MongoDB at Morgan Stanley Conference: AI and Growth Prospects

On Thursday, 06 March 2025, MongoDB (NASDAQ: MDB) addressed its growth strategies and challenges at the Morgan Stanley Technology, Media & Telecom Conference. Despite a disappointing revenue growth outlook for fiscal year 2026, the company remains optimistic about its AI capabilities and long-term prospects. CEO David Acharya and Interim CFO Serge Tanjga discussed the company’s strategic positioning amid market expectations and competitive pressures.

Key Takeaways

• MongoDB aims to return to a historical 20% revenue growth profile, with a current guidance of 12% to 14%.
• Atlas growth has stabilized, and the company sees positive signs in consumption growth.
• MongoDB is focusing on AI capabilities, highlighting its architectural advantages for AI applications.
• The company plans strategic investments in R&D and market awareness to capitalize on future opportunities.
• Relationships with hyperscalers remain strong, allowing MongoDB to effectively partner and compete.

Financial Results

• Overall revenue growth for the last year was 19%, with the cloud business growing by 27%.
• The most recent quarter showed 20% overall growth, with 24% growth in the cloud segment.
• Revenue growth guidance for the current fiscal year is 12% to 14%.
• Non-Atlas revenue is expected to decline in the high single digits.
• Atlas consumption growth is expected to stabilize.
• Multiyear non-Atlas deals are expected to decrease by $50 million.
• The normalized underlying growth rate, excluding the $50 million impact, is roughly 300 basis points higher than the 12-14% guidance.
• Q3 operating margin was 21%, with a target of approximately 10% for the year, down from 15%.

Operational Updates

• Atlas growth has stabilized, addressing issues from the previous year.
• Investments in Voyage AI aim to reduce risks in AI applications.
• Focus on modernizing existing applications and building custom AI applications.
• Integration of embedding models and re-ranking capabilities into the core platform is underway.
• Emphasis on Java apps running on Oracle for the AI relational migrator service.

Future Outlook

• MongoDB aims to return to a 20% revenue growth profile.
• Expanding AI capabilities to drive future growth.
• Focus on high-end market segments with higher sales productivity.
• Plans to build domain-specific models for healthcare and other industries.
• Targeting to become a $20 billion company.

Q&A Highlights

• MongoDB maintains high win rates and addresses customer skills gaps and trust issues.
• The company is well-positioned for AI due to its flexible architecture and real-time data capabilities.
• Strong relationships with hyperscalers enable effective partnerships and competition.
• Efforts to make MongoDB easier to use aim to increase developer adoption.

For a detailed understanding, readers are encouraged to refer to the full transcript below.

Full transcript - Morgan Stanley Technology, Media & Telecom Conference:

Sanjay Singh, Infrastructure Coverage within the Software Team, Morgan Stanley: All right. Welcome to another great session and day four of the Morgan Stanley TPMT conference. I’m Sanjay Singh. I cover the infrastructure coverage within the software team at Morgan Stanley. We’re super excited to have the management team here at MongoDB.

We have CEO, David Acharya. I think Serge is going to be joining us on stage in about a minute. He’s getting mic’d up. But given that we’re a few minutes late, we’re going to get started. Before that, let me just get through the disclosures.

For important disclosures, please see the Morgan Stanley Research Disclosure website at www.morganstanley.com/researchdisclosures. If you have any questions, please reach out to your Morgan Stanley sales representative. So maybe just to level set, Dave, when we think about this year, another solid year, overall revenue growth 19%, your cloud business on a reported basis was 27% growth. Reported earnings results last night, again, solid quarter, 20% overall growth. Cloud business growing 24.

But the outlook did disappoint the market expectations for fiscal year twenty twenty six. Before we dive into the details and the debates around Q4, I guess the high level question that I have for you is from a big picture perspective, do you believe that MongoDB can get back to that 20% revenue growth profile that you’ve historically achieved since you guys have been a public company?

David Acharya, CEO, MongoDB: The short answer is yes. And I think one of the key points we want to emphasize from the call yesterday, and we tried to do that in some of the one on one meetings earlier today, is that Atlas growth has stabilized. Consumption growth has stabilized. And that’s a question a lot of investors were asking us. And if you do the math based on the feedback we gave on the non Atlas business, I think you can reverse engineer into a pretty solid growth rate for Atlas.

