Earnings call transcript: MongoDB Q2 2026 beats estimates, stock dips

MongoDB, a $17.57 billion market cap company with a GOOD financial health score according to InvestingPro, reported its Q2 2026 earnings, surpassing both EPS and revenue forecasts. The company posted an EPS of $1, significantly beating the forecasted $0.67, resulting in a 49.25% surprise. Revenue reached $591.4 million, exceeding expectations of $553.94 million. Despite these strong financial results, MongoDB’s stock saw a decline of 1.82% in after-hours trading, closing at $217.80, down from a previous close of $218.44.

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Key Takeaways

• MongoDB’s EPS of $1 outperformed forecasts by 49.25%.
• Revenue for Q2 2026 was $591.4 million, a 24% increase year-over-year.
• Atlas, MongoDB’s cloud database service, grew 29%, making up 74% of total revenue.
• Despite strong earnings, the stock fell 1.82% in after-hours trading.
• Full-year revenue guidance for FY2026 was raised to $2.34-$2.36 billion.

Company Performance

MongoDB’s performance in Q2 2026 reflects robust growth, driven by its cloud service, Atlas, which now constitutes 74% of the company’s revenue. With a strong revenue growth rate of 19.24% and an impressive current ratio of 5.89, the company demonstrates solid operational execution. The company added 5,000 customers over the last two quarters, reaching a total of 59,900. MongoDB’s strategy of expanding its capabilities in AI and infrastructure has positioned it well in a competitive market, with over 70% of Fortune 500 companies as clients.

Financial Highlights

• Revenue: $591 million, up 24% YoY
• Earnings per share: $1, a 49.25% surprise over forecast
• Non-GAAP operating income: $87 million, 15% operating margin
• 2,564 customers with $100,000+ ARR, a 17% growth YoY

Earnings vs. Forecast

MongoDB reported EPS of $1, significantly above the forecast of $0.67, marking a 49.25% surprise. Revenue was $591.4 million, beating the expected $553.94 million, a 6.76% surprise. This marks a trend of exceeding market expectations, contributing to a positive outlook for the company.

Market Reaction

Despite exceeding earnings and revenue expectations, MongoDB’s stock fell 1.82% in after-hours trading. The stock closed at $217.80, trading near its InvestingPro Fair Value, reflecting a cautious investor sentiment possibly due to broader market conditions or profit-taking after recent gains. The stock maintains a beta of 1.44, indicating higher volatility compared to the broader market.

Outlook & Guidance

MongoDB raised its full-year FY2026 revenue guidance to $2.34-$2.36 billion, reflecting confidence in continued growth. Analysts maintain a bullish consensus on the stock, with price targets ranging from $170 to $405. The company anticipates mid-20s percentage growth for Atlas in the second half of the year and a decline in non-Atlas subscription revenue by mid-single digits.

For detailed analysis of MongoDB’s growth trajectory and comprehensive financial metrics, investors can access the full suite of tools and insights available on InvestingPro, including exclusive ProTips and advanced valuation models.

Executive Commentary

CEO Dave Videcheria emphasized the growing strategic importance of AI, stating, "Most customers overestimate the impact of a new technology AI in the short term, but underestimate in the long term." He also noted that customers are becoming more discerning about cloud versus on-premise deployments.

Risks and Challenges

• Slow adoption of AI technologies could delay anticipated growth.
• Potential decline in non-Atlas subscription revenue.
• Economic uncertainties may impact customer spending.
• Competition in the cloud services market remains intense.

Q&A

During the Q&A session, analysts focused on AI adoption and its implications for MongoDB. Executives noted that while enterprise customers are cautious, the potential for AI to transform business operations remains significant. The company continues to focus on high-quality workloads and efficient go-to-market strategies.

Full transcript - MongoDB (MDB) Q2 2026:

Conference Operator: Good day, everyone, and welcome to MongoDB’s Second Six Quarter Fiscal Year twenty twenty six Earnings Call. At this time, all participants are in a listen only mode. After the presentation, there will be a question and answer session. Please note this conference is being recorded. Now it’s my pleasure to turn the call over to Brian Denio from ICR.

Please go ahead.

Brian Denio, Investor Relations, MongoDB: Thank you, Carmen. Good afternoon and thank you for joining us today to review MongoDB’s second quarter fiscal twenty twenty six financial results, which we announced in our press release issued after the close of market today. Joining me on the call today are Dave Videcheria, President and CEO of MongoDB Mike Barry, CFO of MongoDB and Jess Lubert, MongoDB’s new Vice President of Investor Relations. 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 multiyear 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 the material risks and uncertainties that could affect our actual results, please refer to the risks described in our quarterly report on Form 10 Q for the quarter ended 04/30/2025, filed with the SEC on 06/04/2025. 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

Dave Videcheria, President and CEO, MongoDB: law. Additionally, we

Brian Denio, Investor Relations, MongoDB: will discuss non GAAP financial measures on this conference call. Please refer to the tables in the earnings release on the Investor Relations portion of our website for a reconciliation of these measures to the most directly comparable GAAP financial measure. With that, I’d like to turn the call over to Dave.

Dave Videcheria, President and CEO, MongoDB: Thank you, Brian, and thank you to everyone for joining us today. Before discussing our strong quarter, I want to remind everyone about our upcoming Investor Day, which will take place on September 17 at the Javits Center in New York City during our Dot Local Conference. We’ll spend the day discussing the investments we’re making to drive durable growth and margin expansion and our view of the future. I look forward to seeing you there. Now on to Q2.

I’m pleased to report another strong quarter as we continue to execute against our large market opportunity. Let me start with our results before giving you a broader company update. We generated revenue of $591,000,000 up 24% year over year and above the high end of our guidance. Atlas revenue grew 29% year over year, representing 74% of total revenue. We delivered non GAAP operating income of $87,000,000 for a 15% non GAAP operating margin.

And we ended the quarter with over 59,900 customers. Atlas performance was strong, accelerating to 29% year over year growth, up from 26% in Q1. Our customer additions were also robust. We have added over 5,000 customers over the last two quarters. These results reflect the strength of MongoDB’s platform, our flexible document model, expanded capabilities like search and vector search, enterprise readiness, and the ability to run anywhere.

Many of our recently added customers are building AI applications, underscoring how our value proposition is resonating for AI and why MongoDB is emerging as a key component of the AI infrastructure stack. At the same time, we significantly outperformed on operating margin, demonstrating that we can drive durable revenue growth while expanding profitably. In short, our results show that customers are choosing MongoDB. Let me tell you why. First, MongoDB is an enterprise ready database capable of meeting the most stringent enterprise requirements.

