MongoDB, Inc. (MDB) Earnings Call Transcript & Summary

All right. CJ, let's start with you. You've now had nearly a year in the role, hundreds of customer conversations. What's become clear to you about the role MongoDB can play? And how does that change your ambition for the company?

Absolutely. First, it's great to be here. Thanks for inviting us. I would say, yes, the -- on an average, the customer meetings I do tends to be around 10 to 12 a week and really trying to understand how do customers see us today and where do they see us in the future, right? And this is across AI native, digital natives or Fortune 500 or Global 2000. The biggest learning I had is that we are definitely seen as a very modern database that can scale significantly for massive workloads, right? So to give you an example, one of the Fortune 100 firms in North America told me that after they moved multiple of their workloads on MongoDB, they have now created us as a standard that any new application unless grown otherwise should be on MongoDB, okay, which is a massive thing in a highly competitive database world, as you know. So one is that it is the most modern database. It is the right data platform when you think about creating AI workloads that are real time. It seems like it is a great technology or a great foundation. So it's my learning number one. Learning number two is the modernization opportunity that Dave and the team have talked about in the past, that is real as people are trying to get AI ready. And I know we are early and we said that this year, Mike and I shared with you guys that we are focusing on creating the right product and product-focused approach versus a services-focused approach. But that opportunity, specifically in Fortune 100 and Global 2000 is very real for MongoDB as customers prepare themselves for AI. And then the third thing that I would share is my learning has been as MongoDB grew phenomenally workload by workload over the last many years now, including Atlas, the awareness in the C-suite has been low. So that's the, I would say, the thing that we can improve on is what I realized in having this conversation. And the AI decisions, what is the AI architecture for the workloads where do they see MongoDB in. Those are usually top-down decision made by a Chief Data Officer or Chief AI Officer or Enterprise Architect were -- so my realization was when I would tell them, "Hey, we have now vector integrated fully, we have this great best-in-class embedding model." So you should build your agents on top of MongoDB. They are like, gee, we didn't know that. And with one very large financial services company 6 weeks ago had the conversation, and now they're doing a POC on top of Vector, Voyage and Mongo together. So I found that the awareness was low in the C-suite on MongoDB.

Yes. I think since day one, you've talked about maybe driving more top-down engagement. I guess where are we in that journey over the last kind of 12 months? Like how much higher is engagement around winning some of those newer workloads.

Yes. So sales cycles when you go drop down tend to be always long. But it is early, but it's working. So we are having a lot more strategic conversations with a large telecommunications for or a large retail firm in Europe, and they are saying, okay, we will standardize one of the Fortune 100 companies in Texas once we engage, they said CJ, the reason developers love MongoDB, but you have not served us in the past. So you are not one of the standards on our marketplace, let's fix that so that people can start building on MongoDB. So early, but the signs are very encouraging, and we are getting opportunities for newer workloads, including AI through those top-down conversations.

Okay. Great. Mike, let's bring you into the conversation. Atlas has been sustaining around 29% growth for the last few quarters. It seems to be largely led by large customers, while the newer AI cohorts remain a bit earlier. What is different about the workloads you're landing today? And how does that shape your view of the durability of the current trajectory and its potential to even strengthen over time? .

Sure. So again, thanks for having us, Matt. So let's talk about the workload growth as it relates to the durability. In answer to your first question, we've not seen very much -- there hasn't been a big difference between the workloads that we signed historically versus today. There's always some nuances by geo or by sector. What's really driving the growth is 3 things. One is, as we've continued to increase our go-to-market focus on the larger enterprises, as those workloads grow, we have 2 jobs. One is to get more workloads. And the more Mongo we have in there, it gives us the ability to expand our workloads. That's number one. Number two is the cross-sell, especially related to vector, search and now embeddings. While almost half of our large customers have multiple products, the revenue contribution from that is still quite a bit lower. So that's the job to drive it up. And what underpins all of that, Matt, is -- and team have done a great job on the reliability and the performance of Atlas, and that has enabled us to limit the churn and contraction in that base. So we expect all of that to continue, which is why it gives us confidence in the durable growth. Again, not much difference in the workload trajectory, but that's really what's driving the growth in those large enterprises.

Okay. And then CJ and Mike, for you guys. AI demand is clearly building across the Frontier Labs, AI natives and a little bit on the large enterprise side as well, but they are on a different adoption curve. So can you walk us through the piece of new workload acquisition across those separate cohorts? And how you see each of those beginning to influence Atlas consumption?

