MongoDB, Inc.

Good day, everyone, and welcome to MongoDB's second quarter fiscal year 2026 earnings call. At this time, all participants are in a listen-only mode. After the presentation, there will be a question and answer session. To participate, you will need to press star 1-1 on your telephone. You will then hear a message advising your hand is raised. To withdraw your question, simply press star 1-1 again. Please note this conference is being recorded. Now it's my pleasure to turn the call over to Brian Daniel from ICR. Please go ahead.

Thank you, Carmen. Good afternoon, and thank you for joining us today to review MongoDB second quarter fiscal 2026 financial results, which we announced in our press release issued after the close of market today. Joining me on the call today are David Echeria, President and CEO of MongoDB, Mike Berry, CFO of MongoDB, and Jess Lubert, MongoDB's new Vice President of Investor Relations. During this call, we will make four 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 non-Atlas business and multi-year license revenue, the long-term opportunity of AI, our financial guidance and underlying assumptions, 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. They cause actual results to differ materially from our expectations. For 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 30th, 2025, followed by the SEC on June 4th, 2025. Any forward-looking statements made in 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 on the earnings release on the Investor Relations portion of our website for a reconciliation of these measures to the most directly comparable GAAP financial measure. With that, I'd like to turn the call over to Dave.

Thank you, Brian, and thank you to everyone for joining us today. Before discussing our strong quarter, I want to remind everyone about our upcoming Investor Day, which will take place on September 17th at the Javits Center in New York City during our DOT Local Conference. We'll spend the day discussing the investments we're making to drive durable growth and margin expansion and our view of the future. I look forward to seeing you there. Now, on to Q2. I'm pleased to report another strong quarter as we continue to execute against a large market opportunity. Let me start with our results before giving you a broader company update. We generated a revenue of $591 million, up 24% year-over-year and above the high end of our guidance. Atlas revenue grew 29% year-over-year, representing 74% of total revenue. We delivered non-GAAP operating income of $87 million for a 15% non-GAAP operating margin. And we ended the quarter with over 59,900 customers. Atlas' performance was strong, accelerating to 29% year-over-year growth, up from 26% in Q1. Our customer additions were also robust. We have added over 5,000 customers over the last two quarters. These results reflect the strength of MongoDB's platform, our flexible document model, expanded capabilities like search and vector search, enterprise readiness, and the ability to run anywhere. Many of our recently added customers are building AI applications, underscoring how our value proposition is resonating for AI and why MongoDB is emerging as a key component of the AI infrastructure stack. At the same time, we significantly outperformed on operating margin, demonstrating that we can drive durable revenue growth while expanding profitably. In short, our results show that customers are choosing MongoDB Let me tell you why. First, MongoDB is an enterprise-ready database capable of meeting the most stringent enterprise requirements. Over 70% of the Fortune 500, as well as 7 of the 10 largest banks, 14 of the largest 15 healthcare companies, 9 of the 10 largest manufacturers globally are MongoDB customers. MongoDB is a battle-tested enterprise platform relied on by some of the most sophisticated and demanding organizations in the world. in part because of our strong enterprise posture across security, durability, availability, and performance. Atlas enabled one of the world's largest automakers to overcome Postgres' scalability and flexibility limits while reducing complexity. The company's management console attracts over 8.5 million vehicles, requiring a modern schema to handle both structured and unstructured data, something Postgres could not handle. Ultimately, Atlas consolidated infrastructure, accelerated innovation, and supported the scale of millions of connected vehicles. Second, MongoDB is suitable for a broad range of use cases, including the most mission-critical and transaction-intensive applications. MongoDB has also supported full asset transactions for more than six years, ensuring strong consistency and data integrity at scale. This is why some of the world's most demanding transactional workloads run on MongoDB today. For example, Deutsche Telekom selected MongoDB Atlas as the foundation for its internal developer platform which includes mission-critical workloads like contract management, device purchases, and billing for 30 million customers. With 90 Atlas clusters managing over 60 million customer records, Deutsche Telekom's customer data platform now handles 15 times the concurrent logins of legacy systems. By consolidating these high-volume, transaction-intensive applications on MongoDB, Deutsche Telekom has improved resiliency, accelerated innovation, and delivered a step change in customer engagement. MongoDB has redefined what's core for the database by natively including capabilities like search, vector search, embeddings, and stream processing. Comparing MongoDB to another database like Postgres is not an apples-to-apples comparison. Take a global e-commerce application that manages inventory and order data while enabling product discovery through sophisticated search across millions of SKUs. The choice for this application, not between MongoDB or Postgres, is between MongoDB or Postgres plus other offerings like Pinecone, Elastic, and Cohere For embeddings, MongoDB's complete solution allows developers to spend less time stitching together and maintaining a patchwork of disparate systems and more time building differentiated functionality that drives the business forward. For example, Agibank, a Brazilian neobank with 2.7 million active customers, migrated their content management system storing customer records from Postgres to Atlas. As data volumes grew, Postgres' inflexibility and task execution latency drove performance issues and the database lacked sophisticated secondary indexes and full-text search, hurting sales of core offerings such as loans, insurance, and card approvals. Agibank was constantly updating the database and manually scaling infrastructure, which was both time-consuming and error-prone. With Atlas, Agibank gained a resilient, flexible system that handled rising demand and supported new services, delivering nearly five times better performance and 90% lower costs, all with no outages. Fourth, MongoDB is emerging as a standard for AI applications. Over the last few quarters, we've seen a strength in our self-serve channel, driven in part by AI-native startups choosing Atlas as the foundation for their applications. In the enterprise segment, adoption is real but early. Much of the activity today centers on employee productivity tools and packaged IFC solutions. Enterprises are still in the very early stages of building their own custom AI applications that will transform their business. We consistently hear from customers that when teams try to scale from vibe-coded prototypes built on relational backends to enterprise-grade deployments, these platforms quickly hit limits in flexibility, scalability, and performance. Across startups and increasingly enterprises, our unified platform is resonating strongly. In the enterprise segment, a leading electric vehicle company chose Atlas and VectorSearch to power its autonomous driving platform. After testing VectorSearch against Postgres PG Vector for their in-vehicle voice assistant, They selected MongoDB for superior performance at scale and stronger ROI. They now rely on Atlas to handle over 1 billion vectors and expect 10 times growth in data usage by next year. DevRev, a well-funded AI-native platform with proven founders disrupting the help desk market, built AgentOS, its complete agentic platform that autonomously handles billions of monthly requests on Atlas. DevRev accelerated development velocity lower costs, and scale globally with low latency by using Atlas. AgentOS also leverages Atlas Vector Search for semantic search, enriching its knowledge graph and LLMs with domain-specific content. Companies in nearly every industry and across every geography are choosing MongoDB because we deliver the features, performance, cost-effectiveness, and AI readiness they need, all in one platform. As we look ahead, we remain confident in MongoDB's position to lead both the current wave of digital transformation and the next wave powered by AI. With that, here's Mike.