And that’s a function of a couple of things. One, the base itself is stabilized. Two, the workloads that we acquired last year seem to be bigger and growing slightly faster than the workloads we acquired in the year before period. And that’s also giving us confidence about the workloads we will acquire this year as we move upmarket and address more and more sophisticated use cases.

Sanjay Singh, Infrastructure Coverage within the Software Team, Morgan Stanley: Yes. It’s a great way to frame it out. So let’s talk a little about Serge, maybe you can walk us through the guidance philosophy, the assumptions that underpin that guidance, the puts and takes and why that sort of outputting and netting out to sort of a 12% to 14% guide?

Serge Tanjga, SVP Finance, MongoDB: Yes, yes. So the total guidance is 12% to 14%. What we called out is that the non Atlas revenue will decline in the high single digit range. And that is entirely due to the fact that we’re facing a very difficult compare when it comes to multiyear non Atlas deal. So due to ASC six zero six, when we signed a multiyear deal, we recognized the term license component upfront.

And we had two very strong years, exceptionally strong years in a row of multiyear non Atlas deals fiscal year twenty twenty four and then fiscal year twenty twenty five. And so what that means as we look into fiscal year twenty twenty six, the opportunity set of deals in our renewal base to do multiyear deals is simply lower than it was in the prior two years. And even though we assume that we will be equally as successful, just a smaller opportunity set yields $50,000,000 less in multiyear revenue. And that’s the reason why the non Atlas revenue is declining in the high single digits. Dave already talked about Atlas.

What’s implied in the guide is stable consumption growth in Atlas, fiscal year twenty twenty six versus fiscal year twenty twenty five. We find that very encouraging in the context of a growing business, and that’s no small feat. And again, we feel like we can accomplish it because of the strength we’re seeing in fiscal year twenty twenty five workloads as well as our increased investment upmarket in our strategic accounts where we see better productivity. And so those are the puts and takes when it comes to the guide. I think that perhaps a better way to think about the underlying growth of the business is to normalize for the $50,000,000 And so the way that I would do it is I would remove it out of the fiscal year twenty twenty five revenue base.

So that’s how that 12 to 14 becomes roughly 300 basis points better. And that’s more reflective of sort of the underlying growth of the business minus the sort of the accounting lumpiness.

Sanjay Singh, Infrastructure Coverage within the Software Team, Morgan Stanley: Yes. One of the questions I’m getting this morning from investors is if you just look at big picture and you look at the trend line of growth, you guys, I think, couple of years ago were high 40s, year after low 30s, this year 2019, now guiding as we sort of normalize mid-fifteen. So the question I’m getting is, is there a competitive issue here? Is there any issue with retention in terms of that line of concern? Dave or Serge, how would you address that?

David Acharya, CEO, MongoDB: Yes. So our win rates are still very high. What we see, there’s really three layers to the cake in terms of our growth. One is the base business, workloads that required post two years ago, and obviously, that’s the largest part of our Atlas business. There’s the workloads required in the prior year and then there’s new workloads.

We did have issues last year this time where the base was not growing as fast as we thought, and then the workloads we had acquired in the previous year were not tracking as fast as we thought. And then we had a late start to the year with some organizational just changes at the start of the fiscal year. We fixed all that, so we feel like that’s a big reason why we’re calling out stable consumption growth in Atlas. I would say that we do think this year is a year of transition. We are really excited about the opportunity in AI, but we also do recognize that customers, especially large enterprises, are moving quite slowly in the deployment of custom AI apps.

Most of the AI use cases are fairly simplistic, chatbots, document management use cases, etcetera. But we are seeing people get very interested. And we think, architecturally, we are well designed for the world of AI. One, we support different types of structured semi structured, unstructured data. Two, we’re highly elastic and scalable.

Three, we natively embed text or keyword search and semantic search through our vector search capability. And then we just announced last week the acquisition of Voyage AI, which is the best in class embedding and re ranking models, and that’s all designed to really essentially reduce the risk of hallucinations. And one of the things that we find with our customers is there tends to be two issues holding them back. One is a skills issue, skills gap issue and the second is a trust issue. And so the way we’re addressing the trust issue is to obviously do things like voyage where they can get better predictability and quality of

Serge Tanjga, SVP Finance, MongoDB: the outputs from these AI applications.

David Acharya, CEO, MongoDB: And on the outputs from these AI applications. And on the skills issue, we’re not just coming with technology, but we’re coming with a very solution oriented approach. One way of that is modernizing existing applications using AI and the second one is really helping them build custom AI applications that really transform their business.

