MongoDB, Inc.

Good day and welcome to MongoDB's Q4 fiscal year 2025 earnings call. At this time, all participants are in a listen-only mode. After the speaker presentation, there will be a question and answer session. To ask a question during the session, you will need to press star 1-1 on your telephone. You will then hear an automated message advising your hand is raised. To withdraw your question, press star 1-1 again. Please be advised that today's conference is being recorded. I would now like to hand the conference over to your speaker, Brian Denue from ICR. Please go ahead.

Thank you, Cherie. Good afternoon, and thank you for joining us today to review MongoDB's fourth quarter field 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 Itacharia, president and CEO of MongoDB, and Serge Tamja, MongoDB's interim CFO. During this call, we will make four leading 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, the long-term opportunity of AI, the opportunity of application modernization, our expectations regarding our win rates and Salesforce productivity, our financial guidance and underlying assumptions, 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, and cause actual results to differ materially from our expectations. For discussion of material risks and uncertainties that affect our actual results, please refer to the risk described in our quarterly report on Form 10-Q for the quarter ended October 31st, 2024, followed by the SEC on December 10th, 2024. Any affordability 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 table in 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.

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 fourth quarter results before giving you a broader company update. We generated revenue of $548.4 million, a 20% year-over-year increase and above the high end of our guidance. Atlas revenue grew 24% year-over-year, representing 71% of revenue. We generated non-GAAP operating income of $112.5 million, for a 21% non-GAAP operating margin. We ended the quarter with over 54,500 customers. For the full year, we crossed the $2 billion revenue mark while growing 19% and are roughly 20 times the size we were the year before we went public. Overall, we were pleased with our fourth quarter performance. We had a healthy new business quarter led by continued strength and new workload acquisition within existing Atlas customers. In addition, we again benefited from a greater than expected contribution from multi-year non-Atlas deals. Moving on to Atlas consumption, the quarter played out better than our expectations with consumption growth stable compared to the year-ago period. Surge will discuss consumption trends in more detail. Finally, retention rates remain strong in Q4, demonstrating the quality of our product and the mission criticality of our platform. As I look into fiscal 26, let me share with you what I see as the main drivers of our business. First, We expect another strong year of new workload acquisition. As we said many times in the past, in today's economy, companies build competitive advantage through custom-built software. In fiscal 26, we expect that customers will continue to gravitate towards building their competitive differentiation on MongoDB. Second, we expect to see stable consumption growth for Atlas in fiscal 26 compared to fiscal 25. Usage growth to start fiscal 26 is consistent with the environment we have seen in recent quarters. This consistency coupled with an improved fiscal 25 cohort of workloads gives us confidence that Atlas will continue to see robust growth as it approaches a $2 billion run rate this year. Third, as Serge will cover in more detail, we expect our non-Atlas business will represent a meaningful headwind to our growth in fiscal 26 because we expect fewer multi-year deals and because we see that historically non-Atlas customers are deploying more of their incremental workloads on Atlas. Fourth, We are very excited about our long-term opportunity in AI, as I will explain a bit later. In fiscal 26, we expect our customers will continue on their AI journey from experimenting with new technology stacks to building prototypes to deploying apps in production. We expect the progress to remain gradual, as most enterprise customers are still developing in-house skills to leverage AI effectively. Consequently, we expect the benefits of AI to be only modestly incremental to revenue growth in fiscal 26. Fifth, we'll continue scaling our application modernization efforts. Historically, this segment of the market was not widely available to us because of the effort, cost, and risk of modernizing old and complex custom applications. In fiscal 25, our pilots demonstrated that AI tooling combined with services can reduce the cycle time of modernization. This year, we'll expand our customer engagements so that app modernization can meaningfully contribute to our new business growth in fiscal 27 and beyond. To start with, And based on customer demand, we are specifically targeting Java apps running on Oracle, which often have thousands of complex store procedures that need to be understood, converted, and tested to successfully modernize the application. We address this through a combination of AI tools and agents, along with inspection verification by delivery teams. Though the complexity of this work is high, the revenue opt-in for modernizing these applications is significant. For example, we successfully modernize a financial application for one of the largest ISVs in Europe, and we're now in talks to modernize the majority of the Alexia estate. As I take a step back, I see fiscal 26 as a year of solid Atlas growth enabled by a large market, superior product, and strong go-to-market execution. We expect continued strong win rates as we acquire incremental workloads across our customer base. We will continue building on our core land expand go-to-market motion to further accelerate workload acquisition. In fiscal 25, we saw improved Salesforce productivity, and we are forecasting continued improvements in fiscal 26. In addition, we will continue investing to become a standard in more of our accounts. We are not market-constrained in even our largest accounts. For example, we finished the year with 320 customers with over 1 million in ARR, a year-over-year growth rate of 24%. This reinforces our move-up market. To that end, in fiscal 26, we will make significant incremental investments in our strategic accounts program. Looking beyond fiscal 26, I'm incredibly excited about a long-term opportunity, particularly our opportunity to address the expanded requirements of a database in the AI era. Let me tell you what we're seeing in our customer base as they work to adopt AI. AI is ushering in a new era of accelerated change, and every company will have to adapt. We are witnessing a once-in-a-generation shift that will