MongoDB, Inc.

Thank you for standing by and welcome to MongoDB's Q3 Fiscal Year 24 conference call. At this time, all participants are in listen-only mode. After the speaker's presentations, there will be a question and answer session. To ask a question at that time, please press star 1-1 on your telephone. As a reminder, today's call is being recorded. I would like to turn the call over to your host, Mr. Brian Dinu from ICR. Please go ahead.

Great. Thank you, Valerie. Thank you. Good afternoon. Thank you for joining us today to review MongoDB's third quarter fiscal 2024 financial results, which we announced in our press release issued at the close of market today. Joining me on the call today are Dave Edicheria, President and CEO of MongoDB, and Michael Gordon, MongoDB COO and CFO. During this call, we will make forward-looking statements, including statements related to our market and future growth opportunities, the benefits of our product platform, our competitive landscape, customer behaviors, our financial guidance, and our planned investments. 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 the 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, 2023, that was filed with the SEC on September 1st, 2023. 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 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. I'm pleased to report that we had another strong quarter as we continue to execute well despite challenging market conditions. I will start by reviewing our third quarter results before giving you a broader company update. We generated revenue of $433 million, a 30% year-over-year increase and above the high end of our guidance. Atlas revenue grew 36% year-over-year, representing 66% of total revenues. We generated non-GAAP operating income of $79 million for an 18% non-GAAP operating margin, and we had another solid quarter of customer growth, ending the quarter with over 46,400 customers. Overall, we delivered a strong Q3. We had a healthy quarter of new business acquisition led by continued strength and new workload acquisition within our existing customers. In addition, our enterprise advanced business again exceeded our expectations demonstrating strong demand for our platform and the appeal of our run-anywhere strategy. Moving on to Atlas consumption trends, the quarter played out in line with our expectations. Michael will discuss consumption trends in more detail later. Finally, retention rates remained strong in Q3, reinforcing the mission criticality of our platform, even in a difficult spending environment. This quarter, we held our most recent global customer advisory board meeting where customers across various geographies and industries came together to share feedback and insight about the experience using MongoDB. From these discussions, as well as our ongoing C-suite dialogue with our customers, a few themes emerged. First, AI is in nearly every conversation with customers of all sizes. We're seeing great early feedback from our partnership with AWS's CodeWhisperer the AI-powered coding companion that is now trained on MongoDB data to generate code suggestions based on MongoDB's best practices from over 15 years of history. Microsoft GitHub Copilot is also proficient at generating code suggestions that reflect best practices, enabling developers to build highly-performing applications even faster on MongoDB. And with the recent advances in Gen AI, building AI applications is no longer the sole domain of AI or ML experts. Increasingly, it's software developers who are being asked to build powerful AI functionality directly into their applications. We are well positioned to help them do just that. We saw exceptional interest in our vector search public preview, and we announced general availability yesterday. Customers are building a range of AI use cases from semantic search to retrieval augmented generation, or RAG, where organizations can leverage the use of their private data to increase the accuracy of LLMs. For example, UKG, a human capital and workforce management technology, serves over 80,000-plus customers around the globe, chose to use MongoDB Atlas Vector Search for an AI-powered assistant that helps guide their customers' employees, people managers, and HR leaders. They chose Atlas Vector Search because of its minimal added architectural complexity, flexibility to handle rapidly changing data as applications evolve, and the scale to handle large workloads. UKG is not alone. In a recent state of AI survey report by Retool, Atlas Vector Search received by far the highest net promoter score from developers compared to all other vector databases available in the market. Moreover, developers can combine Vector Search with any other query capabilities available in MongoDB, namely analytics, text search, geospatial, and time series. This provides powerful ways of defining additional filters on vector-based queries that other solutions just cannot provide. For example, you can run complex AI-enriched queries such as find pants, a shirt, shoes in my size that look like the outfit in this image within a particular price range and have free shipping. Or find real estate listings with houses that look like this image that were built in the last five years and are in an area within seven miles west of downtown Chicago with top-rated schools. Second, customers feel more pressure than ever to modernize their data infrastructure. They are aware that their legacy platforms are holding them back from building modern applications designed for an AI future. However, customers also tell us that they lack the skills and the capacity to modernize. They all want to become modern, but are daunted by the challenges as they're aware it's a complex endeavor that involves technology, process, and people. Consequently, customers are increasingly looking to MongoDB to help them modernize successfully. We launched Relational Migrator earlier this year to help customers successfully migrate data from their legacy relational databases to MongoDB. Now we're looking beyond data migration to the full lifecycle of application modernization. At our .Local London event, we unveiled the Query Converter, which uses generative AI to analyze existing SQL queries and stored procedures and convert them to work with MongoDB's Query API. Customers already use the tool successfully to convert decades-old procedures to modernize their backend with minimal need for manual changes. While it's still early days, we're continuing to invest in