Yes. I'll start and then Mike can contextualize in terms of how we think about the durability of it and what does it mean, is even some of the examples that I have publicly used with the permission, so for example, ElevenLabs, ElevenLabs is doing phenomenal. They started originally with a first-party service from a hyperscaler that could not scale as the number of agents were scaling, AIs that business on switches and -- and then the -- one of the founding engineers realize that this is not scaling the agent workload for ElevenLabs, which is now doing north of $500 million in ARR, a very successful company and switch to more doesn't call me by the founding engineer in Europe. And what they shared were 2 things. Number one is having search vector search integrated into the operational layer made it simpler for them to scale before they were taking this data out of this first-party database, putting it somewhere else where search, data pipeline, fragmentation and of performance issues and outages -- so one is now they have peace of mind. If you talk to Marty and the team, they say, malware works, okay? So that's one. Second thing on your question, a year ago, they were not even a customer, okay? -- including some of the Frontier Labs example that I have shared a year ago exactly September, they were not a customer. So we are early. And that's why Mike and I continue to say that we are early. We are optimistic -- and we see that some of these folks don't start with us. And some of the folks do start with us, but that's a minority. Most when they hit scale issues with progress or relational flavor whichever they are using, then they switch over to MongoDB. So that's why we continue to say, including the remarks last week that we are early.

Yes. And just to add to that. Thank you, CJ. We've talked about the growth vectors here. AI native. We love that, lots of customers still early in the process. the labs, the same thing, very large customers. Our goal is to continue to increase that. For us, the big inflection point is when enterprises start to deploy AI and scale. So this is growing still relatively small, but we've seen great traction, and we do think it will continue to be a driver of growth. It's really that enterprise piece, which is the inflection.

Yes. And I think one last thing I do want to touch on is what really encouraged me over the last couple of quarters since I've been here is the new customer acquisition. So when we look at Voyage, predominantly that acquisition is coming via coding agents, right? So whether you go to Claude code or Codex and you say, what's the best-in-class embedding model, given the open AI has their embedding model, the answer typically almost always is voyage, which is helping us on the top of the funnel to be able to upsell and cross-sell Atlas. So that's number one. And number two, getting 2,900 net new customers in Q2, this will become future customers of Atlas in a meaningful way, some of them and then the voice piece is important because the voyage is almost always an AI workload. That's why they are using us, obviously, the embedding model for. And that will -- we'll see how that plays out because Voyage acquisition only happened 18 months ago. But the number of logos and the quality of logos that I see that are signing up with Voyage is high.

Okay. Great. Then let's bring you in, CJ just referenced how voyage is actually generating a lot of activity vis-a-vis the coding agents. But I think when you look at the broader ecosystem of new applications, a lot of that seems to be defaulting to Postgres, right? So how much progress has Mongo made in getting considered from day 1? And are you seeing that translate into more greenfield wins?

Yes. Thanks for the question. So I think a couple of things. So a few weeks ago, we had our build best down here in San Francisco. We just announced another integration yesterday with Percel, but what we announced a few weeks ago was we've always had an MCP server and the MCP is really that a gateway for the agent to talk to something else, right? And we made that significantly better to where the friction between like the sign-up provisioning and then actual usage is now doesn't exist. So when we announced that a few weeks ago as well as what we did yesterday, now the ability for the model or a coding agent to not only select us because it's the right technology decision, but we also will get benefit of the fact that there's no friction to the incident process. So that's really going to be a big, I think, driver to -- from a discoverability perspective. Now we are there in the same spot as some other players. But at the end of the day, the models are making 2 decisions when they test decision, the data model and then technology. they're making the right decision on the data model. They sell Jason. They're making the wrong decision on the technology, and that's the content game that we've been talking about over the last few months, and there's a lot of investment going in there the day you've heard us talk about. And we're going to continue investing in that awareness activity.

Yes. And I would say just -- we shared this on the call back last week is -- we currently between August 13, that's been outlined in today, September 10. The data seems encouraging. Once we help this manage MCP across Codex, Claude and Brockville and our intent is if we get more data points to be able to share at Investor Day, but this was a critical piece of integration that we had to do with coding agents for them to now normal as much as we missed.

The last thing I'll just add just real quick. Our MCP is unique in a sense where a lot of MCPs for database just provide data access. Our actually allows you the data access, but also allows the coding agency do provisioning of clusters. And so by having the MCP to be able to do both, it opens the door up for that for tiles experience. So it's pretty unique in that case.

