$MDB MongoDB Q1 2026 Earnings Conference Call

Thank you, Brian, and thank you to everyone joining us today. I'm pleased to report that we got off to a strong start in fiscal 2026 as we executed well against our large market opportunity. Let's begin by reviewing our first quarter results before giving you a broader company update. We generated revenue of $549 million, a 22% year-over-year increase and above the high end of our guidance. Atlas revenue grew 26% year-over-year, representing 72% of revenue. We generated non-GAAP operating income of $87 million for a 16% non-GAAP operating margin, and we ended the quarter with over 57,100 customers. Overall, we posted a strong Q1 despite a dynamic and fast-changing macro environment. We had a solid new business quarter. We are beginning to see the benefit of our decision to focus our resources on the high end of the market where we have the largest opportunity. Atlas consumption this quarter played out in line with our expectations. Mike will discuss consumption trends in more detail and our expectations for the remainder of the year. Our total customer net adds are the highest in over six years, reflecting the continued strong adoption of MongoDB across a wide range of industries and use cases. Self-serve customer additions were particularly strong this quarter, reinforcing MongoDB's position as the go-to platform for developers building the next generation of applications, including many focused on AI. While these accounts typically start small, the self-serve channel is a powerful engine for long-term growth. Finally, retention rates remain strong in Q1, demonstrating the quality of our product and the mission criticality of our platform. We are pleased with our Q1 performance. As I said before, companies leverage software to execute their business strategy, drive differentiation, and improve operational efficiency. As the operational database that is the core of software applications, MongoDB is undeniably a must-have component of the tech stack. We continue to make progress toward our goal of becoming the standard platform for enterprises and the default for developers building new applications. At the heart of this momentum is MongoDB's modern architecture, which delivers real and measurable advantages for the types of applications being built today: cloud-native, distributed, real-time, and the AI-powered applications of tomorrow. MongoDB's document model and the associated platform enables developers to more easily represent the messiness of real-world data, which includes understanding relationships between structured and unstructured data, and managing data that is constantly evolving and changing. This fundamental architectural advantage provides customers greater flexibility, faster time to market, and the ability to scale without rearchitecting. These capabilities are why customers continue to develop more and more mission-critical workloads in MongoDB, illustrated by our strong customer additions this quarter. As AI redefines how applications are built and how businesses operate, MongoDB is exceptionally well positioned. Real-world AI applications require high-quality, context-rich, and often unstructured data to deliver trustworthy outputs. We continually hear from large enterprises that high accuracy is a critical requirement to drive widescale adoption of AI. Our recent acquisition of Voyage AI enhances our ability to serve this need. Embeddings are the bridge between a large language model and a customer's private data. Voyage's leading embedding and re-ranking models allow customers to feed precise and relevant context into LLMs, significantly improving the accuracy and reliability of the output of AI applications. By producing the most contextually rich, domain-optimized embeddings, MongoDB sits at the gateway of meaning in an AI system. With the release of Voyage 3.5, we've taken another step forward, meaningfully outperforming the next best embedding models while reducing storage costs by more than 80%. This makes it not only powerful, but also cost-effective at scale. So, what does this all mean? MongoDB now brings together three things that modern AI-powered applications need: real-time data, powerful search, and smart retrieval. By combining these into one platform, we make it dramatically easier for developers to build intelligent, responsive apps without stitching together multiple systems. In their desire to keep up with evolving customer needs, some vendors are retrofitting their products, such as adding JSON or vector support as afterthoughts, which are superficial and brittle. This is a tacit admission that MongoDB's approach of using JSON and the document model is the best way to model real-world data. These features may check the box, but they fall apart in production, leading to performance