$MDB MongoDB Q1 2027 Earnings Conference Call

Thank you, Jess, and thank you all for joining us today. I continue to spend a lot of time working with a wide range of customers from AI natives and digital natives to large enterprises and public sector organizations. This customer-driven focus is to deliver meaningful outcomes for MongoDB. The process I follow is tightly linked, so each part strengthens the others. Number one, engage directly with C-suite leaders to elevate MongoDB from a technical decision to a strategic platform commitment. Number two, surface new pipeline by helping customers connect their most pressing modernization and AI opportunities to what MongoDB can uniquely solve. Number three, feed what I learn directly into our product and technology teams to accelerate our customer-driven innovation roadmap. These conversations reinforce my conviction in both what we have built and the scale of the opportunity ahead. That opportunity has two dimensions. The first is core workloads where large customers run their most demanding mission-critical workloads on MongoDB across on-prem, public clouds, and hybrid environments. The second is AI where enterprises, digital natives, frontier labs, and AI natives alike are moving agentic applications into production and choosing MongoDB as the data platform to power them. As you heard from other software companies, these two opportunities are not distinct and in fact reinforce each other. Enterprises are starting to build agentic applications on top of the very data already running on MongoDB. This dual opportunity compounding together is what gives us so much optimism about the road ahead. Today, I'm proud to share with you our Q1 results. We generated total revenue of $688 million, up 25% year-over-year, beating the high end of guidance and accelerating from the 22% growth we reported in fiscal Q1 of the prior 2 years. Top line strength was driven by Atlas, which grew 29.4% year-over-year, including a record $117 million year-over-year dollar growth. Now, at a $2 billion run rate, this is the fourth quarter in a row Atlas delivered year-over-year growth of at least 29%. EA and other, previously referred to as non-Atlas, grew 13% year-over-year. We delivered a non-GAAP operating margin of 18% above the high end of the guidance. We ended the quarter with over 67,700 customers, adding 2,500 customers in Q1, growing year-over-year and quarter-over-quarter. AI adoption of MongoDB technologies across our customer base continues to accelerate. MCP server usage is growing significantly. Voyage customers have more than doubled quarter over quarter. And vector search adoption is far outpacing overall company growth. Let me walk through each dimension of our opportunity. Across my conversations with customers, one shift stands out. MongoDB is starting to become a strategic platform decision in addition to a workload by workload evaluation. This is driven by a powerful combination of our platform technology fundamentals: high performance at scale, the ability to run anywhere, and AI capabilities that are fully integrated in a single data platform. Zoom is a clear example of that. Zoom, a global leader in AI-powered workplace collaboration, runs MongoDB Enterprise Advanced as a unified data platform for Zoom Meetings, Zoom Phone, Zoom Contact Center, and Zoom Virtual Agent deployed across dozens of clusters globally to deliver low latency, highly available communications at scale. By standardizing these workloads on MongoDB, Zoom gains a cloud-agnostic hybrid deployment model that runs anywhere their business requires. This simplifies a previously polyglot data estate, improves app resilience, and reduces total cost of ownership across mission-critical services. We look forward to continuing to support Zoom as they deliver the next generation of workplace experiences. Turning to AI, this opportunity spans three distinct segments. First is the frontier labs. Several of these have selected MongoDB for use cases that are mission-critical to the deployment of their products among the most demanding data workloads in the industry. The depth of engagement varies by lab and by workload and it is still early. But we feel great about the use cases we are winning and the ability to expand within these customers over time. Second is AI-native companies. These customers are choosing MongoDB as the foundation for their AI products from day one because the data layer determines if you can scale to support rapid growth. For example, Andur Labs is an AI-native application security platform protecting over 7 million applications across both human-written and AI-generated code. Andur selected Atlas as its default database to support 225% year-over-year revenue growth. Andur uses Atlas and Atlas search to power its mission-critical security workflows including Orie, its new security intelligence layer for AI coding agents, allowing the company to reduce operational friction and accelerate delivery of its differentiated offerings. Third is enterprises deploying AI. It is still early here, but we are beginning to see customers move from experimentation into production, building AI applications on top of the operational data layer already running their business. Zomato is a great example. The world's second largest food delivery company with 25 million monthly active users built Nugget, an AI-native customer support platform they are now selling to other enterprises on Atlas. After evaluating DynamoDB and DocumentDB, they chose Atlas for its aggregation pipeline, right consistency, and flexible schema. Nugget now orchestrates 15 million conversations per month on MongoDB's platform, reducing costs by 55% and improving human agent productivity by 40%. Another exciting pattern is also emerging across these segments, something I'm really excited about. Customers choosing MongoDB as the memory layer for AI agents themselves. Agentic workloads need memory that's transactional, high velocity, and able to retrieve the right context at the right time. Adobe's Journey Agent is a clear example, a composite multimodal AI agent that unifies Adobe's marketing suite and orchestrates end-to-end customer journeys for