Sanjay Singh, Infrastructure Coverage within the Software Team, Morgan Stanley: Yes, a lot there. And I definitely want to dive deeper into a lot of those points that you just made. Kind of tying the bow on last night’s earnings results, Serge, going into this fiscal year twenty twenty five that we just completed, you guys had historically been kind of prudently conservative on the non Atlas business and the multiyear deals. What is it about fiscal year twenty twenty six that’s going to be different resulting in that sort of high single digit headwind? Because there was, I think, a $40,000,000 anticipated headwind for this year that didn’t seem to materialize.

Why won’t that resolve itself to the upside in fiscal year twenty twenty six?

Serge Tanjga, SVP Finance, MongoDB: Yes. So if we rewind sort of the story of multiyear deals just to make sure everybody’s on the same page, we had an exceptionally strong fiscal year twenty twenty four. The biggest deal was Alibaba, but there was strength across the board, well above sort of the average. So going into fiscal year twenty twenty five, we assume the $40,000,000 headwind, which would result in fiscal year twenty twenty five being roughly an average year when it comes to multi year performance. In Q3, we call that significant outperformance.

In Q4, part of our outperformance was also due to multiyears. So most of that $40,000,000 headwind actually did not materialize, right? So we outperformed our expectations that we had in fiscal year twenty twenty five. And you have to understand that like forecasting multi year deals is inherently particularly difficult. Sometimes those happen at the very last minute.

And so it’s not conservatism, it’s just difficulty of forecasting is how I would put it. And so but what that means now as we look into fiscal year twenty twenty six and we think about sort of what’s available for us to go get using historical sort of occurrence, if you will, of multiyear deals, whether they are renewing multiyears or new multiyears. When when we apply to a lower opportunity set in the renewal base, that’s what results in the $50,000,000 So in order to do better than that, we would need to see better sort of a greater percentage of customers signing up for those than has been the case for the last two years. So that’s not what we’re assuming. It’s just that the opportunity is lower.

Sanjay Singh, Infrastructure Coverage within the Software Team, Morgan Stanley: Got it. And then the last one on coming off of last night’s results on margins. So op margins in terms of the guidance, you look at the midpoint sort of targeting about 10% after delivering about 15% operating margins, it’s just going to be 25%. Understanding the $50,000,000 headwind from the non Atlas business, which is a super high margin, why not philosophically take the decision to protect margins after a week of an expected outlook?

Serge Tanjga, SVP Finance, MongoDB: Yes. So first of all, I’d just echo what you said that $50,000,000 is roughly half of the margin decline from $15,000,000 to $10,000,000. And then the second is related to and I’m sure Dave is going to want to chime in here, but about our confidence in terms of the investments that we’re making and the opportunity going forward. So where we’re disproportionately investing in the business is two areas. One is R and D, because we see an opportunity to continue pushing the envelope in terms of performance in the core database plus the investment in Voyage AI and creating this out of the box Gen AI solution that we think would be unique in the marketplace.

And the second area of investment is awareness. We hear over and over again, even from some of our largest customers that a small percentage of their developers really knows the full capabilities of our platform. So that’s an obvious opportunity to increase our ability to acquire new workloads. And that’s an area where we’re going to invest this year and invest more consistently going forward. So that’s the rest of the bridge down to the from 15% to 10% in addition to the $50,000,000 The only thing that I would add is so then if you kind of turn around to like what’s the philosophy underlying, those are the puts and takes of what’s the philosophy.

So we observe our margin performance over the years, and we have high confidence that the business scales. So we don’t have to go back to the IPO, which you remember when the margin was negative 37% and kind of celebrate the progress that we made from there. Even over the last couple of years, whenever we slow down investments, we just see margins shoot up. That’s the underlying unit economics of the business showing up. So this is a proactive decision at a moment in time that is unique in our opinion to invest in certain areas of the business to maximize the opportunity going forward.

And we have the confidence that the business will continue scaling. This is not a forever state. This is a moment in time state. That’s why Dave refers to it as a transition year. And we’re making that knowing that the opportunity set is coming to us, and we want to make sure that we maximize it with the purposes of being the biggest possible company we can be in five years.

David Acharya, CEO, MongoDB: Yes. I would just double click on a couple of points. One, we just printed a 21% op margin quarter, right? It’s the best quarter of op margin we ever had. So the unit economics for business are very strong.

