Good day everyone and welcome to MongoDB's Second Quarter Fiscal Year 2026 Earnings Call. At this time all participants are in a listen only mode. After the presentation there will be a question and answer session. To participate you will need to press star one one on your telephone. You will then hear a message advising your hand is raised. To withdraw your question, simply press star one one again. Please note this conference is being recorded. Now it's my pleasure to turn the call over to Brian Denyeau from ICR. Please go ahead. Thank you. Good afternoon and thank you for j oining us today to review MongoDB second quarter fiscal 2026 financial results. Announced in our press release issued after. The close of the market today. Joining me on the call today are Dev Ittycheria, President and CEO of MongoDB, Mike Berry, 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 t o 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 cause. Actual results to differ materially from our expectations. For discussion of the material risks and u ncertainties that could affect our actual results. Please refer to the risks described in our quarterly report on Form 10-Q for the quarter ended April 30, 2025, filed. With the SEC on June 4, 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 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 Dev. 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 onto Q2. I'm pleased to report another strong quarter as we continue to execute against a large market opportunity. Let me start with our results before giving you a broader company update. We generated revenue of $591 million, 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 million 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 7 of the 10 largest banks, 14 of the largest, 15 healthcare companies, 9 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 Magic console tracks over 8.5 million vehicles, requiring a modern schema to handle both structured and unstructured data, something Postgres could not handle. Ultimately, Atlas consolidated infrastructure, accelerated innovation, and supported 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 ACID 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 million customers. With 90 Atlas clusters managing over 60 million 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 is not between MongoDB or Postgres; it 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 neobank with 2.7 million 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 lacked sophisticated secondary indexes and full text search, hurting sales of core offerings such as loans, insurance, and card approvals. Agibank was constantly updating the database and manually scaling infrastructure, which is both time consuming and error prone. With Atlas, Agibank gained a resilient, flexible system that handled rising demand and supported 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 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 IFV 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-coded prototypes built on relational backends 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 Vector Search to power its autonomous driving platform. After testing Vector Search against Postgres PGVector 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 billion vectors and expect 10x 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 agent AI 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 LLMs 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. Thanks, Dev. 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 2026. I will be discussing our results on a non-GAAP basis unless otherwise noted. As Dev 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 onto the results. In the second quarter, total revenue was $591 million, 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 second quarter of fiscal 2025 and 72% 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 first half of fiscal 2026. 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 multi-year 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 multi-year 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 AI 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 up market. 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 million, 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 million for a 15% operating margin compared to 11% in the year ago period. We are very pleased with our stronger than expected 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 million of one-time 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 million, or $1 per share, based on 87 million diluted shares outstanding. This compares to a net income of $59 million, or $0.70 per share, on 84 million diluted shares outstanding in the year ago period. Turning to the balance sheet and cash flow, we ended the second quarter with $2.3 billion in cash, cash equivalents, short-term investments, and restricted cash. During the quarter we spent $200 million to repurchase approximately 930,000 shares, which was under our previously announced $1 billion total share repurchase authorization. Operating cash flow was well above our expectations at $72 million and free cash flow was $70 million, which compares to -$1 million and - $4 million, 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 2026. 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. Number three, we are raising our operating margin guidance while still continuing to make incremental investments for growth with a focus on R&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 million, including the $38 million outperformance in Q2. This reflects the strong Q2 consumption benefiting revenue in the second half and our continued confidence in Atlas growth. All this implies mid-20s% 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 for multi-year license revenue for fiscal 2026 to now be $40 million due to the Q2 outperformance compared to our prior expectation of approximately $50 million. 