their global B2C user base with MongoDB as the agent's long-term memory and reasoning layer. Adobe leverages the MongoDB platform, Atlas Search, and Atlas Vector Search together to power the sub-100 millisecond hybrid search the agent needs to act in real time. To be clear, our results today are driven primarily by core workloads, but we are seeing real and growing momentum from AI and agentic workloads, and believe MongoDB is purpose-built to be the generational data platform for the agentic era. Built natively into the platform, MongoDB's innovations in the core database, embeddings, and vector capabilities are moving us beyond a system of record to becoming the real-time system of intelligence. That shift comes down to five core strengths. Number one, MongoDB is architecturally built for AI in two key ways. First, our flexible schema is uniquely suited to how applications get built in the agentic era. A growing share of software is now created through prompt-driven development, natural language iteration rather than line-by-line authorship. Whether the prompt comes from a developer or an agent, the shape of the application shifts with each prompt, and a rigid relational schema becomes a tax on every iteration, compromising agility. In addition, LLMs are the lingua franca for AI, and they speak in unstructured, document-shaped data, the exact form MongoDB was built around. We have been compounding both advantages for 15 years, well before the current AI wave gave them a tailwind. Second, MongoDB is a transactional, high-performance data platform built for how agents actually work. Agents don't behave like traditional applications. They read, write, and act continuously across multiple simultaneous threads, with a single agent spawning sub-agents that each make independent reads and writes in real time. Analytical systems built for offline processing weren't designed for this, and it shows in the performance when you run agents on top of them. MongoDB 8.3, released this month, takes that one step further, delivering up to 45% more reads, 35% more writes, and 15% more ACID transactions over 8.0 without changing a line of application code. Third, MongoDB is a data platform that delivers the retrieval accuracy agents need to be trusted while optimizing tokens and cost in production. For internal tools, occasional errors may be tolerable, but for customer-facing applications such as clinical decision support, fraud detection, financial transactions, insurance transactions, accuracy is non-negotiable. MongoDB delivers best-in-class retrieval through integrated vector search and Voyage embeddings and rerank all models purpose-built to surface the most relevant context when an agent needs it. This quarter, automated Voyage AI embeddings entered public preview, removing weeks of infrastructure work and enabling developers to deliver semantic search in minutes. Fourth, MongoDB runs wherever the agent needs to run across all three major clouds, on prem, and in hybrid environments. The assumption that every workload eventually migrates to the public cloud is being challenged by real factors: cost at scale, capacity challenges, latency requirements, and regulatory mandates on data residency. Many customers run Atlas and EA simultaneously, and they need a platform that doesn't force a choice. Fifth, MongoDB is embedded in the tools developers and agents actually use to build agentic applications. LangChain is the world's most widely adopted agent framework with over 1 billion downloads. We delivered 10-plus native integrations with LangChain for vector search, hybrid retrieval, semantic caching, and agent memory. We recently announced that MongoDB Checkpointer for LangSmith deployment, which collapses what used to be a dedicated PostgreSQL instance per agent into a single shared Atlas cluster state, memory, and operational data unified in one place. Last month, we also launched the MongoDB plugin and agent skills on the Cloud Code Marketplace, where we are already seeing strong early traction with developers. Whenever agents are built, MongoDB is already there. Executing on this opportunity requires a world-class team. On the product side, we recently announced two CPO appointments. Ben Cefalo, a long-time MongoDB leader, is now Chief Product Officer for core products, overseeing Atlas and Enterprise Advanced. Pablo Stern Plaza, who is based in San Francisco, joined as Chief Product Officer for AI and emerging products, with responsibility for our AI product portfolio and our strategic relationships with top AI-native and frontier customers. Over the years, Pablo has worked for many software companies in technical roles, helping scale their product lines into meaningful thriving businesses. Anchoring our technology organization is Jim Scharf, our Chief Technology Officer, who continues to focus on the enterprise requirements that matter most: security, durability, availability, and performance. On the go-to-market side, Erica Volini joined as Chief Customer Officer earlier in Q1, bringing two decades of enterprise growth experience, most recently architecting the partner-led motion that drove ServiceNow from $5 billion in revenues to more than $10 billion. Ryan McBean joined us as Chief Revenue Officer bringing 20 plus years scaling global go-to-market organization, most recently as CRO of Confluent, where he led a cloud-native consumption-oriented platform business with strong parallels to our own, and previously in senior roles serving large enterprise customers at VMware and Cisco. Erica and Ryan are partnering as a unified go-to-market team jointly responsible for the full customer life cycle. With this team in place, I'm confident in our ability to capture the opportunity ahead. I also want to extend my deepest thanks to the entire MongoDB team, and especially our go-to-market organization, whose hard work and sharp execution delivered a stellar Q1. One last note before I hand it over to Mike. I would like to personally invite you to our investor day, which will be in New York City on September 29th. Please email [email protected] if you'd like to attend. We hope to see many of you there. With that, Mike, please take it away.

