MongoDB Inc (MDB) 2025 Q2 法說會逐字稿

Good day and thank you for standing by. Welcome to the MongoDB's second-quarter fiscal year 2025 conference call. (Operator Instructions)

您好，感謝您的支持。歡迎參加 MongoDB 2025 財政年度第二季電話會議。（操作員指令）

Please be advised that today's conference is being recorded. I would now like to turn the call over to your speaker for today, Brian Denyeau. Please go ahead.

請注意，今天的會議正在錄音。現在我想將電話轉給今天的發言人 Brian Denyeau。請繼續。

Thank you, Lisa. Good afternoon and thank you for joining us today to review MongoDB's second-quarter fiscal 2025 financial results, which we announced in our press release issued after the close of market today. Joining me on the call today are Dev Ittycheria, President and CEO of MongoDB; and Michael Gordon, MongoDB's COO and CFO.

謝謝你，麗莎。下午好，感謝您今天加入我們，共同回顧 MongoDB 2025 財年第二季的財務業績，我們在今天收盤後發布的新聞稿中公佈了這一業績。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 的營運長兼財務長 Michael Gordon。

During this call, we will make forward-looking statements, including statements related to our market and future growth opportunities, our expectations for the macroeconomic environment in fiscal 2025 and the impact of AI, the benefits of our product platform, our competitive landscape, customer behaviors, our financial guidance, 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有關的陳述、我們對 2025 財年宏觀經濟環境的預期和人工智慧的影響、我們產品平台的優勢、我們的競爭格局、客戶行為、我們的財務指導以及我們在人工智慧方面的規劃投資和成長機會。

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

這些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，這些因素可能導致實際結果與我們的預期有重大差異。

For a discussion of the material risks and uncertainties that could affect our actual results, please refer to the risks described in our quarterly report on Form 10-Q for the quarter ended April 30, 2024, filed with the SEC at May 31, 2024. 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.

有關可能影響我們實際結果的重大風險和不確定性的討論，請參閱我們於 2024 年 5 月 31 日向美國證券交易委員會提交的截至 2024 年 4 月 30 日的季度 10-Q 表季度報告中所述的風險。本次電話會議中所做的任何前瞻性陳述僅反映我們截至今天的觀點，我們不承擔更新這些陳述的義務，除非法律要求。

Additionally, we will discuss non-GAAP financial measures in 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 their most directly comparable GAAP financial measure.

此外，我們將在本次電話會議中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 Dave.

說完這些，我想把電話轉給戴夫。

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 second-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二季業績。

We generated revenue of $478 million, a 13% year-over-year increase against a very difficult year-over-year compare and above the high end of our guidance. Atlas revenue grew 27% year over year, representing 71% of revenue. We generated non-GAAP operating income of $52.5 million for 11% non-GAAP operating margin, and we ended the quarter with over 50,700 customers.

我們實現了 4.78 億美元的營收，與去年同期相比成長了 13%，並且高於我們預期的最高值。Atlas營收年增27%，佔總營收的71%。我們創造了 5,250 萬美元的非 GAAP 營業收入，非 GAAP 營業利潤率為 11%，本季末我們擁有超過 50,700 名客戶。

Overall, we were pleased with the performance in the second quarter. We had a strong new business quarter and we saw improving sales productivity year over year. We saw strength across the board with both Atlas and Enterprise Advanced exceeding our expectations, demonstrating the enduring appeal of our run anywhere strategy. Our Q2 performance reinforced our belief the slow start to the new business in Q1 was purely operational, and we feel good about our new business outlook in the second half of the year.

總體而言，我們對第二季的表現感到滿意。我們新業務季度表現強勁，銷售效率也逐年提升。我們看到 Atlas 和 Enterprise Advanced 均表現出超出我們預期的全面實力，證明了我們的「隨處運作」策略的持久吸引力。我們第二季度的業績強化了我們的信念，即第一季新業務起步緩慢純粹是營運方面的，我們對下半年新業務前景充滿信心。

Moving on to Atlas consumption, the quarter played out modestly better than our expectations. Michael will discuss consumption trends in more detail. Finally, retention rates remained strong in Q2, demonstrating the quality of our product and the mission criticality of our platform.

談到 Atlas 的消費，本季的表現略優於我們的預期。邁克爾將更詳細地討論消費趨勢。最後，第二季的留存率依然強勁，證明了我們產品的品質和平台的關鍵性。

Our performance this quarter reinforce our confidence in our ability to execute on our long-term opportunity. As we said before, companies today rely on software to express their business strategy. This trend has driven our success for the past decade, and we anticipate it will continue to do so for the foreseeable future.

本季的表現增強了我們對實現長期機會的能力的信心。正如我們之前所說，當今的公司依靠軟體來表達他們的商業策略。這一趨勢推動了我們過去十年的成功，我們預計在可預見的未來它仍將繼續發揮作用。

Even with our success to date, we only have a low single-digit share in one of the largest and fastest-growing markets in all of software. When you combine this foundational tailwind with the opportunities for our customers to incorporate generative AI into their businesses and modernize their legacy application state, it is clear that MongoDB has multiple long-term growth opportunities.

即使我們迄今為止取得了成功，但在整個軟體領域最大且成長最快的市場之一中，我們只佔有個位數的份額。當您將這一基礎性順風與我們的客戶將生成式人工智慧融入其業務並使其遺留應用程式狀態現代化的機會結合起來時，很明顯 MongoDB 擁有多個長期成長機會。

Turning to AI. AI continues to be an additional long-term opportunity for our business. At the start of the fiscal year, we told you that we didn't expect AI to be a meaningful tailwind for our business in fiscal year 2025, which has proven accurate. Based on recent peer commentary, it seems that the industry now mostly agrees with this view. Companies are currently focusing their spending on the infrastructure layer of AI and are still largely experimenting with AI applications.

轉向人工智慧。人工智慧將繼續成為我們業務的另一個長期機會。在財年開始時，我們告訴大家，我們並不認為人工智慧會成為 2025 財年我們業務的重大推動力，事實證明這項預測是正確的。根據最近的同行評論，似乎業界現在大多同意這一觀點。目前，各公司都主要將支出集中在人工智慧的基礎設施層上，並且仍在對人工智慧應用進行大量試驗。

Inference workloads will come and should benefit MongoDB greatly in the long run, but we are still very early when the monetization of AI apps will take time. AI demand is a question of when, not if, and our discussions with customers and partners give us increasing conviction that we are the ideal data layer for AI apps for a number of key reasons.

推理工作負載將會出現，從長遠來看，這將極大地有利於 MongoDB，但我們仍處於 AI 應用貨幣化階段，這需要時間。人工智慧的需求是一個時間問題，而不是是否的問題，我們與客戶和合作夥伴的討論使我們越來越相信，由於以下幾個關鍵原因，我們是人工智慧應用的理想資料層。

First, more than any other type of modern workload, AI-driven workloads require the underlying database to be capable of processing queries against rich and complex data structures quickly and efficiently. Our flexible document model is uniquely positioned to help customers build sophisticated AI applications because it is designed to handle different data types, your source data, vector data, metadata, and generated data right alongside your live operational data, updating the need for multiple database systems and complex back-end architectures.

首先，與任何其他類型的現代工作負載相比，人工智慧驅動的工作負載更需要底層資料庫能夠快速有效地處理針對豐富而複雜的資料結構的查詢。我們靈活的文件模型具有獨特的優勢，可以幫助客戶建立複雜的 AI 應用程序，因為它旨在處理不同的資料類型、來源資料、向量資料、元資料和生成的資料以及即時操作數據，從而更新對多個資料庫系統和複雜後端架構的需求。

Second, MongoDB offers a high performance and scalable architecture as the latency of LLMs improve the value of using real-time operational data for AI apps will become even more important.

其次，MongoDB 提供了高效能和可擴展的架構，因為 LLM 的延遲提高了使用即時操作資料對 AI 應用的價值將變得更加重要。

Third, we are seamlessly integrated with leading app development frameworks and AI platforms, enabling developers to incorporate MongoDB into their existing workflows while having the flexibility to choose the LLM and other specific tools that best suit their needs.

第三，我們與領先的應用程式開發框架和人工智慧平台無縫集成，使開發人員能夠將 MongoDB 納入他們現有的工作流程，同時可以靈活地選擇最適合他們需求的 LLM 和其他特定工具。

Fourth, we meet or exceed the security and compliance requirements expected from an enterprise database, including enterprise-grade encryption, authorization, and auditability.

第四，我們滿足或超越了企業資料庫所期望的安全性和合規性要求，包括企業級加密、授權和可審計性。

Lastly, customers can run MongoDB anywhere, on-premise or as a fully managed service in one of the 118 global cloud regions across three hyperscalers giving them the flexibility to run workloads to best meet their application use cases and business needs.

最後，客戶可以在任何地方運行 MongoDB，可以在本地運行，也可以在三個超大規模資料中心的 118 個全球雲端區域之一中以完全託管的服務運行，從而使他們能夠靈活地運行工作負載，以最好地滿足其應用程式用例和業務需求。

We see three main opportunities where we believe AI will accelerate our business over time. The first is that the cost of building applications in the world of AI will come down as we've seen with every previous platform shift, creating more applications and more data requiring more databases.

我們看到了三個主要機遇，我們相信人工智慧將隨著時間的推移加速我們的業務發展。首先，正如我們在先前的每次平台轉變中看到的那樣，在人工智慧領域建立應用程式的成本將會下降，從而創建更多的應用程式和更多的數據，需要更多的資料庫。

The second opportunity is for us to be the database of choice for customers building greenfield AI applications. While we see that there's tremendous amount of interest and planning for new AI-powered applications, the complexity and fast-moving nature of the AI ecosystem slows customers down.

第二個機會是，我們可以成為客戶建立綠地 AI 應用程式的首選資料庫。雖然我們看到人們對新的人工智慧應用程式有著極大的興趣和計劃，但人工智慧生態系統的複雜性和快速發展的特性減慢了客戶的速度。

That's why we launched the MongoDB AI applications Program, or MAAP, which became generally available to customers last month. MAAP brings together a unique ecosystem, including the three major cloud providers, AWS, Azure, and GCP as well as Accenture and AI pioneers like Anthropic and Cohere.

這就是我們推出 MongoDB AI 應用程式（MAAP）的原因，該計劃已於上個月向客戶全面開放。MAAP 匯集了一個獨特的生態系統，包括三大雲端供應商 AWS、Azure 和 GCP 以及埃森哲和 Anthropic、Cohere 等人工智慧先驅。

MAAP offers customers reference architectures and end-to-end technology stack that includes prebuilt integrations, professional services, and a unified support system to help customers quickly build and deploy AI applications.

MAAP 為客戶提供參考架構和端到端技術堆疊，包括預先建置的整合、專業服務和統一的支援系統，以協助客戶快速建置和部署 AI 應用程式。

The third opportunity is to help customers modernize their legacy application estate. As you know, this segment of the market is a massive opportunity for us as most of the existing $80 billion-plus database industry is built on dated relational architecture.