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 2026. We now expect revenue to be in the range of $2.34 billion- $2.36 billion, an increase of $70 million from our prior guide. We are raising our non-GAAP income from operations expectations by $44 million and are now targeting a range of $321 million- $331 million, and non-GAAP net income per share to be in the range of $3.64- $3.73 based on 87.4 million diluted shares outstanding. Note that the non-GAAP net income per share guidance for the third quarter and fiscal year 2026 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 multi-year outperformance we experienced in Q3 of fiscal year 2025. As a reminder, Q3 of last year was our strongest multi-year revenue quarter and is the largest portion of the multi-year 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&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 million- $592 million. We expect non-GAAP income from operations to be in the range of $66 million- $70 million, and non-GAAP net income per share to be in the range of $0.76- $0.79 based on 87.7 million 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 will 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 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. Thank you so much. As a reminder, that is star one one to get in the queue and wait for your name to be announced. To withdraw the question, simply press star one one. Again, our first question is from Sanjit Singh with Morgan Stanley. Please proceed. Hi, thank you for taking that 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 has now accelerated for two quarters, and I just look at the sequential dollar adds, I had that up more than $40 million in Q2, which is kind of the strongest sequential dollar adds we've seen in quite some time in what's been a pretty sober cloud spending environment. I wonder if you can give us some sense of the drivers of the strong sequential additive this quarter. I know you pointed to May. If anything you can give us from a workload perspective or new factors, maybe the workloads from last year are starting to ramp. I just love to understand that trajectory a little bit better. Yeah, Sanjit, thanks for the question. Clearly we're really pleased by the quarter and really pleased by the accelerating growth in Atlas. I would say a lot of it was due to the workloads that we acquired over the past year, especially with a move up market that are growing faster and becoming bigger than previous workloads we've seen. 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. 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. We also have added lots of customers over the last six months and I think that's also helping drive some of the growth. Yeah, that's great c olor. 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? In some sense, if I look at the customer ads, it seems like things are humming quite well. Just to get to understand how like what the state of the organization today, that'd be really helpful. Yeah, sure. Nothing really has changed. We're just doubling down on what we said previously. We are moving up markets. We're focusing our high-end salesforce focus on the most sophisticated and demanding customers. These are typically enterprise customers all around the world. We're using a self-serve channel to better serve the SMB market. I know there are a lot of questions about whether we're kind of abandoning the early-stage market by this move, and I think the results over the last couple 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 our wallet share in these larger accounts. We're really just continuing with the strategy that we articulated before, and obviously we're pleased with the results. Appreciate the thought, Dev. Thank you. Thank you, Sanjit. Thank you. Our next question is from Raimo Lenschow with Barclays. Please proceed. Perfect. Thank you. First of all, congrats to Jess. All the best. Two quick questions for me. Staying on that theme of self-serve, that acceleration, Dev, obviously you changed things. It's accelerated despite you actually moving up market. Can you help us understand that? What's driving that a little bit? I had one follow-up for Mike. Yeah, I mean clearly the output metrics look really good. I would say the work around self-serve began, 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 helping them understand the value proposition of MongoDB. Even running things like office hours where we spend time with SQL developers to explain the benefits of modeling data on a document database. All these experiments and tactics that we're doing, which are very data-driven, are really paying off. Mae Petrie, who 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. We don't want to declare a victory too early. Obviously we're very pleased with the results. Yeah, no, that's really nice to see. Then Mike, things. First of all, for all the extra disclosure, the ARR for the non-Atlas or EA part is kind of really. Helpful if you think about the. I get the logic around the renewal cohorts, especially Q3, but am I doing? The Roth correctly that actually next year. That part of the business looks more interesting because the cohort looks better. Just trying to get your idea or maybe you might not even give. Thank you. Sure. Thanks for the question. I'm going to hold that answer till we get to Q3 of next year because it kind of depends on what happens in Q3 of this year. The one thing is, as we talked about, the big impact in Q3 of this year is the multi year. We'll see how it comes back next year. It really depends, Raimo, how we do in Q3 this year. Okay, perfect. Thank you. Thanks for the disclosure. Really helpful. You're welcome. Thanks, Raimo. Thank you so much. Our next question comes from Tyler Radke with Citi. Please proceed. Hey, thanks for taking the question and nice job on the Atlas growth. Wanted to dig into the AI commentary that you had, Dev. Obviously last year quarter you talked about Cursor, which obviously is ramping up significantly in terms of their ARR. I think you called out many examples this quarter, including autonomous vehicle company. Sounds like expecting pretty significant growth there. How much of that is playing into the Atlas strength that you're seeing here in the quarter? Any way to quantify that cohort or use cases, whether it's, you know, vector search or maybe even 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. 