MongoDB, Inc. Class A 2027 Q2 Earnings Call

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 Enterprise Advanced 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 risks described in our quarterly report on Form 10-Q for the quarter ended July 31st, 2026, filed with the SEC 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 am pleased to share our very strong Q2 results. Total revenue of $772 million, up 30% year-over-year, and representing the highest level of quarterly growth seen since fiscal year 2024. 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-GAAP operating margin of 24%, driven by the strong revenue growth we delivered. We ended the quarter with 70,600 customers, adding a record 2,900 net new customers in the period.

Voyage customer count nearly doubled quarter-over-quarter, and Atlas Vector Search adoption continues to outpace the growth of the rest of the company, showing our strong early momentum for AI workloads. Our core business remains strong, and our run anywhere advantage is a key differentiator for this quarter's growth across both Atlas and EA. Enterprises across financial services, 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 AI 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 am 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 am 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 is 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 AI, the Financial Times 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 voyage-4 model and serving over 100,000 daily queries on the cost-efficient voyage-4-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 PostgreSQL due to performance lags and outages affecting user experience. They migrated their chat memory system onto Atlas in just 4 weeks and now run at 10x faster reads than PostgreSQL. Beyond that, labs use us 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 Claude Code, Codex, and Grok Build, as well as popular coding tools like Cursor and Devin 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 Claude by noting, "The best AI applications need a strong database, which is why we have long pointed to developers building on Claude to MongoDB Voyage for embeddings. More recently, demand from those developers drove MongoDB to build a new managed MCP server, which has seen fast adoption since launch and now lets developers explore, query, and manage their MongoDB data without ever leaving Claude." 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 arrives. 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 1 million-plus organizations and has processed over 7 billion meeting minutes. Fireflies 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 fuel their hypergrowth seamlessly. We are also seeing strong traction with Voyage, our embedding and reranking 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 reranking 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 Advanced, 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.