第三個機會是幫助客戶實現其遺留應用程式的現代化。如您所知，這個細分市場對我們來說是一個巨大的機遇，因為現有的價值 800 多億美元的資料庫產業大部分都是建立在過時的關係架構上的。

Modernizing legacy applications has always been part of our business, and we have taken steps over the years to simplify and demystify this complex process through partnerships, education, and most recently, our relational migrator product. AI offers a potential step function improvement, lowering the cost and reducing their time and risk to modernize legacy applications.

對遺留應用程式進行現代化改造一直是我們業務的一部分，多年來，我們透過合作、教育以及最近的關係遷移產品採取措施簡化和消除這一複雜過程。人工智慧提供了潛在的階躍功能改進，降低了成本並減少了對遺留應用程式進行現代化改造的時間和風險。

For that reason, earlier this year, we launched several pilots with our customers where we work with them to modernize mission-critical applications, leveraging both AI tooling and services. The early results from these pilots are very exciting as our customers are experiencing significant reductions in time and cost of modernization. In particular, we have seen dramatic improvements in time and cost to rewrite application code and generate test suites.

因此，今年早些時候，我們與客戶一起啟動了多個試點項目，與他們合作，利用人工智慧工具和服務對關鍵任務應用程式進行現代化改造。這些試點的早期結果非常令人興奮，因為我們的客戶在現代化時間和成本方面都顯著減少了。特別是，我們看到重寫應用程式程式碼和生成測試套件的時間和成本得到了顯著改善。

We see increasing interest from customers that want to modernize their legacy application state, including large enterprise customers. As a CIO of one of the world's largest insurance companies said about our pilot, this is the first tangible return he's seen on his AI investments.

我們看到越來越多的客戶（包括大型企業客戶）對更新其遺留應用程式狀態感興趣。正如全球最大的保險公司之一的資訊長在談到我們的試點計畫時所說，這是他從人工智慧投資中看到的第一個實際回報。

While it's still early days and generating meaningful revenue from this program will take time, we are excited about the results of our pilots and the growing pipeline of customers eager to modernize the legacy estate.

儘管現在還處於早期階段，從該計劃中獲得可觀的收入仍需要時間，但我們對試點的結果以及越來越多渴望對傳統資產進行現代化改造的客戶感到興奮。

Finally, I understand that there are a lot of questions about the current business conditions and the macro environment more broadly. So let me give you a sense of what we're seeing across the business. As a reminder, when think of the macro influence on our business, it's important to distinguish between consumption of existing workloads and new business.

最後，我知道對於當前的商業狀況和更廣泛的宏觀環境存在許多疑問。那麼讓我來為你介紹一下我們在整個業務中看到的情況。提醒一下，當考慮對我們業務的宏觀影響時，區分現有工作負載和新業務的消耗非常重要。

Starting with consumption of existing application on our platform, this is where we have historically seen a macro impact as usage of applications impacted by the underlying business conditions of our customers. As we discussed on our last earnings call, in Q1, we did see broad-based consumption growth slowdown suggesting some macro softening. Our usage trends suggest a similar macro environment in Q2 as in Q1, even though Q2 Atlas consumption growth was modestly ahead of our expectations.

從我們平台上現有應用程式的使用開始，這是我們歷史上看到的宏觀影響，因為應用程式的使用受到客戶底層業務條件的影響。正如我們上次財報電話會議上所討論的那樣，在第一季度，我們確實看到廣泛的消費成長放緩，表明宏觀經濟有所軟化。我們的使用趨勢表明，第二季的宏觀環境與第一季類似，儘管第二季 Atlas 消費成長略高於我們的預期。

Moving on to new business, we generally have not seen the macro environment impact our ability to win new business, and that was true in Q2 as well. We realized that this is different from what you hear from some other software vendors.

談到新業務，我們總體上沒有看到宏觀環境影響我們贏得新業務的能力，第二季也是如此。我們意識到這與您從其他一些軟體供應商聽到的消息不同。

Ultimately, software application development continues even in uncertain environments as customers know they need to continue investing in internally developed software to run the business as well as to drive competitive differentiation. In addition, we still have relatively low market share in a large market, which means we have an opportunity to gain share in any environment.

最終，即使在不確定的環境中，軟體應用程式開發仍會繼續，因為客戶知道他們需要繼續投資內部開發的軟體來經營業務並推動競爭差異化。此外，在一個很大的市場中，我們的市佔率仍然相對較低，這意味著我們在任何環境下都有機會獲得份額。

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 on MongoDB Atlas, leveraging the full power of our developer data platform, including Fanatics, Occidental Petroleum, and Indeed. Fanatics Betting and Gambling, a division of the sports ecosystem company, Fanatics, leverages MongoDB to significantly enhance their user experience of their mobile app.

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、遍布世界各地的客戶都在 MongoDB Atlas 上運行關鍵任務項目，充分利用我們開發人員資料平台的全部功能，包括 Fanatics、Occidental Petroleum 和 Indeed。Fanatics Betting and Gambling 是體育生態系統公司 Fanatics 的一個部門，它利用 MongoDB 顯著增強其行動應用程式的使用者體驗。

Initially, the team launched a platform on Postgres but faced challenges with scalability, flexibility and excessive complexity. After migrating to MongoDB Atlas, the team also integrates Atlas Search to provide users with a better experience to find all available betting options. With Atlas having scaling, partitioning, and operations, developers can focus on writing code and improving the user experience. Looking ahead, Fanatics plans to continue to expand on MongoDB Atlas as they ensure they can operate at scale as they prepare for the start of the NFL season.

最初，該團隊在 Postgres 上推出了一個平台，但面臨著可擴展性、靈活性和過度複雜性的挑戰。遷移到 MongoDB Atlas 後，團隊還整合了 Atlas Search，為用戶提供更好的體驗來尋找所有可用的投注選項。由於 Atlas 具有擴展、分區和操作功能，開發人員可以專注於編寫程式碼和改善使用者體驗。展望未來，Fanatics 計劃繼續擴展 MongoDB Atlas，以確保他們能夠在為 NFL 賽季開始做準備時大規模運作。

L'Oreal, McKesson, and Nationwide Building Society are turning to MongoDB to modernize applications. L'Oreal's Tech Accelerator, a department dedicated to catalyzing digital innovation at L'Oreal is utilizing MongoDB for an application designed to bring products and solutions to market while quickly improving employee efficiency.

歐萊雅 (L'Oreal)、麥克森 (McKesson) 和全美建築協會 (Nationwide Building Society) 正在採用 MongoDB 來實現應用程式的現代化。歐萊雅技術加速器是一個致力於催化歐萊雅數位創新的部門，它利用 MongoDB 開發應用程序，旨在將產品和解決方案推向市場，同時快速提高員工效率。

The team's previous database solution had limited out-of-the-box functionality and was unable to handle the complex calculations needed to retrieve and restructure large amounts of data from their data warehouse. L'Oreal migrated to MongoDB Atlas to streamline the application architecture and simplify a previously highly complex and time-consuming data access layer.

該團隊先前的資料庫解決方案開箱即用的功能有限，無法處理從資料倉儲中檢索和重組大量資料所需的複雜計算。歐萊雅遷移到 MongoDB Atlas 以簡化應用程式架構並簡化先前高度複雜且耗時的資料存取層。

With this migration, L'Oreal achieved a 40-fold performance improvement. On Atlas, the existing code is easier to maintain, more scalable and more efficient, making life easier for developers.

透過此次遷移，歐萊雅實現了 40 倍的效能提升。在 Atlas 上，現有的程式碼更易於維護、更具可擴展性和更有效率，讓開發人員的工作更輕鬆。

Mature companies and startups alike are using MongoDB to help deliver the next wave of AI-powered applications to their customers, including Delivery Hero, Generali, and Questflow. Delivery Hero, a long-time MongoDB Atlas customer is the world's leading local delivery platform, operating in 70-plus countries across four continents. Their quick commerce service enables customers to select fresh produce for delivery from local grocery stores.

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 AI 驅動的應用程序，包括 Delivery Hero、Generali 和 Questflow。Delivery Hero 是 MongoDB Atlas 的長期客戶，也是全球領先的本地配送平台，業務覆蓋四大洲 70 多個國家。他們的快速商務服務使客戶能夠從當地雜貨店選擇新鮮農產品進行配送。

Approximately 10% of the inventory is fast-moving perishable produce that can go quickly out of stock. The company risk losing revenue and increasing customer churn if the customer didn't have viable alternatives to their first choice.

大約 10% 的庫存是快速流通的易腐爛產品，可能很快就會缺貨。如果客戶沒有可行的替代選擇，公司就會面臨收入損失和客戶流失的風險。

To address these risks, they are now using state-of-the-art AI models and MongoDB Atlas Vector search to give hyperpersonalized alternatives to customers in real time if items they want to order are out of stock. With the introduction of MongoDB Atlas Vector search, the data science team recognized that they could build a highly performant, real-time solution more quickly and for less cost than alternative technologies.

為了應對這些風險，他們現在使用最先進的人工智慧模型和 MongoDB Atlas Vector 搜索，如果客戶想要訂購的商品缺貨，則實時為客戶提供超個性化的替代方案。隨著 MongoDB Atlas Vector 搜尋的推出，資料科學團隊意識到，他們可以比其他技術更快、更低成本地建立高效能即時解決方案。

In summary, we had a healthy Q2 with both Atlas and EA exceeding expectations. We saw a strong new business quarter and improved sales productivity, and we are confident in our ability to keep winning new business in the second half of the year. Looking forward, we see a great opportunity to help our customers modernize legacy applications and build the next generation of AI-powered applications.

總而言之，我們第二季度的業績表現良好，Atlas 和 EA 都超乎預期。我們看到了強勁的新業務季度和提高的銷售效率，我們有信心在下半年繼續贏得新業務。展望未來，我們看到了一個巨大的機會，可以幫助我們的客戶實現傳統應用程式的現代化，並建立下一代人工智慧應用程式。

With that, here's Michael.

以下是麥可的發言。

Thanks, Dave. I'll begin with a detailed review of our second-quarter results and then finish with our outlook for the third quarter and full fiscal year 2025. First, I'll start with our second-quarter results. Total revenue in the quarter was $478.1 million, up 13% year over year and above the high end of our guidance.

謝謝，戴夫。我將首先詳細回顧我們的第二季業績，然後介紹我們對第三季和 2025 財年全年的展望。首先，我先介紹一下我們的第二季業績。本季總營收為 4.781 億美元，年增 13%，高於我們預期的最高值。

As a reminder, while we have difficult compares throughout fiscal '25, we are facing a particularly difficult year-over-year comparison in Q2, given we had a number of large multiyear partnership licensing deals in Q2 last year.

提醒一下，雖然我們在整個 25 財年都很難進行比較，但考慮到去年第二季度我們達成了許多大型多年期合作許可協議，我們在第二季度面臨的同比比較尤其困難。

Under 606 accounting rules, we recognize the license component upfront, which creates a difficult compare a year later. Shifting to our product mix, let's start with Atlas. Atlas grew 27% in the quarter compared to the previous year and now represents 71% of the total revenue compared to 63% in the second quarter of fiscal 2024 and 70% last quarter.

