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

Okay. Hi, everybody. Let's get started. Let's have a fun day 3 today. I'm actually excited with at least the lineup that I've got. I was only half joking with CJ when I met him this morning that having hosted him at previous conferences in addition to him being a very, very capable tech sector executive, he's also one of the more fashionable executives that I know. So when I was pulling my wardrobe together this morning, I knew I had to totally raise my game. So this is the best I could do. I think CJ wins out.

Thank you, Karl.

CJ, Mike and Ben, thanks so much for coming to our conference. Your presence always makes it much better. You're also coming off of a wonderful print, which evidently most investors appreciated. So Mike will talk a little bit about the financials in a moment. But CJ, welcome to MongoDB. Welcome back to Scottsdale. And maybe where I wanted to start is a little bit about your early vision for Mongo. I know you've only been here probably 30 days or something. But I guess, I was struck by your comment on the call that you envisioned MongoDB as being a modern data platform. When I hear that, it sounds like you want Mongo to be more than amazing document store database. And so maybe you could lay out that vision even though it's quite early.

Absolutely. So at the highest levels, Mongo participates in a large market which is always a good thing that it participates in a large market. And this market, my career started at Oracle Corporation.

You know a few things about database.

I do know a few things about databases. And I stayed with Oracle for 7-plus years when Oracle was a high organic growth company in late '90s. And so database as a technology has been around for a long time. And I would say 70s, 80s and 90s, 70s is when Oracle started was very structured data, rows and columns, and that's what we obsessed about in terms of performance, and it was always scale-up architecture. And looking at MongoDB and I've been a customer of MongoDB at ServiceNow, we did a lot of technical integrations with MongoDB discovering MongoDB in cloud or on-prem and so on. And so I have a lot of respect, first of all, on what the founders created in the first decade of this century. And enterprises, Karl, when I speak to customers, enterprises everywhere they have so much semi-structured and unstructured data. There are so many workloads nowadays with as enterprises are looking at AI, and everybody is looking at it, I would say, they're piloting. I would say with a large financial services company last week, and they said, we have 45 to 50 agents that we are piloting, which is an important term whether it's about maybe 3 or 4 are in production employee-facing, not really in a meaningful way customer-facing. And when I see all that, the opportunity for MongoDB to go from a great database, document database with native JSON support and others to become a truly data platform. That opportunity excited me and that's why I joined. And one last thing I would say is that product to platform transition that I've done at previous companies, MongoDB has all those ingredients on when customers, and I've already spoken to 30-plus customers in 30 days, when they look at -- when I speak to them, 3 things they tell me. One, the cloud migration to a GCP or any hyperscaler you pick, that's still going on, and it will still go on for 4 to 5 years. As part of that, there is some, of course, lift and shift, but there is also, hey, we are going to modernize these workloads and given how much unstructured data they have, MongoDB has the right position to it. Second thing is, AI workloads, how do we clean up the data. Of course, everybody is going to these customers saying, we have agentic platform built on us. We have this, we have that. But customers are like, if I'm going to fundamentally change my business or make a truly efficient business, I want something at scale. And to have real-time data store with the right platform is the high ground where MongoDB is positioned. And the third last thing that I would say is that the data estate still continues to grow. So you have a confluence of this cloud factor, AI factor and just core data. And that's what excited me and that's why I joined.

Yes. And you've also got an amazing opportunity to leverage the developer mind share that Mongo has built over the years. I followed your story from way before when Dave became CEO and one of the tenets of Mongo has always been that developers love the product. So that's an amazing base to work off of here. CJ, let's talk a little bit about some of the broad themes out there. I think I'm certainly not the only one noticing that across the software space, there are pockets where we're not really seeing any growth acceleration. Frankly, it's flat to down. The one area where there's consistency and it feels like the whole category is inflecting is interestingly in the data space, where Mongo is accelerating Snowflake. We had Ali at Databricks on stage yesterday, Palantir. So the whole data category in software is accelerating right now. Can you just pinpoint the 1 or 2 things that are causing that? Certainly, when I talk to enterprises, I consistently hear this theme about needing to better utilize their corporate data to make it AI ready. But something broad is clearly happening. And could you perhaps help the audience understand exactly what that is?