Thank you, Matt. We wanted to make sure on behalf of our products and technology organization that we are ready to scale when somebody wants to create an agentic workload in production that is customer-facing, which is typically where the scale is much higher, and have all the capabilities in a single platform, so you are not doing search somewhere else, you are not doing vectorization somewhere else, and embeddings, which I was still trying to understand the power of embeddings and what that would do for agentic workloads, but now seeing that with some of the large financial services and healthcare companies gives me a lot of confidence that our data platform can truly act as a real-time system of intelligence. So, the answer is I'm seeing it's still early, Matt, just to be clear because the security governance, observability, there are many, many aspects to the agents and what kind of outcomes they deliver if it is agents at scale, but we feel that we are ready and you know, just yesterday Matt, I was with a Fortune 25 firm and when we outlined what we already have where MongoDB can not only act as an operational data layer, but can also act as a long-term memory and some of the things that we are building right now, they got really, really excited as they think about rolling out production agents at scale. So, early, but I'm seeing very encouraging signs and we are ready.

Thanks, Mike.

So, Ryan, first is that AI natives, what we are finding, and you know, I shared the example of somebody like 11 Labs at dot local in London a few weeks ago. They were using a first-party database for operational data. They were using another software for search. And basically most of those product lines were really choking as 11 Labs was growing significantly, right? They are now at a 500 million ARR. So, when asked the team, technically the engineer who made that decision saw that the growth of the company, as in that AI native company, 11 Labs, was being held up by the data layer. And us having search, vector search, and operational data in a single platform, they made the decision to move to MongoDB not too long ago. And two things they said that really resonated with me, Ryan. Number one, they are like, "Gee, we should have done this a lot sooner. Otherwise, we would have not had to deal with all these outages and other things they dealt with on the previous platform." And number two, now choosing MongoDB, even though they have scaled significantly on their ARR as an AI native company, gives them peace of mind. I'm hearing that from other AI native companies who also chose maybe a Postgres or something, and Postgres completely choked on the performance. So, that just gives me a lot of confidence that if an AI native company where AI is the business, or the agentic layer is the business, and they feel that they can scale with MongoDB, when that moves over to the enterprises, whether banks, healthcare, and other firms, they will also realize the same thing a little bit later. And as Mike shared, and I shared earlier, the contribution is there. We are seeing very encouraging signs right now, but a lot of growth was still driven by core enterprise workloads, which I would argue are also getting ready for the AI world.

Raimo, great question. So, we definitely see... I would say, and Raimo, thanks for acknowledging, but in Q1, just in Q1, I individually met 200 customers. Okay? So, I have lots of data points, and what we actually see is a lot more modernization acceleration, where somebody is moving to Atlas so that they're ready on scaling out for AI workloads, rather than a consolidation play. What I see, yeah, there are some examples where they're saying, "Okay, CJ, now you have search and vector search in the database. That improves our data pipelines. We don't need to ETL now to some other search provider. We tried to use open source, that didn't work." So, we are seeing some movement of data, and we are also seeing some migration from Postgres and others into MongoDB, given that we do unstructured data really, really well, and LLMs speak the language of JSON or love JSON. So, that's how I would describe it more than data consolidation, modernization, and also getting ready where you are not ETLing out data, and just use MongoDB as the layer for AI.

Thank you.

Yeah, Ittai, I'll give you a straightforward answer. This is work in progress. So, what we find is that some of these AI native companies come through our self-serve motion. We constantly watch, you know, we add so many customers through our self-serve motion, and that motion has been working really, really well as a lot of venture investments have gone into AI native companies. So, post 2023, first I want to acknowledge through our self-serve motion, we are getting some of these iconic logos that have now become truly companies with 100 billion ARR plus. With Ryan now in place, we are figuring it out. What is the right point to intervene, and that is a work in progress. Okay, what are the characteristics? It's a tier one VC company, maybe it's not. Like for example, a customer that grew in Q1, we found out that there was an AI/robotics company, and they were growing a lot on Atlas, and then our team reached out to them right away. So, we see that some of these companies are coming via self-serve motion, and then one, when do we intercept and put a field wrap on it. And number two, is that how do we scale and focus on that motion because we are a great database for those kind of companies. So, work in progress, but we are making definitely improvements as we learn.