Over 70% of the Fortune 500 as well as seven of the 10 largest banks, 14 of the largest 15 health care companies, nine of the 10 largest manufacturers globally are MongoDB customers. MongoDB is a battle tested enterprise platform relied on by some of the most sophisticated and demanding organizations in the world, in part because of our strong enterprise posture across security, durability, availability, and performance. Atlas enabled one of the world’s largest automakers to overcome Postgres’ scalability and flexibility limits while reducing complexity. The company’s management console tracks over 8,500,000 vehicles requiring a modern schema to handle both structured and unstructured data, something Postgres could not handle. Ultimately, Atlas consolidated infrastructure, accelerated innovation, and support the scale of millions of connected vehicles.

Second, MongoDB is suitable for a broad range of use cases, including the most mission critical and transaction intensive applications. MongoDB has also supported full asset transactions for more than six years, ensuring strong consistency and data integrity at scale. This is why some of the world’s most demanding transactional workloads run on MongoDB today. For example, Deutsche Telekom selected MongoDB Atlas as the foundation for its internal developer platform, which includes mission critical workloads like contract management, device purchases, and billing for 30,000,000 customers. With 90 Atlas clusters managing over 60,000,000 customer records, Deutsche Telekom’s customer data platform now handles 15 times the concurrent logins of legacy systems.

By consolidating these high volume, transaction intensive applications on MongoDB, Deutsche Telekom has improved resiliency, accelerated innovation, and delivered a step change in customer engagement. Third, MongoDB has redefined what’s core for the database by natively including capabilities like search, vector search, embeddings, and stream processing. Comparing MongoDB to another database like Postgres is not an apples to apples comparison. Take a global e commerce application that manages inventory and order data while enabling product discovery through sophisticated search across millions of SKUs. The choice for this application, not between MongoDB or Postgres, is between MongoDB or Postgres plus other offerings like Pinecone, Elastic, and Cohere for embeddings.

MongoDB’s complete solution allows developers to spend less time stitching together and maintaining a patchwork of disparate systems and more time building differentiated functionality that drives the business forward. For example, Agibank, a Brazilian neo bank with 2,700,000 active customers migrated their content management system storing customer records from Postgres to Atlas. As data volumes grew, Postgres’ inflexibility and task execution latency drove performance issues and the database lacks sophisticated secondary indexes and full text search, hurting sales of core offerings such as loans, insurance and card approvals. Agibonc was constantly updating the database and manually scaling infrastructure, which is both time consuming and error prone. With Atlas, Agibang gained a resilient flexible system that handle rising demand and support new services, delivering nearly five times better performance and 90% lower costs, all with no outages.

Fourth, MongoDB is emerging as a standard for AI applications. Over the last few quarters, we’ve seen a strength in our self serve channel, driven in part by AI native startups choosing Atlas as the foundation for their applications. In the enterprise segment, adoption is real, but early. Much of the activity today centers on employee productivity tools and packaged IoT solutions. Enterprises are still in the very early stages of building their own custom AI applications that will transform their business.

We consistently hear from customers that when teams try to scale from Vibe coated prototypes built on relational back ends to enterprise grade deployments, these platforms quickly hit limits in flexibility, scalability and performance. Across startups and increasingly enterprises, our unified platform is resonating strongly. In the enterprise segment, a leading electric vehicle company chose Atlas and Vectrus Search to power its autonomous driving platform. After testing VectorSearch against Postgres PG Vector for their in vehicle voice assistant, they selected MongoDB for superior performance at scale and stronger ROI. They now rely on Atlas to handle over 1,000,000,000 vectors and expect 10 times growth in data usage by next year.

DevRev, a well funded AI native platform with proven founders disrupting the help desk market built AgentOS, its complete agentic platform that autonomously handles billions of monthly requests on Atlas. DevRev accelerated development velocity, lowered costs and scaled globally with low latency by using Atlas. Agent OS also leverages Atlas Vector Search for semantic search enriching its knowledge graph and LMs with domain specific content. 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, all in one platform. As we look ahead, we remain confident in MongoDB’s position to lead both the current wave of digital transformation and the next wave powered by AI.

With that, here’s Mike.

Mike Barry, CFO, MongoDB: Thanks, Dave. I’ll begin with a detailed review of our second quarter results and then finish with our outlook for the third quarter and fiscal year twenty twenty six. I will be discussing our results on a non GAAP basis unless otherwise noted. As Dave mentioned, we had a great quarter as we exceeded all of our guidance ranges and are increasing our full year guidance across the board. Now on to the results.

In the second quarter, total revenue was $591,000,000 up 24% year over year and above the high end of our guidance. Shifting to our product mix. Atlas revenue outperformed our expectations and year over year growth accelerated to 29% in the quarter and now represents 74% of total revenue. This compares to 71% in the 2025 and seventy two percent last quarter. We had an impressive Atlas growth quarter, which benefited in part from the strong start to consumption in May that we referenced on our last call as well as broad based strength, especially in larger customers in The U.

S. Let me provide some context on Atlas consumption in the quarter. In Q2, Atlas consumption growth was strong and relatively consistent with last year’s growth rates. This drove the acceleration in revenue as well as the growth in absolute revenue dollars year to date for the ’26. Turning to non Atlas, revenue came in ahead of our expectations in the quarter as we continue to have success selling incremental workloads into our existing EA customer base.

Non Atlas ARR, which reflects the underlying revenue growth of this product line without the impact of changes in duration, grew 7% year over year. In addition to the good underlying trends in non Atlas, in Q2, we also benefited from more multiyear deals than expected, reflecting our customers’ desire to commit to building with MongoDB long term. Approximately half of the non Atlas revenue outperformance versus guidance was attributable to multiyear outperformance. We had another strong quarter for customer adds in the second quarter as we grew our customer base by approximately 2,800 sequentially, bringing the total customer count to 59,900, which is up from over 50,700 in the year ago period. This quarter, we incorporated new customers added from the Voyage acquisition to our customer count, representing 300 of the 2,800 added.

The growth in our total customer count is being driven primarily by Atlas, which had over 58,300 customers at the end of the quarter compared to over 49,200 in the year ago period. It is important to keep in mind the growth in our Atlas customer count reflects new customers to MongoDB in addition to existing EA customers deploying workloads on Atlas for the first time. Of our total customer count, over 7,300 are direct sales customers, a decline of 200 customers sequentially and flat year over year. These metrics are largely due to our decision to reallocate a portion of our go to market resources from the mid market to the enterprise channel starting in the second half of last year. This does not impact our total customer count, but is an output of fewer self serve originated customers being elevated to our direct sales team as we move upmarket.