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. The growth was really driven by our core business and our core customer base. While we're very happy with the AI customers increasingly choosing MongoDB, it was not a material mover of the needle for our growth. Great. Then follow up on the migration opportunity. I know you've been investing in Relational Migrator, you're working with companies like Cognition to accelerate the code migration opportunity and you've seen professional services ramp up a little bit. Where have you started to see sort of the time to migration 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. Yeah, sure. Yes, we're super excited about what we call app monetization or legacy app monetization. You'll hear a lot more about this at Investor Day in September, Tyler. What I will say 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 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 really help drive the delivery and the go-to-market efforts around AppMod. We're definitely beefing up resources and I would say that we're investing a lot in product and there's a lot more to do. I would say this is something that we're very excited about, but it'll drive more of our longer-term growth. It won't be as pronounced in terms of this year, but we're very, very excited about the opportunity and we definitely will spend more time discussing this and what we're actually doing on the product side in September. Thank you. One moment for our next question, it comes from Jason Ader with William Blair. Please proceed. 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 h ow you differentiate. Let me tackle them one by one. Clearly, what we are seeing is that the strategic high ground for AI, especially when it comes to inference, is OLTP. We talked about this on the last call, where some companies had acquired early stage OLTP startups, and what it really spoke to when those companies had spoken about their organic efforts to build an OLTP platform, and I think what it spoke to was the fact that building an OLTP platform that's ready and mission critical and enterprise can serve the most demanding requirements of enterprises is not trivial. I think they basically threw in the towel and decided to do these acquisitions. What it just reinforces is that OLTP is the strategic high ground for AI. We believe that if now customers are going to be choosing what OLTP platform 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 constantly 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 suggests shows is that real JSON is much more important now with AI than ever before. The clones and bolt-ons that have traded off features and performance and developer experience have just not met customer expectations. Candidly, what I see 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. Great, thank you. Just one quick follow up. Why do we hear so much about Postgres adoption for AI startups? You talked about the success you guys are having. If Postgres has the disadvantages that you've talked about multiple times, scalability, JSON support, how come we hear so much about that You know, at least. In their early stages of AI? That's a really good question. 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 is a lot of these startup founders don't think that hard about their database choice. They kind of go with what they know. What we are seeing is that as some of these startups are scaling, they're running into real scaling challenges with Postgres. We've talked about this in the past, like when you add a JSON, when you use JSON B on Postgres, a 2 KB document or bigger starts really creating performance problems because Postgres has to do something called off row storage, which creates enormous performance overheads. Developers need a platform that can handle structured, semi-structured, unstructured data. They need a platform that performs well, and they need a platform that can scale as they grow. 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. We feel really good about our position. The reality is that a lot of these AI founders kind of start with what they know or what they've used in the past. Only when the business starts scaling do they start recognizing the challenges. We realize we need to do more developer education and do more work. 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. 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. Thank you. One moment for our next question. That comes from Mike Cikos with Needham. Please proceed. Hey, thanks for taking the questions, guys. I just wanted to come back to Atlas specifically, and Mike, appreciate last quarter you gave us some very granular color around Atlas trends. 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. Sure. Thanks for the question, Mike. 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 was 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. Nothing to call out there, but we did see notable strength in the larger customers in the U.S., and if we dive deeper on that one, as Dev talked about, we are seeing some workloads from our larger customers grow for longer and expand more than we have seen in the past. That's good. While there are many moving parts in the consumption business, we also expect that there is benefit from our go-to-market changes. Given the preponderance of our strategic accounts being in the U.S., no surprise that we saw that growth mostly in the U.S. Lastly, Mike, there is some benefit from comparing it to a little slower growth in Q1. That would be the detail on Q2 as it relates to consumption growth. Thank you for that. If I could just squeeze maybe one more in on the outperformance that we saw this quarter from the multi-year deals. Maybe I'm just misunderstanding here, but my assumption was the reason we were facing this outperformance was really tied to the fact that in prior years we've had some pretty big deals on the multi-year front. 