MongoDB, Inc.

Good day, and thank you for standing by. Welcome to the MongoDB's second quarter fiscal year 2025 conference call. At this time, all participants are in a listen-only mode. After the speaker's presentation, there will be a question and answer session. To ask a 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. 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, then you. Please go ahead.

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 Dave Itacheria, President and CEO of MongoDB, and Michael Gordon, MongoDB's COO and CFO. 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. 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 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, 2024, filed with the SEC on May 31st, 2024. Any quarterly 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 in this conference call. please refer to the tables in our earnings release on the investor relations portion of our website for reconciliation of these measures to their most directly comparable GAAP financial measure. With that, I'd like to turn the call over to Dave.

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. 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. Overall, we were pleased with our 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 Advance 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 Q1 was purely operational, and we feel good about our new business outlook in the second half of the year. 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 remain strong in Q2, demonstrating the quality of our product and the mission criticality of our platform. Our performance this quarter reinforced our confidence in our ability to execute on a 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. 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 estate, it is clear that MongoDB has multiple long-term growth opportunities. 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. 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. 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 backend architectures. 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. 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. Fourth, we meet or exceed the security and compliance requirements expected from an enterprise database, including enterprise-grade encryption, authorization, and auditability. 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. 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. 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 in and planning for new AI-powered applications, the complexity and fast-moving nature of the AI ecosystem slows customers down. That's why we launched the MongoDB AI Applications Program, or MAP, which became generally available to customers last month. MAP 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. MAP offers customers reference architectures, an end-to-end technology stack that includes pre-built integrations, professional services, and a unified support system to help customers quickly build and deploy AI applications. 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. 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. For that reason, earlier this year, we launched several pilots with our customers where we worked 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. 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. While it's still early days and generating meaningful revenue from this program will take time, we're excited about the results of our pilots and the growing pipeline of customers eager to modernize the legacy estate. 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 thinking of the macro influence on our business, it's important to distinguish between consumption of existing workloads and new business. Starting with consumption of existing applications on our platform, this is where we have historically seen a macro impact as usage of applications is 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. 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 realize that this is different from what you hear from some other software vendors. 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. 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 the user experience of their mobile app. 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 handling 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. 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. 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. 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. 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 longtime 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. Approximately 10% of the inventory is fast-moving, perishable produce that can go quickly out of stock. The company risks losing revenue and increasing customer churn if the customer didn't have viable alternatives to their first choice. To address these risks, they are now using state-of-the-art AI models and MongoDB Atlas Vector Search to give hyper-personalized 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. 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're confident in our ability to keep winning new business in the second half of the year. Looking forward, we see great opportunity to help our customers modernize legacy applications and build the next generation of AI-powered applications. With that, here's Michael.