In Q2, our total company net AR expansion rate was approximately 119%, which is consistent with recent quarters. We ended the quarter with 2,564 customers with at least $100,000 in ARR, representing 17% growth versus the year ago period. Moving down the income statement, gross profit in the second quarter was $436,000,000 representing a gross margin of 74, which is down from 75% 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. Our income from operations was $87,000,000 for a 15% operating margin compared to 11% in the year ago period.

We are very pleased with our stronger than expected margin result operating margin results, which benefited mainly from our revenue outperformance. Additionally, I’d like to provide a little context on the modest restructuring we undertook in the quarter. It impacted less than 2% of employees and resulted in approximately $5,000,000 of onetime charges, which we have excluded from our non GAAP financials. This action is consistent with the key priorities I outlined for you last quarter to identify ways to both reallocate existing spend to higher ROI opportunities and be more disciplined about incremental spending. We are focused on running an efficient, scalable business that supports growth in revenue and profitability to drive long term shareholder value.

Net income in the second quarter was $87,000,000 or $1 per share based on 87,000,000 diluted shares outstanding. This compares to a net income of $59,000,000 or $0.70 per share on 84,000,000 diluted shares outstanding in the year ago period. Turning to the balance sheet and cash flow. We ended the second quarter with $2,300,000,000 in cash, cash equivalents, short term investments and restricted cash. During the quarter, we spent $200,000,000 to repurchase approximately 930,000 shares, which was under our previously announced $1,000,000,000 total share repurchase authorization.

Operating cash flow was well above our expectations at $72,000,000 and free cash flow was $70,000,000 which compares to negative $1,000,000 and negative $4,000,000 respectively in the year ago period. Our strong cash flow results were driven primarily by strong operating profit and higher cash collections. Before turning to our outlook in greater detail, I’d like to share the key points driving how we are looking at the rest of fiscal year twenty six. Number one, we are raising our expectations for revenue based on our confidence in Atlas as well as a strong performance in the first half of the year, providing a higher starting point for Atlas heading into the second half. Number two, we are increasing our operating margin guidance by 150 basis points at the high end, reflecting our strong Q2 performance and continued focus on margin improvement.

And number three, we are raising our operating margin guidance while still continuing to make incremental investments for growth with a focus on R and D and developer awareness. Now moving on to our full year guidance. I’d like to provide some incremental comments on our expectations. First, as we discussed, we had a strong start to the year and are confident in our ability to drive continued revenue and profitability growth. We are raising our full year revenue guidance by $70,000,000 including the $38,000,000 outperformance in Q2.

This reflects the strong Q2 consumption benefiting revenue in the second half and our continued confidence in Atlas growth. All in, this implies mid-20s percentage growth for Atlas in the second half of the year. Second, incorporating our strong performance in the first half, we expect non Atlas subscription revenue will now be down in the mid single digits for the year compared to our prior expectation of high single digit decline. We also expect the headwind from multiyear license revenue for fiscal to now be $40,000,000 due to the Q2 outperformance compared to our prior expectation of approximately $50,000,000 Please note, we expect non Atlas ARR will continue to grow year over year. Finally, we are raising our expectations for operating margin to 14% at the high end, up from 12.5% in our prior quarter guidance.

This reflects the better than expected revenue performance, the impact of our more disciplined approach to investing for growth, and our increased focus on efficiency. For fiscal year twenty six, we now expect revenue to be in the range of $2,340,000,000 to $2,360,000,000 an increase of $70,000,000 from our prior guide. We are raising our non GAAP income from operations expectations by $44,000,000 and are now targeting a range of $321,000,000 to $331,000,000 and non GAAP net income per share to be in the range of $3.64 to $3.73 based on 87,400,000.0 diluted shares outstanding. Note that the non GAAP net income per share guidance for the third quarter and fiscal year twenty six assumes a non GAAP tax provision of 20%. Moving on to our Q3 guidance, a few things to keep in mind.

First, we expect to see a low 20% year over year percentage decline in the non Atlas business after the strong multiyear outperformance we experienced in 2025. As a reminder, Q3 of last year was our strongest multiyear revenue quarter and is the largest portion of the multiyear headwind. Second, we expect operating margin will be lower than in Q2, primarily due to the expected sequential decline in non Atlas revenue, which is very high margin revenue. In addition, it is also impacted by the timing of operating expenses, specifically R and D hiring and seasonality of our marketing investments. With that context, I will now turn to our outlook for the third quarter.

For the third quarter, we expect revenue to be in the range of $587,000,000 to $592,000,000 We expect non GAAP income from operations to be in the range of $66,000,000 to $70,000,000 and non GAAP net income per share to be in the range of $0.76 to $0.79 based on 87,700,000.0 diluted shares outstanding. To summarize, we had a very strong quarter. We are pleased with our ability to drive revenue growth across the business and increase our operating profit expectations. We remain incredibly excited about the opportunity ahead and we’ll continue to invest responsibly to drive long term shareholder value. I would also like to take a moment to extend a warm welcome to Jess Lubert, our new Vice President of Investor Relations, who started with us yesterday.

Jess joins us from Juniper Networks, where he led their Investor Relations effort, including the most recent including most recently helping the company navigate the acquisition by Hewlett Packard Enterprise. We’re excited to have him on board and eager to see the impact of his work. Last but not least, we look forward to seeing many of you in a few weeks at our Investor Day. Please reach out to our Investor Relations team at ir@mongodb.com with any questions. With that, we’d like to open it up for questions.

Carmen, take it away.

Conference Operator: Our first question is from Sanjit Singh with Morgan Stanley. Please proceed.

Sanjit Singh, Analyst, Morgan Stanley: Hi, thank you for taking the question and congrats on a heck of a quarter in Q2. I wanted to dive into some of the drivers into Q2. When I look at the acceleration atlas, which is now accelerated for two quarters in a row, and I kinda just look at the sequential dollar ads, I had that up, you know, more than 40 more than 40,000,000 in q two, which is kind of the strongest sequential dollar ads we’ve seen in quite some time in what’s been a a pretty sober sort of cloud spending environment. So I was wondering if you could, you know, give us some sense of the drivers of, you know, of of of the strong sequential ads this quarter. I know you pointed to May, but if anything you can give us from like a workload perspective or any other new factors, maybe the workloads from last year starting to ramp.

I’d just love to understand that trajectory a little bit better.

Dave Videcheria, President and CEO, MongoDB: Yes. Sajid, thank you. Thanks for the question. So clearly, we’re really pleased by the quarter and really pleased by the accelerating growth in Atlas. I would say lot of it was due to the workloads that we acquired over the past year, especially with our move up market that are growing faster and becoming bigger than previous workloads we’ve seen.

