MongoDB, Inc.

Good day, and thank you for standing by. Welcome to the MongoDB's third quarter fiscal year 2026 earnings conference call. At this time, all participants are in a listen-only mode. After the speaker's presentation, there will be a question and answer session. To ask a question during the session, you'll need to press star 11 on your telephone. You will then hear an automated message advising your hand is raised. To withdraw your question, please press star 11 again. Please be advised that today's conference is being recorded. I would now like to turn the conference over to your speaker for today, Jess Lubert, VP of Investor Relations. Please go ahead.

Thank you, Operator. Good afternoon, and thank you for joining us today to review MongoDB's third quarter fiscal 2026 financial results, which we announced in our press release issued after the close of market today. Joining me on the call today are CJ Desai, President and CEO of MongoDB, and Mike Berry, CFO of MongoDB. Following our prepared remarks, David Echeria, MongoDB's former President and CEO and current member of the board, will join us for Q&A. During this call, we will make forward-looking statements, including statements related to our market and future growth opportunities. our opportunity to win new business, our expectations regarding Atlas consumption growth, the impact of non-Atlas business and multi-year license revenue, the long-term opportunity of AI, our financial guidance, and underlying assumptions and our investments and growth opportunities in AI. These statements are subject to a variety of risks and uncertainties, including the results of operations and financial conditions that could cause actual results to differ materially from our expectations. For a discussion of material risks and uncertainties that could affect our actual results, please refer to the risk described in our quarterly report on Form 10-Q for the quarter ended July 31, 2025, filed with the SEC on August 27, 2025. Any forward-looking statements made on this call reflect our views only as of today, and we undertake no obligation to update them except as required by law. Additionally, we will discuss non-GAAP financial measures on this conference call. Please refer to the tables in our earnings release on the investor relations portion of our website for a reconciliation of these measures to the most directly comparable GAAP financial measures. With that, I'd like to turn the call over to CJ.

