MongoDB, Inc. Class A Investor Day 2026

Where data lives for 3 reasons. First, regulation: in many industries, in countries dictate how data can be stored and where. Enterprises want control and are concerned about intellectual property. They need to know exactly where their data lives and how it is handled. And now, with AI in the mix, they don't want their proprietary data exposed to risks around AI access, training, or ownership. Plus, business continuity and resiliency means enterprises need a safety valve. They can't be locked into a single environment. If there is an outage at a data center or they want to change vendors, they need their applications to be truly portable. And we're seeing these reasons play out with our customers today. For example, nationwide UK, the second-largest provider of household mortgages and savings in the UK, runs across both EA and Atlas to meet their regulatory requirements, while giving members real-time access to the accounts and their transactional data. A major US bank uses EA to serve more than 100 production applications across payments, fraud detection, and account services. By self-managing, they keep the sensitive customer data in conversational data inside of their own environment. And then lastly, Zoom runs EA-deployed across dozens of environments globally to deliver low-latency, highly available communications at scale and wherever their business needs dictate.

This need for flexibility is exactly what our run-anywhere strategy is built for. And what makes run-anywhere even more powerful is having EA as our self-managed option alongside of Atlas in the cloud. Last month marked the 10-year anniversary of MongoDB Atlas. Back in 2016, we were convinced that the cloud would fundamentally change how organizations build and use the database. So we built Atlas, a fully managed database as a service with all the benefits of cloud-native infrastructure. And since then, we've delivered a steady stream of innovation from our initial launch and support for the three major hyperscalers to client-side field-level encryption, to a advanced networking options we've stayed ahead of the curve in defining what a world-class managed database should be. Today, Atlas can run across AWS, Azure, GCP, and 130 cloud regions. Customers can even run a single cluster across multiple clouds simultaneously. And Atlas is also available in all the cloud marketplaces. So regardless of what hyperscaler our customers choose, they can transact through their cloud provider with ease. But that's not a forget that this is the same database that you can run in any environment. This is not something you can easily do with Postgres.

Since there are so many flavors out there, and each behaves just a little differently, as Dave said, Amazon Aurora is not the same as Neon, which is not the same as Supabase, which is not the same as open-source Postgres. You think you're betting on one database, but you're really limiting yourself to that specific flavor. This means applications are not portable. With MongoDB, our database can run on a developer's laptop in self-managed enterprise environments and even in air-gapped deployments for the most sensitive workloads like public sector or regulated industries. This is the definition of run-anywhere. Because of this innovation and flexibility, Atlas has been used by nearly 20 million users in processes more than 3 trillion queries each day. To put that into a little perspective, that's 35 million queries a second, and about 340 queries for every man, woman, and child on Earth per day. Let's leave that for a second. It's more than double what it was just a few years ago. But that's not forget. Atlas is also a great way for builders to get started. There's this perception out there and you all ask me about it every time we talk. But the data tells a different story.

Not everyone starts with Postgres. On average, we see 500,000 free-tier clusters each quarter, and this has been growing steadily each year. And when you combine that with 150 million community downloads, that's a huge number of builders that begin their journey with us. Putting it all together, we believe that our customers deserve the same capabilities no matter where they choose to run. And it's this portability that really matters. Run-anywhere gives our customers unique flexibility advantages and is a huge competitive moat for us. That is why we're also continuing to invest in EA. In June, we GA'd Search and Vector for self-managed customers. And as a reminder, we introduced Vector Search in 2023 in Atlas, which was actually before the ChatGPT launch we were in private preview with our Vector Search. And today we see customer workloads with billions of vectors. Vector Search is a core building block for our AI applications, and customers told us clearly they needed it to run in their own self-managed environments, not just in our cloud. And with that, teams end up stitching together with that, out that, excuse me, teams would end up stitching together a database and a vector store themselves.

And this is what they have to do today with relational databases. Which adds complexity in an infrastructure tax teams cannot afford when trying to ship AI applications fast. So by making Search available for enterprise advance and customers get the core database and Vector Search running anywhere, they need without the complexity. The demand came in immediately, and across different use cases. A large multinational bank is simplifying its AI architecture by bringing together operational data, vector embeddings, and full-text search on top of enterprise advance. And another large multinational financial services company wants MongoDB to serve as a system of record for all the customer information using our search capabilities. Enterprise choice matters more than ever. Our customers want to know we will keep investing in our self-managed options, which we've made that commitment, and we don't plan to stop there. Now, all of that only matters if the core database can carry the load which brings us to MongoDB 9.0. Since our inception, we've never taken the foot off the gas when it comes to the database innovation. As we've moved deeper into the enterprise, the applications we power grew more demanding. We matched that growth with a concent drumbeat of innovation, staying ahead of the customers' needs so they could keep building on us at any scale.