根據 606 會計規則，我們預先確認許可證部分，這使得一年後難以進行比較。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了 27%，目前佔總營收的 71%，而 2024 財年第二季為 63%，上個季度為 70%。

We recognize Atlas revenue primarily based on customer consumption of our platform, and that consumption is closely related to end-user activity of their applications. Let me provide some context on Atlas consumption in the quarter.

我們確認 Atlas 收入主要基於客戶對我們平台的消費，並且該消費與最終用戶對其應用程式的活動密切相關。讓我提供一些本季度 Atlas 消費的背景資訊。

First, as a reminder, in Q1, Atlas consumption growth was below our expectations, and we updated our growth assumptions for the remainder of the year. In Q2, Atlas consumption growth was modestly ahead of those updated expectations across the board. While this is encouraging, consumption growth is still below our original forecast from the beginning of the year.

首先，提醒一下，在第一季度，Atlas 消費成長低於我們的預期，我們更新了今年剩餘時間的成長假設。在第二季度，Atlas 的消費成長略高於最新預期。雖然這令人鼓舞，但消費成長仍然低於我們年初的最初預測。

Turning to non-Atlas revenue. non-Atlas came in modestly ahead of our expectations in the quarter as we continue to have success selling incremental workloads into our existing customer base. On a year-over-year basis, non-Atlas revenue was down 13% due to the especially difficult compare I referenced earlier.

轉向非 Atlas 收入。由於我們持續成功向現有客戶群銷售增量工作負載，本季非 Atlas 業務的業績略高於我們的預期。與去年同期相比，非 Atlas 業務收入下降了 13%，原因是我之前提到的比較特別困難。

Turning to customer growth. During the second quarter, we grew our customer base by approximately 1,500 customers sequentially, bringing our total customer count to over 50,700, which is up from over 45,000 in the year-ago period. Of our total customer count, over 7,300 are direct sales customers, which compares to over 6,800 in the year ago period.

轉向客戶成長。在第二季度，我們的客戶群環比增加了約 1,500 名客戶，使我們的總客戶數量超過 50,700 名，高於去年同期的 45,000 名。在我們的總客戶數中，超過 7,300 名是直銷客戶，而去年同期數字超過 6,800 名。

The growth in our total customer count is being driven primarily by Atlas which had over 49,200 customers at the end of the quarter compared to over 43,500 in the year ago period. It is important to keep in mind that growth in our Atlas customer count reflects new customers to MongoDB in addition to existing EA customers adding incremental Atlas workloads.

我們總客戶數量的成長主要得益於 Atlas 的成長，截至本季末，Atlas 的客戶數量超過 49,200 名，而去年同期的客戶數量超過 43,500 名。重要的是要記住，除了現有 EA 客戶增加 Atlas 工作負載外，我們的 Atlas 客戶數量的成長也反映了 MongoDB 的新客戶。

Continuing on. In Q2, our net AR expansion rate was approximately 119%. The decline versus historical periods is attributable to a smaller contribution from expanding customers. We ended the quarter with 2,189 customers with at least $100,000 in ARR and annualized MRR, up from 1,855 in the year ago period.

繼續。第二季度，我們的淨應收帳款擴張率約為119%。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 2,189 名 ARR 和年化 MRR 至少 100,000 美元的客戶，高於去年同期的 1,855 名。

Moving down the income statement, I'll be discussing our results on a non-GAAP basis unless otherwise noted. Gross profit in the second quarter was $360.8 million, representing a gross margin of 75%, which is down from 78% in the year ago period. Our year-over-year margin decline is primarily driven by a lower mix of high-margin upfront license revenue compared to last Q2 as well as Atlas growing as a percentage of the overall business.

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。第二季毛利為3.608億美元，毛利率為75%，低於去年同期的78%。我們的利潤率同比下降，主要是由於與去年第二季度相比，高利潤的前期許可收入佔比下降，以及 Atlas 佔整體業務的比例增長。

Our income from operations was $52.5 million or an 11% operating margin for the second quarter compared to a 19% margin in the year ago period. The primary reason for a more favorable operating income results versus guidance is our revenue outperformance.

我們第二季的營業收入為 5,250 萬美元，營業利益率為 11%，而去年同期的利潤率為 19%。營業收入結果優於預期的主要原因是我們的收入表現優異。

In addition, Q2 operating income benefited from the timing of certain marketing and other spend which we now expect to incur in the second half of the year. Net income in the second quarter was $59 million or $0.70 per share based on 83.8 million diluted weighted average shares outstanding. This compares to a net income of $76.7 million or $0.93 per share on 82.5 million diluted weighted average shares outstanding in the year ago period.

此外，第二季的營業收入受益於某些行銷和其他支出的時機，我們目前預計這些支出將在今年下半年發生。根據 8,380 萬股稀釋加權平均流通股計算，第二季淨收入為 5,900 萬美元，即每股 0.70 美元。相比之下，去年同期的淨利潤為 7,670 萬美元，即每股 0.93 美元，攤薄加權平均流通股數為 8,250 萬股。

Turning to the balance sheet and cash flow. We ended the second quarter with $2.3 billion in cash, cash equivalents, short-term investments and restricted cash. Operating cash flow in the second quarter was negative $1.4 million. After taking into consideration approximately $2.6 million in capital expenditures and principal repayments of finance lease liabilities, free cash flow was negative $4 million in the quarter.

轉向資產負債表和現金流。截至第二季度，我們的現金、現金等價物、短期投資和受限現金總額為 23 億美元。第二季經營現金流為負140萬美元。考慮到約 260 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負 400 萬美元。

This compares to free cash flow of negative $27.3 million in the year ago period. In addition, this quarter, we also received $170.6 million in cash from the settlement of the CAP calls associated with our 2024 convertible notes. I'd now like to turn to our outlook for the third quarter and full fiscal year 2025.

相比之下，去年同期的自由現金流為負 2,730 萬美元。此外，本季我們也從與 2024 年可轉換票據相關的 CAP 催繳結算中獲得了 1.706 億美元現金。現在，我想談談我們對 2025 年第三季和全年財年的展望。

For the third quarter, we expect revenue to be in the range of $493 million to $497 million. We expect non-GAAP income from operations to be in the range of $57 million to $60 million and non-GAAP net income per share to be in the range of $0.65 to $0.68 based on 84.6 million estimated diluted weighted average shares outstanding.

我們預計第三季的營收將在 4.93 億美元至 4.97 億美元之間。我們預計非公認會計準則下的營業利潤將在 5,700 萬美元至 6,000 萬美元之間，非公認會計準則下的每股淨利潤將在 0.65 美元至 0.68 美元之間（基於預計的 8,460 萬股稀釋加權平均流通股）。

For the full fiscal year 2025, we expect revenue to be in the range of $1.92 billion to $1.93 billion, non-GAAP income from operations to be in the range of $187 million to $195 million and non-GAAP net income per share to be in the range of $2.33 to $2.47 based on 84.3 million estimated diluted weighted average shares outstanding.

對於 2025 財年全年，我們預計收入在 19.2 億美元至 19.3 億美元之間，非 GAAP 營業收入在 1.87 億美元至 1.95 億美元之間，非 GAAP 每股淨利潤在 2.33 美元至 2.47 美元之間（基於 8430 萬股預計加權平均流通股）。

Note that the non-GAAP net income per share guidance for the third quarter and full fiscal year 2025 includes a non-GAAP tax provision of approximately 20%. I'll now provide some context around our updated guidance.

請注意，2025 財年第三季和全年非 GAAP 每股淨收入指引包括約 20% 的非 GAAP 稅務準備金。我現在將提供有關我們更新指南的一些背景資訊。

First, because of the stronger-than-expected Atlas consumption in Q2, our starting Atlas ARR for the back half of the year is higher than we anticipated in May. As a result, we're raising our Atlas revenue forecast to reflect that higher starting point. We are not changing our underlying Atlas Q3 and Q4 consumption growth rate assumptions.

首先，由於第二季 Atlas 消費量強於預期，我們下半年的起始 Atlas ARR 高於我們 5 月的預期。因此，我們上調了 Atlas 收入預測，以反映更高的起點。我們不會改變 Atlas 第三季和第四季的消費成長率假設。

Second, we are slightly increasing our EA assumptions for the rest of the year to reflect the strength of the second half EA pipeline.

其次，我們稍微提高了今年剩餘時間的 EA 假設，以反映下半年 EA 通路的實力。

Finally, thanks to the performance in Q2 and the increased revenue outlook, we now expect a 10% operating margin at the midpoint of our fiscal '25 guidance. We will continue investing to capture our long-term opportunity with a focus on our strategic priorities.

最後，由於第二季的業績和收入前景的成長，我們目前預計 25 財年指引中位數的營業利潤率將達到 10%。我們將繼續投資，抓住長期機遇，並專注於我們的策略重點。

Separately, although we don't provide guidance on cash flow, we wanted to call out two items that will impact fiscal '25 cash flow but benefit our long-term financial profile. In Q2, we started paying some of our cloud provider commitments upfront in exchange for better economics.

另外，雖然我們不提供現金流的指導，但我們希望指出兩項將影響 25 財年現金流但有利於我們長期財務狀況的事項。在第二季度，我們開始預付部分雲端供應商的承諾，以換取更好的經濟效益。

We see this as a very low risk, high ROI use of our cash and one that will benefit our gross margin going forward. These prepayments will represent a negative impact to our operating cash flow in the back half of the year of roughly $20 million per quarter. In addition, in Q3, we will incur approximately $20 million to $25 million of CapEx to acquire IPv4 addresses, which will allow us to further reduce our cloud infrastructure costs in the future.

我們認為這是一種風險極低、投資報酬率極高的現金使用方式，並且有利於提高我們未來的毛利率。這些預付款將對我們下半年的營運現金流產生負面影響，每季約 2,000 萬美元。此外，在第三季度，我們將花費約 2,000 萬至 2,500 萬美元的資本支出來取得 IPv4 位址，這將使我們能夠在未來進一步降低雲端基礎設施成本。

To summarize, we're pleased with our second quarter results and especially our ability to win new business. We have a small share, one of the largest and fastest-growing markets in all of software. The secular tailwinds at our back are getting stronger in the age of AI, and we're excited about the future. We'll continue investing judiciously and focusing on our execution to capture this long-term opportunity.

總而言之，我們對第二季的業績以及我們贏得新業務的能力感到滿意。我們佔有一小部分份額，但卻是整個軟體領域最大且成長最快的市場之一。在人工智慧時代，我們背後的長期順風越來越強勁，我們對未來充滿興奮。我們將繼續明智地投資並專注於執行，以抓住這一長期機會。

With that, we'd like to open it up to questions. Operator?

現在，我們想開始回答問題。操作員？

(Operator Instructions) Sanjit Singh, Morgan Stanley.

（操作員指示） 摩根士丹利的 Sanjit Singh。

Hi, thank you for taking the questions and congrats on Q2. Nicely, Mongo, beating and raising again. Dave, I wanted to follow up on some of the operational changes you're making to the business that you announced last quarter.