Thank you, Jess, and thank you to everyone for joining. I am honored and genuinely excited to speak with you as the CEO of MongoDB. This is an incredible company, and stepping into this role is a privilege. I want to start by thanking our customers, partners, and employees for everything you have done to build MongoDB into what it is today. I especially want to acknowledge Dev, whose leadership and vision created a phenomenal company which has strong momentum and a tremendous market opportunity ahead. Many have asked why I chose MongoDB. I had multiple opportunities to lead other technology companies, but MongoDB stood apart. We are at a true inflection point driven by major shifts across cloud, data, and AI. MongoDB has the potential to become the generational modern data platform of this evolving era, an opportunity that comes once in a lifetime. I am a truly customer-obsessed leader, so during my diligence, I spoke with multiple customers. Across these conversations, the message was clear. MongoDB already powers core mission-critical workloads for enterprises that are modernizing their technology stack. At the same time, MongoDB is uniquely positioned at the center of the AI platform shift. Few technology companies have that combination of durable core strength and emerging platform relevance. Throughout my career, I have driven product to platform transformation at some of the most respected technology companies. Looking at MongoDB today, I see all the ingredients needed to build an iconic modern data platform company. World-class technology, a strong innovation engine, deep developer and customer pool, and exceptional talent. We have everything required to become the generational data platform of choice in the AI era. Now on to this quarter's results. Atlas performance was strong, accelerating to 30% year-over-year growth, up from 29% in Q2 and 26% in Q1. We generated total revenue of $628 million, up 19% year over year, and about the high end of our guidance driven by strength in Atlas. We delivered non-GAAP operating income of $123 million, or a 20% non-GAAP operating margin. We ended the quarter with over 62,500 customers, adding 2,600 in the quarter and 8,000 year to date reflecting 65% growth in customer additions on a year-to-date basis, driven by the strong performance of our self-serve motion. Q3 was an exceptional quarter that was driven by our continued go-to-market execution and the broad-based demand we are seeing across business. At the same time, we significantly outperformed on operating margin, demonstrating that we can drive durable revenue growth while simultaneously expanding profitability. Now, let me explain why I see such a large opportunity ahead for both core operational data and emerging AI workloads. Our core business is strong across self-serve and enterprise customers even before any AI tailwinds. In my first three weeks, I've met with over 30-plus customers from AI-native companies to C-suite technology leaders at Fortune 500 companies. Those conversations have only strengthened my conviction in MongoDB's opportunity. Customers already depend on us for mission critical workloads today, and they are leaning in even further, betting on MongoDB to power the AI applications that will shape their future. The expansion opportunity in front of us is immense. We already serve more than 70% of the Fortune 100 and many of the world's largest banks, healthcare organizations, and manufacturers run their mission-critical workloads on MongoDB. Even with this foundation, there is still significant room to broaden our footprint within the enterprise. A strong example of this expansion opportunity is a major global insurance provider that has adopted MongoDB broadly across its enterprise. The company selected MongoDB Atlas to modernize several mission-critical systems, including its next-generation policy administration platform, analytics rating engine, unstructured data repositories, and hundreds of supporting services. Since moving its policy platform to Atlas, the insurer has expanded from just a small set of regions to nationwide and significantly accelerated the rollout of new products and distribution channels. Standardizing on Atlas has given the organization the scalability and reliability to improve customer experience, support more advanced data and AI capabilities, and increase development velocity, all central to its transformation and growth ambitions. All of this momentum in the core business is happening before the AI wave has meaningfully impacted our results. We are still early, but the signs are encouraging. From AI native startups building intelligent applications on MongoDB, to large enterprises developing AI agents that will reshape how they operate. AI applications must connect what LLMs know with what companies know, which is their proprietary data, systems, and real-time context. This is fundamentally an information retrieval problem, and it requires a very different architecture than the last generation of software. Rapidly evolving AI models uncover new, complex properties about entities, and rigid tabular stores cannot deliver the real-time, high-accuracy performance that AI systems require. At the same time, AI is dramatically increasing the speed at which applications are built and iterated, and fixed database schemas simply cannot keep pace. This is where MongoDB has a structural advantage. Our document model, natively JSON, is built for diverse, class-changing, and interdependent data. Our integrated search, vector search, and voyage embeddings remove the need for brittle bolt-ons and we are seeing industry-leading results. Number one on the Hugging Phase Retrieval Embedding Benchmark with Voyage MongoDB models and the number one vector database on DB engines. Advances in our embedding and re-ranking models drive meaningful accuracy gains, enabling AI applications to deliver more grounded responses with fewer LLM hallucinations. while lowering storage cost and query cost through smaller, more efficient embeddings. Because all of this is delivered in a unified platform that runs anywhere, customers can keep operational and AI workloads together, simplify their architecture, and innovate faster. As AI adoption accelerates, MongoDB's position not just to participate in the wave, but to help define it. We are already beginning to see this play out with AI native customers like Mercore, which is redefining hiring with its fully automated platform that uses AI to assess and match talent with the opportunities they are best suited for. Mercore uses MongoDB Atlas to store the AI data behind its platform that directly connects professionals, to AI model training and evaluation roles. Originally a self-serve customer, the company is also utilizing Voyage AI embeddings and Atlas Vector Search. Atlas has scaled to support Mercore's 50% month-over-month growth, allowing the company to keep its software engineering team lean and agile as it expands to over $10 billion in value. This is just one example of how customers are building AI native applications and companies on MongoDB. We are also seeing meaningful traction among large enterprises that are starting to build AI applications that have a material impact on their business. For example, a highly influential global media company aimed to increase engagement via enhanced content recommendation for its vast repository of multimodal assets across its 70-plus websites. Their existing stack, powered by Elasticsearch, hit a performance wall, struggling with the complexity of new embedding models. Recognizing that rigid systems stifle innovation, the engineering team re-architected on MongoDB Atlas and MongoDB Atlas Vector Search. Working with MongoDB experts to deliver a proof of concept in just weeks, They integrated Voyage AI models directly alongside their data. The solution scaled effortlessly, cutting latency by 90%, reducing operational spend by 65%, and driving a 35% increase in click-through rates, ultimately providing millions of global readers with a seamless, deeply personalized discovery journey. The bottom line is that the business is performing exceptionally well, existing customers are expanding with us, and net new customer additions continue to show strength. Companies in nearly every industry and across every geography are choosing MongoDB because we deliver the features, performance, cost effectiveness, and AI readiness they need in single data platform. Given the continued robust performance of Atlas, along with the healthy underlying fundamentals we are seeing in the business, we are raising our financial guidance for the fourth quarter and the full fiscal year 2026 and reiterating our commitment to the long-term financial model outlined at our recent investor day. Over the next few months, my focus is straightforward. Deepening customer relationships, advancing our innovation agenda as we build the generational modern data platform for the multi-cloud and AI era, scaling our go-to-market efforts, and supporting our people so they can do their best work. I believe MongoDB is a company that has only begun to realize its vast potential, and I look forward to unlocking this potential in the years to come. With that, I'll now hand the call over to Mike to discuss the financial results and outlook in greater detail.

