MongoDB, Inc.

Hello, and welcome to MongoDB's second quarter fiscal 27 earnings call. At this time, all participants are on a listen-only mode. After the speaker's presentation, there will be a question and answer session. To ask the question during the session, you will need to press star 11 on your telephone. You will then hear an automated message advising your hand is raised. To withdraw your question, please press star 11 again. I will now like to hand the conference over to Jess Lubert, Vice President of Investor Relations. You may begin.

Thank you, Operator. Good afternoon, and thank you for joining us today to review MongoDB's second quarter fiscal 2027 financial results, which we announced in our press release issued after the close of market today. Joining me on the call today are CJ Desai, President and CEO of MongoDB, and Mike Berry, CFO of MongoDB. During this call, we will make forward-looking statements, including statements related to our market and future growth opportunities, our opportunity to win new business, our expectations regarding Atlas consumption growth, the impact of EA and other business, and multi-year license revenue, and the long-term opportunity of AI, our financial guidance, and underlying assumptions, including expectations regarding profitability and operating margin, and our investments and growth opportunities in AI. These statements are subject to a variety of risks and uncertainties, including the results of operations and financial conditions that could cause actual results to differ materially from our expectations. For a discussion of material risks and uncertainties that could affect our actual results, please refer to the risk described in our quarterly report on Form 10-Q for the quarter ended July 31st, 2026 filed with the SAC on September 1st, 2026. Any forward-looking statements made on this call reflect our views only as of today, and we undertake no obligation to update them except as required by law. Additionally, we will discuss non-GAAP financial measures on this conference call. Please refer to the tables in our earnings release on the investor relations portion of our website for a reconciliation of these measures to the most directly comparable GAAP financial measures. With that, I'd like to turn the call over to CJ.