With that innovation, I just want to remind you that we were one of the first distributed databases to support transactions, and that we natively supported horizontal scaling, which is how you distribute data across machines to support a larger scale from the very beginning. Our 8.0 release went GA two years ago right around now, which delivered meaningful performance gains over the previous release. This release has experienced faster adoption with nearly 80% of the Atlas fleet is already on 8. Since launch, we're also seeing higher customer satisfaction and improvements in customer retention. So clearly, our customers have been pleased with what 8.0 has delivered, and now this brings us to today with the release of 9.0. 9.0 is the best release of MongoDB we've ever shipped. We can continue to harden the core to give the most demanding enterprises a strong foundation, and with 9.0 we're setting a new bar for security, durability, availability, and performance. We've made a significant improvement in the way we look at performance and scale. Up to 20% higher transactional throughput, 35% faster reads, 30% faster updates, and two times more throughput on larger instances. So to put it a little bit in perspective is 9.0 is faster than 8. 8.0, which was faster than 7.

And if you use the release before 2023, we're about 100% faster than we were back then. The customers have also noticed this. This has made MongoDB a better fit for more opportunities. All of this adds up to greater efficiency, which means greater consumption. And we saw this pattern with 8.0: better performance and scale means customers save more serve more users transactions and AI-driven activity. In other words, it's Jevin's paradox in action. When we introduced better price performance, we've seen stronger growth in subsequent periods. Our customer Coinbase has been putting 9.0 through its paces at the real production scale so let's take a look at their video.

Coinbase is the largest crypto exchange in the United States. In addition to trading tools, we also provide a developer platform to empower agents to discover and buy things with crypto.

Back early in 2024, we saw an opportunity to revive the HTTP 4.02 standard. However, realize that we needed to build a way for agents to be able to autonomously discover all the different services that existed on the internet, and that's why we decided to build the bazaar.

One of the advantages of running our X4.02 facilitation and bazaar discovery on MongoDB's multi-region architecture is that it allows us to have low latency as well as high availability across regions, while still keeping our core functionality available. This enables us to have more flexibility and observability into how we're generating our search results without having to maintain several separate search infrastructures on our own.

MongoDB 9.0 is a great step forward for us as we think about how internet-native payments are going to scale to billions of transactions over time. It's going to be parallel searches, all of that being handled and abstracted away behind the scenes is yet another example of how our team can stay lean, focused on building, and have all of the implementation details and nitty-gritty of our database handled for us. I can say with confidence we don't have to worry about details around our latency, uptime, operational SLAs and SLOs, and all of that backend work is going to be handled seamlessly. Working with MongoDB means that our team can focus on the core competency of bringing agentic payments to the mainstream, supporting hundreds of thousands, if not millions, of AI agents transacting seamlessly and frictionlessly across the internet.

Thanks to Coinbase for that awesome collaboration. We switched the screens in the back, please. Thank you. So performance and scale are non-negotiables for their business, and that's exactly what 9.0 has been built to deliver. In 9.0, we've also enhanced our industry-first and industry-only queryable encryption capability. Last year at our investor day, our CTO, Jim, talked about queryable encryption which is a powerful security capability that only MongoDB has. In most databases, the data remains in the clear so it can be queried. Queryable encryption lets customers keep the data encrypted and still query it. So let's say a customer wants to find a name that starts with the beginning parts, or find a credit card number that ends with 6789, or a medical record note that contains fever. They stay they can run these queries while the data stays encrypted, and the query itself is also encrypted. This is why queryable encryption makes MongoDB really suitable for applications with super highly sensitive data. For example, the France's internal digital force manages and secures digital communication systems for law enforcement. They needed a defense in-depth strategy and queryable encryption let them encrypt the data knowing that they would only they're the only people that could ever decrypt it.

And to meet regulatory requirements, a leading financial institution needs to retain all the customer notifications about their financial products. They leverage queryable encryption to protect the PII data contained in those communications, which helps safeguard their customer identities. And like everything else we do, we always are thinking about run anywhere. And again, you can use queryable encryption no matter where you're running. 9.0 is the best release we've ever delivered for our customers. The demands of modern and AI applications require us to keep building the most performant and fastest database we can. But there's another level of demand that's on the horizon. And we're skating to that puck today. We're bringing even greater elasticity and scale to our platform with Atlas Infinite, making it easier to handle the bursting nature of AI agents. Infinite is Atlas's biggest architectural innovation since its inception in 2016. You we just heard from Coinbase about how important scale is as they bring more and more agents online. What makes agent scale requirements different from those we've seen in the past? Let's take a look. User-driven applications can be more predictable based on timing or seasonality. They're intensity also scales with the number of users in the system.

So think about Cyber Monday. You know the traffic is coming. You can plan for it. On the other hand, agents don't generate steady, predictable traffic. They can sit idle for long stretches and then suddenly introduce massive unpredictable demand that hits the database all at once. And even with users interacting with the application, agents will also make purchase adjust purchases adjust prices, manage inventory, and handle fulfillment on that same Cyber Monday. So you need a database designed for this unpredictable and intense agentic activity. This is what Infinite delivers: the ability to handle both of these workload shapes simultaneously. It's built for extreme elasticity. Infinite is designed to scale up and scale down rapidly. So when agents run in an autonomous loop or fan out and spawn additional agents, the database is able to respond. This is the exact shape of workload Infinite was designed for from the ground up. As an application scales, customers want more performance per dollar and pay only for what they use. So it offers tunable performance. But to be clear, this is not about spending less. It's about making sure businesses aren't paying for idle capacity that could be used elsewhere.