Mike. Thank you, CJ. I want to extend a big welcome to you from all of the employees at MongoDB. We are excited to have you join the team. I look forward to working with you to continue to execute on our business plans and drive meaningful shareholder value. I also want to thank Dave for the partnership and our time working together. I believe we accomplished a lot in a short period of time and appreciate all of your guidance and leadership. Best of luck in the next stage of your life journey. Okay, now let's move on to the financial results. I will begin with a detailed review of our third quarter results and then finish with our outlook for the fourth quarter and fiscal 26. I will be discussing our results on a non-GAAP basis unless otherwise noted. As CJ mentioned, we had another strong quarter as we exceeded all of our guidance ranges and are increasing our full year outlook across the board. In the third quarter, total revenue was $628 million up 19 percent year-over-year and above the high end of our guidance. Shifting to our product mix, Atlas revenue outperformed our expectations as year-over-year growth accelerated to 30 percent in the third quarter and now represents 75 percent of total revenue. This compares to 68 percent of total revenue in the third quarter of fiscal 25 and 74 percent last quarter. In the third quarter, Atlas consumption growth was relatively consistent with last year's growth rates, which drove the acceleration in revenue as well as growth in absolute revenue dollars for the third straight quarter. Atlas growth was driven by continued strength with our largest customers in the US and broad-based strength in EMEA. This strength is being driven both by new workloads and growth of existing workloads. We believe these dynamics reflect our growing strategic importance to many customers and our ability to win more critical workloads due to the strength of Atlas. You can see that progress in our total company net ARR expansion rate, which increased to 120% in the third quarter, up from 119% last quarter. Turning to non-Atlas, revenue came in ahead of our expectations in the quarter, as we continue to have success expanding within our existing non-Atlas customer base. Non-Atlas ARR, which reflects the underlying revenue growth of this product without the impact of changes in duration, grew 8% year over year. We continue to see consistent trends in non-Atlas in the third quarter, which reflects the desire of some of our largest customers to build with MongoDB long-term for their most mission-critical applications. We also benefited from higher than expected multi-year revenue in the third quarter as approximately two-thirds of the non-Atlas revenue outperformance versus the high end of guidance was attributable to multi-year outperformance. We had another strong quarter for customer ads as we grew our customer base by approximately 2,600 sequentially, bringing the total customer count to over 62,500, which is up from over 52,600 in the year-ago period. The growth in our total customer count is being driven primarily by Atlas, which had over 60,800 customers at the end of the third quarter compared to over 51,100 in the year-ago period. We ended the quarter with 2,694 customers with at least $100,000 in ARR, representing 16 percent growth versus the year-ago period. Moving down the income statement, gross profit for the third quarter was $466 million, representing a gross margin of 74 percent, which is down from 77 percent in the year-ago period. Our year-over-year gross margin decline is primarily driven by Atlas growing as a percent of the overall business. Although Atlas gross margins are slightly below the total company gross margins, they continue to improve year over year. Our income from operations was $123 million for a 20 percent operating margin compared to 19 percent in the year-ago period. We are very pleased with our stronger-than-expected operating margin results, which benefited from both our revenue outperformance and lower-than-expected operating expenses. Net income in the third quarter was $115 million or $1.32 per share based on 86.9 million diluted shares outstanding. This compares the net income of $98 million or $1.16 per share on 84.2 million diluted shares outstanding in the year-ago period. Turning to the balance sheet and cash flow, We ended the third quarter with $2.3 billion in cash, cash equivalents, short-term investments, and restricted cash. During the quarter, we spent $145 million to repurchase approximately 514,000 shares, which was executed under our previously announced $1 billion total share repurchase authorization. Operating cash flow was well above our expectations at $144 million, and free cash flow was $140 million, which compares to $37 and $35 million, respectively, in the year-ago period. Our cash flow results were driven primarily by strong operating profit and improving working capital dynamics, particularly related to higher cash collections. We remain confident in our ability to drive higher and more consistent free cash flow going forward. Before we go into our guidance for the rest of fiscal 26, let me recap some of the enhancements we have made to our approach to guidance since I joined MongoDB. Importantly, we are providing more visibility into our expectations for Atlas growth, as well as non-Atlas ARR growth each quarter. That being said, we will continue to be prudent in our forecasting of multi-year deals and only include those deals where we have very clear visibility. Our goal is to give you a more transparent view into our expectations for the business and our approach to guiding the non-Atlas business. Now, let me share some of the assumptions driving our outlook for the rest of fiscal 26. Number one, we are continuing to see strong momentum in Atlas. which has experienced relatively consistent consumption growth through the first three quarters of the year and comparable seasonal patterns as compared to fiscal 25. We are seeing strength with existing customers along with momentum in new accounts as customers large and small increasingly recognize the strategic value of Atlas. As a result, We now expect Atlas to see approximately 27 percent revenue growth in the fourth quarter of fiscal 26, which is higher than our previous expectations of growth in the mid-20 percent range. This outlook reflects our continued confidence in Atlas while taking into account the historical seasonal variability and consumption patterns during the holiday period. Number two. We continue to experience steady ARR growth in our non-Atlas business and have good line of sight to several large multi-year deals we either already have or expect to close in the fourth quarter of the year. Based on these dynamics, we now expect our non-Atlas business to grow in the upper single digit percent range year over year in the fourth quarter. Number three, we continue to make strategic investments in engineering, marketing, and direct sales capacity to drive continued growth. Some of these planned investments have taken longer to implement than expected and have shifted into the fourth quarter of fiscal 26 and fiscal 27, which has benefited our operating margin during fiscal 26. Fourth, we continue to make progress on free cash flow conversion which is now expected to exceed 100 percent for fiscal 26. Finally, we will continue to execute our share buyback program to help offset dilution from employee equity awards. In addition to our buyback, this past quarter we began settling the taxes due on the vesting of employee RSUs with cash instead of issuing new shares. We also expect to receive over 1 million shares of stock for the cap calls associated with our 2026 notes that mature in January 2026. All of these actions will help us manage share count for the long term and illustrates our commitment to being good stewards of your capital. Now let's shift the guidance in the fourth quarter and fiscal 26. For the fourth quarter, we now expect revenue of $665 to $670 million which equates to 21 to 22% year-over-year growth. We expect non-GAAP income from operations to be in the range of $139 to $143 million for an operating margin of approximately 21%. We expect non-GAAP net income per share to be in the range of $1.44 to $1.48 based on 86.5 million diluted shares outstanding. For fiscal 26, we now expect revenue to be in the range of 2.434 to $2.439 billion, an increase of $79 million from the high end of our prior guide, and representing full-year revenue growth of 21 to 22 percent. We are raising our non-GAAP income from operation expectations by $109 million at the high end and are now targeting a range of $436.4 to $440.4 million for an operating margin of approximately 18%. We expect non-GAAP net income per share to be in the range of $4.76 to $4.80 based on 86.7 million diluted shares outstanding. Note that the non-GAAP net income per share guidance for the fourth quarter and fiscal 26 assumes a non-GAAP tax provision of 20 percent. While we will provide detailed guidance for fiscal 27 on our fourth quarter call, I would like to comment on how we were thinking about a few metrics as we sit here today. First, We remain committed to the long-term model presented at our investor day in September and continue to make great progress against all of the objectives highlighted at the event. We have seen strong margin expansion and free cash flow performance in fiscal 26, and both of these metrics are tracking well above the long-term targets we discussed in September. As we look ahead to fiscal 27, we will continue to make strategic investments to focus on driving growth going forward. With these planned investments and the timing of headcount ads, we continue to target 100 to 200 basis points of margin expansion on average and 80 to 100 percent for free cash flow conversion outlined in our long-term model. Second, Our non-Atlas business is on track to exceed our prior expectations for fiscal 26 due to the stronger performance, including greater than expected large multi-year deals. Given this outperformance and our current bottoms-up forecast for fiscal 27, we currently do not expect non-Atlas multi-year transactions to provide either a meaningful headwind or tailwind to revenue in fiscal 27. To summarize, we had another very strong quarter. We are pleased with our ability to drive both revenue growth across the business while increasing our operating profit expectations and driving meaningful free cash flow. We remain incredibly excited about the opportunity ahead, and we will continue to invest responsibly to drive long-term shareholder value. With that, Lisa, we would now like to open the call up for questions.

