business and stable atlas consumption trends.

感謝大家今天的參加。我只是想提醒並總結一下，我們本季表現非常強勁，因為我們抓住了一個巨大的機會。我們預計，即將到來的財年將與去年類似，新業務發展良好，地圖消費趨勢穩定。

We are more excited than ever about the long-term outlook, particularly around our opportunity to address expanded requirements of the database in the AI era, and we'll continue to invest judiciously to focus on our execution and focus on our execution to capture the long-term opportunity ahead of us. So thank you for joining us, and we'll talk to you soon. Take care.

我們對長期前景比以往任何時候都更加興奮，特別是圍繞我們在人工智慧時代滿足資料庫擴展需求的機會，我們將繼續明智地投資，專注於我們的執行，並專注於我們的執行，以抓住我們面前的長期機會。感謝您加入我們，我們很快就會與您交談。小心。

This concludes today's program. Thank you all for participating. You may now disconnect.

今天的節目到此結束。感謝大家的參與。您現在可以斷開連線。