bottlenecks, operational headaches, and spiraling infrastructure costs. Fundamentally, these vendors are constrained by their relational underpinnings. It's important to understand that superficial compatibility with modern data types is not the same as deeply integrated, production-grade functionality. MongoDB, by contrast, was purpose-built to address these needs natively. We see this dynamic in our customer base every day. To bring this to life with an example, Zepto, an India-based quick-commerce platform with 1.5 billion in annual sales, migrated to MongoDB from PostgreSQL after experiencing scalability challenges. Zepto offers users a choice of over 15,000 products with a promised 10-minute delivery and has grown rapidly since its founding in July 2021, recording 20% month-over-month growth. After this rapid growth, Zepto faced performance issues with its previous infrastructure powered by PostgreSQL and Redis clusters that could no longer scale. By migrating to MongoDB Atlas, Zepto overcame these challenges through built-in features like in-memory caching, sharding, and real-time analytics. This transition enabled them to reduce latency by 40%, handle six times more traffic, and improve page load times by 14%, directly enhancing customer experience and enabling their fast growth. As we look ahead, we're confident that MongoDB's combination of architectural advantage, enterprise trust, and broad developer adoption positions us to lead in both the current wave of digital transformation and the next wave powered by AI. We also remain focused on our other strategic priorities we've discussed in previous quarters: moving upmarket and modernizing legacy apps. We're seeing good progress on these initiatives, which will fuel growth into fiscal 27 and beyond. This quarter, we hired a new leader who has nearly 30 years of experience in technology transformation at leading systems integrators to lead our application modernization program. We continue to see significant demand to modernize legacy applications, and we're making great progress on tooling to automate this effort and standardize and productize this offering. While we continue to invest in the long term, we are also sharpening our focus on operating efficiency. We view this as healthy discipline, regularly reassessing the return on our spend, identifying what's working and what's not, and reallocating resources to high-conviction areas and improving profitability. To help usher in our next stage of growth, I'm delighted to introduce two new leaders to the executive team. Mike Barry, our new CFO, joins us from NetApp, where he served in the same role for the past 5 years. Mike is a seven-time CFO with over 30 years experience in technology and software and has a proven track record of driving profitable growth. We have also promoted May Petri to be our new CMO. May joined MongoDB in early 2022 as VP of Digital and Growth Marketing and brings the right mix of enterprise experience and results orientation to lead our marketing organization. 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 the European Commission, Lenovo, Nokia Networks, and CSX. CSX, a leading US railroad transportation company, migrated its mission-critical railroad transportation operations portal, which is responsible for real-time monitoring and alerts across 21,000 miles of track and ensuring uninterrupted 24x7 availability, onto MongoDB Atlas. CSX can now dynamically scale workloads and optimize database management. With this modernization, CSX is positioned to achieve greater operational performance while driving long-term sustainable growth. Startups and mature companies are using MongoDB to help deliver the next wave of AI-powered applications to their customers, including Cursor, Halion, Vonage, the Financial Times, and LGU Plus. LGU Plus, a South Korean mobile network operator owned by the LG Corporation, built its agent assist AI solution on MongoDB Atlas, which supports thousands of agents in accessing information and delivering accurate responses to customers quickly. They use MongoDB Atlas Vector Search to enable real-time AI capabilities such as identifying customer intent and providing guidelines on how to respond to inquiries. The solution has significantly enhanced customer experiences and decreased the average processing time per call. In summary, we had a strong Q1 and we remain confident in our ability to execute the long-term opportunity. We're steadily advancing toward our vision of becoming the go-to platform for enterprises and the first choice for developers creating new applications. Before I turn it over to Mike, I would personally invite you to the investor session at the MongoDB local event to be held at the Javits Center on September 17th. Please email [email protected] if you're interested in attending. With that, here's Mike.