Thank you. As a reminder, if you would like to ask a question, please press star one on your telephone. You'll hear the automated message advising your hand is raised. We also ask that you please wait for your name and company to be announced before you proceed with your question. One moment while we compile the Q&A roster. And our first question of the day will be coming from the line of Sanjit Singh of Morgan Stanley. Your line is open.

Yeah, thank you for taking the questions. Fiscal year 26 is turning out to be quite the year for MongoDB, so congrats to the team all around. CJ, I wanted to start with you since this is your first earnings call. I heard you loud and clear in terms of what the goal is here to make MongoDB a foundational data platform for the AI era. In terms of making that happen in your kind of first 45 days on the job, maybe even less than that, are there some initial things that you're looking at, some kind of... things that might fit in the sort of quicker wind bucket, and then longer term, what is, you know, what are some of the changes you think that the company can make or evolve to, to get to that, to get to that, to see your place in that sort of AI era?

Thank you, Sanjit. Here is, you know, this is my day 28 on the job, and I have been speaking to customers as well as our innovation team, including our Voyage AI team as well as our core database teams. The first thing I would say is the opportunity for MongoDB to be that data platform for AI workloads is very real because you need real-time operational data, you need the right context, you need to make sure that you are keeping up to date between the proprietary data of the company as in the enterprise, as well as the LLM learnings that the LLM model brings to the table. And most importantly, when I think about all of that combined together, MongoDB has all the elements needed to be the right foundational platform for AI workloads. In speaking to customers, it is still early. There are various co-pilots when it comes to productivity types of applications that are happening inside of an organization, whether it's a bank or a healthcare organization or a manufacturing organization. But what I have not seen is truly AI agents running in production that fundamentally transform the business or serve customers better. There are many, many pilots still going on. When I contrast that with the AI native companies, and there is a really good, fast growth at scale AI native company that currently switched from Postgres to MongoDB because Postgres could not just scale. There is another AI company that highlighted that is using our embeddings as well as our vector database besides our operational platform. So when I combine all this together, Sanjit, what I see is as truly skilled agentic platforms where you can have enterprises creating agents that transform their business, MongoDB has a very important role to play. And from a Low-hanging fruit standpoint, I would argue that our embedding model and re-ranking model is something that customers can start with today. Then they can move on to our vector database and use us for also real-time operational store. So that's how I'm thinking, and some of my initial customers' conversations have validated that theory.