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. Thanks for the golf analogy. No, it did not fill the divot. In Q2, it was really good underlying strength in ARR growth and then greater than expected multi-year. There were really no pull forwards, Mike, and this is a hard business to forecast because sometimes even customers don't know whether they're going to opt for an annual renewal or a multi-year. There were no pull forwards and there was nothing out of the ordinary. Very importantly, we left the non-Atlas assumptions consistent with our last guidance, hence pulling down the multi-year headwind from 50- 40. Again, nothing to call out on Q2. No pull forwards and there were really no large multi-years in there. It was just across a good subset of customers. Thank you again. Thank you. Our next question comes from the line of Alex Zukin with Wolfe Research. Please proceed. Yes, thanks for squeezing me in and I'll echo the congrats on a truly amazing quarter I guess. Dev, when you think about the AI comments that you've talked about both in the press release and in the call, maybe just a little bit more nuance in the use cases, not necessarily that you're seeing kind of contribute materially today, but the differentiation of 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? When do you think we should expect to kind of actually start seeing that contribute more materially to the growth in consumption. Yeah, so thanks for the question. Alex, a couple of points again, you know, we're really pleased with the results of this quarter, but I would say the AI cohort, you know, 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. 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. That's point number one. It's much easier to do that on MongoDB than to do that on some kludge, you know, kind of set up on top of a 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. What this means is that rather than stitching together multiple systems, you can do this all in MongoDB. It becomes less complexity and lower cost. The third thing is that we've now embedded Voyage AI models on our platform, right? If you control the embedding layer, you sit at the gateway of meeting of AI, right? What the embedding models do is really are a bridge between a company's private data and the LLM. That becomes really important because the better the quality of the embedding model, the better the quality of the signal of your own data. That reduces things like hallucinations or just bad outputs. Customers are now, as people start caring more and more about, like, higher stake use cases, they really want to ensure those outputs are high. The fact that it's part of our platform, we can enable you to do auto embeddings. It becomes an incredibly compelling feature in terms of the market. What I would say is that the enterprise uptake of AI is still early. I've said this for a couple years now and I think a lot of people were a little skeptical of what I said, but it's proving to be true, as we predicted. The lack of skills and the lack of trust with AI systems is kind of slowing. 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 systems with 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. The real enduring value will come 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 to drive new revenue opportunities or dramatically reduce their existing cost structure. 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 bet big on MongoDB. DevRev, the company going after the help desk space, has built their whole agentic platform on MongoDB. We feel really good about what this all portends for the future. As I said, it was a small part of our growth this quarter. Very helpful. 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 the 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, sustaining this kind of accelerating pace and investing in things like the Bay Area 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. I think it's the funnest part of my job, quite frankly. 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 MongoDB is the revenue growth. Those two are directly connected. It's a great business model where when we can grow Atlas in the 20%+ range and then keep that ARR or VA in that single digit, it generates a ton of gross profit that funds a lot. The team has done a really 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. I felt good about it when I started. Candidly, I feel better about it 90 days later. Excellent. Thank you, guys. Congrats again. Thank you, Alex. Thank you. Our next question comes from Kash Rangan with Goldman Sachs. Please proceed. It's always tough to go after Alex because he asks such good questions, but that's not going to stop me. So Dev and Mike, congratulations on the quarter. You know it's super interesting you were talking about how some of the Silicon Valley AI startup founders don't have time to think about databases, but our good friend Dheeraj Devrav seems to have made a wise choice here. As you set encampment up in the Bay Area and start to evangelize the need for an Atlas consumption AI savvy database, how do you reconcile that with the fact that at the same time enterprise is where we really saw that the bread and butter value proposition of MongoDB resonate? Could what is happening with Devrav be a leading indication of what's going to happen in the enterprise? We've all, much to your observation, not seen much of a productive impact from the enterprise because of AI at the business level. What could be that unlock is what are folks like Dheeraj doing correctly? That could be a precursor if it is for what is to come in the enterprise. Yeah. Kash, thanks for the question. Obviously, I have so much respect for Dheeraj. He built Nutanix into a really great business and he's going to do the same at DevRev. I will tell you that the AI cohort, as I said earlier, was not really material to our growth. I think these are all customers earlier in their journey. 