Yes, so thanks for the question. What I would say is that we still talk to customers who have very near-term needs for running their business, building new applications to drive operational efficiency, building new products and services through software to drive to take advantage of new revenue opportunities and to continue to drive more innovation in their business. I think what people find attractive about MongoDB is that you really can use it for a wide variety of use cases. You can support very transactional intensive use cases, you can support more modern use cases, things like IoT, streaming, and so on and so forth, as well as being able to also support some of these more modern use cases like AI, and the fact that you can do this all in one platform where you don't have to stitch together multiple tools, but the underlying architecture is designed to really help you model the real world, to be able to handle complex, nested, and evolving data, to be able to scale elastically, to be able to run these applications on any cloud, across clouds, or on-prem just makes MongoDB a very attractive solution. And we feel really good about the fact that we added a lot of customers, so what it really shows is that customers and developers are voting with their feet to really adopt MongoDB.

Thanks, Raimo. To your first question, I think the moves by both Databricks and Snowflake validate one thing: that OLTP or the operational data store is the strategic high ground, especially for AI. That's where inference happens. Inference is the big market, that's where everyone wants to go, and you need to have an operational data store to do that. And I think the other thing it points out is that building organically an OLTP store is really hard, especially when you need to meet the requirements of enterprise scale, availability, resiliency, and security. And both organizations had signaled that they were working on organic approaches. You know, Snowflake talked about UniStore, Databricks have talked about their own organic efforts, and it's clear that they couldn't make it happen, so this is not an easy task. The second point I'd make is that just because they're buying small PostgreSQL companies, I think Neon was in the vibe coding space and Crunchy Data is a small relational company based in South Carolina. It's not clear to me why the world needs a 15th or 16th PostgreSQL derivative database. I think we'll find that out. And there's also some noise about how Neon is 80% of its instances are provisioned via code. I should point out that nearly 80% of MongoDB instances on Atlas are provisioned via code. And so we do that to help our customers provision and scale clusters very quickly. And so the real advantage is architecture. And we believe that the fact that PostgreSQL and other relational platforms are now adding JSON is a tacit admission that the core tabular architecture just doesn't get the job done in the world of AI. Developers need to be able to model real-world data, which is complex, messy, nested, which means it has highly interdependent relationships and is constantly evolving and changing. And then when you look at the fact that they bolted on these capabilities, if you add a document size greater than 2 kilobytes, it's going to deliver very poor performance. And so superficial compatibility does not mean it's native, does not mean it's production grade, does not mean it's designed for enterprises. And so if the competition's now going to compete for these complex AI workloads, we welcome that challenge because architecturally, we think we have a huge advantage.

First of all, there are few companies that are greater than $2 billion of revenue where their main business is growing 20% plus, the total business growing double digit with 70% plus gross margin. So stop there. That's a scale business that has a lot of leverage built in. So the three things, or the four things I looked at: already had the scale from an international and from a product perspective, and the main business now is growing very quickly. Number two, this is a business model that has leverage because you can bring additional revenue in. It's going to come through the gross margin line at high margins. That leaves a ton of room for investing in the business, but also candidly for driving more efficiency as well. And that was the third piece. When you look at it, and nothing against the company as it sits today, as we grow, I'm completely confident we can continue to invest in the business but become more efficient. And then the fourth part was, it's really nice to come to a business that has a super clean balance sheet and a bunch of cash on there as well. That leaves a bunch of flexibility going forward. So all of that I looked at and said, 'Wow, what a great opportunity.' And then of course I looked at where you folks were valuing the business and I said, 'Wow, that's a really good opportunity.'

Thank you.

Yeah, Jason, thanks for the question. I want to clarify some of these misconceptions that are out there. One is that this is a big market. It's a hundred billion dollar plus market, so there can be multiple winners. Second, Postgres' popularity is really a function of the consolidation of the SQL market. People are leaving Oracle, leaving SQL Server, leaving MySQL and going to Postgres. The third thing I should mention is that Postgres does have this veneer of being open source, open standard, not owned by any one vendor, but there are literally every vendor, including the hyperscalers, have their own version of Postgres that build proprietary extensions and other capabilities that actually make it very difficult to go from one version of Postgres to another version of Postgres. With MongoDB, you can actually run any workload on any cloud, across clouds, and on-prem without changing a line of code. And lastly, I would say architecturally we are far better optimized for this new world of complex modern applications, especially in the world of AI. JSON was designed to really address the needs of this modern world: how data is very messy, how it's very interdependent, how it changes often, there's no predictability in the format, there's no uniformity on the structure. MongoDB is designed to handle that world. And when relational databases start trying to mimic our features, what does that tell you? It tells you that their existing architecture is not designed for this world. Now, there's no question when a technology has been available for 40, 50 years, there's a large group of people who understand that technology, but we feel we're well positioned. We have more work to do, but we feel well positioned to be a winner in this next wave of applications being built. And we feel confident about our position.

Yeah, I would say I definitely think that there will be multiple winners. This is not a zero-sum game. I also believe that the other point I want to clarify is a lot of people compare MongoDB to Postgres, and that's actually a false comparison. By us embedding keyword search, by us embedding a native vector search, by us embedding embedding models, you're really comparing MongoDB to Postgres plus Elastic plus Pinecone plus something like Cohere. So the value for customers is that they don't have to stitch all these capabilities together. They get all these capabilities in a very elegant, natively built way that allows them to move fast. It's not a very complex architecture, and it's much more cost-effective. But I do think there will be multiple winners, and for people who want to stay on relational, Postgres is a very viable option, but we think that we have a big opportunity in front of us.

Thank you.