I will also tell you that obviously, we were private at the time, but we saw similar moments happening with the cloud and we invested very aggressively in building Atlas. Obviously, that wasn’t available to all of you, but like our existing investors saw the investment and obviously that’s paid massive dividends over the last seven, eight years of our growth. We see a similar opportunity with AI. Again, I cannot reinforce how if you believe in a world where the world is going to change only more quickly with AI, you need a data foundation that’s designed to be flexible and adaptable. There’s no more flexible and adaptable platform than MongoDB, and we’re well optimized for the world of AI.

And so I think we are investing as a vote of confidence. It’d be candidly, it would be a much simpler conversation with all of you to say, Hey, we’re going to keep margins where they are or maybe even increase margins just because of what’s happening in the marketplace. But we’re actually investing as a vote of confidence because we think we see that opportunity. Customers are telling us that they want to use us to do both build new Gen AI apps and help modernize their existing legacy infrastructure. And I think those things will pay dividends in the years to come.

Sanjay Singh, Infrastructure Coverage within the Software Team, Morgan Stanley: Yes. I want to spend the bulk of we have roughly twenty minutes left. I want to spend the bulk of the time diving into why MongoDB is well positioned to power the next wave of modern AI applications. And maybe we sort of level set the conversation. Dave, give us your thoughts about roughly the last twelve to eighteen months As 2024 progress, what is your latest thoughts on where we are in the cycle?

Are we moving out of the test eval, proof of concept phase of the market and actually getting applications into production?

David Acharya, CEO, MongoDB: Yes. You’re talking about AI applications. Yes. So I think this is a gradual journey. When I look at the enterprises today so I’ll give you a couple of anecdotes.

I was meeting with two large financial services company in New York, and I asked them how many AI apps do you have in production? One person told me about 25, 30, another person told me about 20 to 15. And I asked them how many of those AI apps are customer facing? Both of them said zero, right? Because they’re very, very nervous about the risk of hallucinations, especially in a regulated business like financial services, right?

So while they’re very interested, the use cases they’re looking at is more around document management, customer service, just streamlining efficiencies to sort of play with agents. But the challenge you have is that unless you really feel confident about the outputs you’re getting, you’re going to be very measured in terms of the deployment model. That being said, when we think about and I’m just kind of I’m old enough to remember the Internet era where like people are kind of building simplistic static web pages and no one thought the Internet would transform not only everyone’s business but everyone’s life, right? I think AI will have that same impact. And I think you’re seeing the innovation happen.

You start with basic infrastructure, basic apps. Now you get into more sophisticated infrastructure and you’re starting to see more sophisticated apps. I think that’s happening. Kindly, I think the first generation of Gen A apps will show up as ISVs. You have companies like Harvey and like the cogeneration tools and all that.

I think that’s where it’s going to start showing up first. But I think the only way an organization can really differentiate themselves is by building custom purpose Gen A apps that are meaningful to their business. Then why is MongoDB well positioned, right? So let’s talk about that. One, fundamentally, we are a distributor architecture where we use a document based approach to manage structured, semi structured and unstructured data.

A lot of people say, well, Postgres supports JSON. Can Postgres do what you do? If you look at any performance results, as soon as you introduce a two kilobyte JSON document or object, Postgres starts having performance issues because Postgres or relational databases need to do something called off road storage. And off road storage is a different technique for managing data that can fit in rows and columns. In fact, Postgres has this approach called the oversized attribute storage technique or Toast, which is a way for them to deal with unstructured data, but it comes at a performance hit.

Postgres is also very rigid in a schema, so it’s not very easy to to change. Postgres is not designed to be a single node system, not designed to be a scalable system. Some people have tried to make it a little bit more scalable, but it still has inherent scalability limitations. So when we think about a world that’s going to change and adapt and deal with different types of datas and different types of modalities, we think we are well positioned. And just to be clear, a lot of I get the question, what is the role of the database in the world of AI?

I think of the LLM as the brain. I think of the database as the state machine and the memory machine. And then like I get question like, well, what’s Voyage AI? Think of Voyage AI as a very fancy librarian. So let me explain what I mean.