你好，感謝您回答問題，並祝賀您獲得第二季度的成績。很好，蒙哥，再次擊敗並加註。戴夫，我想跟進一下您上個季度宣布的針對業務所做的一些營運變革。

Just in terms of the update in terms of realigning some of the sales incentives to drive higher workload quality and while maintaining some of that record workload growth that you saw last year. Could you walk us through what the changes were that you put in place? And how much confidence do you have that you will drive better workload growth as we get into early next year?

就更新而言，我們重新調整了一些銷售激勵措施，以提高工作量質量，同時保持去年創紀錄的工作量成長。您能否向我們介紹一下您實施了哪些變革？當我們進入明年年初時，您對推動工作量成長有多大信心？

Yes, sure. So first of all, thanks, Sanjit. Just a reminder for everyone, we made some slight incentive comp changes to really get our reps to focus to have a little bit more balance on size versus volume of workloads acquired. These changes were well received by the field. we had a good workload quarter, as you just described.

是的，當然。首先，感謝 Sanjit。只是提醒大家，我們對激勵補償進行了一些細微的調整，以便讓我們的銷售代表真正集中精力在所獲得的工作量規模與數量之間取得更多的平衡。這些變化受到了業界的一致好評。正如您剛才所描述的，我們本季的工作量很好。

Workloads start small so it's too early. While we're pleased with our results, obviously, it's too early to declare victory and to really see if this is going to have a material impact on changes. But clearly, we're happy with what we've seen so far.

工作量一開始就很少，所以還太早。雖然我們對結果感到滿意，但顯然現在宣布勝利還為時過早，也無法真正看出這是否會對變化產生實質影響。但顯然，我們對目前所看到的情況感到滿意。

And then as a follow-up for Michael. I remember at the Q4 last year, you gave us the update on the unused credit dynamics. As we start to think about the back half of this year, is there anything else that we should think about that? I mean you just mentioned some of the cash flow impacts just now. Is there anything on like the revenue side of the equation that we should keep in mind?

然後作為對邁克爾的後續行動。我記得去年第四季度，您向我們提供了未使用信用動態的最新情況。當我們開始考慮今年下半年時，還有什麼事情我們該考慮嗎？我的意思是您剛才提到了一些現金流的影響。在收入方面，我們應該記住什麼嗎？

I noticed at the end of this year, there's just more holiday days in the quarter in Q4, does that have an impact? Anything that you would like to call out in terms of our modeling when we think about expectations for Atlas more broadly going into the end of the year?

我注意到今年年底，第四季的假期更多了，這會造成影響嗎？當我們更廣泛地考慮 Atlas 年底的預期時，您想就我們的模型提出什麼建議？

Yes. I think there are a few things to take into account that have been throughout the year, but of course, are reflected in the updated guide. Specifically on Atlas, throughout the year, we have tough compares given the headwinds from the back of the unused commitments. That's obviously hardest. As the year goes on, that number builds.

是的。我認為有一些事情需要考慮，這些事情已經存在一年多了，但當然，這些都反映在更新的指南中。尤其是對於 Atlas，由於未使用的承諾所帶來的阻力，我們全年都面臨著艱難的比較。這顯然是最難的。隨著時間的推移，這個數字不斷增加。

So that makes for a tough year-over-year comparison. I think the other thing to think about in the context of Atlas is the ending year dynamic is really a compounding over the course of the year, as we talked about at the beginning of the year, we got off to a slower start and some of the prior year workloads, those cohorts were growing at slower rates.

因此，進行同比對比非常困難。我認為在 Atlas 的背景下要考慮的另一件事是，年末的動態實際上是全年的複合增長，正如我們在年初所討論的那樣，我們的起步較慢，而且一些前一年的工作量、那些群體的增長速度較慢。

And so that does affect and compound over the course of the year. And so I think that's important to keep in mind is factored and is factored into our guidance. And then looking at the revenue picture more broadly, moving away from Atlas and thinking about EA, we've had throughout the year a headwind on EA and we'll have that as a multiyear headwind in the back half as well.

所以這確實會產生影響，並且會在一年內加劇。因此，我認為重要的是要記住這一點，並將其納入我們的指導中。然後更廣泛地看一下收入情況，除了 Atlas 之外，我們還要考慮 EA，我們全年都面臨著 EA 的阻力，並且在下半年我們還會面臨多年的阻力。

The only other thing I think I would call out is we saw a slower seasonal rebound in Q1, and so we're expecting that slower seasonal rebound to occur in Q3 as well. And then as you mentioned, Q4 is a seasonally weaker quarter. And so all those are some of the considerations to think about when you're thinking about the rest of the year.

我認為我要指出的另一件事是，我們在第一季看到了較慢的季節性反彈，因此我們預計第三季也會出現較慢的季節性反彈。正如您所說，第四季度是季節性較弱的季度。所以，這些都是你在考慮今年剩餘時間的事情時需要考慮的一些事情。

Appreciate that color. Thank you.

欣賞那種顏色。謝謝。

Raimo Lenschow, Barclays.

巴克萊銀行的 Raimo Lenschow。

Hey, thank you. Two quick questions from me. Michael, on the EA, you talked about like a slightly better pipeline in the second half. Can you talk a little bit -- is that still the continuation of what we saw in Q2, Q3 last year where people wanted to modernize their self-service footprint ahead of the move to the cloud or what's the dynamic there?

嘿，謝謝你。我要問兩個簡單問題。邁克爾，在 EA 上，你談到了下半部分的管道稍微好一點。您能否稍微談談——這是否仍然是我們去年第二季度和第三季度看到的情況的延續，當時人們希望在遷移到雲端之前實現自助服務足跡的現代化，或者那裡的動態是什麼？

And then one for Dave, like -- there's a big debate about like reference architecture on AI. And I think it's a little bit too early, but it comes up with investors a lot like what are you seeing there? I know AI infrastructure, we're still in the early innings, but what are you seeing there in terms of people engaging with you? Thank you.

然後是戴夫的問題，關於人工智慧的參考架構存在很大爭議。我覺得現在還為時過早，但它已經引起了很多投資者的關注，就像您所看到的一樣？我知道人工智慧基礎設施還處於早期階段，但從人們的參與程度來看，您看到了什麼？謝謝。

Thanks, Raimo. On the EA question, I would say there are lots of reasons why we're seeing strength of EA. I wouldn't uniquely or exclusively tie it to AI, I think it's broadly in support and shows the strength of the run anywhere strategy.

謝謝，Raimo。關於 EA 的問題，我想說有很多原因可以解釋為什麼我們看到 EA 的實力。我不會將其與人工智慧獨特或排他性地聯繫起來，我認為它廣泛地支援並展示了隨處運行策略的實力。

The other thing that I'd call out is given the 606 dynamics as it relates to EA, we're always sensitive to the multiyear aspect, and that's what's provided given the strength in multiyear in fiscal '24, some of the tough compares here in fiscal '25. I think what we've seen in terms of the pipeline is strength in EA broadly.

我要指出的另一件事是，鑑於 606 與 EA 相關的動態，我們始終對多年期方面很敏感，而這正是考慮到 24 財年多年期的強勁表現，以及 25 財年的一些艱難比較所提供的。我認為，就通路而言，我們看到 EA 整體上實力雄厚。

So not just a multiyear phenomenon, but really a volume phenomenon as well that we're seeing we look out on the horizon, and that is reflected in the updated guide.

因此，這不僅是一個多年的現象，而且實際上也是一個我們看到的規模現象，這在更新後的指南中有所反映。

Thanks. And Raimo, on the question about AI, I think in terms of reference architectures, I think it's important to understand, and I said this in the prepared remarks, is that unlike most of the workloads, AI-driven workloads really require the underlying database to be capable of processing queries against very rich and complex data structures both quickly and efficiently. And MongoDB is well positioned to do that. We can basically unify and handle source data, vector data, metadata, generated data from your LLM right alongside your live operational data.

謝謝。Raimo，關於人工智慧的問題，我認為就參考架構而言，重要的是要理解，我在準備好的發言中說過，與大多數工作負載不同，人工智慧驅動的工作負載確實需要底層資料庫能夠快速有效地處理針對非常豐富和復雜的資料結構的查詢。MongoDB 完全有能力做到這一點。我們基本上可以統一並處理來源資料、向量資料、元資料、來自您的 LLM 的生成資料以及您的即時操作資料。

And then as the performance of these LLMs and latency of these LLMs increase, accessing real-time data becomes really important, like say, you're calling and talking to a customer support chatbot that you want that chatbot to have up-to-date information about that customer so that they can provide the most relevant and accurate information possible. There are some questions about LLMs, whether a general-purpose LLM or a fine-tune LLM, what the trade-offs are.

然後，隨著這些 LLM 的性能和延遲的增加，訪問實時數據變得非常重要，比如說，您正在呼叫並與客戶支持聊天機器人交談，您希望該聊天機器人擁有關於該客戶的最新信息，以便他們可以提供盡可能最相關和最準確的信息。有一些關於LLM的問題，無論是通用LLM還是精細LLM，其權衡是什麼。

Our belief is that given the performance of LLMs, you're going to see the general purpose LLMs probably win and we'll use RAG as the predominant approach to marry generally available data with proprietary data.

我們相信，鑑於 LLM 的表現，您將看到通用 LLM 可能會獲勝，並且我們將使用 RAG 作為主要方法，將普遍可用的數據與專有數據結合。

And then you are starting to see things like advanced RAG use cases where you get much more sophisticated ways to ask complex questions, provide more accurate and detailed answers and better adapt to different types of information inquiries. And so that's what we're seeing. I think it's a quickly evolving space, but we feel very good about our positioning for AI, even though it's still very early days.

然後您會開始看到類似高級 RAG 用例的東西，您可以透過這些用例以更複雜的方式提出複雜的問題，提供更準確、更詳細的答案，並更好地適應不同類型的信息查詢。這就是我們所看到的。我認為這是一個快速發展的領域，但我們對自己在人工智慧方面的定位非常滿意，儘管它還處於早期階段。

Okay. Thank you.

好的。謝謝。

Kash Rangan, Goldman Sachs.

高盛的卡什·蘭根 (Kash Rangan)。

That's perfect. I was about to call Raimo and say, can you please explain reference architectures, and I'll buy you a nice glass of wine. So hopefully, he'll take me up on that offer because that was a very technical question. But anyway, coming back to the call here, Dave, a question for you. Why is EA doing so well? So you talked about a pipeline in the second half of the year.

那很完美。我正要打電話給 Raimo 說，你能解釋一下參考架構嗎，我想請你喝杯好酒。所以希望他能接受我的提議，因為這是一個非常技術性的問題。但無論如何，回到這裡的通話，戴夫，我問你一個問題。EA 為何表現如此出色？所以您談到了下半年的管道。

Last year, you had some significant wins for EA. We were supposed to be on this cloud journey. So definitely, Atlas has reached parity in many senses. It can support big scale applications, whatnot. So curious to get your thoughts on why is EA still an important piece of the business and I suppose when you look at AI, could something surprise us?