Yes, I'll touch on it and then Mike will add. First is we see tremendous opportunity in federal business, not only just United States, but in Europe and other places as well. Federal business, when you think about whether it's tax agencies, whether you think about other types of agency, for example, administrations of various kinds, there is a lot of unstructured data. And there's a lot of unstructured data that needs to be stored properly or documents for a lack of better term and that needs to be retrieved. Performance has to be high and cost has to be lower. So, I am 100% a believer that this is a large TAM for us. We have not invested significantly both from a go-to-market perspective as well as product perspective in the past, but the good news is we will have FedRAMP High certification for US Federal this year. That comes with other set of requirements on how we support these federal customers. And one of the things that I have observed after being here is that a lot of these customers are still using our community version, and they would love to understand as we get FedRAMP High certification, can we sell to them properly and serve them properly and have enough coverage. So, massive potential, and that's why the acquisition.

So, short answer to your first question, yes, it is plural and it was chosen carefully. Thank you for noticing, Patrick. Number two, as we work with them and as they have tried whether it's a Postgres alternative or others, they have come to realize that and these are, you know, truly at the forefront of innovation in AI space or driving innovation, that MongoDB is just a great data platform for some of the workloads. And the point around, of course, we cannot go into specific details with our agreements with them on the type of use cases, but they vary and there are multiple use cases depending on the lab that we are working with them. And it's early, but we will continue to expand.

Absolutely. So, I'll answer your first question. LangChain, great partner. I'm really proud of what Harrison and the team are doing. And the simplicity when we talk to customers is three legs of the stool for any agentic workload: harness, LLM, and data layer. And if they are being used as in LangChain, you know, they have significant traction even when I talk to some of the large banks, whether it's on-prem or in the cloud, there's significant traction on the harness layer. And then they say, "Okay, what about the data layer?" And data layer, MongoDB being a choice for the data layer just makes sense. So, we've done many integrations with them and we are seeing this being played out at some of the large enterprise customers who say, "Hey CJ, I'm glad that the data layer as in MongoDB really works with the harness layer. And of course, we can choose whichever LLMs we want." So, that is actually being played out right now in some large customers who are trying to create agentic applications at scale. Number two, in terms of the CPOs, I'm really, really proud of Ben and his long tenure here and focus on somebody who wakes up every day focused on our foundational layer whether it's Atlas and EA, and he will continue to do that. And with Pablo who is based in San Francisco, he will look at emerging products and because the AI ecosystem right now is very concentrated actually in San Francisco city, working not only with just the frontier labs, but also with a lot of our AI native customers who tend to be in Silicon Valley. He wakes up every day to make sure how we are relevant in that ecosystem and he's a product and technology guy who has scaled many, many product lines over time. So that really gives me one person focused on foundation, second person focused on emerging products as well as AI workloads. And what I'll just want to share with you briefly, I am really fired up about our innovation roadmap that is accelerating and you will continue to hear new potential products as we move through this year at various dot local conferences.

I'll have Mike answer how that plays out given those were long-term deals and how we think about it.

Yeah. And I would say, Carl, that you also see some of that as we continue to move forward from Q4 to Q1. More than RPO, the CRPO number that Mike outlined and how whether it's long-term commitments across EA or Atlas is really, really encouraging for us.

Sanjit, I can't believe you asked me to stack rank. But here is how I would say it. What I'm seeing today is that our ability to be that... because AI workloads fundamentally, the requirements keep on changing. The tech stack that these large enterprises are building AI workloads on, whether it's LLMs, they want to use multiple LLMs or SLMs they want to use, continues to change. And as people are building these agents, as in developers are building these agents, us being super flexible with a no schema rather than rigidity of relational that you understand well, definitely helps us. So, I would say that architecture of MongoDB on native JSON, even the chat conversations that you want to store could become a long-term memory, so next time you come in and ask a question, it knows the context. But I would say that architecture, it is almost... our founder calls it really well, that we would rather be lucky than smart. And when we created MongoDB, this is from Dwight, we didn't have AI workloads in mind, but this architecture is perfectly suited for AI workloads. So, I would argue that that's the first part of the stack rank. And then the second part is our ability to do real-time and provide real-time intelligence on operational data, and having embeddings so that your token costs are lower and you have right retrieval that is accurate would be the second in the stack rank.

Yeah. And Sanjit, what I will just add is that what Mike outlined, we were really, really pleased that our go-to-market teams globally executed on what we asked them to execute on in Q1, which definitely helped that metric.

So, thank you everyone. We delivered a strong first quarter with broad-based momentum across Atlas, Enterprise Advanced, and our AI workloads. We are issuing strong guidance for Q2 and full year fiscal 27, and we remain committed to expanding profitability while investing for growth in line with our long-term financial model. Our results, our customer engagements, and the leadership team we have assembled all point to the same conclusion. MongoDB is on its way to becoming the generational data platform of choice for the AI era. Thank you very much for dialing in today.