Understood. I know it's early, so great to get out of that perspective. And then one follow-up for me, it's sort of a mark-to-market question. The calendar year 24, fiscal year 25 workload sort of improved in quality versus the prior year. I just want to get a sense of your sort of view on how the calendar year 25 workloads are shaping up as they will unlikely be a factor in terms of thinking about growth next year. So in terms of the quality of the workloads this year, can you give us a sense of the quality of those workloads?

Yeah. Hey, Sandeet, it's Mike. So what we'll say there is, as we said during the prepared remarks, and we saw this in Q2 as well, what we're really seeing is strength in the larger customers. It's not only from new workloads, but it's from the existing workloads. We don't want to bifurcate between which calendar year those were added. What we'd say is that we continue to see growth in the larger customers. They are growing longer and They're getting bigger and growing for longer, which is great, and we're seeing that across both the United States and then broad-based in EMEA as well. And, hey, as Atlas gets bigger and bigger, all of those kind of munch together because they're expanding, they're adding. So what we'll do is we'll focus on the growth in our larger customers, especially in the U.S. and EMEA, without going into each year.

Understood.

Thanks, Mike. Hope that helps.

Thank you. One moment for the next question.

And our next question will be coming from the line of Matt Martino of Goldman Sachs. Your line is open.

Hey, thanks for taking my questions and nice to see another quarter of acceleration. CJ, I appreciate you're only a few weeks in, but I'd be curious to hear what customers are telling you is top of mind for MongoDB. You know, what are the repeated themes in customer conversations as you take a fresh lens to the business?

Absolutely, Matt. Great to hear from you. First thing I would say is that the modernization effort, whether it's a workload that may be just running on-prem in a large enterprise or a workload that is moving to cloud or sometimes to multiple clouds for resiliency, That transformation in speaking to a large telecommunications company, a large healthcare company, a large tech company, and I can cite you many other examples, I was pretty overwhelmed to understand that those transformations are still going on. That is just a recent conversation I had with CTO of a large telecommunications company who said that they are moving 1300 plus applications to another hyperscaler and trying to determine which workloads are best suited for MongoDB. So the whole multi-cloud or a public cloud transformation is still going on and just my intuitive sense in speaking to these customers will be going on for at least next five to seven years. So that specific TAM still very much exist for MongoDB. Now, these are the same set of customers, while they are trying to modernize their application stack, they are also experimenting, I would say, because I have not seen agents at scale that are customer-facing or sometimes even employee-facing. They may have 10, 15, 20, but not that many compared to thousands of applications they run. In those AI applications area, They are experimenting sometimes with our embedding models or with our vector database or using MongoDB for real-time operational database. So that second aspect, which is still fairly early, but we are very well positioned as you think about AI workloads in enterprises and large enterprises. And last but not the least, spending time, as you know, or you may know, that I spend half of my time in New York City and half of my time in Silicon Valley, and speaking to my network in Silicon Valley with AI-native companies or digital-native companies, what I hear from them is that certain alternatives and relational database just do not scale because AI workloads are fundamentally around unstructured and semi-structured data. and then they decide sometimes explicitly to use MongoDB. So I put this in three buckets. One bucket is our core, and still the cloud transformation, digital transformation, modernization, whichever term you want to use, our core will still continue to grow. As people create AI agents at scale, MongoDB has a role to play. and for AI-native companies, and some at scale, are already using MongoDB because the alternatives in relational world just do not scale. So those are my three buckets and initial mental model on how these conversations are proceeding and what we can do for them.

Really clear. Thanks for sharing your thoughts there, CJ. And then, Mike, just a quick follow-up for you. It was good to see the outperformance on both Atlas and non-Atlas. But, you know, with margins now about 200 basis points shy of your midterm framework, how should we think about the philosophy around reinvestment and any considerations around non-Atlas and the ability to expand margins as we look out into fiscal 27? Thanks a lot.

Yeah, thanks for the question, Matt. I'm sure everyone's focused on 27, so what we'd say is we will guide 27 on the next call. What we would say is, and it's built into the guidance that you have in Q4, and I also talked about it on the prepared remarks, we are continuing to invest and we will continue to invest. Some of the investments that we wanted to make, especially around engineering, marketing less so, but certainly around sales capacity, has been pushed into Q4. So you should expect to see OpEx continue to grow in fiscal 27. But we also want to make sure, and that's why, Matt, we took the time to say, hey, we want to reorient you to what we talked to you about in September. We still expect to see margin expansion. But you really see it in the fiscal 26 numbers is that is coming mostly from revenue growth. That is the expectation next year. We'll continue to grow revenue. We're going to continue to invest in the business. but the business model will continue to drive that expansion. So you should expect to see us continue to invest, especially across those three areas.

Thanks, Mike.

Thank you.

Thank you. One moment for the next question. And our next question will be coming from the line of Carl Karstad of UBS. Your line is open.