去年，你們為 EA 取得了一些重大勝利。我們本應踏上這趟雲端之旅。因此可以肯定的是，Atlas 在許多方面已經達到了同等水平。它可以支援大規模應用程式等等。我很好奇，想知道您對為什麼 EA 仍然是業務的重要組成部分的看法，我想當您關注 AI 時，可能會有什麼讓我們感到驚訝的事情嗎？

I know that you've had this view that we're building infrastructure first and then the platform than applications. Could it be by any chance, a different way to approach AI in this cycle and that we don't really need applications, but somehow these LLMs are going to be a perfect replacement for the way we think about old world applications? Just curious to see what the devil's advocate you might be somewhat very skeptical of the whole AI applications build out on top of the infrastructure and platform. Thank you so much.

我知道你有這樣的觀點：我們先建立基礎設施，然後建立平台，然後再建立應用程式。是否有可能，在這一周期中，我們有另一種方法來處理人工智慧，而且我們並不真正需要應用程序，但這些 LLM 卻可以完美地取代我們對舊世界應用程式的思考方式？只是好奇地想看看，作為魔鬼的擁護者，您可能會對在基礎設施和平台之上構建的整個人工智慧應用程式持懷疑態度。太感謝了。

Yes. So thanks, Kash. What I would just say is we did have a better-than-expected both Atlas and EA quarter. There's no question about that. I don't want to suddenly say there's some inflection point on EA in our business.

是的。所以謝謝你，Kash。我只想說，Atlas 和 EA 本季的業績確實好於預期。這是毫無疑問的。我不想突然說我們的業務在 EA 方面出現了一些轉折點。

I think I would really attribute it to, one, better execution, and two, customers do really appreciate our run anywhere strategy. There's still lots of customers, either for regulatory reasons or other reasons who want to run workloads on-premise and they're not going away.

我認為我應該將其歸功於：第一，更好的執行；第二，客戶確實欣賞我們的隨處運行策略。仍然有很多客戶，無論是出於監管原因還是其他原因，想要在本地運行工作負載，而且他們不會離開。

I mean, we've been in this cloud journey for 10 years and some workloads are just very hard to move to the cloud or some workloads for many customers don't make sense to work -- move to the cloud, at least not anytime soon. So the fact that they can build it on MongoDB and have the optionality to move it to the cloud later very easily is something that's very compelling for customers.

我的意思是，我們已經在雲端運算之旅中走過了 10 年，有些工作負載很難遷移到雲端，或者有些工作負載對許多客戶來說根本沒必要遷移到雲端，至少短期內不會。因此，他們可以在 MongoDB 上建立它，並且可以選擇稍後非常輕鬆地將其移動到雲端，這一事實對客戶來說非常有吸引力。

With regards to AI, I mean, we predominantly see most of the AI workloads in the cloud, but there are definitely lots of customers who are looking at using open source LLMs in particular, things like Llama and running those workloads locally.

關於人工智慧，我的意思是，我們主要看到雲端的大多數人工智慧工作負載，但肯定有很多客戶正在考慮使用開源 LLM，特別是 Llama 之類的東西，並在本地運行這些工作負載。

Obviously, it means that they have also have to have access to NVIDIA hardware like GPUs but we do see some customers do that. Again, I wouldn't suggest that, that's also an inflection point or cause for the EA outperformance. I think it's very, very early days and most of those are experiments that customers are running. But clearly, we feel really good about run anywhere strategy.

顯然，這意味著他們還必須能夠使用 GPU 等 NVIDIA 硬件，但我們確實看到一些客戶這樣做。再說一次，我不會建議這樣做，這也是 EA 表現優異的轉捩點或原因。我認為現在還處於非常早期的階段，其中大多數只是客戶正在進行的實驗。但顯然，我們對隨處運作策略感到非常滿意。

And as we've said in the past, we are investing in basically introducing search and vector search to our community product and that will then ensure up in EA. So EA is definitely an area where we're also investing from a product point of view.

正如我們過去所說的那樣，我們正在投資將搜尋和向量搜尋引入我們的社群產品中，然後將確保在 EA 中實現。因此，從產品角度來看，EA 絕對是我們投資的一個領域。

Thank you.

謝謝。

Tyler Radke, Citi.

花旗銀行的泰勒拉德克（Tyler Radke）。

This is Tyler, that geared I think the question is from you can name, but thanks for taking the question. Dave, you talked about a Postgres displacement in the quarter. And I think that's the first time you've talked about that at least in recent quarters. So I was wondering if you could just frame for us what use cases do you come across them?

我是泰勒，我認為這個問題是你提出來的，但還是感謝你提出這個問題。戴夫，您談到了本季的 Postgres 位移。我認為這是至少最近幾季你第一次談論這個問題。所以我想知道您是否可以為我們描述一下您遇到的用例？

I know it is popular in terms of a sequel displacement for migrating out of legacy applications. But if you could just frame for us the competitive environment in Postgres both the open source stuff and some of the new venture-backed start-ups in the space.

我知道它作為一種用於從遺留應用程式遷移出來的後續替代產品而頗受歡迎。但是如果您可以為我們描述 Postgres 中的競爭環境，包括開源產品和該領域的一些新的風險投資支持的新創公司。

Yes, it's important to understand that Postgres has been around for almost 40 years. I mean it's -- Postgres, the name has turned from post ingress. So that technology has been around a long time. As you said, they're really the beneficiary of lift and shift from Oracle, SQL Server, and MySQL, so they're consolidating the relational market. In terms of why do we compete or why do we win?

是的，了解 Postgres 已經存在近 40 年了，這一點很重要。我的意思是 —— Postgres，這個名字已經從 post ingress 轉變而來。所以這項技術已經存在很久了。正如您所說，他們實際上是 Oracle、SQL Server 和 MySQL 升級和轉變的受益者，因此他們正在整合關係市場。我們為什麼要競爭或為什麼我們會贏？

I would say it's a few things. One, our scheme of flexibility. MongoDB has a very flexible scheme allowing you to store documents in a JSON-like format. So this is beneficial for application structures that evolve over time. We can horizontally scale so we're making it very easy to distribute data across multiple servers or virtual servers for applications that require massive amounts of data.

我想說有幾件事。第一，我們的方案靈活。MongoDB 具有非常靈活的方案，可讓您以類似 JSON 的格式儲存文件。這對於隨著時間推移而發展的應用程式結構是有益的。我們可以水平擴展，因此對於需要大量資料的應用程序，我們可以輕鬆地將資料分佈在多個伺服器或虛擬伺服器之間。

Performance of large data sets. Again, we can handle that better than Postgres. The built-in shortings allows for automatic data distribution. And then we also, I think, developer productivity, that JSON-like format and flexible schema can lead to faster development cycles, especially for customers who really work in agile environments. So we feel like we compete, our win rates against Postgres are high.

大型資料集的效能。再次，我們可以比 Postgres 更好地處理這個問題。內建短路可實現自動資料分配。然後，我還認為，開發人員的生產力，類似 JSON 的格式和靈活的模式可以縮短開發週期，特別是對於真正在敏捷環境中工作的客戶而言。所以我們感覺我們在競爭，我們與 Postgres 的勝率很高。

But again, there's lots of decisions being made where we're not party to where people are just doing a lift and shift of legacy platform or they just want to stay on relational because that's what they know. And obviously, it's our job to educate them on the benefits of MongoDB, but we feel good about our competitive position against Postgres.

但同樣，我們並不是所做的許多決定的一方，人們只是在對傳統平台進行提升和轉變，或者他們只是想維持關係，因為這是他們所知道的。顯然，我們的工作是讓他們了解 MongoDB 的優勢，但我們對自己與 Postgres 的競爭地位感到滿意。

Thank you. And a follow-up question for Michael. You talked about how consumption in Atlas in the quarter tracked a bit better than planned. It sounds like it was not a macro-related improvement. What do you think the driver of that was? And are you seeing any improvement in some of the recently acquired workloads, those starting to ramp up better?

謝謝。還有一個後續問題要問麥可。您談到了本季度 Atlas 的消費情況比計劃要好一些。這聽起來不像是個與宏觀相關的改進。您認為造成這現象的原因是什麼？您是否看到最近獲得的一些工作量有所改善，這些工作量開始更好地增加？

Or maybe they have different seasonal patterns than you thought. Any color on that driver of outperformance would be helpful. Thank you.

或者它們的季節模式可能與你想像的不一樣。任何有關推動業績優異的具體資訊都會有所幫助。謝謝。

Yes, sure. I would say that, yes, Q2 consumption growth was better than our expectations. That was great to see. I would describe it as within a reasonable or typical range of outcomes. And so there are no signs that we've seen that would specifically point to anything, any material changes in the underlying macroeconomic environment, better or worse.

是的，當然。我想說，是的，第二季的消費成長比我們的預期好。看到這個真是太好了。我會將其描述為在合理或典型的結果範圍內。因此，我們還沒有看到任何跡象表明宏觀經濟環境會發生任何實質變化，無論是好轉還是惡化。

We've certainly seen -- to your question on the workloads and some of the cohorting, we did see a little better performance there as well and in line with what we saw elsewhere, but they're still below our original expectations. So I think that's probably the key couple of things there.

我們當然看到 - 對於您關於工作量和一些群組的問題，我們確實看到了那裡的表現略有改善並且與我們在其他地方看到的一致，但仍低於我們最初的預期。所以我認為這可能是其中的幾個關鍵點。

Thank you.

謝謝。

Brad Reback, Stifel.

布拉德·雷巴克（Brad Reback），Stifel。

Great. Thanks very much. Dave, on your commentary around the modernization pilots, how should we think about timing of that being a bit of a tailwind to the overall growth rate?

偉大的。非常感謝。戴夫，針對您對現代化試點的評論，我們應該如何看待這對整體成長率產生順風作用的時機？

Yes. So again, just to make sure everyone understands, the legacy relational application market or database market is quite large, it's over [$80 billion], and it's a massive opportunity for us. And since day one, since our IPO, we've been getting customers to migrate off relational to MongoDB. But one of the biggest friction points has been that while it's easy to move the data, you can map the schema from a relational schema to a document schema and you can automate that.

是的。所以，我再說一遍，為了確保每個人都理解，傳統的關係應用程式市場或資料庫市場相當大，規模超過 800 億美元，這對我們來說是一個巨大的機會。從我們上市的第一天起，我們就讓客戶從關聯式資料庫遷移到 MongoDB。但最大的障礙之一是，雖然行動數據很容易，但您可以將模式從關係模式對應到文件模式，並且可以自動執行該操作。

The biggest stumbling buck is that the customer or some third party has to rewrite the application, which, by definition, creates more costs, more time and in some cases, more risk especially for older apps, where the development teams who built those apps no longer exist.

最大的問題是客戶或第三方必須重寫應用程序，而這必然會產生更多的成本和時間，在某些情況下還會產生更多風險，特別是對於較舊的應用程式而言，因為構建這些應用程式的開發團隊已不復存在。

So what's been compelling about AI is that AI has finally created a shortcut to overcome that big hurdle. And so essentially, you can start basically diagnosing the code, understand the code, recreate a modern version of that code and generate test suites to make sure the new code performs like the old code.