Thanks, Kash. Let me start. When we talked about the strategic initiatives, we really called out three things: one, R&D investments; two, moving up market; and three, putting more focus on awareness and education. On the R&D investments, I would tell you that we're already seeing returns on investment. We said we're going to double down on the core. We introduced MongoDB 8.0, which is the most performant release we've ever issued. I will also point out that it's also had the fastest uptake of any major release. Customers are adopting it two times faster than our last major release. We're also expanding our engineering efforts around AI and Voyage because that's a super exciting area for us, and we're also investing in product tooling for app modernization. Last but not least, we're bringing more senior talent to really complement the existing team so that we can have a broader ambition. So that investment is paying handsomely. The move up market is also going well because a part of our results are a function of the fact that we made that move starting last year, and we're starting to do bigger deals. We've signed some very, very large deals with some very, very large enterprises. The productivity of that team has always been quite high, and I would say a complementary move is that our self-serve business is starting to acquire mid-market logos, serving them more efficiently without ceding ground to anyone else. That shows up as you see in our customer account this quarter. In terms of awareness and education, we are aggressively investing in a few areas. One, we're aggressively investing in the Bay Area. That's where the next-gen AI companies and the next-gen AI developers are highly concentrated, and we're starting to see some traction there. We have some high-profile AI customers already on our platform and lots of other smaller customers. We're investing in attracting relational developers to learn more about MongoDB, attending relational conferences, putting together more training and more skills for people to upskill their abilities, and also providing certifications. What we also find is a lot of the new Atlas registrants are actually new to MongoDB. So we're spending a lot of time making sure they're onboarded properly and taking full advantage of all our capabilities. As part of the training, we're upskilling developers on modern databases. Certifications, self-paced courses, and all that. We also expanded our documentation to Mandarin, Portuguese, Korean, and Japanese because MongoDB truly has a global business and there are developers all around the world who want to use MongoDB. I think we're just getting started. There are more things we're doing that I just can't talk about right now, especially around awareness and education. But the key point is there are some misconceptions about MongoDB that we know we need to address, and we're quite excited about the opportunity to do so.

Thank you.

Yeah, so what I would say is the following. We see thousands of customers building thousands of apps on MongoDB, and that's growing quarter over quarter. We are seeing some high-profile, well-known AI companies. I mentioned Cursor on the call, and there are a few other high-profile companies who are building on top of MongoDB, and obviously those businesses are really taking off. But what we see is that enterprises are still early in the adoption of AI. The barriers include a limited set of skills and experience with AI, trust with AI systems that are probabilistic, which is another way of saying the risk of hallucinations. So we see some early use cases around operating efficiency, chatbots, codegen, and domain-specific ISVs like Harvey. Customers are using them, and we've already seen, as I mentioned on the call, LG U+, Swisscom, Novo Nordisk, CentralReach, a bunch of customers I mentioned in the past who have already deployed our AI capabilities. But the real enduring value will come when people start building custom AI apps. The point I want to make is that anyone can use an ISV to run their business, but that doesn't give them a competitive advantage because their competitors could use the same ISV. What really gives them a competitive advantage is building custom solutions using AI to transform their business, whether it's to see new opportunities, respond to new threats, or drive more operating efficiency. When people start really learning about MongoDB, the document model can handle these complex data structures. We have best-in-class Voyage embeddings to improve the accuracy of these results to help people get comfortable with using AI. By integrating text search, vector search, and embeddings with operational data, that's a unique differentiator. It makes the developer's life easy, reduces cost and complexity. So we feel we're well positioned for this, but it's still early as most enterprises are still early in the adoption of AI.

No meaningful changes. We feel good about what's happening at the high end of the market, and we also feel good about our self-serve business being able to acquire customers more efficiently. So we feel like those motions are working.

Thank you, Brad.

Yeah, we're very proud of our results in Q1. Much to what Mike mentioned earlier, when you look across the landscape, there are not many companies who have their core business growing at 26% year-over-year at our scale. Atlas is a very large business, and there are not many companies who are growing at that rate. We feel like what's on the horizon in terms of AI, and I haven't talked about app modernization where we can help customers more efficiently reduce the cost and significantly reduce the time to modernize legacy applications, that gives us easy access to a large market. Those are also initiatives that we're spending a lot of time and investment on. There are lots of customers who still need to run their business and continue to build applications that are core to their business strategy. So we feel good about our opportunity. Our guide is our guide. We feel good about the quarter, and we also feel great about the fact that our customer adds were very strong this quarter, which shows that people are embracing MongoDB.