Okay, great. Thank you. First of all, CJ, welcome aboard. I'm excited to work with you over the coming years. I had a question for you. So it seems as if you're describing these good set of numbers as strength in the core, essentially even before that AI tailwind kicks in. I'd love if you could define what you think is fundamentally driving that core strength. And do you feel like it's possible that actually Mongo is already getting an AI tailwind in the sense that there's a heightened focus on modernizing your data in advance of AI such that this core strength is actually AI related.

Carl, great to hear from you and looking forward to seeing you on Wednesday. I would say the core strength from my perspective is workloads that are, you know, need modernization has a lot of unstructured or semi-structured data and ideally suited for MongoDB. Now, when it comes to AI, could AI potentially drive more modernization efforts, that is possible but not deterministic. As in, we see, as we shared in the remarks, that in the high end of the enterprise, the consumption of the workloads we acquired maybe a year ago, a year and a half ago, that continues to move up in the right direction as our go-to-market teams are focused on the high end of the enterprise. We also saw broad-based strength in Europe, And that is pretty much to the core business like the large insurance company on the claims engine and other things that I spoke about related to policies. So I particularly see that as, okay, does that mean that if core is modern, it helps with AI workloads? Absolutely, that is true because they are not mutually exclusive. And, Carl, one thing I would say, this is my personal experience in building systems AI technologies in the past, that the AI team is typically a separate team from the core data team. An AI team relies on the core data team, and if the core data team moves slow, then AI teams get really frustrated because innovation velocity is how they measure themselves on. So my personal experience was, hey, when the core team is not agile, their schemas are not flexible, it actually slows AI down. So there is definitely some facts behind your theory that it is potentially the AI revolution, which we are still in the early stages, is driving modernization in the other part of the enterprise.

Okay, CJ, thank you. And then, Mike, for you, I think everybody on the line appreciates the more definitive guidance on Atlas for the following quarter, so thank you. I wanted to ask what's driving that. Is it simply a function of you and Jess in your relatively new seats wanting to be more transparent in the guidance? Or, Mike, is there something actually changing in Atlas such that now that it's at scale, it's becoming predictable enough that it now makes more sense to give precise guidance? Thank you.

So thanks, Carl. Thank you for the question. I would say it's probably a little bit of both. One is, hey, we want to give you folks a little bit more visibility to what's behind the guidance that we provide. That was number one. Also, as Atlas gets to be, gosh, now almost a $2 billion business, you know, we feel better about the forecasting. The team has done a wonderful job forecasting that part as well. So when we gave the number for Q4, we want to make sure and give you the visibility. But we also have a pretty good view of what we hope it would be. Understanding that, keep in mind, Q4, we want to be prudent because there are some seasonal holiday patterns that can be somewhat unpredictable. And we've seen that play out in the past Q4s. So I just want to note that for the guidance that we just gave.

Got it. Thank you.

Thank you.

One moment for the next question. And the next question will be coming from the line of Remo Linschao of Barclays. Your line is open.

Thank you, CJ. All the best from me as well. I have two questions, one for CJ, one for Mike. CJ, one of the core things in terms of adoption of Mongo will be on the developer side because they're at the end of the day, developers are a big driver of what's getting used, etc. At the moment, a lot of AI is on the West Coast. What's your thinking around getting developer engagement up with Mongo to go against that Postgres narrative that happens a lot in the Valley? And then, Mike, for you, since next year, EA... is not seeing benefits for multi-year. Should we anchor our numbers on the ARR performance then? Is that the right way to think about it? Thank you.

Thank you, Remo. Great to hear from you. I'm going to first ask, there is a little bit historical context in terms of your point on the West Coast. I'm going to ask our previous CEO, David Tacharia, to talk about Reclaim the Bay, the initiative that him and the team started. And then I'm going to specifically talk about how I think about it on the West Coast.

Hey, Raimo, it's Dave here. As CJ mentioned, we've talked about this in previous calls, but we made a concerted effort to reinvest in the Bay Area because during COVID and post-COVID, we felt that we had neglected that region. And obviously, there was a whole new corpus of AI-native companies that were getting launched. So there's been a real concerted effort, both in terms of putting more feet on the street putting more marketing efforts in terms of supporting that part of the world, investing more in the startup community and also in the venture community to get people to understand the true value proposition of MongoDB. We've done things like hackathons and other events in that area as well. And there's a team really focused, dedicated to really supporting and servicing these early AI native companies and that is starting to yield some results. And we feel really good about the progress there, but I'll let CJ talk about what happens going forward.

Thank you, Dave. And this is the Reclaim the Bay in San Francisco area on the West Coast. It is 100% true, Remo, that there is a lot of investment with AI-native companies, and we could benefit from increased mind share and being in front of them, as in the developer community that you talked about, which is a super important community to us, on the West Coast. So me spending personally time on the West Coast Health, I do also have deep network in the West Coast community, both venture community as well as tech companies at scale, and I've already started leveraging that network to get their feedback. We are really excited in this quarter as in the 4Q, we are relaunching our DOT Local after a few years in San Francisco on January 15th, where we are going to invite companies that have built on MongoDB, some great speakers on why they should build on MongoDB, and show hands-on experience to the developer community in that conference on January 15. And what I see is, you know, just speaking to many CEO founders as well as developers of smaller companies or mid-sized companies, that all these efforts of the marketing investment that Mike and Dave originally approved is going to start yielding results as we move into the next fiscal year. Thank you.