人工智慧引人注目的地方在於它終於創造了一條捷徑來克服這一巨大障礙。因此，本質上，您可以開始基本診斷代碼、理解程式碼、重新建立該程式碼的現代版本並產生測試套件，以確保新程式碼的效能與舊程式碼相同。

So that definitely gets people's interest because now all of a sudden, what may take years or multiyears, you can do in a lot less time. And the pilots that we have done the time and cost savings have been very, very compelling. That being said, we're in the very early days.

這肯定會引起人們的興趣，因為現在突然之間，原本需要幾年甚至幾年才能完成的事情，可以在更短的時間內完成。而且我們進行的試點證明，節省的時間和成本非常有吸引力。話雖如此，我們還處於早期階段。

There's a lot of interest. We have a growing pipeline of customers across, frankly, all parts of the world, from North America to EMEA and even the PacRim. And so we're quite excited about the opportunity. But again, I would say it's very early days. But there's a number of reasons why I would say that customers are very focused on this.

人們對此很感興趣。坦白說，我們的客戶群遍布世界各地，從北美到歐洲、中東和非洲，甚至環太平洋地區。因此我們對這個機會感到非常興奮。但我還是要說，現在還太早。但我認為客戶非常關注這一點有幾個原因。

What the cost of licensing and maintaining legacy apps, is it becoming too high to bear? In many cases, the regulatory and compliance requirements are forcing customers to upgrade. There's a whole end-of-life of critical technologies, notably side-based as forcing customers to act.

許可和維護遺留應用程式的成本是否高得難以承受？在許多情況下，監管和合規要求迫使客戶升級。關鍵技術的整個生命週期都會終結，特別是基於迫使客戶採取行動的技術。

There's a ton of technical depth on these legacy platforms that limit the organizational flexibility to do things with AI. And candidly, customers have also soured on the traditional approach of using large systems integration projects that are very costly and take a long time.

這些傳統平台上有大量的技術深度，限制了組織使用人工智慧的靈活性。坦白說，客戶也對傳統的使用大型系統整合專案的方法感到厭惡，因為這種方法成本高昂，耗時長。

So this whole approach is definitely getting their attention.

所以整個方法肯定會引起他們的注意。

That's great. And then one last follow-up. It feels like we've spoken about EA more than typical on this call, which is great. As we think about the back half pipeline, is the composition similar in so much as it's predominantly existing customers? Or are you beginning to see an uptick in net new customers there as well? Thanks.

那太棒了。然後是最後一次跟進。感覺我們在這次電話會議中談論 EA 的次數比平常多，這很好。當我們考慮後半部分管道時，其組成是否相似，因為其主要都是現有客戶？或者您是否也開始看到那裡淨新客戶數量的上升？謝謝。

Yes. I would say that it's predominantly existing customers that are doing this, who are maybe doubling down on EA and expanding the footprint to EA. And that's typically the driver for our EA business.

是的。我想說，主要是現有客戶在這樣做，他們可能會加倍投資 EA，並將業務範圍擴大到 EA。這通常是我們 EA 業務的驅動力。

Thanks very much. Thank you.

非常感謝。謝謝。

Karl Keirstead, UBS.

瑞銀的卡爾·科爾斯特德（Karl Keirstead）

Okay, great. Hey, Dave, at the beginning of the call, you mentioned that Mongo's slow start to the year was purely operational but on the Q1 call, when you were describing what happened, you described it as very much a broad-based macro issue. So I'm curious, should I interpret that comment as you, Mike and the team have done a bit of a rethink and might be changing modestly at least your explanation about the Q1 results?

好的，太好了。嘿，戴夫，在電話會議開始時，你提到 MongoDB 今年開局緩慢純粹是運營方面的問題，但在第一季度電話會議上，當你描述發生的事情時，你將其描述為一個非常廣泛的宏觀問題。所以我很好奇，我是否應該將該評論解讀為您、麥克和團隊已經進行了一些重新思考，並且可能會至少適度改變您對第一季結果的解釋？

Yes. Actually, just to be clear, I think just to make sure everyone understands, what we called out was that the consumption of existing workloads was broad-based. The slowdown was broad-based across both geos and channels. It was new business, it just got off to a slower start. So that was what we called out in our Q1 call.

是的。實際上，為了明確起見，我認為只是為了確保每個人都理解，我們所指出的是，現有工作負載的消耗是廣泛的。這種放緩是跨地域和跨渠道的廣泛現象。這是一項新業務，只是起步較慢。這就是我們在第一季電話會議上提出的。

And with our Q2 call and results, as you can see, new business performed better as expected than expected on both Atlas and EA, which is why we believe the Q1 new business was an operational issue, not a macro issue. The macro issue was all about the slowdown in consumption because Q1 typically tends to be a seasonally strong quarter for us, and that didn't happen.

透過我們第二季的電話會議和業績，你可以看到，Atlas 和 EA 的新業務表現都優於預期，這也是我們認為第一季新業務是營運問題而不是宏觀問題的原因。宏觀問題在於消費放緩，因為第一季通常對我們來說是一個季節性強勁的季度，但這種情況並沒有發生。

Yes. I think that's really important, Karl, just to keep in mind, when we talk about macro, it could have those two different effects, right? The new business piece and then the expansion of existing workloads. And so what we're talking about, as Dave said in Q1 was the existing workload expansion. The operational piece was on the new business side.

是的。卡爾，我認為這真的很重要，只要記住，當我們談論宏觀時，它可能會產生兩種不同的影響，對嗎？新的業務部分，然後是現有工作量的擴展。正如戴夫在第一季所說的那樣，我們談論的是現有的工作量擴展。營運部分屬於新業務方面。

And I think the key thing that we've observed really in a wide range of macroeconomic conditions in our almost seven years as a public company is with the exception of that one quarter, we've been able to execute quite well in the new business opportunity really in most environments, given that we have a huge market, relatively low share, strong product and a talented team that executes well. And so I think that's probably helpful just to understand as we're using the terms, just to make sure we don't trip ourselves up.

我認為，在我們作為一家上市公司近七年的時間裡，我們在各種宏觀經濟條件下觀察到的關鍵情況是，除了那個季度之外，我們在大多數環境中都能夠在新業務機會中表現得相當好，因為我們擁有巨大的市場、相對較低的份額、強大的產品和一支執行力強的優秀團隊。所以我認為這可能有助於理解我們正在使用的術語，以確保我們不會犯錯。

Okay. That's great. And then my follow-up for you, Mike, is when you were pressed in prior questions about the second half, most of the variables that you brought up were , frankly, headwinds, things like tough compares. But if I look objectively your 3Q total revenue guide of [$497 million], and this is my assumption, but I assume a normal beat.

好的。那太棒了。然後我要跟進的是，麥克，當你在之前的問題中被問及下半年時，你提到的大多數變數坦率地說都是逆風，諸如艱難的比較之類的事情。但如果我客觀地看待您第三季的總收入預期 [4.97 億美元]，這是我的假設，但我認為這是一個正常的成長。

That's going to result in sequential total revenue growth. That's actually the highest, I think, that MongoDB has ever put up. So clearly, there's something good that you're embedding in that 3Q guide. What is that specifically?

這將導致總收入連續成長。我認為這實際上是 MongoDB 有史以來的最高紀錄。因此顯然，您在第 3Q 指南中嵌入了一些好的東西。那具體是什麼？

Yes. So I'm not sure I follow all the math on the fly and I'll happy to follow up, obviously, with any of that. But if I just think about the Q3 guide, I won't belabor the headwinds, but they exist and we've been through those, as you called out. But maybe to talk about the couple of positive things that we see that lead to our improved outlook or increased revenue forecast for the balance of the year. I'd say it's really two things.

是的。因此，我不確定我是否能夠即時理解所有的數學知識，但顯然，我很樂意跟進其中的任何內容。但如果我只考慮第三季指南，我不會詳細闡述阻力，但正如你所說的，阻力確實存在，而且我們已經經歷過了。但也許可以談談我們看到的幾個積極因素，這些因素可以改善我們的前景或增加今年剩餘時間的收入預測。我認為這實際上是兩件事。

In Atlas, it's reflecting the stronger-than-expected Q2 and therefore, the starting ARR at the beginning of Q3 is higher because we haven't seen any change in the underlying macro, the actual growth assumptions for Q3 and Q4, we have remained unchanged. And so you're supplying growth to a higher base, but as the base keeps getting bigger and bigger, that does matter. And that flows through.

在 Atlas 中，它反映了強於預期的第二季度表現，因此，第三季度初的起始 ARR 更高，因為我們沒有看到底層宏觀發生任何變化，對於第三季度和第四季度的實際增長假設，我們保持不變。因此，你是在更高的基礎上提供成長，但隨著基礎越來越大，這一點就很重要了。而且這流暢通無阻。

And then secondly was the positive impact of the increased strength in the EA pipeline for the second half of the year and the impact on that revenue. So those are probably will be the two things that I'd call out on the positive side of the revenue in addition to, of course, having everyone keep in mind the tough compare and some of the headwinds we talked about.

其次是下半年 EA 通路實力增強及其對收入的正面影響。因此，除了讓大家記住我們談到的艱難比較和一些不利因素之外，這些可能是我要指出的關於收入的正面方面的兩個因素。

Okay. That's helpful, Mike. Thanks. Thank you.

好的。這很有幫助，麥克。謝謝。謝謝。

Jason Ader, William Blair.

傑森·阿德，威廉·布萊爾。

Yes, thank you. I'm trying to understand the Q4 implied revenue guide, Michael. Just the implied sequential growth there is only 1.5%. If I look at your full year, relative to what you said specifically on Q3, that would be well below the historical seasonal pattern in Q4. So I just want to understand, is there something going on in Q4 that's different, just sequentials -- I understand the year over year is tough, but the sequential seem well below where you've normally been. And is there something specific that we should think about there?

是的，謝謝。我正在嘗試了解第四季度隱含收入指南，邁克爾。那裡隱含的連續成長率只有 1.5%。如果我看一下您的全年情況，相對於您在第三季具體提到的情況，這將遠低於第四季度的歷史季節性模式。所以我只是想要了解，第四季是否發生了一些不同的事情，只是連續的——我知道同比去年同期很艱難，但連續的業績似乎遠低於正常水平。我們應該思考一些具體的事情嗎？

Yes. I think there are two things. So obviously, it sounds like you get the year-over-year, but just to make sure everyone is on the same page is making sure that people understand the tough compare for Atlas specifically around the unused commitments and then EA, the multiyear. And then more broadly, we had less growth in Q1 that compounds. And then also, we talked about how we're assuming just in the same way that we saw in Q1, we're assuming less of a seasonal rebound in Q3, that has implications for Q4.

是的。我認為有兩件事。因此，很明顯，這聽起來像是你得到的是同比數據，但是為了確保每個人都在同一頁上，就是確保人們了解 Atlas 的艱難比較，特別是圍繞未使用的承諾，然後是 EA，即多年期。更廣泛地說，我們第一季的複合成長率較低。然後，我們還討論了我們如何假設，就像我們在第一季看到的那樣，我們假設第三季的季節性反彈較少，這對第四季有影響。

Got you. Okay. And then just one quick follow-up on gross margins. Did you talk about Atlas gross margins in the quarter? And then how material will the impact be from the prepayments and the IPv4 for purchases on Atlas gross margins over time?