So I would say, your observations are correct that infrastructure software related to data, definitely benefiting right now. And there are still questions in enterprises' mind, hey, what do we do with SaaS and what is the long-term vision for SaaS? But you contrast that with data, though, right now, you mentioned some data warehouse companies. Data warehouse companies always are very good at, hey, I can ask these kind of analytical questions, my top 10 trends in the holiday weekend, just give me those answers. And then for data scientists and AI folks in those enterprises data warehouse technologies are helping them out with those questions. From my perspective, so that's one piece that trying to get more value out of the data you have to make the business decisions. Now companies are focused on it rather than in '22, '23, you saw, Karl, there was a lot of efforts on optimization. Do I really want to do this right now? I have other supply chain and issues. If you're a manufacturing company, or if you're a financial services company, you had other concerns related to inflation and so on. And you contrast that on the OLTP side. On the OLTP, now people are saying, okay, we have time to really modernize the application stack, as you saw in our results, they speak for themselves. And what we discussed, Mike and I, 2 days ago, is that where we are seeing is, of course, the high end of the enterprise where the consumption that we reported for 3Q, but also broad-based strength in Europe as well. And then our self-service motion, coming back to your developer point, we are seeing a nice customer growth to say, I finally have time to create a new application or modernize a new application, and that's both working in our favor. So you have data warehouse, the analytics phenomena, and you have the OLTP with us, a phenomena that's helping.

I'm not asking for guidance, that will come in 3 months, but maybe I'll phrase it this way. These drivers you're talking about, CJ, how durable are they? Is there anything that you can share with us, maybe it's anecdotally, backlog color to make the point that these drivers can persist next year or year after. Ultimately, what I'm getting at is, what's the duration of this data investment cycle you appear to be benefiting from today?

I would say, we are always -- Mike and I even spoke at this -- about this thing that how do we think about the future and so on, on consumption. What we saw is that we don't want to be at the highest level, say, this will continue forever, right? I mean, because we just don't know what we don't know, and you saw what happened in '22 and '23. But from my perspective, when I see -- speaking to customers, as they think about the IT budgets in next fiscal year, there is nothing that they are giving the indication that the budgets are going to shrink like what you saw...

I'm not hearing that...

Yes, in '22, '23. They are telling me that AI is still going to be a priority, right? And they still have to show the ROI. I'm talking about CIO, CTOs and so on. And I think from my perspective, we just have to, of course, execute on our side in serving the customers at the highest level and continue to provide right guidance that Mike is now on top of as we move forward. Mike, would you add anything?

So thank you, CJ. I would just do one thing on that, Karl, is that's the part of the industry, and we get a lot of questions. Keep in mind, the changes we've made internally to address those industry trends, we feel really good about the, call it, the durability of that. We've moved resources upmarket. We still have a huge amount to go get in the -- call it, in the Fortune 500 or 1000. So we feel like there's still a ton of room to run there as we continue to shift resources. So the industry trends are great. We also feel good about our change in resources to go attack that market.

Okay. Let's talk a little bit about more specifically the effect of AI on your results. My impression from listening to you the other night is that you put up that stronger-than-expected results. But not because of some direct AI lift. I think the way you were describing it was that it was a core strength, actually, which makes Mongo interesting to a lot of people in the audience because if you've put up that strong performance in the core even before that direct AI lift comes, well, that's interesting because then you've got a potential growth catalyst sitting in front of you. So can -- maybe this is for you, Mike, can you draw that distinction in terms of where the strength came from core versus AI most recently?

Sure. So to that point, and we've said it for the last couple of quarters, we continue to see really good traction in AI native. We see the work going on in the large corporations related to AI, but it has not been a material driver to our results. And it was not in Q3 either. The growth in consumption that we saw in the drivers, call it, in the core. It's been the large customers who are doing mission-critical workloads for their business. So if it's an insurance company, claims processing, if it's a bank, it may be check depositing, it's core workloads that we've seen. Do we see a lot of activity in customers in terms of testing? Sure. but that's not driving a lot of revenue. So we do think that, that is a future driver. Again, we believe it is not if, it is when, but it is not driving the results today, nor is it a big part of the guidance that we did for Q4.

And is the catalyst, Mike, that we're waiting for as simple as enterprises like UBS start writing more robust enterprise-grade AI applications that create a pull-through for database that maybe today, a lot of the AI applications are somewhat lightweight and they're not super database heavy. But sort of Phase 2 might be that catalyst? Is that conceptually the right way to think about it?

I think that's the right way. And they may be doing inferences as they're not creating a lot of data internally. But once they actually do it with their internal data, that will create a lot more data. And we feel that will be the pull-through.