And, Ramo, thanks for the question. It's Mike. On non-ATLAS next year, we wanted to make sure we've had a lot of questions about the multiyear headwinds, so thank you for the question there. We are not guiding for fiscal 27. However, sitting here today, I would steer you more towards if you look at the full year revenue growth of non-Atlas, it's about 4%. Somewhere in that mid kind of low single digits is probably a good range to think about for next year as we sit here today.

Okay, that's clear. Thank you.

Thank you. One moment for the next question. And our next question. We'll be coming from the line of Brad Reback of FIFO. Your line is open.

Great. Thanks very much. Not sure who this is for, but on the commentary around new customer strength within Atlas, are you seeing new customers ramp faster for net new workloads than they have been historically? And if so, why?

Brad, my initial observation is that the team needs engineering team has done a fantastic job when they launch 8.0 and all the subsequent point releases. That allows Atlas to be adopted faster and remove the friction, whether you are coming via our self-serve channel or whether you are a large enterprise moving on to Atlas. So that's one thing I would say. And I'm going to ask Dave to provide commentary as well from a context perspective.

Yeah, I think what I would also say is that, Brad, is that I think the self-serve team has really removed the friction to enable customers to onboard more quickly and more easily. And given the performance, price performance gains that we've seen in 8 and now even better in 8.2, I think that's really driving a lot of the traction we're seeing in our new customers. They quickly see the performance benefits and they're scaling nicely. And so that's allowing us to continue to acquire customers efficiently.

And one last thing on that, Brad. If you look at the revenue from that, it hasn't changed materially. It's still, keep in mind, a pretty small number when they first onboard, so it's not going to move the needle much. We haven't seen much change in that cohort over the last couple of years.

Great. And then, CJ, a quick follow-up for you. Philosophically, how do you think about M&A as it relates to Mongo? You know, what types of things, if anything, do you think you need to acquire?

Brad, you know me well, and I'm a big believer in organic growth. Dave and the team have laid a very strong foundation on our technology platform. I think Voyage AI in February was a brilliant acquisition where we got an unbelievable team in Palo Alto. My goal on behalf of MongoDB is to always believe in our own teams and our technology, we participate in a large market, and where it makes sense where we can get a particular adjustment technology or a great team that can help us accelerate the roadmap, we would always consider that type of M&A.

Perfect. Thank you. Operator, we'll take two more questions.

Okay. Thank you. And one moment for the next question. And our next question will be coming from the line of Alex Zukin of Wolf Research. Your line is open.

Hey, guys. Thanks for taking my question. CJ, maybe for you, I mean, you shared, I think, a lot of thoughts about your initial vision. You shared the three pillars of the core, the enterprise AI opportunity and the AI natives. I just want to maybe lean in. Where do you see your particular skill set and network offering kind of not the lowest hanging fruit, but your ability to make kind of the biggest impact in call it the next 12 to 24 months? Like, where do you really see that incremental opportunity for growth inflection?

I would say, Alex, and, you know, you are aware of the enterprises and the customer obsession I have and the relationships that I have formed over many, many years with the technology leaders at large companies. So from my perspective, there are two areas where I can benefit our go-to-market teams immensely. Number one is Fortune 500. where MongoDB can still penetrate even at a higher rate than it is penetrating today, both within the existing accounts as well as the new accounts we get. So that's Fortune 500. And then I was with our sales teams in Europe and there are many customers that they are targeting, including existing customers, large banks, manufacturing companies, and so on, where they're trying to expand, where my personal relationships with those technology buyers can help. So that's bucket number one, is make no mistake, high end of the enterprise, as in Fortune 500 and Global 2000. Number two, on the other extreme, would be AI-native companies lived in Silicon Valley for a very long time. I understand where the venture community is investing. Folks who are creating, whether it's domain-specific AI companies or foundational companies, have relationships there as well across 101, 280, and 237. And that's where I also plan to, I would say, plant the seeds in a correct fashion so that over time that becomes a meaningful business for MongoDB if we are the underlying infrastructure for those companies. So those are the two extremes that I'm going to spend personally a lot of time on.

Excellent. And you mentioned Voyage AI, the acquisition this year being kind of a crown jewel in the portfolio. Maybe just help us understand with the AI native, specifically the opportunities there, are those starting? Are you guys landing with Voyage? Are you landing with Atlas? Are you landing with both now at a more kind of constant pace? Help us understand kind of that incremental differentiator.