So I think the move up market is really paying off. And what we’re also seeing is that there’s a great uptick of some of the other capabilities we offer like search and vector search that are also adding to that growth of those workloads. And then as we mentioned, we also acquired a ton of new customers. Obviously, the self serve customers tend to spend less on a per customer basis, but we also have added lots of customers over the last six months. And I think that’s also helping drive some of the growth.

Sanjit Singh, Analyst, Morgan Stanley: Yes. That’s great color. I wanted to follow-up on the go to market side. Over the last couple of years, we’ve been sort of tinkering and optimizing the go to market organization across sort of territory investment, but also sort of quotas and moving to incremental consumption. Could you give us an update on the state of operations for the Salesforce today?

And in some sense, know, if I look at the customer ads, it seems like things are humming quite well. But just to get to understand, you know, how like, what’s the state of the organization there? That’d really helpful.

Dave Videcheria, President and CEO, MongoDB: Yeah. Sure. So nothing really has changed. We’re just doubling down on what we’ve said previously. We are moving up markets.

We’re focusing our high end sales force, focus on the most sophisticated and demanding customers. These are typically enterprise customers all around the world. And then we’re using our self serve channel to better serve the SMB market. I know there are a lot of questions about where we kind of abandoning the self serve the early stage market by this move. And I think the results over the last couple of quarters have shown that we are not.

I think we’re just becoming much more effective in serving that market while also being very effective in growing wallet share in these larger accounts. So we’re really just continuing with the strategy that we articulated before and obviously we’re pleased with the results.

Sanjit Singh, Analyst, Morgan Stanley: Appreciate the thoughts Dave. Thank you.

Dave Videcheria, President and CEO, MongoDB: Thank you, Sanjit.

Conference Operator: Thank you. Our next question is from Raimo Lenschow with Barclays. Please proceed.

Raimo Lenschow, Analyst, Barclays: Perfect. Thank you. First of all, congrats to Jess. All the best. Two quick questions from me.

Staying on that theme of of self-service, that that acceleration, Dave, obviously, you know, you you changed things around, but it it kind of it’s accelerated despite kind of you actually moving upmarket. Like, can you help us understand then what’s driving that a little bit? And then I have one follow-up for for Mike.

Dave Videcheria, President and CEO, MongoDB: Yeah. I mean I mean, clearly, the output metrics look really good, but I would say the work around self serve began, you know, has been going on for a while. The team is really good at running experiments using a data driven approach to figure out what’s working, to figure out what’s not working. A new motion that we’re also doing that’s showing good results is going after SQL developers who don’t really know MongoDB and attracting them to our platform, really, you know, helping them understand the value proposition of MongoDB, even running like things like office hours where we spend time with, you know, SQL developers to explain the benefits of of modeling data in a on a document database. And all these experiments and tactics that we’re doing, which are very data driven, have are really paying off.

And May Petrie used to run that group, is now our CMO, and she has a strong team under her, and we feel really good about what that self serve team has been doing. But, again, we don’t wanna declare victory too early, but, obviously, we’re very pleased with the results.

Raimo Lenschow, Analyst, Barclays: Yeah. No. That’s really nice to see. And then, Mike, the things first of all, for all the extra disclosure, the ARR for for the non ATLAS or EA part is kind of really helpful. If you think about the I guess, in logic around the renewal cohorts, especially q three.

But in am I doing the graph correctly that actually next year, that part of the business looks more interesting because the cohort looks better? Like, just trying to get your idea or and maybe you might not even give it to us because you just do ARR. Thank you.

Mike Barry, CFO, MongoDB: Sure. So thanks for the question. So I’m gonna hold that answer till we get to q three of next year because it kinda depends on what happens in q three of this year. So the one thing is, as we talked about, the big impact in q three of this year is the multiyear. We’ll see how it how it comes back next year, but it really depends, Raimo, on how we do in q three this year.

Raimo Lenschow, Analyst, Barclays: Yeah. Okay. Perfect. Thank you. But thanks for the disclosure.

Really helpful.

Unidentified Speaker: You’re welcome. Thanks, Raimo.

Conference Operator: Thank you so much. And our next question comes from Tyler Radke with Citi. Please proceed.

Tyler Radke, Analyst, Citi: Hey, thanks for taking the question. And and nice job on the the Atlas growth. Wanted to dig into the AI commentary, that that you had, Dave. Obviously, last quarter, you talked about Cursor, which which obviously is is ramping up significantly in terms of their ARR. And I think you called out many examples this quarter, including a, autonomous vehicle company.

It sounds like, you know, expecting pretty significant growth there. But how much of that is is playing into the Atlas strength that that you’re seeing here in the quarter? Any way to quantify, you know, that cohort or or use cases, whether it’s, you know, vector search or maybe even if you if you throw in voyage? Just help us understand if that’s starting to move the needle because it sounds like there’s some pretty high profile wins in there.

Dave Videcheria, President and CEO, MongoDB: Yeah. So thanks for the question, Tyler. While we’re adding thousands of AI native customers, I will tell you that the growth that we delivered this quarter was not material to that growth. Growth is really driven by our core business and our core customer base. And so while we’re very happy with the AI customers increasingly choosing MongoDB, it was not a material mover of the needle for our growth.

Tyler Radke, Analyst, Citi: Great. And then follow-up on the migration opportunity. I know you’ve been investing in Relational Migrator. You know, you’re working with with companies like Cognition to to accelerate the the code, migration opportunity. And and you’ve seen professional services ramp up a little bit.

But where have you started to see sort of the the time to migration or or replatform improve a bit? Just anything you could share, in terms of that, migration opportunity if that’s started to improve in terms of velocity or size of workload migration would be helpful. Thank you.

Dave Videcheria, President and CEO, MongoDB: Yeah. Sure. So, yes, we’re super excited about what we call app modernization or legacy app modernization. You’ll hear a lot more about this at Investor Day in September, Tyler. But what I will say to you is that the value proposition is very clear.

Customers are very, very motivated to try and modernize these legacy systems for a wide variety of reasons. We are seeing a lot of progress. We’ve actually brought in a new leader, new product leader who brings a lot of depth and scale, especially around AI to help us build the tooling to leverage AI to really drive more automation in terms of how we analyze and refactor the code. We brought in a new leader last quarter to help really help drive the delivery and the go to market efforts around AppMod. So we’re definitely beating up resources.

And I would say that we’re investing a lot in product and there’s a lot more to do. And I would say this is something that we’re very excited about, but it’ll drive more of our longer term growth. Less it won’t be as pronounced in terms of this year, but we’re very, very excited about the opportunity and we’re definitely will spend more time discussing this and what we’re actually doing on the product side in September.

Brad Sills, Analyst, Bank of America: Thank you.