明白了。好的。然後我們再快速跟進毛利率。您談到了 Atlas 本季的毛利率嗎？那麼，隨著時間的推移，預付款和購買 IPv4 對 Atlas 毛利率的影響有多大？

Yes. So we didn't specifically break out Atlas gross margin in the quarter, but they continue to be lower. But obviously, we've pretty significantly shrunk the delta. That does explain some of the comparison on a year-over-year basis. We didn't give specific guidance going forward.

是的。因此，我們沒有具體公佈本季度 Atlas 的毛利率，但其毛利率仍然較低。但顯然，我們已經相當顯著地縮小了差距。這確實解釋了同比的一些比較情況。我們沒有給出未來的具體指導。

If you think about the two changes that we called out on the cash flow side, those will benefit us in terms of gross margin. There won't be a significant benefit in fiscal '25. So we'll obviously talk about that more when we get to the fiscal '26 guide. But it's a very excellent use of our cash and good ROI in terms of improving those economics further.

如果你考慮一下我們在現金流方面提出的兩項變化，那麼這些變化將使我們的毛利率受益。25財年不會有顯著的收益。因此，當我們了解財政 26 指南時，我們顯然會更多地談論這一點。但從進一步改善經濟效益的角度來看，這是對我們現金的絕佳利用，並且獲得了良好的投資回報。

Thank you.

謝謝。

Brad Sills, Bank of America.

美國銀行的布拉德·西爾斯。

Great. Thank you so much. Great to hear the continued strength in new workloads here. That's been very consistent theme here throughout all of this. I did want to ask about some of the newer services like vector and stream processing. How are those contributing to the sustained strength you're seeing in new workloads?

偉大的。太感謝了。很高興聽到這裡的新工作量持續強勁成長。這是貫穿整個過程的一個非常一致的主題。我確實想詢問一些較新的服務，例如向量和流處理。這些對於您在新的工作量中看到的持續成長有何貢獻？

es, Brad, thanks for the question. So in terms of Search, we're seeing solid momentum in Search. We're having success with that business is starting to grow, and we just introduced a new capability called Search Nodes, which allows customers to optimize their search deployments by asymmetrically scaling specific nodes dedicated for Search versus the rest of their nodes on their cluster.

是的，布拉德，謝謝你的提問。因此，就搜尋而言，我們看到了強勁的搜尋勢頭。我們在該業務開始成長方面取得了成功，我們剛剛推出了一項名為「搜尋節點」的新功能，該功能允許客戶透過不對稱地擴展專用於搜尋的特定節點與其叢集上的其他節點來優化他們的搜尋部署。

It also helps dealing with use cases that are very search intensive. One of the largest gaming companies in the world replatform the content moderation platform from [DuckDB, Elastic and DYNAMO] to Atlas and Atlas Search and using Atlas Search Nodes for workload isolation, high performance.

它還有助於處理搜尋密集型的用例。全球最大的遊戲公司之一將內容審核平台從 [DuckDB、Elastic 和 DYNAMO] 遷移到 Atlas 和 Atlas Search，並使用 Atlas Search Nodes 進行工作負載隔離和實現高效能。

On Vector, we're continue to see growth in adoption, and we see vectors effective in attracting new customers to the MongoDB platform. A world-renowned financial news organization, which is already running at Atlas migrated from Elastic Search to Atlas Search using search nodes to take advantage of our vector search capabilities to build a site search that combines lexical search with semantic search to find the most relevant articles for user query.

在 Vector 上，我們繼續看到採用率的成長，並且我們看到向量能夠有效地吸引新客戶使用 MongoDB 平台。一家全球知名的財經新聞機構已經在 Atlas 上運行，它使用搜尋節點從 Elastic Search 遷移到 Atlas Search，利用我們的向量搜尋功能建立一個結合詞彙搜尋和語義搜尋的網站搜索，以找到與用戶查詢最相關的文章。

And a European energy company built a geospatial search application using Atlas Search and Vector search and the Atlas are built on-prem but -- and to cloud to vectorize geospatial data and facilitate research and discovery.

一家歐洲能源公司使用 Atlas Search 和 Vector search 建立了一個地理空間搜尋應用程序，Atlas 是在本地構建的，但也可以在雲端向量化地理空間數據，促進研究和發現。

And then we recently announced stream processing GA in (inaudible) local, New York in May, and we've seen strong interest. It's still early days, but we're seeing interest from a variety of industries ranging from automotive to retail to transportation who work with streaming data and wanted to be able to take actions on that data to drive their business.

然後，我們最近在 5 月在紐約當地（聽不清楚）宣布了流處理 GA，並且我們看到了濃厚的興趣。現在還為時過早，但我們看到汽車、零售、運輸等各個行業都對流數據感興趣，並希望能夠根據這些數據採取行動來推動他們的業務。

And so customers are very pleased with the performance of the product and how easy it is to use. But again, it's just early days since we just launched it only in May.

因此，客戶對產品的性能和易用性非常滿意。不過，由於我們五月才剛推出它，所以現在還為時過早。

And one more, if I may, please. On the last earnings call, you mentioned how the slowdown in consumption was broad-based across industries and workload types, which led you to believe that it was a macro-related impact.

如果可以的話，請再說一句。在上次財報電話會議上，您提到消費放緩是跨行業、跨工作量類型的普遍現象，這讓您認為這是一種宏觀相關影響。

With some of the improvement you've seen this quarter, I know it's early, but was that also broad-based? And could you deduce that maybe that might mean some improvement in the underlying macro? Or was it more outsized to certain types of industries and services? Thank you.

對於本季度看到的一些改進，我知道現在還為時過早，但這也是廣泛的嗎？您能否推斷出這可能意味著底層宏方面的一些改進？或者它對某些類型的行業和服務來說更為不利？謝謝。

Brad, I would describe it as broad based, but I would describe it within a reasonable range of outcomes, and so no clear indication that macro is improving or deteriorating and just a good quarter.

布拉德，我會將其描述為廣泛的，但我會將其描述在合理的結果範圍內，因此沒有明確跡象表明宏觀經濟正在改善或惡化，只是一個好季度。

Wonderful. Thank you.

精彩的。謝謝。

Rishi Jaluria, RBC Capital Markets.

Rishi Jaluria，加拿大皇家銀行資本市場。

A wonderful. Hey, this is Rishi Jaluria from RBC. Two questions. Maybe, Dave, I wanted to first start out going back to some of what you talked about, which is the MongoDB versus Postgres debate. One of the popular debates out there is which architecture is better suited to new generative AI applications, especially if we truly do believe AI will lead to a replatforming, a software similar to what we saw with the cloud.

太棒了。嘿，我是 RBC 的 Rishi Jaluria。兩個問題。也許，戴夫，我想先回顧一下你所談論的一些內容，即 MongoDB 與 Postgres 的爭論。目前流行的爭論之一是哪種架構更適合新的生成式人工智慧應用，特別是如果我們真的相信人工智慧將導致平台重構，即類似於我們在雲端運算中看到的軟體。

I know you talked a little bit about reference architecture earlier in Q&A, but maybe could you walk us through why you believe MongoDB is better suited to new generative native applications versus Postgres? And then I've got a quick follow-up.

我知道您之前在問答環節談到了參考架構，但您能否向我們講講為什麼您認為 MongoDB 比 Postgres 更適合新的生成原生應用程式？然後我會快速跟進。

Yes, sure. So very quickly, the reason we believe that we're well positioned to win these new workloads is that AI-driven workloads require the underlying database to be capable of processing queries against rich and complex data structures. As you know, with AI, the data structures can be very, very complex. So this means that the data can be large and obviously not in any consistent size. MongoDB is designed to handle these different data structures.

是的，當然。因此，我們相信我們有能力贏得這些新工作負載的原因是，人工智慧驅動的工作負載要求底層資料庫能夠處理針對豐富而複雜的資料結構的查詢。眾所周知，人工智慧的資料結構可能非常非常複雜。所以這意味著數據可能很大，而且顯然不存在任何一致的大小。MongoDB 被設計用來處理這些不同的資料結構。

And I talked about we can help unify metadata, operational data, vector data and generated all in one platform. Relational databases, and Postgres is one of them, have limitations in terms of what they can, how they can handle different types of data.

我說過，我們可以幫助統一元資料、操作資料、向量資料並產生所有資料在一個平台上。關聯式資料庫（Postgres 是其中之一）在其功能和如何處理不同類型的資料方面有其限制。

In fact, when the data gets too large, these relational databases have to do what's called off-road storage. And it becomes -- it creates a performance overhead on these relational platforms. Postgres has this thing called TOAST, which stands for The Oversized-Attribute Storage Technique.

事實上，當資料變得太大時，這些關聯式資料庫必須進行所謂的越野儲存。並且它會對這些關係平台產生效能開銷。Postgres 有一種稱為 TOAST 的東西，它代表超大屬性儲存技術。

And it's basically a way to handle these different data types, but it creates a massive performance overhead. So we believe that we are architecturally far better for these more complex workloads than relational databases. And we believe that, ultimately, we are the ideal data layer for AI applications. Obviously, it's early days. As I said, we haven't seen a lot of inference workloads in production.

這基本上是一種處理不同資料類型的方法，但它會產生巨大的效能開銷。因此，我們相信，從架構上來說，與關聯式資料庫相比，我們的資料庫更適合處理這些更複雜的工作負載。我們相信，最終我們是人工智慧應用程式的理想資料層。顯然，現在還為時過早。正如我所說，我們在生產中沒有看到大量的推理工作負載。

But architecturally, we feel very good about our ability to compete against relational databases and Postgres in particular. That being said, Postgres is popular because they're the beneficiary people who want to stay on relational and who've lived in relational for the last 30, 40 years. But head-to-head, we feel like we can win our share of business.

但從架構上來說，我們對自己與關聯式資料庫尤其是 Postgres 競爭的能力感到非常滿意。話雖如此，Postgres 之所以受歡迎，是因為他們是那些希望保持關係並在過去的 30、40 年裡一直生活在關係中的受益者。但透過面對面的較量，我們覺得我們能夠贏得自己的份額。

Got it. Thank you. That's really helpful. And then maybe just sticking on the theme of AI. We've talked before in the past that AI is just driving a lot of new code, being developed is significantly more productive. Have you seen that behavior in any of your existing customers on Atlas where maybe their utilization rate goes up or the number of applications built for customer goes up, anything like that, that understanding it's super early, might at least give you some early indication that the increased developer productivity could turn into a real tailwind in terms of consumption of the underlying Atlas stream? Thank you.

知道了。謝謝。這真的很有幫助。然後也許只是堅持人工智慧的主題。我們以前曾談到，人工智慧正在推動大量新程式碼的開發，其效率將顯著提高。您是否看到您現有 Atlas 客戶的行為，他們的利用率可能上升了，或者為客戶構建的應用程式數量上升了，諸如此類的事情，現在了解這些還為時過早，至少可以給您一些早期跡象，表明開發人員生產力的提高可能會成為底層 Atlas 流消費方面的真正順風？謝謝。

Yes. So this is a common question I ask our customers when I meet with them in terms of what cogeneration tools that they're using and what benefits they're gaining. The answers tend to be a little bit all over the map. Some people see at 10%, 15% productivity improvement, some people say 20%, 25% productivity improvement, some people say it helps my senior developers be more productive, some people say it helps my junior developers become more like senior developers. So the answers tend to be all over the map.