Yeah. I would say one example, and this, you know, in my remarks I shared, that there is a super high growth AI company that is doing very, very well and will become a very large company. I have absolutely no doubts about that. They were not able to scale with Postgres and few other technologies, Redis and so on that they were using, and they moved completely to MongoDB. And seeing that week over week and month over month growth, is super inspiring, and I spoke to the hyperscaler where this workload is running, and they are seeing the same that, wow, this company is doing really well. So that's built on MongoDB because Postgres had scaling issue. The other extreme, I spoke to a fairly successful AI native company that is doing decent ARR, growing very fast, and when I said, hey, have you considered MongoDB to the founder CEO who is very technical. He said, CJ, we built our own vector database and so on. And while I was speaking to him, Alex, about 10 days ago, he basically said, once he looked at the portfolio, he said, let me start with embeddings first. So we are going to try. Of course, we have to prove it to him by our embeddings improves his accuracy on search and so on and improves the performance. So he said, let's start with embedding models first from OAJI. Once that works, CJ, I'm willing to replace my vector DB that we have homegrown, created it with MongoDB. And oh, by the way, if that works well, eventually I'm willing to swap out my operational database as well and use MongoDB. So in those kind of scenarios where they are already on a certain track, we can land with Voyage AI embeddings. And I'm also seeing in a very large customer of MongoDB, I spoke to somebody who is running the AI initiatives, and they love the Voyage AI embeddings and re-ranking model, and they've already approved it for two big workloads. So we can absolutely land with that is the short answer.

Operator, we'll take our last question.

Sounds like a beautiful synergy.

Thank you.

Thank you. And the last question for the day will be coming from the line of Ryan McWilliams of Wells Fargo. Your line is open.

Hey, guys. Thanks for the question. The consumer app development environment seems to be getting stronger as new iOS app development has surged to multi-year highs. We think it's due to agenda coding, and I know it's early. But on the enterprise side, are you seeing stronger product velocity from your customers in building their enterprise applications?

I'm going to ask Dave to provide his opinion, and then I'll provide mine.

Yeah, I think what we're seeing is we're clearly seeing a lot of, I would say, prototyping and iteration. I would say the enterprise requirements still have pretty strong and stringent requirements around security and durability and performance. So while there's a big difference between coming out with a prototype and having a production-grade system that an enterprise can truly rely and trust, And so there is still a lot of work required to, you know, make those applications enterprise class. But clearly with the advent of code gen tools, the rate and pace of software development is only going to increase. And as I think we said in the past, that's one of the big reasons why we think AI is a tailwind. It's just that, you know, the ability to produce more software and more and more strategies is then encapsulated in software. So from that point of view, we think that's all good news for us.

Yep. And the only thing I'll add on is when I speak to customers who have been speaking for a long time, you know, in regulated industries, which is financial services, which is healthcare, which is public sector, the requirement for an AI agent to be in production versus prototype are vastly different. And they are looking for governance, auditability, this and that, while the innovation is and the need for the speed is very high. So I have not seen, like, you know, customers will tell me, CJ, I have 10 agents in production, 15 agents in production, and when I really ask them, I say, are they really customer-facing? Can they be audited on the you know, probabilistic outcome they derived, the answer is, oh, we are still working through that. That doesn't mean that it will not happen soon or it will never happen, but I still feel we are fairly early. And even the environment on which they are building agents, they are telling me they try one, it doesn't work, they move on to the next one. So the churn for some of these AI companies that deliver these tools is also very real. And that's why I'm very encouraged by the MongoDB opportunity. We have the platform for operational data. We have the best vector database, and we have the embedding models where they can comfortably, at enterprise scale, build a real AI agent using MongoDB platform.

Excellent. Really appreciate that detail. And then for Mike, on the Atlas 4Q growth guidance, appreciate the color there. Just a quick clarification there. On this 4Q Atlas guidance, should we expect results closer to 10 or a guidance philosophy consistent with your historical precedent? Thanks.

Yeah, so thanks. So I don't want to go into the golf analogy. And besides, Ryan, you know I like hockey analogies better. What I would say is that, hey, we feel really good about Atlas. It's had a great year so far. We feel good about it going into Q4. We remain excited about the growth. That being said, we are being prudent for Q4. As the seasonal holiday patterns, hey, they can be somewhat unpredictable, and we've seen that play out in the past Q4s. So what I would say is, hey, we just need to be prudent as we enter the holiday season.

Sounds like you're getting pucks on the net. Thanks, guys. Nice.

Very nice.

Thank you. That does conclude today's Q&A session. I would like to go ahead and turn the call back over to... MongoDB's President and CEO, CJ Desai. Please go ahead.

Thank you, Lisa. In summary, we delivered an exceptional third quarter, highlighted by accelerating Atlas growth, robust customer additions, and significant operating margin output. We are raising our revenue and operating income guidance for the fourth quarter and full fiscal year 2026, and reiterating our commitment to the long-term financial model we outlined at Investor Day. Our results underscore that MongoDB's core business is firing on all cylinders even before any meaningful AI tailwinds. At the same time, we are uniquely positioned to become the generational modern data platform for the AI era, all while driving durable, efficient growth. Thank you, everyone, for joining, and thank you for listening.

This concludes today's conference call.

You may all disconnect. Very nice. Yeah, good work. Well done, guys. Thank you.