Imagine you hired Albert Einstein to be your personal assistant, the smartest person in the world. You asked him a question about chemistry or biology. Even no matter how smart that person is, they still need to go do research to essentially find give you the right answer to a hard problem. And the challenge is like they could go read every book in the library or and they could essentially get all the information to come back with an answer, but that will take a long time and cost a lot of money. Or you can say for this particular question, go to this section of the library, go to this aisle, this shelf, this book, this page, this and this section of this page to get the answer you’re looking for to formulate a response.

And that enables you to be much more efficient about how you do very sophisticated search and retrieval. Remember, this is data sitting in an enterprise. Obviously, the LMS have been trained on Internet data, but they don’t have data in the businesses that you’re in or large banks or financial services, telcos, media companies, tech companies. So you need embedding models to really help the LMS become much more efficient to in order to produce the results and produce high quality results. And so we feel like as the world produces more software and you’re going to see the software use cases expand dramatically that traditional software cannot dealing with open ended questions, reasoning, natural language kind of interaction, different kinds of user interfaces, etcetera.

So if the envelope of software is going to increase, then by definition, you need more data infrastructure to support that software and you need real time data. That’s the other question I get is what about you versus Snowflake or Databricks? Imagine you’re again, back to an agent doing investment decisions. For you to make an investment decision, you might have been trained, but to act on that decision, you need to know what is that stock trading at, what is the volumes, any other kind of real time data to make a buy or sell decision. If you’re a customer chatbot, you need to know exactly what’s happening with that customer to be able to respond appropriately in terms of how to answer that customer’s question.

So that’s where real time data becomes incredibly important for these kind of mission critical applications. And we think when you look at all the requirements, we are well positioned for that.

Sanjay Singh, Infrastructure Coverage within the Software Team, Morgan Stanley: Yes. To pick up on some of those things that you just laid out and sort of incorporating the Voyager acquisition, kind of rewind back two years ago, vector database companies is kind of the hot thing. Increasingly, database companies across the market have embedding vector search capabilities. And now you guys are seeing to pushing the puck forward with having world class embedding models, re ranking capabilities. So it sounds like the unlock here is about bringing a solution to the customers.

So And not just

David Acharya, CEO, MongoDB: a solution, but a solution in an elegant user experience, right? It’s back to the point like the most successful companies have been able to take friction out of the user’s workflow and be able to do things. Customers still had to go get their data embedded to use a you can’t use a vector database without using without embedding your data. But they didn’t have to go to OpenAI. They’d have to go to Cohere.

They’d have to go to some other third party. And we said that’s a very painful process. Most enterprises don’t know which embedding model to use, don’t know which operational store to use, not sure which LMM to use. And then they got to figure out what vector store to use and stitch it all together. That’s why we want to make it much more simple and easy for customers because we can bring everything to bear in a very elegant user experience.

Sanjay Singh, Infrastructure Coverage within the Software Team, Morgan Stanley: The classic kind of MongoDB value proposition, right? Exactly. And so could you talk about what have been what’s been the storyline in terms of vector search adoption, RAG adoption? You guys released it generally available earlier last year. How has that been building momentum?

And do you think the acquisition with Voyager is going to unlock more of those RAG and AgenTic use cases?

David Acharya, CEO, MongoDB: Yes. So the uptick has been good. We have we’ve talked on the last call, not this call, like we have had thousands of small customers building AI apps. We have a couple large well known AI companies using us as their memory and state machine for the use cases they’re doing. Obviously, with the competitive dynamics of AI, they don’t really want us to talk about who they are right now.

But we’re seeing starting to see some of those apps starting to take off, and these are like 7 figure workloads. And what I would say is, in terms of your question around vector, we’re seeing adoption, but I think the voice thing really makes it so much easier and so much more compelling to use MongoDB. So that is truly a one stop shop.

Sanjay Singh, Infrastructure Coverage within the Software Team, Morgan Stanley: What’s going to be the timeline to integrate the embedding models and the re ranking capabilities into the core of the year?

David Acharya, CEO, MongoDB: Yes. So the way we’re doing it today, you can go to Voyage AI and you can get their models either from them or you can get them from AWS, Bedrock and a few other places. What we’re going to do later this year is do something called auto embedding. So you can choose as data comes into MongoDB, you can choose if you want that data embedded. So out of the box, all that is taken care of for you.

Then we will focus on building domain specific models. Right now, they have models for financial services. They have models for coding. But we see a lot of customers, like health care customers, saying, I want models, but a lot of health care data is not publicly available. So we can go to health care customers and say, we can build models for you that are optimized for your particular use cases, your particular data and then enable you to leverage the power of AI to really do profound things in your business.