Okay. Perfect. CJ, one of the more interesting examples this quarter was a Frontier Labs using Atlas as a memory layer for inference. So as agents become more persistent and personalized like how significant could memory become as a distinct new workload for MongoDB?

I would say, first, when I really understood the use case with one of the labs, they started with 1 product to users as a both short-term and long-term conversational memory that gets saved in MongoDB. And MongoDB from unstructured data perspective is the best place to save it. So it kind of makes sense, both across read and writes. And then the same lab for another emerging product also is using us for the same use case. This is something that customer drove the demand in this case. And now we are using that with enterprises to say, here is how a particular lab for this particular use case uses us for memory. So we are, I would say, early in positioning that correctly. And then depending on the customer, they understand it. So I was with a large insurance firm and we said this is how MongoDB is used as a conversational memory and this particular lab is using it. And they said, "Oh, we have the exact same problem. The current solution we have is not working well, let us do a POC on that. So emerging use case will share more at the Investor Day around this whole memory phenomena, but that is definitely a clear MongoDB advantage right now.

Okay. I want to switch gears to EA. It seems for years, the infrastructure conversation was only moving in one direction toward public cloud. More companies are now looking at self-hosted and sovereign environments as deliberate choices. It's not MongoDB specific. We're seeing this across the broader software ecosystem. So I guess, CJ, the question for you is what's driving that change? And how durable do you think that will be?

Yes. So Mike and I speak about this all the time in what is driving this demand? That's the question we also got last week because 36% growth in a long time across industries on enterprise advance, self-managed was phenomenal. And for the first time, as you saw we raised the guidance in double digits for EA, 11% that we have not done in 3 years, right? Last 2 years was 7%. So one thing is -- we do want to meet customers where they are. So that -- I would start there first. And in my early days with MongoDB, the customer feedback, whether it's related to a customer, say, in France or the U.K. or whether it's in the United States or in Canada, for that matter, was for a variety of reasons, whether there were public cloud-related constraints or whether they wanted to build the AI layer in-house on-prem because if the memory prices are going up, clouds are going to charge you more. economics may not work. So I'm going to now run this in on-prem. And our ability to run anywhere was a clear advantage. But what changed in 2026 from my standpoint, we are 9 months in is confluence of 3 things. One is public clouds definitely have capacity issues that they are talking to even regulated industries, whether it's banks and other places. Number two is certain workloads because of the cost-related issues besides the capacity-related issues, they want to run on-prem now. And then specific to AI, getting these workloads AI ready, they would rather run it in-house. And from a database standpoint and a modern database standpoint, we are the only one who can say that you can run that. And one last example I would share a large financial services firm right here on the U.S. West Coast told me they will move even more workload to Atlas if we can do a proper failover between Atlas and EA. So this is not coming at expense of Atlas. This is also helping us with Atlas because certain workloads will go to Atlas, but certain workloads, they like that we have this option. So that's how I see it. The initial demand is high. Ben and the team got search and vector search done on June 30, which has now created pipeline robust for AI workloads in self-managed environment. And that's why Mike and I felt comfortable last week to say we will grow double digit for the year. Mike, do you want anything.

Mike, maybe let me just double-click on this because I think if self-managed demand is becoming more structural than, let's say, episodic, like how is that going to change the longer-term profile of MongoDB?

So we think it will have a material impact on the growth. So in the past, if you look back a couple of years ago, and again, we'll talk about EA ARR to get the duration out of there. If that was growing in low single digits, now what we're talking about is ARR, 3 straight quarters above 10%. Well, it's not 29%. We understand that the ARR growth is significant. We now look at it that we have 2 durable growth drivers, between Atlas and EA or self-managed. And we do expect that to continue all the things that CJ just talked about. The other thing I want to make sure we get this question a lot, which is if a customer uses EA does it come at the expense of Atlas. And our answer to that is a definitive no. If you look at the EA population, a good percentage of those also have Atlas, especially in the larger customers. If you look at the Atlas consumption growth in that cohort that has EA, it's actually higher than the total company. And that's what CJ is talking about. More Mongo is good. More Mongo, either Atlas or self-managed. We do not see it cannibalizing EA, I'm sorry, Atlas, we actually see it contributing to that growth. At some point, does some of those workloads move to the cloud, possibly. We'll see what happens, but we want more Mongo to drive both EA and Atlas, and that's what we're seeing in that cohort.