the query converter and other AI features with the goal of significantly reducing the effort involved in modernizing legacy applications to run on MongoDB. To be clear, application modernization will take time to ramp, but is one of the largest long-term growth opportunities for our business. Third, our run-anywhere strategy continues to be a real differentiator as customers greatly appreciate the optionality that our platform provides as they manage often conflicting priorities on the way to the cloud. On one hand, the movement to the cloud continues unabated. Customers in industries and geographies who are at first hesitant to move to cloud, such as financial services in Southern Europe, are now moving to the cloud with urgency to become more nimble and to reduce costs. Many of our customers find that the all-in cost of maintaining legacy workloads on-prem is higher than the cost of migrating them to the cloud. On the other hand, our largest enterprise customers tell us they are planning to maintain a meaningful on-prem footprint for the foreseeable future. The reasons for keeping workloads on-prem include regulatory requirements, the desire to keep using their existing on-prem infrastructure, or the enormity of the task of migrating all their apps to the cloud. In the meantime, they still want a modern data platform to deploy new and existing applications the continued outperformance of our EA business demonstrates that our customers value our ability to run anywhere and to future-proof their eventual move to the cloud by building on EA. Finally, our customers remain focused on cost management. They are looking to do more with less by consolidating vendors and reducing the complexity of their data architecture. MongoDB dramatically increases developer productivity and supports a wide variety of use cases, eliminating the need for many point solutions. This combination resonates with customers in this macro environment. For example, Atlas Search now powers the homepage of one of the most recognizable sports media brands in the world. The customer replaced an incumbent search technology with Atlas Search because they were drawn to the operational ease of running search queries alongside other queries on Atlas, as well as the overall cost savings from consolidating functionality onto a single platform. In short, customers view MongoDB as a true partner, a partner that not only accelerates the pace of innovation, but also drives them to become more efficient. We are deepening investments in our product, partnerships, and customer-facing teams to continue to enable customers to do both. 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 applications on Atlas, leveraging the full power of our developer data platform. These customers include AT&T, Fishbowl by Glassdoor, and Trend Micro. AT&T's selected Atlas is a key element of their modernization journey. The location management application validates 380 million unique customer addresses and handles about 14 million transactions per day. But the various disparate data management solutions led to technical debt and there were duplicative sources of information. The company turned to Atlas as a developer data platform to simplify their data infrastructure, merge their data into a single view, and free their teams from managing database operations. Now they rely on Atlas chain streams to easily track changes with data as well as Atlas' native search capabilities and built-in geospatial functions to quickly identify location information and accelerate time to market for mission-critical products and services. EY, DeliveryHEO, and ASAPLOG are examples of customers turning to MongoDB to free up their developers' time for innovation while achieving significant cost savings. One of the 2023 MongoDB Innovation Award winners is EY. Ernst & Young LLP manages high volumes of transactional data and its clients and internal teams work under strict timelines to file taxes and meet regulatory deadlines. The cloud-based Global VAT Reporting Tool, or GVRT, automates and digitizes the preparation of 242 different types of returns across 79 countries. EY migrated from their previous non-relational database solution to Atlas and experienced a significant performance boost, reduced costs by as much as 50%, and are able to scale without limitations to handle increased data volumes, transaction loads, and concurrent user requests during peak periods. Evernorth Health Services, a division of the Cigna Group, Manulife, and PlayWox are turning to MongoDB to modernize applications. Manulife, one of the largest life insurance companies in the world, migrated to Atlas when it became clear that their relational database caused a drag on innovation and increased the time to bring new digital products to market. Manulife selected Atlas because the flexible document model speeds up development, scales easily, supports ACID transactions, and offers seamless data migration. Using Atlas device syncs, they successfully launched one critical app's offline mode to ensure uninterrupted app usage when offline or in low connectivity areas to improve mobile data synchronization. Using Atlas allows Manulife to broaden its digital capabilities and enhance the personalization of customers' interactions cost-effectively. In summary, I'm pleased with our third quarter results. Our run-anyway strategy allows customers flexibility over where they deploy, and MongoDB is emerging as a platform of choice for their AI-powered applications. And customers are using MongoDB to modernize and become more efficient. Before I turn it over to Michael, I'm excited to share that Ann Lunas, the former Chief Marketing Officer and Executive Vice President of Corporate Strategy and Development at Adobe, just joined MongoDB's Board of Directors. Anne held leadership roles at Adobe from 2006 to 2023. She was instrumental in driving Adobe's transition from a perpetual to a subscription-based business model and has experienced marketing to creative professionals, whether they are in a small agency, a medium-sized business, or a very large enterprise. If you replace creative professionals with developers, this strategy is very similar to what MongoDB is doing, and Anne did it at the next level of scale. Prior to Adobe, Anne held a variety of leadership positions at Intel during her 20-year tenure at the company, including Vice President of Sales and Marketing. We are thrilled for the exceptional perspective Anne will bring to the board. With that, here's Michael.