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. What that really confirms is that our move up market made sense. The quality of those workloads, the durability of their growth, they grow for longer and become bigger than what we've seen in the past, is really making us feel good about that decision. To juxtapose that, we also decided to double down on self-serve to better serve the small and medium-sized business market. That's also obviously becoming more and more effective, given the number of customers that we've added over the last six months. 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. There's also going to be continued work to continue to drive that efficiency even better. We also are investing for the long term, and we're just constantly debating those decisions internally. We feel good about what's working and we feel good that someone like Dheeraj is betting early on MongoDB because that's a good signal for other founders who are thinking about doing the same. Awesome. We'll drill into this more in a couple of weeks when we see you in San Francisco. Absolutely. Thank you. One moment for our next question. Is Brad Re back with Stifel? Please proceed. Great. Thanks very much. The 7% EA ARR growth seems fine. I'm assuming you're not satisfied with single digit growth there, Dev. Any sense of where we should think about that longer term? Thanks. Clearly, EA is a large enterprise motion and what we've seen is that it's typically less new customers choose EA and it's more of our existing customer base who have a mix of EA and then sometimes also start deploying Atlas. I think one thing that's becoming more and more clear is that customers are becoming much more thoughtful about how to think about using deployments on premise versus using the cloud. Four or five years ago, there was a belief that everything was going to 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 premise and some workloads make sense to run in the cloud. I think that's where the MongoDB story becomes really attractive because the same code base can be used. It also gives them optionality for the future where they can move from on premise to the cloud. A lot of our EA customers have done that either with new workloads and some existing workloads, and they can also move from cloud to cloud and they can also move back to on premise if they choose to do so. That optionality becomes a very powerful value proposition for our customers. Great. Thank you very much. Thank you, Brad. Thank you. Our next question is from the line of Ittai Kidron with Oppenheimer. Please proceed. Thanks. Great numbers and congrats to Jess and good luck in the new role. Dev. I wanted to dig into the AI opportunity again, but take it from the perspective of a go to market motion. Clearly it can power a lot of AI use cases that are embedded with bigger platforms through a self serve motion. It sounds like to really capture the big workload opportunities it's going to have to be more of an enterprise pull. I'm wondering how do you think about targeting the AI opportunity from go to market motion that doesn't just fall into if you're a big enterprise, I'm going to send you to an enterprise salesperson and all the rest call our self serve and do it yourself. Is it something a little bit more, you think, target perhaps that you need to take here in order to capitalize on this opportunity? Yeah. Yeah. What I would say, Ittai, is that we've seen this movie before with the cloud where some early stage customers started growing very, very quickly and then we put dedicated sales focus on those accounts and they grew then even faster. We're clearly watching the market and when self serve customers are to a point where they really need that 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 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, early sta ge 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 developers using CodeGen 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 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. I think that's kind of a microcosm into the enterprise market where I think they're still quite early in their AI journey. 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 like AI in the short term but underestimate it in the long term. I think we're just in that classic journey right now. Appreciate that. Maybe as a follow up, Mike, I just want to make sure I dig in a little bit into the non-Atlas business, the EA, predominantly EA business. Can you tell us roughly what% of your customers here are on multi-year deals versus just annual deals? I'm just kind of curious where we are now and what was it, say, a year or two ago, and where do you think that mix is going to be a year or two from now? Thanks for the question. We don't break out the percentage of customers on multi year versus one year. What I would say is in fiscal 2025 obviously we saw a lot of larger multi year deals and you see that in the numbers this year. We will always see multi year deals. They haven't been, I would call it, as large. It's more widespread. 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 Dev talked about, it's going to be a mix of Atlas and on-prem and that mix has stayed relatively consistent. 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? No reasons are the same. It's typically there if it aligns with their long-term strategy. They want to be able to lock in the pricing, and as everybody knows, data has gravity. Moving data around is not fun for everybody. They want to be able to lock in and guarantee their prices for that period of time. Appreciate it. You bet. Thank you. Our next question comes from the line of Siti Panigrahi with Mizuho. Please proceed. Thanks for taking my question. 