Conference Operator: Thank you. One moment for our next question. It comes from Jason Ader with William Blair. Please proceed.

Jason Ader, Analyst, William Blair: Yes. Thank you. Dave, I was hoping you could talk about some of the kind of latest industry developments just on the technology side. In particular, I’m thinking about Lake Base from Databricks and then DocumentDB and the Linux Foundation. Can you just comment on both those things and how they might impact MongoDB and how you differentiate?

Dave Videcheria, President and CEO, MongoDB: Yeah, so let me tackle them one by one. Clearly what we are seeing is that the strategic high ground for especially when it comes to inference, is OLTP. So we talked about this on the last call where some companies that acquired early stage OLTP startups. And what it really spoke to and those companies had spoken about their organic efforts to build an OLTP platform. And I think what it spoke to was the fact that they building an OLTP platform that’s ready and mission critical and enterprise can serve the most demanding requirements of enterprises is not trivial.

And, I think they basically threw in the towel and decided to do these acquisitions. And, what it just reinforces that, OLTP is the strategic high ground for AI. And we believe that if now customers are gonna be choosing what OLTP platform that they want for AI, just given our architecture, just given the fact that we have a durable architectural advantage in terms of JSON support, which addresses messy, complicated, and highly interdependent and costly changing data structures. The fact that we integrated search and vector search, I think really helps us position going after AI. With regards to your second question around the Linux Foundation, I think what this really also suggest shows is that, you know, real JSON is much more important now with AI than ever before, and the clones and bolt ons and you know, that have traded off features and performance and developer experience have just not met customer expectations.

And candidly, what I see this is that the hyperscalers are investing less and really handing off to the open source community to kind of really take on the bulk of the work in terms of product development. Our hyperscaler partnerships remain strong. And I think we have the right open source model where we can balance the access to free software while preserving the ability to both generate and capture value.

Jason Ader, Analyst, William Blair: Great. Thank you. And then just one quick follow-up. Do we hear so much about Postgres adoption for AI startups? You talked about the success you guys are having.

But if Postgres has the disadvantages that you’ve talked about multiple times, scalability, JSON support, how come we hear so much about that, at least in the early stages of AI?

Raimo Lenschow, Analyst, Barclays: Yeah,

Dave Videcheria, President and CEO, MongoDB: that’s a really good question. And I think it’s important to understand. And we spend a lot of time, we have now invested in a team in the Bay Area that spends a lot of time with the startup community. What’s become clear a lot these startup founders don’t think that hard about their database choice. They kinda go with what they know.

And what we are seeing is that as some of these startups are scaling, they’re running to real scaling challenges with Postgres. And what you know, and we’ve talked about this in the past, like, when you add a a JSON when you use JSONB on Postgres, a two kilobyte document or or or bigger starts really creating performance problems because Postgres has to do something called off road storage, which creates enormous performance overheads. And so the, you know, developers need a platform that can handle structured, semi structured, unstructured data. They need, obviously, a platform that performs well, and they need a platform that can scale as they grow. And what we’re hearing clearly from the startup community is that Postgres in many cases is not scaling for them and they’re now coming to us.

And so we feel really good about our position, but the reality is that a lot of, you know, these AI founders kind of struggle with what they know or what they’ve used in the past. And only when the business starts scaling do they start recognizing the challenges. And and we realize we need to do more developer education and do more work, and so we’re investing a lot in the startup community. We’re running a big event in October in San Francisco with a big hackathon and we’re inviting lots of customers to participate. But that’s just the start of a meaningful investment we’re making in the Bay Area and the AI startup community to rethink their decisions around just going with what they know.

Brian Denio, Investor Relations, MongoDB0: Thank you.

Conference Operator: Thank you. One moment for our next question that comes from Mike Sykos with Needham. Please proceed.

Brian Denio, Investor Relations, MongoDB1: Hey, thanks for taking the questions guys. I just wanted to come back to Atlas specifically. And Mike, I appreciate last quarter you gave us some very granular color around Atlas trends. I was hoping we could get an update on how Atlas trends played out this quarter or just at the very least why we did see such broad based strength from large customers this quarter? Thank you.

Mike Barry, CFO, MongoDB: Sure. Thanks for the question, Mike. So when we talk about consumption in the second quarter for Atlas, as we talked about, it performed well, grew 29% year over year. As we talked about, Mike, the consumption growth were relatively consistent with last year. And as we talked about on the last call, we started out with a strong May, and we saw broad based strength across most of the geos and segments, so nothing to call out there.

But we did see notable strength in the larger customers in The US. And if we dive deeper on that one, as Dave talked about, we are seeing some workloads from our larger customers grow for longer and expand more than we have seen in the past, so that’s good. While there’s many moving parts in the consumption business, we also expect that there is benefit from our go to market changes. And given the preponderance of our strategic accounts being in The US, no surprise that we saw that growth mostly in The US. And then lastly, Mike, there is some benefit from comparing it to a little slower growth in Q1.

So that would be the detail on Q2 as it relates to consumption growth.

Brian Denio, Investor Relations, MongoDB1: Thank you for that. And if I could just squeeze maybe one more in. On the the outperformance that we saw this quarter from the multiyear deals, and maybe I’m just misunderstanding here, but I I my assumption was the reason we were facing this outperformance was really tied to the fact that in prior years, we’ve had some some pretty big deals on the multiyear front. And so to see some of these deals come in this year, is that a function of customers renewing earlier, which is helping fill that larger divot that we previously expected? Is that a fair assumption?

Or can you help me think through that a little bit more? Thank you.

Mike Barry, CFO, MongoDB: So thanks for the golf analogy. No, it did not fill the divot. So in Q2, it was really good underlying strength in ARR growth and then greater than expected multiyear. There were really no pull forwards, Mike. And it this is a hard business to forecast because sometimes even customers don’t know whether they’re gonna opt for an annual renewal or a multiyear.

So it was there were no pull forwards, and there was nothing out of the ordinary. Very importantly, we left the net the non Atlas assumptions consistent with our last guidance, hence, pulling down the multiyear headwind from 50 to 40. And again, nothing to call out on Q2. No pull forwards. And there were really no large multiyears in there.

It was just across a good subset of customers.

Brian Denio, Investor Relations, MongoDB1: Thank you again. Yep.

Conference Operator: Thank you. Our next question comes from the line of Alex Sukin with Wolfe Research. Please proceed.