Yes. And can I add just something to what Mike said. Agreed, here is why I'm personally paying attention being based both half and half in San Francisco area as well as New York is we are learning a lot from AI native company. Because AI native companies today, right, there are so many, and venture capitalist who are on the sidelines, as you remember in '22 and '23, started funding in 2024, whether it's a vertical-specific AI company or a foundational model company and so on. And my observation, even when I was doing diligence on MongoDB and after joining MongoDB and speaking to them, Karl, is that they are saying, okay, to truly create a killer AI app, which AI native companies, that's the aspiration, right? What does really MongoDB have? And can we build on top of MongoDB, the next-generation killer AI company. And so I ask them questions, hey, are you using our vector search functionality? If yes, tell me why. If not, why not? As you know that Dave and the team bought Voyage and have you thought about our embedding models and reranking models that improves your search accuracy and so on. And they're like, I didn't know that, let me try that out. That's an easy few lines of code change that I can do. And the third most important thing is scaling. And if I become successful because everything that becomes big, start small in AI native companies, and some of them are getting traction, as you know. And what I see with them is, yes, we are actually growing a lot, CJ and we used the example of Mercor in the earnings script. And because we are growing and we are scaling, MongoDB is the right OLTP database for us. That learning from the AI native companies, we want to take that to the enterprise to say, hey, because the AI team calls, when I talk to these large enterprises, what I find is that the AI team, as in the agentic team, is separate from the core database team and the core workloads team, and they are dependent on those workloads team to get the data via APIs and so on, and they want to move fast. Because they are like, hey, I have a lot of pressure to experiment on this agent X or agent Y. And then when I tell them, hey, do you know that this foundational model company is using MongoDB in this way or this Mercor is using it and this is why they are using it. They're like, ah, thank you for telling us because we just wanted to move fast, so we didn't really make an explicit decision.

Got it. CJ, let me ask about a subject that's somewhat relevant in the markets today. The markets are a bit heavy today. I think on additional media stories about the pace of enterprise agentic adoption. I think Dave and you, more recently have been on the more reasonable, I'd say, accurate, actually, end of the spectrum on that debate, hyping it versus being quite reasonable. Do you want to weigh in with your review? You talked to a lot of customers, not just since you've joined Mongo, but prior. Where do you think enterprises are in taking truly agentic apps out of pilot actually into production. And Mike said when, not if. Can you give your best guess as to when the when is?

I don't like to do that, Karl. But I would say in speaking to customers, this is not days and weeks, right? This is not days and weeks. It's a few quarters depending on. Because enterprises, if they want to transform their business, which is a very important point, using an agent, whether it's single-purpose agent or a multipurpose agent, you have scale, durability, availability, real-time learning, context switching for the agent versus human. There are a lot of factors to be considered. And yes, I created a pilot and it does something cute internally for productivity that I can write e-mail fast or I can do this Copilot or Codegen. But to truly create an agent that transforms your business, right, or creates a new business for you. My viewpoint is still a few quarters away. I have -- in like I've been speaking to customers, as you know, for a long time, nobody has come said, CJ, this completely transformed my business today. And we are piloting, tinkering, I have this financial services firm I talked to, they said we have 20s of agents but they're still doing this 1 task, but nothing at scale.

Got it. Let's switch the subject maybe to competition in your space. There's always been intense competition in the database space for as long as I can remember. Still the case, but in your defense, what I would say is that 10 years ago, you were 1 of probably 5 or 10 at the time, no SQL database vendors, and you've actually been the one to emerge from the pack, well, a lot of them have still stuck at $100 million, $200 million or sold themselves. So you've won that race. But today, you're still looking at some competition. So let's go through 3 categories, and Ben maybe went away in as well. One is a lot of us have been hearing about the popularity of Postgres relational databases, where Postgres running on Azure and AWS have partly, maybe not entirely solved some of the scaling issues that used to be a blocker years ago. Ben, do you want to take this one? When you contrast Mongo against Postgres, where do you live on this debate? Is it sort of room for 2 because the overall demand is so strong? Or do you feel like you've got an opportunity to take some share from Postgres?