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 return. How do you see what kind of do you think the inflection point? When do we think we'll start seeing some of the adoption of this AI like you say they're testing but what can trigger. I know you have been talking about a year ago probably we are a few years out but it's good to see some of the traction. How do you first of all what will be your view on that report and how should we think about in terms of revenue contribution, material contribution from AI. Yeah. I think it just comes down to the 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. You can't always guarantee the output. You can hope that you've trained the models well, you've hoped that you've given it the right information, but you can't always guarantee the output. As I mentioned, I had meetings with two financial services customers earlier today and both of them are still hesitant to roll out end user facing AI applications for those specific reasons. It's going to take a little bit of time for people to really get comfortable that they can really deal with the last mile issues and make sure that they don't have any errors that potentially could be impacted their brand or really cause a lot of customer problems. That's point number one. There are issues around obviously 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. I think we're just in that learning journey. I don't know if there's going to be some massive tipping point. What we are seeing with the frontier models is that all these frontier models are clustering around the same ballpark in terms of performance and the efficacy of their models. I think what's going to start happening is how people start leveraging these insights to build what I call scaffolding around these frontier models to address the needs of their business. Obviously everyone's talking about agents and people are very, very focused on essentially using agents to drive a lot of work. Agents require. 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. You need platforms that can massively scale up and down, which is again a good sign and support indicator for MongoDB. I think it's going to take a little bit of time. It's going to take time being comfortable with technology, it's going to take time where people start with low stakes use cases, start gravitating to higher stake use cases. I don't think there's going to be some seminal inflection point. I think it's just going to take time. I think that time is coming. That's great. Color Dev, thank you. Thank you. Our next question is from Bradley Sills with Bank of America. Please proceed. Oh, 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&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? We'd love to get some color on just where you're investing in the stack. Thank you. Yeah, sure. We talked about the fact that R&D is a big part of our investment focus for this year. One, we came out with 8.0, which was the most performant release ever. We already started to see dividends of our investment in our platform. 8.1 is even better. We're also making investments in the expansion parts of our platform. What I will say is we're going to go into a lot more detail around this Investor Day. If you can hold until September 17th, we'll go into a lot of things that we're doing on the R&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 will give you a lot more color. Got it. Great. Thanks for that, Dave. 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. To what extent would you attribute some of this upside to that effort and maybe just an update on that effort as you make? 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 acquired, say, in earlier years. I think that's a big part for why you're seeing this growth happen now. Great. Thank you, Carmen. I think we have time for one more question. All right. One moment, please. We have the line of Rishi Jaluria with RBC. Please proceed. Oh, wonderful. Thanks for squeezing me in at the deadline. I'll keep myself to one question. Dev, really nice to see the early traction with AI-native companies. You know, 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, 10 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 30 years, we actually enter 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. Yeah, sure. Again, just to make sure we're all talking the same language, we believe that agents essentially do three things. One, they perceive or understand the state of things. 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. Basically, you have to come up with a plan saying I want to take this action or these sets of actions, and then you have to act. You actually have to go execute those actions. Right. Why is MongoDB good for 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. That's why our document database, I think, is the best way to do that. Two, we obviously support search and vector search. You can do very sophisticated hybrid search. That becomes super important. With memory, 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. Memory lets agents connect the dots across time and situation. You have different kinds of memory, things like short-term context, past experiences, knowledge, skills etc, 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. 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. We think that we'll be positioned to be a winner as people deploy more and more agents in their enterprise. All right, very helpful. Thank you so much. Thank you so much. With that, we conclude the Q&A session, and I will pass it back to Dev Ittycheria for his final comments. 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 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. Thank you and we'll talk to you soon. Take care. Thank you. This concludes our conference. Thank you for participating and you may now disconnect.