Thank you.

Well, I will tell you that I think we already have meaningful traction. We previously disclosed that 75% of the Fortune 100 are existing MongoDB customers and 50% of the Fortune 500 are MongoDB customers. So that tells you that we already have meaningful traction, and what we realize is the biggest opportunity for us is to expand in those accounts. I just recently had the CIO of one of the largest healthcare companies in the world in our office. I just met with the senior leadership team from one of the largest financial services companies here in New York, and then I met another team from another financial services company in New York, and they're bringing us in saying we want to have a more strategic relationship with you. So I feel like the motion is working. We're doing larger deals, and the productivity of the sales team focused on those accounts is materially higher than the typical sales rep. So we feel like it's a motion that we will invest in for the long term, and the results will obviously speak for themselves, but we feel really good about our move up market. I also want to point out that our self-serve motion is a nice complement to that move up market because it allows us to acquire lower-end customers, mid-market customers much more efficiently. So we're not ceding ground to anyone in that segment of the market.

Thank you.

Thanks for the question. I just want to again say we're going after a big market. I think Postgres' popularity is a function of people basically leaving other relational platforms, in particular Oracle, SQL Server, and MySQL. So that's why you're seeing developers move to Postgres. But I would tell you that Postgres is a tabular database, much like all relational databases. So the question you have to ask yourself is, they announced support for JSON. Why did they do that? They did that because it was a tacit admission that that architecture just doesn't get the job done in a world that has to deal with data in the real world. Data in the real world is complex. Data in the real world has a lot of dependencies. I'll give you some examples. If you want to model a message that has attachments or reactions or is part of a threaded conversation, how do you do that in a structured table? If you want to deal with adding new fields or new values, how do you do that? For example, if you have a user who has multiple phone numbers, how do you model that quickly? How do you deal with nested structures? A customer record could include past orders, each with their own line items and order history. How do you do that with a relational database? It's much more difficult. You can model that so much more easily in MongoDB. How do you deal with messy, inconsistent data that has no uniformity? We recognize that some people who don't know MongoDB may not really understand all these advantages, which is why we're putting more emphasis on awareness and education. But fundamentally, if you see why these relational database users are adding JSON support, it is an acknowledgment that their existing architecture cannot natively evolve to serve these new needs. That's why we think we're well positioned, because MongoDB is a native JSON database. It's a document database, and it's distributed. It is designed to scale, and the latest release is the most performant release. We're even more excited about 8.1 that's coming out soon. We acquired Voyage. That's going to be natively part of the platform. Later this month, we will enable people to seamlessly generate embeddings from data sitting inside MongoDB, and that'll be in private preview. That's within 4 months of the acquisition. So we're moving fast. We're innovating quickly. That doesn't even mention our core vector search engine as well as our keyword search engine. When you put all these things together, it becomes a very compelling platform. But we recognize that some customers and some users just don't understand all these things, and that's what we're focused on addressing.

Thank you.

Yeah, I mean you have to remember our self-serve business was a new skill that we developed organically here. May Petri, who's been promoted to CMO of the company, was the one who led our self-serve business since early 2022. She and her team have really done a great job of growing that business, being much more sophisticated, running experiments on how to attract the right level of customers. That is showing up in the numbers. As we move up market, we want to take advantage of that self-serve capability to be able to acquire more customers in the mid-market. So that's something that we're going to do. We feel really good about the combination of our direct sales force as well as our self-serve business in terms of how we approach the market. I would say that when we are able to get in front of customers and explain our differentiation, customers understand and want to use MongoDB. Our biggest challenge is making sure people really understand the differentiation and don't have certain misconceptions of what we do or what others do.

Thank you. Thanks, Mike.

Well, thank you for joining our call. First of all, I would like to thank Mike. Eight days in, preparing for an earnings call is hard work, and to do it in 8 days is pretty impressive. So I really appreciate everything he's done to prepare for the call today. Again, we had a strong quarter with a record total customer net additions. We're raising our revenue and operating margin guidance for the full year. We're moving forward with a billion dollar share repurchase program, reflecting our confidence in the business and our commitment to delivering value to shareholders. And we're more excited than ever about our long-term outlook, particularly our position to fundamentally address the needs of workloads in both today's era and tomorrow's era driven by AI. So thank you very much for the call, and we'll talk to you soon.