是的。因此，當我與客戶會面時，我經常問他們一個問題：他們使用什麼熱電聯產工具，以及他們獲得了什麼好處。答案往往有點雜亂無章。有些人認為生產力提高了 10%、15%，有些人說生產力提高了 20%、25%，有些人說它可以幫助我的高級開發人員提高工作效率，有些人說它可以幫助我的初級開發人員變得更像高級開發人員。因此答案往往五花八門。

There's no question in my mind that with this platform shift, like previous platform shifts, the cost of building apps will come down. And so by definition, more apps will be produced, which will generate more data, which requires more databases.

我毫不懷疑，隨著這次平台轉變，就像以前的平台轉變一樣，建立應用程式的成本將會下降。因此，根據定義，將會生產更多的應用程序，這將產生更多的數據，從而需要更多的資料庫。

Now obviously, we're in the early days and much like -- I've lived through other platform shifts like going to the Internet, a lot of people built these -- there's all this dark fiber that was built out to service all this potential demand. It didn't happen. People thought it was maybe overinflated and then before we needed it, the Internet transform the way we work, the way we live, the way we socialize.

顯然，我們尚處於早期階段，就像我經歷過的其他平台轉變一樣，例如互聯網，許多人構建了這些，所有這些暗光纖都是為了滿足所有潛在需求而構建的。這並沒有發生。人們認為它可能被過度誇大了，但在我們需要它之前，網路就改變了我們的工作方式、生活方式和社交方式。

And so I think that's the same thing is happening here. I think it's all on the come. But I think we're well positioned for that opportunity.

所以我認為這裡也發生了同樣的事情。我認為一切都會到來。但我認為我們已準備好迎接這一機會。

Wonderful. Thank you.

精彩的。謝謝。

Brent Bracelin, Piper Sandler.

布倫特·布雷斯林，派珀·桑德勒。

Thank you. This is Brent Bracelin at Piper Sandler. Dave, I wanted to go back to Atlas. And the question here is, given all the moving parts here around accounting, tough compares, unused commitments. Atlas growth looked really strong, I think 30% plus on a normalized basis after adjusting for unused commitments.

謝謝。我是 Piper Sandler 的 Brent Bracelin。戴夫，我想回到阿特拉斯。這裡的問題是，考慮到會計方面的所有變動因素、嚴格的比較和未使用的承諾。Atlas 的成長看起來確實強勁，我認為在調整未使用的承諾後，正常情況下的成長幅度將超過 30%。

That's before any benefit from AI. Can you just go back to the growth algorithm for Atlas here, I ask because 30% normalized growth would be 3x faster than industry growth, implying a meaningful share capture of new software build.

這是在人工智慧帶來任何好處之前。您能否回到 Atlas 的成長演算法上，我之所以問這個問題，是因為 30% 的標準化成長率將比產業成長率快 3 倍，這意味著新軟體建置的份額將顯著增加。

So maybe it would be worthwhile if you just go walk through what is driving the momentum on the Atlas side that is, again, 2 times, 3 times faster than the industry?

因此，如果您能弄清楚是什麼推動了 Atlas 方面的發展勢頭，那麼它的速度是否比行業平均快 2 倍、3 倍？

Well, what I would say is, obviously, we're pleased with our results. What I would say is, one, Atlas is the most widely available cloud service by definition because it runs across the largest hyperscalers in 118 regions around the world. So no matter who you are, you can run on Atlas almost anywhere in the world.

嗯，我想說的是，我們顯然對結果感到滿意。我想說的是，首先，從定義上來說，Atlas 是最廣泛使用的雲端服務，因為它運行在全球 118 個地區的最大超大規模伺服器上。因此，無論您是誰，您幾乎可以在世界任何地方使用 Atlas。

Second, because MongoDB is a general-purpose database, you can use us for almost any conceivable use case. So we're not limited to some narrow set of use cases or narrow set of users who have a very particular set of needs, that helps.

其次，由於 MongoDB 是一個通用資料庫，因此您可以將它用於幾乎任何可以想像的用例。因此，我們不局限於某些狹窄的用例或具有特定需求的狹窄用戶群，這很有幫助。

Three, we have a large community of developers. We have millions of developers all around the world from Asia to North America to EMEA who are using MongoDB to address some problem. And so that also, obviously, because of the popularity of MongoDB, we are the world's most popular modern database. I think that also speaks to the strength of the product. And then I would say we try and execute well.

三，我們擁有龐大的開發者社群。從亞洲到北美再到歐洲、中東和非洲，世界各地有數百萬的開發人員正在使用 MongoDB 來解決一些問題。因此，顯然，由於 MongoDB 的流行，我們成為世界上最受歡迎的現代資料庫。我認為這也證明了該產品的實力。然後我想說我們會盡力執行。

Obviously, we got off to a slower start than we liked in Q1 but we are very, very focused on execution. We have a really good team, both in terms of our go-to-market team as well as our product and the supporting ecosystem teams, our partner team, engineering team. And so we all try and put our heads down and we were obviously disappointed by Q1, and we're really focused on growing the business as fast as we can.

顯然，我們第一季的起步比預期要慢，但我們非常非常專注於執行。我們擁有一支非常優秀的團隊，包括我們的行銷團隊、產品和支援生態系統團隊、合作夥伴團隊和工程團隊。因此，我們都盡力埋頭苦幹，我們顯然對第一季的表現感到失望，我們真正專注於盡快發展業務。

Perfect. And then Michael, just a quick follow-up on margins. Guidance implies 10% plus op margins in the second half, particularly as the revenue run rate here gets to $2 billion exiting the year. Can you continue to invest in sales capacity while balancing double-digit margins at this scale?

完美的。然後是邁克爾，我們來快速跟進一下利潤率的問題。指引暗示下半年的營業利潤率將超過 10%，特別是當今年年底這裡的收入運行率達到 20 億美元時。您能否在平衡此規模的兩位數利潤率的同時繼續投資銷售能力？

Yes. So specifically around the second half, we've obviously increased the full year guide and that flows through. We did talk about some of the timing issues between halves. We are incrementally on a marginal basis investing in the back half of the year in our AI initiatives. But I think if you look at the two-year view, we're still making 500 basis points of margin progress over the last two years.

是的。因此，具體到下半年，我們顯然提高了全年業績預期，而這項預期會持續下去。我們確實討論了一些半場之間的時間問題。我們將在今年下半年逐步對人工智慧計畫進行邊際投資。但我認為，如果從兩年期來看，我們在過去兩年中仍然實現了 500 個基點的利潤率成長。

So yes, I think it's a balancing act, Brent. I think the key thing is we have such a large opportunity in such attractive investment areas that we'll try and continue to do both.

所以是的，我認為這是一種平衡行為，布倫特。我認為關鍵在於我們在如此具有吸引力的投資領域中擁有如此巨大的機會，因此我們會嘗試並繼續同時做好這兩件事。

Thank you.

謝謝。

Eric Heath, KeyBanc Capital Markets.

KeyBanc 資本市場公司的 Eric Heath。

Hey, good afternoon and thanks for taking the question. Just a couple of quick ones for me. Mike, I was curious to get a better understanding and just maybe some of your assumptions on why you're expecting a slower than typical rebound coming out of 3Q. And then, Dave, thinking about the third quarter here and fiscal year-end, just what are some of your expectations for that vertical, which I assume is usually a big EA opportunity. Thanks.

嘿，下午好，感謝您回答這個問題。對我來說，只有幾個簡單的問題。麥克，我很好奇，想得到更好的理解，也許只是一些關於為什麼您預計第三季的反彈速度會比正常水平慢的假設。然後，戴夫，考慮一下第三季和財年末，您對這個垂直行業有什麼期望，我認為這通常是一個巨大的 EA 機會。謝謝。

Yes. So quickly, just on Q3, I think we're informed by our recent experience, including the fact that the seasonal rebound in Q1 of this year was slower than what we've typically seen. And so that's what we're taking into account when we think about Q3, and that's really what's driving it.

是的。那麼，就第三季而言，我想我們根據最近的經驗了解了這一點，包括今年第一季的季節性反彈比我們通常看到的要慢。這就是我們在考慮第三季時所考慮的因素，這也是真正推動第三季發展的因素。

Yes. And on the -- I think you said, if I heard you correctly, you talked about the Fed -- your public sector and the Fed in particular? Yes. I mean, obviously the fiscal year ends in September. We have a good team there, we have FedRAMP Moderate.

是的。而且——如果我沒聽錯的話，我想您說過，您談到了美聯儲——特別是您的公共部門和美聯儲？是的。我的意思是，財政年度顯然在九月結束。我們有一個優秀的團隊，我們有 FedRAMP Moderate。

So we are well suited for workloads that have that requirement. We are not yet FedRAMP high certified. So that is something that we're working on as well to try and expand the envelope of opportunities we can pursue.

因此我們非常適合有這種要求的工作量。我們尚未獲得 FedRAMP 高級認證。所以這也是我們正在努力的事情，試圖擴大我們可以追求的機會範圍。

And then on -- as you implied, there's also an opportunity to sell EA to these customers, and we're doing that as well. Obviously, all that is baked into our guide for how we think about Q3 and the second half of the year. But we feel good about the opportunities in that segment.

然後——正如您所暗示的，還有機會向這些客戶推銷 EA，我們也在這樣做。顯然，所有這些都已融入我們對第三季和下半年的展望。但我們對該領域的機會充滿信心。

Thank you. And this does conclude today's Q&A session. I would now like to turn the call back to Dave Ittycheria for closing remarks. Please go ahead.

謝謝。今天的問答環節到此結束。現在我想請戴夫·伊蒂切裡亞作結語。請繼續。

Thank you. And again, I want to thank everyone for joining us today. We are pleased with our Q2 results and our continued execution against our large market opportunity. While the macro environment remains mixed, our ability to win new business remains strong across both Atlas and EA.

謝謝。我再次感謝大家今天的出席。我們對第二季的業績以及我們在巨大市場機會中的持續表現感到滿意。雖然宏觀環境仍然好壞參半，但我們在 Atlas 和 EA 上贏得新業務的能力仍然強勁。

And as we look ahead, we believe we are the ideal data layer for AI applications, and we continue to see increased demand from customers to modernize their legacy applications.

展望未來，我們相信我們是人工智慧應用程式的理想資料層，並且我們繼續看到客戶對現代化其傳統應用程式的需求不斷增加。

And last but not least, we will continue investing judiciously and focusing on our execution to capture this long-term opportunity. Thank you for joining us today, and we'll talk to you soon. Take care.

最後但同樣重要的一點是，我們將繼續明智地投資並專注於執行，以抓住這一長期機會。感謝您今天加入我們，我們很快就會與您交談。小心。

This does conclude today's conference call. You may all disconnect.

今天的電話會議到此結束。你們都可以斷開連線。