Thank you, Jess, and thanks everyone for joining us today. I'm pleased to share our very strong Q2 results. Total revenue of $772 million, up 30% year over year, and representing the highest level of quarterly growth seen since fiscal year 24. Atlas revenue grew approximately 29% year over year for the fifth straight quarter driven by large enterprise customers and building AI momentum. EA and other had a standout quarter growing 36% year over year due to widespread strength driven by our run anywhere capabilities. We generated a non-gap operating margin of 24% driven by the strong revenue growth we delivered. We ended the quarter with 70,600 customers, adding a record 2,900 net new customers in the period. Boyage customer count nearly doubled quarter over quarter, and Atlas Vector Search adoption continues to outpace the growth of the rest of the company, showing our strong early momentum for AI workloads. Our core business, CementStrong, and our Run Anywhere advantage is a key differentiator for this quarter's growth across both Atlas and EA. Enterprises across financial services, healthcare, tech are running their most demanding mission-critical workloads on MongoDB, and we are winning more workloads each quarter. Increasingly, these same enterprises, as well as AI natives, are choosing our platform for AI workloads evidenced by the adoption of Atlas Vector Search and Voyage Embeddings. My team and I spent another quarter with the C-suite of our customers discussing our data platform for their most pressing core AI and modernization needs. Our Q2 performance is exactly why I'm confident that we are emerging as the real-time intelligent data platform for modern application in the multi-cloud and AI era. I will begin with what I'm seeing in the enterprise. For customers that already run large part of their data estate on MongoDB, building an agent on top of that data is a natural extension because the data an agent actually needs is live operational data, not a stale copy sitting in a warehouse. Search, vector search, and embeddings are built in not bolted on, so rather than agents connecting to many separate systems, they connect to one platform. We are seeing this show up across industries in a range of use cases, whether it's retrieval of internal knowledge, customer-facing chatbots and agents, or fraud and identity workflows. It is still early, but we are seeing more of these workloads reach production, such as the Financial Times, which leverages us to power AI-driven discovery, reaching millions of readers with interactive experiences at scale. With Vector Search and Voyage, the financial time now unifies their operational data and vector embeddings on a single platform, building a hybrid full-text and semantic search solution, eliminating the complexity of syncing separate systems and accelerating time to production. By indexing content with the high-accuracy OS4 model and serving over 100,000 daily queries on the cost-efficient OS4 Lite model, the Financial Times has significantly cut retrieval costs with minimal performance impact. What used to take weeks of manual index monitoring is now finished in a day. Moving on to the momentum we are seeing with Frontier Labs, who are both customers and partners for us. Multiple leading labs leverage Atlas for workloads that are mission critical to how they ship their products. One lab uses us for inference and chat workloads after moving away from Postgres due to performance lags and outages affecting user experience. They migrated their chat memory system onto Atlas in just four weeks and now run at 10x faster reads than Postgres. Beyond that, Labs uses for research workloads to store experimental results, evaluation data, and training artifacts for model development. These relationships are still early and engagement varies lab by lab, but we are energized by the traction we are seeing with them. As partners with these frontier labs, we are enabling the developers and agents building on their platforms to leverage Atlas. Just recently, We launched a fully managed MCP server, making it easier for developers and agents to connect directly to MongoDB when they are using Cloud Code, Codex, and Crockbill, as well as popular coding tools like Cursor and Daven from Cognition. This is how we stay embedded in the AI supply chain for how new applications get built. Paul Smith, Chief Commercial Officer at Anthropic, described our technology partnership and recent integration with cloud by noting the best AI applications need a strong database, which is why we have long pointed to developers building on cloud to MongoDB VoH for embeddings. More recently, demand from those developers drove MongoDB to build a new managed MCP server, which has seen faster adoption since launch, and now lets developers explore and manage their MongoDB data without ever leaving cloud. The final piece of the AI opportunity is AI natives. Companies whose data layer determines whether the product can support rapid scale. Some choose us from day one, others start elsewhere like prompt-driven development platforms and migrate to us as they hit scaling limits and real usage arise. That pattern is showing up in the numbers. We added a record 2,900 net new customers this quarter, and many of them are AI natives. Fireflies, a unicorn AI native startup, is building what it calls the number one AI assistant for work, helping people unlock the knowledge buried in their conversations. Fireflies serves more than 20 million users across a million plus organizations. and has processed over 7 billion meeting minutes. Firefly chose Atlas from day one for its flexible document model over a rigid relational schema and today runs more than 40 microservices with change streams powering real-time pipelines for analytics and growth intelligence. That lean, scalable foundation has helped their fuel their hypergrowth seamlessly. We are also seeing strong traction with Voyage, our embedding and re-ranking models, which consistently rank at the top of independent leaderboards. In August, we brought automated Voyage embeddings to Atlas for one-click vector search setup, launched Voyage Code 4, a model purpose-built for code, and shipped an upgraded re-ranking API, all keeping Atlas Retrieval Accuracy for AI ahead of the market. Voyage traction is showing up on both ends of the market. Some of our largest existing Atlas customers are beginning to adopt Voyage for AI use cases, while a large majority of new Voyage customers are AI natives and have no prior relationship to MongoDB. Eve is one of them, a unicorn AI native that automates legal case intake, Medical Chronologies, and Demand Letter Drafting for Plaintiff Law Firms. EVE uses Atlas Embedding and Reranking API powered by Voyage AI's Rerank 2.5 to surface the most relevant evidence from large sets of case documents. This improves retrieval quality directly into EVE's RAG layer while simplifying the infrastructure needed to build and evolve these AI experiences. Turning to Enterprise Advance, This quarter's strength was widespread across our install base, particularly within financial services, tech, and the public sector. Two patterns in how customers are using EA stand out, and both point to why this business is strategic for us. The first is AI in governed, self-managed environments. This quarter, we brought search and vector search to EA closing a gap between our cloud and self-managed experiences. Demand came in immediately and across industries from customers looking to take a consolidated approach to building AI in their own governed self-managed environments. A major US bank shows what that looks like in practice. EA already serves as the standardized data platform for more than 100 production applications across payments, fraud detections, document processing, customer and account services. This quarter, that bank extended that same environment to gen AI and semantic search for employee advisors, chatbots, product search, and document intelligence. By bringing operational data, search and vector retrieval together, self-managed with EA, they keep sensitive customer and conversational data inside their own governed environment without sending up separate systems. That gives them a practical foundation to expand AI across the bank on the same platform already running their most critical operations. The second is hybrid deployment. More and more of my customer conversations involve running across multiple clouds and self-managed environments at the same time. For customers on both EA and Atlas, it is an and, not an or. For example, one of the largest cybersecurity companies runs a substantial estate across both Atlas and EA, and both parts grew meaningfully in the quarter. Nationwide UK, the world's largest building society, is also a good example. They now run their growing speed layer application across both EA and Atlas, simultaneously giving members real-time access to account and transaction data across every digital channel and supporting more than 24 million weekly app logins. Splitting that workload across EA and Atlas gives Nationwide stronger operational resilience and helps satisfy UK regulatory requirements while simplifying an estate that used to be far more fragmented. Nationwide already runs with us for faster payments, up to 3 million transactions and 1.5 billion pounds in value on a peak day on a self-managed dual cloud EA cluster. Bringing AI self-managed opens net new demand for us, and hybrid deployment often means that the strong EA estate opens the door to net new Atlas conversations within the same customers. EA's profitability also lets us invest more heavily in R&D and go-to-market, furthering Atlas growth and our AI roadmap. Finally, I feel great about the leadership team driving innovation across both Atlas and EA. Ben Cefalo owns core products, and Pablo Sternplaza owns AI and emerging products. And on the go-to-market side, Ryan McBain has hit the ground running as our new CRO, giving me real confidence in our ability to capture the opportunity ahead. Before I close, I would like to remind everyone that we will be hosting our Investor Day in New York City on September 29th and our DOT Local New York user event on September 30th. We look forward to seeing many of you there. With that, I will turn over to Mike.