Brian Denio, Investor Relations, MongoDB2: Yes. Thanks for squeezing me in, I’ll echo the congrats on truly amazing quarter. I guess, Dave, when you think about the AI comments that you’ve talked about in the press release and in the call, maybe just a little bit more nuance on the use cases, not necessarily that you’re seeing kind of contribute materially today, but the differentiation in the platform that you’re able to incrementally take market share as it becomes available, both in net new kind of AI native companies, but also in some of your larger existing companies or customers that are starting to modernize for this kind of conversational or AI native era. Where are you seeing the most momentum in terms of workload construction and scale? And when do you think we should expect to kind of actually start seeing that contribute more materially to the growth in consumption?

Dave Videcheria, President and CEO, MongoDB: Yes, so thanks for the question, Alex. A couple of points. Again, we’re very pleased with the results of this quarter, but I would say the AI cohort was not a material driver of the growth. That being said, what we are seeing is a lot of customers very, very interested in our architecture. Let me again walk through why.

You know, one, we’re a JSON database. JSON is the best way to express and model the complicated and messy and highly interdependent and constantly evolving data structures that you have to deal with in the real world. So that’s point number one. So it’s much easier to do that on MongoDB than to do that on some kludgy, you know, kind of setup on top of relational database. Second is that we integrate search and vector search.

So you can do very sophisticated things to what people call hybrid search and retrieval. You can do very sophisticated things in finding information quickly, which is a very unique differentiator for us. So what this means that rather than stitching together multiple systems, you can do this all in MongoDB, so it becomes less complexity and lower cost. The third thing is that we’ve now embedded voyage models on our platform. Right?

So the you know, if you control the embedding layer, you sit at the gateway of meeting, of AI. Right? What what the embedding models do is really our bridge between a company’s private data and the LLM. So that becomes really important because the better the the quality of the embedding model, the better the quality of the signal of your own data. So that reduces things like hallucinations or just bad outputs.

And so customers are now as people start caring more and more about, like, you know, high higher stakes use cases, they really wanna ensure those outputs are are high. And the fact that it’s part of our platform, we can enable you to do auto embeddings, it becomes an incredibly, you know, compelling feature. In terms of the market, what I would say is that, you know, the enterprise uptake of AI is still early. I’ve I’ve said this for a couple years now, and I I think a lot of people were a little skeptical of what I said, but it’s proving to be true. As we predicted, like, you know, the the lack of skills and the lack of trust with AI systems is kind of slowing you know, people are being very cautious about deploying AI.

Where it is being deployed is really on end user productivity, whether it’s developers with cogen tools or business users using tools to summarize documents, extract data, or things like deflecting tickets from people to to, you know, systems with, like, conversational AI. I think you are starting to see the first steps in people deploying agent based systems, and I can talk a little bit about that. But that’s still very, very early. We’re seeing small ISVs, some of them are taking off who are really driving most of the impact. But the real enduring value will come you know, when you talk to a customer today, most of them, when you ask them, is AI really transforming your business?

They’ll say no. Yes. We’re seeing some productivity gains here and there, but it’s not really transforming my business. I think the real enduring value will come when they build custom AI solutions that truly transform their business, whether it’s to drive new revenue opportunities or dramatically reduce their existing cost structure. But we’re really pleased.

I mentioned this electric car company that’s very tech savvy that’s using MongoDB. I should mention one of the fastest growing startups in the Bay Area has has bet big on MongoDB. DevRev, the company going after the help desk space, has built their own agentic platform of MongoDB. So we feel really good about what this all portends for the future. But as I said, it was a small part of our growth this quarter.

Brian Denio, Investor Relations, MongoDB2: Very helpful. And then maybe if I could just sneak one in for Mike. You’ve been kind of saying from, I think, the first day you started about how margin profile of this business, it’s not an or, it’s an and, and it’s clearly coming through in both the growth acceleration but also the meaningful margin outperformance. As you think about sustaining this kind of accelerating pace, and investing in things like the, you know, the Bayer startup community, how are you finding, that balance, that and versus or, balance that, quite frankly, is elusive to a lot of companies that are doing what you guys are doing?

Mike Barry, CFO, MongoDB: Well, I think it’s the funnest part of my job, quite frankly. So I would give kudos to not only the management team, but everybody at MongoDB to really jump in this. I think that this has been a company wide effort. And as we look forward and as we talked about, Alex, the number one driver of margin expansion for Mongo is the revenue growth. So those two are directly connected.

It’s a great business model where when we can grow Atlas in the 20% plus range and then keep that ARR or VA in that single digit, it generates a ton of gross profit that funds a lot. And the team has done a really has done a great job of making sure that we are investing in growth, that we go back and look at what we’re doing, making sure that it’s driving growth. If it’s not, then we have an open discussion about whether we should reallocate. So I felt good about it when I started. Candidly, I feel better about it ninety days later.

Brian Denio, Investor Relations, MongoDB2: Excellent. Thank you, guys. Congrats again.

Dave Videcheria, President and CEO, MongoDB: Thanks. Thank you, Alex.

Conference Operator: Thank you. Our next question comes from Kash Regan with Goldman Sachs. Please proceed.

Brian Denio, Investor Relations, MongoDB0: It’s always tough to go after Alex because he asked such good questions, but that’s not gonna stop me. So, Dave and Mike, congratulations on the quarter. You know, it it’s super interesting. You were talking about how some of the Silicon Valley AI startup founders don’t have it have time to think about databases, but our good friend, Biraj at Deborah, seems to have made a wise choice here. So as you set encampment up in the Bay Area and start to evangelize the need for a Atlas consumption AI savvy database, how do you reconcile type of the fact that same time enterprise is where we really saw the the bread and butter value proposition of Mongo resonate.

So could could what is happening with DevRel be a leading indication of what’s gonna happen in the enterprise? Because we’ve all much to your observation, not seeing much of a productivity impact from the enterprise because of AI at the business level. And so what what could be that unlock is is what about what are folks like Dheeraj doing correctly that is a per could be a precursor, if it is, for what is to come in the enterprise?

Dave Videcheria, President and CEO, MongoDB: Yeah. So, Kash, thanks for the question. Obviously, have so much respect for Dheeraj. He built Nutanix into a real great business, and he’s gonna do the same at DevRev. I will tell you that the AI cohort, as I said earlier, is you know, was not really material to our growth.

So I think, you know, these are all customers kind of earlier in their journey. So I you know, what we are seeing, what’s driving the growth right now is these, large enterprises with workloads that we acquired both last year and this year that are really driving the growth, especially the Atlas growth that we saw this quarter. And what that really confirms is that our move up market made sense, The quality of those workloads, the durability of their growth, they become grow for longer and become bigger than what we’ve seen in the past is really making us feel good about that decision. And to juxtapose that, we also obviously decided to double down on self serve to better serve the small and medium sized business market. And that’s also become, you know, obviously becoming more and more effective and gets us given that number of customers that we’ve added over the last six months.