Well, I definitely think we have a lot of opportunity, but let's just go back to what Postgres is because it's not just one, it's 30, 40 different flavors of this, right? And I talk to customers all the time and we started out with Postgres in AWS or you name your hyperscaler. And now for whatever reason, they want to go to a different hyperscale or they want to bring it back on prem, you can't just move that application. It's actually -- just because it's Postgres doesn't mean it's truly portable. So one thing that really especially resonates with enterprises is that MongoDB actually has a truly portable database, right, without any application change. So we can move your data from one cloud to another, and bring it internally, go back to cloud, no application change. And so especially enterprises, Europe is big with sovereign cloud. We're seeing a lot of this portability be a massive driver of a requirement of like what data platform they're going to choose. So I would say that's number one. And then number two is, and you touched on this earlier about the developer experience that we started out with that we really still to this day focus on, is that developer experience, that developer velocity, that efficiency. And so if you look at what we've been doing over the last few years, building up to this AI era is we haven't been bolting on other workarounds with different API layers to add new use cases. It's all part of our idiomatic API that we've always invested in, which is MQL. So we added search, part of the same query language, we added vector, same query language. We're keeping that developer velocity and efficiency in mind at all times. And that's just something that the competitors are not doing.

Got it. The second category, which we were thinking about yesterday because we had Ali at Databricks on stage is some of the OLAP vendors are stepping into the OLTP database space. And Ali was quite proud of their new Lakebase offering. So when you think about those guys, the likes of Databricks and snow taking early gentle steps into the operational database space, what do you think of that?

I think it's cute. Like look, like they tried for many years to build an organic OLTP, right? Because they saw it very early on that OLAP wasn't going to be fit for purpose for these real-time production applications that are going to be business mission critical as CJ was just kind of pointing out and why all these applications are in pilot. So they realized that they couldn't do it organically, so now they bought someone. But they didn't buy the A players. And so we're -- from a place of an OLTP landscape, I think we're 15 years ahead of where we need to be from an OLTP landscape for these AI application.

And I'll add a one thing as it is a question that comes up a lot, and we have great investors here. Like make no mistake, even when I was at Oracle, Oracle was known for OLTP. And we experimented a lot with data warehouses. I'm talking late 90s, early 2000s, and then you know what happened with business objects and all those firms back then, too. And OLTP and OLAP were always 2 sides within an enterprise, different buyers and based on the use case, what you're solving for. And from my perspective, when somebody that is just focused on data warehouses, starts buying OLTP based companies, it's a validation of that our space is great, and it has durable advantage, and they are trying to expand the TAM. And then I'll just add to what Ben said, that even Postgres selling support for JSON and when you actually really look at that why they did that is JSON is popular with developers. Why? Because of MongoDB, and that's a bolt-on versus we just naturally work with that. And for AI high grounds, I would argue that us, being native JSON support and just how MongoDB works for it, even if you look at any of your popular ChatGPT today. They talk about images, videos and then there's so much unstructured data. So we are correctly positioned for them.

The third and last one is the hyperscalers. They're native databases. None of us can leave a meeting with Microsoft without such in company bragging about Cosmos DB and how customers are coming to them for their native databases. Has anything changed in terms of competitiveness versus the hyperscalers, Ben, CJ?

For my personal view of what I'm talking to customers about is it usually starts out with, it's really easy. I click it. It's in the console. It's available so they just start there, right? And even all the hyperscalers claim compatibility with Mongo. When we run our own compatibility test, we come out a lot less than that. And the way we do that, we actually published the benchmarks publicly. So it's available on GitHub, everyone can download the same test and do it themselves. We have thousands of database schematic, so you can query against. When they look at -- when they do their benchmarking, we think it's sub-100 of the actual features of the MongoDB API to give them a better percentage of compatibility. When we take a conservative view of, let's say, 270 of the most critical, mostly used pipelines, we are into the 40s to 50s percent. So even when we give a little bit extra. So I just think that people start there because it's easy similar to why people start with Postgres, it's just like easy. So we eventually get it. I want to get it earlier, don't get me wrong, but we actually get it.

And on the high end of enterprise on that point, besides the features and functionality and scale out and this and that, recently, I mean, if you look at 2025, you had GCP outage, you had AWS outage and you had Azure outage. And speaking to customers, including large financial services as well as health care companies just recently in the last couple of weeks. As Ben said that, hey, on one hand, it's easier for me to turn on and use the first-party database resiliency perspective, now you're locking in yourself. So if one of the hyperscalers is out, your workload is out. And MongoDB on Ben's first point, that I can -- are talking to this large customer, and they said, from a resiliency perspective, CJ, we chose MongoDB rather than the first-party hyperscaler because I can now replicate MongoDB from AWS to Azure without a problem. So not just an intra-region of AWS, US-East-1 and so on, but I can do that across clouds. And that is a massive advantage compared to the first part of it.