fundamentally reshape industries, accelerate the pace of innovation, and redefine competitive dynamics in ways we've never seen before. We joke that the world will move so fast that tomorrow's plans will happen yesterday. The winners will be those companies that can transform and adapt quickly to this new pace of change. Those cannot will fall rapidly behind. AI is transforming software from a static tool into a dynamic decision-making partner. No longer limited to predefined tasks, AI-powered applications will continuously learn from real-time data. But this software can only adapt as fast as the data infrastructure is built on, and legacy symptoms The systems simply cannot keep up. Legacy technology stacks were not designed for continuous adaptation. Complex architectures, batch processing, and rigid data models create friction at every step, slowing development, limiting organizations' ability to act quickly, and making even small updates time-consuming and risky. AI will only magnify these challenges. MongoDB was built for change. MongoDB was designed from the outset to remove the constraints of legacy databases enabling businesses to scale, adapt, and innovate at AI speed. Our flexible document model handles all types of data, while seamlessly scalability ensures high performance for unpredictable workloads. With the Voyage AI acquisition, MongoDB makes AI applications more trustworthy by pairing real-time data and sophisticated embedding and retrieval models that ensure accurate and relevant results. We also simplify AI development by natively including vector and text search directly in the database providing a seamless developer experience that reduces cognitive load, system complexity, risk, and operational overhead, all with the transactional, operational, and security benefits intrinsic to MongoDB. But technology alone isn't enough. MongoDB provides a structured, solution-oriented approach that addresses the challenges customers have with the rapid evolution of AI technology, high complexity, and a lack of in-house skills. We are focused on helping customers move from AI experimentation to production faster with best practices that reduce risk and maximize impact. Our decision to acquire Voyage AI addresses one of the biggest problems customers have when building and deploying AI applications, the risk of hallucinations. AI-powered applications excel where traditional software often falls short, particularly in scenarios that require nuanced understanding, sophisticated reasoning, and interaction in natural language. This means they are uniquely capable of handling tasks that are more complex and open-ended. But because AI models are probabilistic and not deterministic, they can hallucinate or generate false or misleading information. This creates serious risks. Imagine a financial services agent that autonomously allocates capital on behalf of its customers, or a cancer screening application in the hospital that analyzes scans to detect early signs of pancreatic cancer. For any mission-critical application, inaccurate, or low-quality results are simply not acceptable. The best way to ensure accurate results is through high-quality data retrieval, which ensures that not only the most relevant information is extracted from an organization's data with precision, high-quality retrieval is enabled by vector embedding and re-ranking models. VOI-JS embedding and re-ranking models are among the highest rated in the Hugging Face community for retrieval, classification, clustering, and re-ranking and are used by AI leaders like Anthropic, Langchain, Harvey, and Replit. Voyage AI is led by Stanford professor, Tangyu Ma, who has assembled a world-class AI research team from AI labs at Stanford, MIT, Berkeley, and Princeton. With this acquisition, MongoDB will offer best-in-class embedding and re-ranking models to power native AI retrieval. Put simply, MongoDB democratizes the process of building trustworthy AI applications right out of the box. Instead of cobbling together all the necessary piece parts, an operational data store, a vector database, and embedding and re-ranking models, MongoDB delivers all of it with a compelling developer experience. As a result, MongoDB is redefining the database for the AI era. 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 Atlas, leveraging the full power of our platform, including Informatica, Sonos, Zebra Technologies, and Grab. Grab, Southeast Asia's leading super app, which provides everyday services like delivery, mobility, digital financial services, and serves over 700 cities in eight Southeastern Asian countries, successfully migrated its key app, Grab Kiosk, to Atlas. Atlas provides Grab with an automated, scalable, and secure platform, which empowers engineering teams to focus on product development, to accommodate GRAB's rapid growth. By leveraging Atlas, GRAB achieves significant efficiency gains, saving around 50% of the time previously spent on database maintenance. The Associated Press, the Catalan Department of Health, Urban Outfitters, and Lombard ODA are turning to MongoDB to modernize applications. Urban Outfitters chose MongoDB as its database platform to provide a flexible, scalable foundation for its infrastructure. With a vision of integrating data across systems for elevated and consistent customer experiences, the retailer found legacy databases inadequate. By adopting Atlas and its flexible document model, Urban Outfitters accelerated development, boosted scalability, while seamlessly integrating data. This transformation facilitated the introduction of AI-driven personalization and cutting edge search features, enriching the shopping experience across both digital and physical spaces. Mature companies and startups alike are using MongoDB to help deliver the next wave of AI-powered applications to their customers, including Swisscom, NTT Communications, and Paychex. Swisscom, Switzerland's leading provider of mobile, internet, and TV services, deployed a new-gen AI app in just 12 weeks using Atlas. Swisscom implemented Atlas to power a RAG application for the East Foresight Library, transforming unstructured data such as reports, recordings, and graphics into vector embeddings that large language models can interpret. This enables vector search to find any relevant context, resulting in more accurate and tailored responses for users. In summary, we had a healthy Q4. We saw stabilizing Atlas consumption growth, along with a strong new business quarter, and we remain confident in our ability to execute on a long-term opportunity. Fiscal 26 is a transition year as we execute on a go-to-market motion while investing to prepare to capture the AI opportunity both through Greenfield AI applications and AI-assisted modernization of legacy applications. We want to capitalize on a once-in-a-generation opportunity. With that, here's Serge.