So we feel like those motions are working well in concert together. And we feel like this allows us to be much more efficient about how we go to market. And there’s also gonna be continued more work to continue to drive that efficiency even better, but we also are investing for the long term. And so we’re just constantly debating those decisions internally, but we feel good about what’s working. And we feel good that someone like Adiraj is betting early on MongoDB because that’s a good signal for other founders who are thinking about doing the same.

Brian Denio, Investor Relations, MongoDB0: Awesome. We’ll drill into this more in a couple of weeks when we see you in San Francisco.

Dave Videcheria, President and CEO, MongoDB: Absolutely.

Conference Operator: Thank you. One moment for our next question. It’s Brad Reback with Stifel. Please proceed.

Jason Ader, Analyst, William Blair: Great. Thanks very much. The the 7% EA ARR growth seems fine. I’m assuming you’re not satisfied with single digit growth there. Dave, any sense of where we should think about that longer term?

Thanks.

Dave Videcheria, President and CEO, MongoDB: You know, clearly, EA is a large enterprise motion, and what we’ve seen is that it’s typically, you know, less new customers choose EA and it’s more of our existing customer base who have a mix of EA and then sometimes they they they then also start deploying Atlas. I think one thing that’s becoming more and more clear customers are becoming much more thoughtful about, like, how to think about using, you know, deployments on premise versus using the cloud. I think four or five years ago, there was a belief that everything was gonna move to the cloud. I think large enterprises have become much more sophisticated and nuanced in their thinking, and they believe that some workloads make sense to run on prem and some workloads make sense to run-in the cloud. And I think that’s where the MongoDB story becomes really attractive because the same code base can be used.

And so it also gives them optionality for the future where they can move from on prem to the cloud, and a lot of our EA customers have done that either with new workloads and some existing workloads. And then they can also move from cloud to cloud. And they can also move back to on prem if they choose to do so. So that optionality becomes a very powerful value proposition for our customers.

Jason Ader, Analyst, William Blair: Great. Thank you very much.

Dave Videcheria, President and CEO, MongoDB: Thank you, Brad.

Conference Operator: Thank you. Our next question is from the line of Ittai Kidron with Oppenheimer. Please proceed.

Brian Denio, Investor Relations, MongoDB3: Thanks. Great numbers, and congrats to Jess, good luck in the new role. Dev, I wanted to dig into the AI opportunity again, but take it from a perspective of a go to market motion. Clearly, you can power a lot of AI use cases that are embedded with bigger platforms through a self serve motion, but it sounds like to really capture the big workload opportunities, it’s gonna have to be more of an enterprise push. So I’m kinda wondering how do you think about targeting the AI opportunity from go to market motion?

Does it that doesn’t just fall into if you’re a big enterprise, I’m gonna send you to an enterprise salesperson and all the rest call self serve and do it yourself. Is Is it something a little bit more you think, target perhaps that you need to take care in order to capitalize on this opportunity?

Dave Videcheria, President and CEO, MongoDB: Yeah. What what I would say, Itay, is that, you know, we’ve seen this movie before with the cloud where some early stage customers started growing very, very quickly. And then we just we then put, you know, dedicated sales, know, on those accounts and they grew then even faster. So we’re clearly watching the market. And when sales to our customers are to a point where, they really need a higher touch kind of engagement model, then we’re more than happy to do that.

And we have a team that kind of helps transition customers from self serve to more of a direct sales approach And that has worked for us. I think what we have learned is that line by which we actually engage a high touch model can move higher because we’ve become so sophisticated with self serve that we can really serve customers for early stage customers for a long period of time. In terms of the enterprise, what I would say is what I’ve said earlier is that the enterprise is still quite early in their journey to AI. Most of the investments right now are more on end user productivity, like, you know, developers using cogen tools and what I call low stakes use cases. In fact, I had two meetings today with two different leaders of two different financial institutions here in New York and they both talked about what they’re doing in AI.

They both admitted that they’ve kind of started with low stakes use cases, but their appetite to start doing more is increasing as they get more and more comfortable with the technology and they’re quite excited to leverage MongoDB as part of that journey. But again, I think that’s kind of a microcosm into the enterprise market where I think there’s still quite early in their AI journey. And if you remember, this is something I’ve been saying for a while that most customers, most people overestimate the impact of a new technology AI in the short term, but underestimate in the long term. And I think we’re just in that classic journey right now.

Brian Denio, Investor Relations, MongoDB3: Appreciate that. And maybe as a follow-up, Mike, I just want to make sure to dig in a little bit into the non Atlas business, EA, the predominantly EA business. Can you tell us roughly what percent of your customers here are on multiyear deals versus just annual deals? I’m just kinda curious how where we are now and what was it, say, a year or two ago, and where do you think that mix is gonna be a year or two from now?

Mike Barry, CFO, MongoDB: Yeah. That thanks for the question. We don’t break out the percentage of customers on multiyear versus versus one year. What I would say is in fiscal twenty five, obviously, we saw a lot of larger multiyear deals, and you see that in the numbers. This year, we will always see multiyear deals.

They haven’t been, I would call it, as large. So it’s more widespread. So we that’s really the change that we’ve seen. We haven’t broken that out. I don’t think that it has changed much, especially over the year.

As Dave talked about, it’s it’s gonna be a mix of Atlas and on prem, and and that mix has stayed relatively consistent.

Brian Denio, Investor Relations, MongoDB3: When you look at the customers that are choosing multiyear deals, has anything changed in the way they think about the reasoning behind doing that versus not?

Mike Barry, CFO, MongoDB: No. Reasons are are are the same. It’s typically their if if it aligns with their long term strategy, they wanna be able to lock in that the the pricing. And, you know, as as everybody knows, hey. Data has gravity.

Moving data around is is not fun for everybody. So they wanna be able to lock in and guarantee their prices for that period of time.

Brian Denio, Investor Relations, MongoDB0: Appreciate it.

Mike Barry, CFO, MongoDB: You bet. Thank you.

Conference Operator: Our next question comes from the line of Siti Panigari with Mizuho. Please proceed.

Unidentified Speaker: Thanks for taking my question. And Dave, I think some of the comments you were talking about AI slowdown, and you heard about recent MIT report about 95% AI implementation not getting any kind of, you know, return. How do you see what’s kind of do you think the inflection point when we think we’ll start seeing some of the adoption of this AI? Like, you said they’re testing, but what can trigger I know you have been talking about a year ago, you know, probably we are a few years out, but it’s good to see some of the traction. So how do you, first of all, what would be your view on that report and how how should we think about the, you know, in terms of revenue contribution, material contribution from AI?