Mike, let's turn the conversation to you for a moment. Congratulations on the October quarter results.

Thank you. From all the employees at Mongo.

I think one thing that stood out to me in my judgment, the 3Q results were very good, but what I thought was really outstanding was actually your fourth quarter outlook, where you raised it quite substantially. And if I know you wouldn't encourage us to do this, but if we assume sort of a normal-ish beat on that guidance, you're getting a fairly marked acceleration in your total revenue growth rate in the January quarter. Can you unpack 1 or 2 things, Mike, that gave you that confidence for that acceleration in the January quarter? Because to me, that was the highlight of the print.

Sure. So let's break it up into Atlas and then non-Atlas if we could. So for Atlas, as we looked at the second half, when we guided Q3 we were confident, call it, around that mid-20% growth rate. What we saw coming out of Q3 was and we said, hey, it was very much what we thought, and it was very consistent throughout the quarter. The nice part is when that consumption builds, then that's the starting point for Q4. So when we forecasted Q4 then it's a higher base and we felt better about raising that to the 27%. With the caveat, which is, hey, folks, it's the holiday season, happy holidays, everybody. There are -- we've always seen a little bit of unpredictability based on where days fall. So we just want to take that into account. The big thing for us was around the EA business. And from that business, not only did we see a little bit better for multiyear deals, and I'll say this publicly, folks, this is not Poland. This is regular business that was coming due in the second half where it's tough to forecast. Are they going to do a 1-year deal? Are you going to do 3? Possibly even 5. The great part about that is these are the largest customers at Mongo, and we love them all, committing to us long term, and we saw a much better commitment not only in the multiyear, but just then call it the run rate business. So when we built that bottoms-up pipeline, we felt better about that. So those were -- if you look at the Q4 raise, about 1/3 of that was Atlas and about 2/3 of that was EA.

Mike, the other thing that stood out to me was that since you came aboard, I'd say on the margin, you've been giving a little bit of color about how to model Atlas, but more directional. You went one step further on this call where you gave a point guidance for Atlas, which I think everybody appreciates. It leaves out a little bit of a guessing game, how to apportion total revenues. But one interpretation of that is that you wouldn't be making that disclosure improvement, if I could put it that way, unless you felt fairly confident about the Atlas business. Was that the correct interpretation, Mike?

So I never want to give a number externally that I don't feel good about. And what we would say is we debate this all the time, and you can look at ranges, we felt really good about that number in terms of, hey, from a pragmatic point of view, that's where we think we are. Hopefully, things go better. Hopefully, they do, but that -- we felt good about giving that number.

The last thing the -- I wouldn't even call it a flaw because I think you can explain it. But one of the only metrics that stood out to a couple of investors that hit me, Mike, was that the direct customer count fell slightly for the second quarter in a row. But I think there's an explanation for that, but it might be good to just give you a chance to explain that one.

Yes. So thank you about that. And, Karl, we'll probably change this going to next year. Keep in mind that when we talk about direct versus self-serve, this is how we internally categorize those customers. Self-serve is not -- they started and self-serve and then they stay there forever. At some point, they will shift to a direct customer. Sales will take over. So what you have is a little bit of the apples and oranges. As we have added more sales to go direct, we've pushed that team up, focus a little bit higher. And then what happens is self-serve covers that gap. So it looks like direct is actually going down. Folks look at the total customer count, that's what matters. The bifurcation between direct and self-serve is our own internal categorization of how we go to market and just pay a little bit of warning. In '27, folks, we were probably not going to give this number because of this exact issue. Look at the business as a whole, look at consumption growth, look at revenue growth and total customers. How we bifurcate internally is how we go to market. It's not a driver of the business.

And 8,000 customers added this year on year-to-date with 67% growth. But even adding 2,500 in the quarter, specifically, which, if you look at last Q3, that's a 40% growth on just new customer adds. Karl, I mean, you know how much obsessed I was about new logos and others in the past. These are very good leading indicators for the future.

CJ, Mike and Ben, thanks so much for coming to the event. I think, along with everybody here looking forward to watching the Mongo story unfold over the next 12 months.

Thank you for having us.

Thank you.