And that’s something that customers are quite excited by. I was talking to early stage company that’s growing very, very quickly in the life sciences and biotech space, and they’re super excited about being able to leverage some of these models to just improve the performance and the accuracy of the outputs that they’re getting. So there’s a big opportunity. And then there’s other sophisticated things we can do like instruction tuning, where you can do even very sophisticated things around the models that we will just have a roadmap for going into next year.

Sanjay Singh, Infrastructure Coverage within the Software Team, Morgan Stanley: You talked a little bit about the advantages or maybe the limitations of Postgres. If you look at big picture at the operational database market, relational is still two thirds to 70% of the operational database market. So my question is, is that it seems like relational has this sort of, I’ll call it, a supply side advantage. Kids go to school, they undergrads in computer science, they still learn SQL, so you’re pumping out thousands of students a year that know SQL and relational. So if we take a step back, what is the MongoDB strategy to penetrate that supply side and get developers to learn MongoDB, whereas every year you have new developers being pumped out learning relationships.

Yes.

David Acharya, CEO, MongoDB: There’s an old Chinese proverb we love to use inside MongoDB saying, if you tell me, I will hear. If you show me, I will see. If I experience it, I will learn. And what we find is MongoDB is one of those technologies that’s well known but not known well. I’ll give you a simple example.

We have a large financial services customer two years ago who was spending a little bit over $1,000,000 with us. Fast forward twenty four months later, now they’re spending $22,000,000 with us. So 8 figure account, first digit starts with a it’s not a one. Okay, pretty good growth. How much upside is there in this account?

During the account review, I almost fell off my chair when I found out that only 5% of the developers know how to use MongoDB. Now they have tens of thousands of developers, but only 5% know how to use MongoDB. So that 8 figure account could easily be a 9 figure account. And our ish what we have to work on is being able to get those developers to more easily understand how to use MongoDB, not just know about MongoDB, how easy is to work with and organize data, how easy is to shard or scale out data, how easy is when you natively embed text and vector into your platform and then now with embedding models, the user experience is so much more simple and less complex. And when we do that, we see the growth, which is why we’re also investing upmarket because we see a disproportionate the sales productivity is disproportionately higher in the high end of the market than the mid market.

And because of that exact reason that there’s so much opportunities that counts even when we have a large presence.

Sanjay Singh, Infrastructure Coverage within the Software Team, Morgan Stanley: A key part of your strategy for growth, one of the pillars, I should say, for your growth is your AI relational migrator service. Now there’s been several pilots going on at some very large customers. What have been the results thus far from the customer’s perspective? And what is the go to market and investment plan to scale these early successes to the rest of the customer base?

David Acharya, CEO, MongoDB: Yes. So just to level set, the database market is one of the largest markets in software, but a big part of that market is these trapped legacy applications running on legacy databases. That’s frankly very, very hard to move or change. And they’re hard because one, there’s lots of lines of code and two, they’re crown jewels of an enterprise, and so people get very nervous about messing around with them. However, those companies are facing tactical debt, so 90% to 95% of the budgets are just spent on maintaining those applications.

They’re running into end of life issues where a lot of those technologies are getting end of life. There’s compliance and regulatory issues where the regulators are saying those applications are becoming a systemic risk, especially in like financial services or health care and other places. And then when you say, okay, how are you going to leverage AI to modernize your business, they can’t do that on those existing applications. So there’s a confluence of events happening with saying we got to do something different. So when we approach customers about modernizing the applications, the receptivity is very high.

And then when we show them that there’s typically two objections that surface. One, are you selling me snake oil because customers tend to be a little cynical? So we run typically like a six week proof of concept where we say, pick an app and we’ll show you how you can modernize. And the results end up being very positive. And in some cases, the customers have stopped those pilots or POCs because they’ve seen enough to say, you’ve convinced me.

The second question is, okay, why MongoDB? And then we walk them through what I just talked about in terms of our architecture and everything that comes with it, and we get by on that front. And then we start monetizing. We already have a couple proof points. We already have issued some press releases.

And what we have realized is that there’s so much demand, we want to move be able to move fast. So we’ve actually decided to focus on Java apps running on Oracle. Just to give you an example, we’re working with one large insurance company to basically modernize their key underwriting application. There’s tens of thousands of lines of stored procedures sitting inside their Oracle database, right? Typically, the way people built applications previously was to write application logic, but also application logic at the app tier as well as application logic at the database tier to get better performance.