Dave Videcheria, President and CEO, MongoDB: So I think it just comes down to fundamental principles. I think customers need to feel one, that the quality of the output of these AI systems is high. Obviously, AI systems are probabilistic in nature, not deterministic in nature, so you can’t always guarantee the output. You can hope that you’ve trained the models well. You hope that you’ve given it the right information, but you can’t always guarantee the output.

So as I mentioned, I I had meetings with two financial services customers earlier today, and both of them are still hesitant to roll out an end user facing AI applications for those specific reasons. So it’s gonna take a little bit of time for people to really get comfortable that they can really, you know, deal with the last mile issues and make sure that they don’t have any errors that potentially could be, you know, impacting their brand or really cause a lot of customer problems. So that’s point number one. Then there’s issues around, obviously, the the security of these systems, the stability and reliability of these systems, the scalability of these systems. As I mentioned, some of these early stage companies are running into scaling issues with existing architecture, which is why they’re coming to us.

So I think we’re just in that learning journey. I I don’t know if there’s gonna be some massive tipping point. I think what we are seeing with the frontier models is that all these frontier models are kind of clustering around the same ballpark in terms of performance and the efficacy of their models. So I think what’s gonna start happening is how people start leveraging these insights to build what I call scaffolding around these frontier models to address the needs their business. Obviously, everyone’s talking about agents, and people are very, very focused on on essentially, you know, using agents to drive a lot of work.

Agents require you know, if you think about if you’re using agents, agents will use your systems much more intensely than humans will because they can do things much more quickly. So you need platforms that can massively scale up and down, which is again a good sign and support indicator for MongoDB. So I think going to take a little bit of time. It’s going take time being comfortable with technology. It’s going to take time where people start with low stakes use cases and start gravitating to higher stake use cases.

So I don’t think there’s going to be some seminal inflection point. I think it’s just going to take time. But I think that time is coming.

Unidentified Speaker: That’s great color there. Thank you.

Raimo Lenschow, Analyst, Barclays: Thank you.

Conference Operator: Our next question is from Brad Sills with Bank of America. Please proceed.

Brad Sills, Analyst, Bank of America: Great. Thank you so much. I wanted to ask about some of the investments that you alluded to earlier that you’re making in R and D. How are you thinking about that? Is it incremental investments in some of these newer offerings like vector and streaming?

Are there new workloads that you’re thinking of addressing here? Would love to get some color on just where you’re investing in the stack? Thank you.

Dave Videcheria, President and CEO, MongoDB: Yes, sure. So we talked about the fact that R and D is a big part of our investment focus for this year. One, we came out with eight point zero, which is the most performing release ever. So we’re already starting to see dividends of our investments in our platform. 8.1 is even better.

And then we’re also making investments, you know, in the, you know, expansion parts of our platform. What I will say is we’re gonna go into a lot more detail around this Investor Day. So if you can hold until September 17, we’ll go into a lot of things that we’re doing on the R and D side as well as what we’re doing on application modernization and the tooling that we’re building there that will really speak to those investments that we’re making and we’ll give you a lot more color.

Brad Sills, Analyst, Bank of America: Got it. Great. Thanks for that Dave. And one more if I may please. I know there’s been an effort to focus on driving higher quality workloads in that larger account base.

I mean to what extent would you attribute some of the subside to that effort? And maybe just an update on that effort as you make?

Dave Videcheria, President and CEO, MongoDB: I would attribute a lot to that effort. I would say a big part of this growth is the fact that we’re acquiring higher quality workloads that are growing faster and for longer than the workloads required say in earlier years. And I think that’s a big part for why you’re seeing this growth happen now.

Brad Sills, Analyst, Bank of America: Great. Thank you.

Mike Barry, CFO, MongoDB: Carmen, I think we have time for one more question.

Conference Operator: All right. One moment please. And we have the line of Rishi Jaluria with RBC. Please proceed.

Brian Denio, Investor Relations, MongoDB4: Oh, wonderful. Thanks for squeezing me in at at the deadline. I’ll keep myself to one question. Dave, really nice to see the early traction with AI native companies. You know, it’s it’s always made sense to us, especially given your scalability and your ability to work with unstructured data.

If we were to fast forward five, ten years, and we start to see a real paradigm shift where instead of agents built on kind of the traditional GUI mobile interface that we’ve been in for the past thirty years, we actually entered kind of a multi agentic world where maybe the interaction vector may move away from what we’ve been used to into more natural language. Can you talk about why MongoDB still has a strong role and some of the investments that you might be making to position yourself well for that world, understanding that’s at the very least several years away? Thanks.

Dave Videcheria, President and CEO, MongoDB: Yeah, sure. So again, just to make sure we’re all talking in the same language, we believe that agents essentially do three things. One, they perceive or understand the state of things. So you need essentially a way to understand the state of what’s happening in your business. Then you need to decide what to do or plan.

So basically, you have to come up with a plan saying, I wanna take this action or these sets of actions. And then you have to act. You actually have to go execute those actions. Right? So why is MongoDB good agents?

One is, as I said before, the JSON document database is the best at being able to model the real world. The messiness, the complicated nature, the real world does not fit easily in rows and columns, and that’s why our document database, I think, is the best way to do that. Two, we obviously support search and vector search, so you can do very sophisticated hybrid search. So that becomes super important. And then with memory, you know, if if agents didn’t have memory, they would act like goldfish.

They could only react to the last thing last piece of information that they saw. So memory lets agents connect the dots across time and situation. So you have different kinds of memory, things like short term context, past experiences, knowledge, skills, etcetera, that you need to be able to share quickly. You need to be able to orchestrate those agents because you may have multiple agents doing a certain task. You need to register and have governance policies around those agents.

You know, we think that the underlying platform needs to be able to support those things. While there’s a lot more work that needs to be done, the underlying architecture that we have in MongoDB is well suited to address those needs. And we think that we’ll be positioned to be a winner as people deploy more and more agents in their enterprise.

Brian Denio, Investor Relations, MongoDB4: All right. Very helpful. Thank you so much.

Dave Videcheria, President and CEO, MongoDB: Thank you.

Conference Operator: Thank you so much. And with that, we conclude the Q and A session and I will pass it back to Dev Itisheria for his final comments.

Dave Videcheria, President and CEO, MongoDB: Sure. Thank you again for joining us today. In summary, I think it’s clear that we delivered another strong quarter highlighted by the accelerating Atlas growth, the continued adoption of for AI applications and our expanding profitability. We are raising our revenue and operating margin guidance for the full year fiscal year 2026. And these results really reinforce that MongoDB is well positioned to capture the next wave of AI application development while driving durable and efficient growth.

So with that, thank you, and we’ll talk to you soon. Take care.

Conference Operator: Thank you. And this concludes our conference. Thank you for participating, and you may now disconnect.

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