The problem is that over time, it becomes the spaghetti code that becomes very difficult to unravel and you kind of get locked into the platform. And what we can now show is that we can reason through all that code. We can chunk up that code, peel off pieces of the functionality, And we’re doing engagements right now with this global insurance company in Asia, and they’re going country by country. And as soon as we knock off a country, they bring us to another country. And the pipeline of opportunity just in that account is growing dramatically.

That’s just one small example of what we see across other financial services customers, telecom customers, older ISVs who want to modernize. We have an ISV in Germany that asked us to modernize a financial application, which you think financial application is all structured data, but they said they can get far better performance and be able to add features more quickly if they built it on MongoDB and we’ve done that.

Sanjay Singh, Infrastructure Coverage within the Software Team, Morgan Stanley: And it sounds like super exciting opportunities. And so if I look at like, I’m going to call it, your new workload opportunity, including the AI relational migrator service, Let’s put Vector Search RAG in there as well. Let’s put stream processing there. If we think about these as a bucket or a class of opportunities, when do you think that starts to move benefit growth in your cloud business? Is that something that happens this year?

Or is it more

David Acharya, CEO, MongoDB: I mean, I think part of the reason for stable consumption growth is just starting to show up in the numbers. At least the search is starting to show up in our numbers. Again, it’s all part of Atlas consumption, so it’s hard to disaggregate, but that’s part of driving our confidence on the stable consumption growth that we talked about in Atlas. And I think, as I said, this is a year of transition. We clearly have an appetite to grow faster and deliver better margins.

I recognize there’s a lot of people in the audience who might be wondering, is this what we expect the business to do? And I can tell you absolutely not. We are very, very motivated to grow much more quickly and do it much more efficiently.

Sanjay Singh, Infrastructure Coverage within the Software Team, Morgan Stanley: Yes. And so maybe with our last minute or so, maybe Dave, just take the opportunity to sort of speak to what excites you about MongoDB today. The business is at $2,000,000,000 scale. You got a world class cloud business. Looking forward, what excites you and why do you think MongoDB is a good investment opportunity at these levels?

David Acharya, CEO, MongoDB: Well, I’m happy that we’re a $2,000,000,000 company, but in some ways, I’m a little pissed that we’re not a $20,000,000,000 company, right? And I think that’s the long term opportunity sitting in front of MongoDB. And as excited I was about building Atlas, and again, we were a private company, a lot of people were skeptical, wait a minute, you’re going to partner and compete with the hyperscalers? Who’s done that? How can you prove that?

Why won’t they strip mine your product? You know all the best cases against MongoDB, and we were able to prove that. I’m equally excited, if not more excited, by the opportunity that AI presents. And I think it will be I think you’re starting to see the market shake out. We saw DataStax get acquired by IBM.

I think a lot of these kind of single function databases or point solutions just can’t scale and grow very, very quickly. And I think that gives us more opportunity.

Sanjay Singh, Infrastructure Coverage within the Software Team, Morgan Stanley: Maybe just to leave a loss on one of the debates that we’ve been getting this morning since we started a little bit late. Common question I get, hyperscaler competition. Is there any truth to that in terms of that being their ability to bundle maybe Cosmos or DocumentDB? Are you seeing any signs of that as a potential headwind on the facility?

David Acharya, CEO, MongoDB: Yes. That’s been a question we’re getting ever since AWS launched DocDB in January of twenty nineteen, and our Atlas business only grew faster since then. But I would say is our relationship with the hyperscalers has never been better. It’s actually really good. They’re actually working well on some of these app monetization efforts and programs, and they do that by funding some credits and so on and so forth to get customers to move more quickly.

And then we’re partnering in the field from a sales point of view. Clearly, there’s an area of coopetition. They do have the first party services, but we’ve been in this business long enough. We know how to partner and compete. And what we find is like if there’s a hyperscaler who doesn’t want to work with us, there’s two others who are happy to go after that opportunity together.

So we know how to kind of use that motion to help drive business.

Sanjay Singh, Infrastructure Coverage within the Software Team, Morgan Stanley: Awesome. With that, we’re out of time. Thank you, Dave. Thank you, Serge. You’re updating us on the MongoDB opportunities.

Thank you.

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