MongoDB, Inc.

Hello, and welcome to MongoDB's first quarter fiscal year 27 earnings conference call. At this time, all participants are on a listen-only mode. After the speaker's presentation, there will be a question and answer session. To ask the question during the session, you will need to press star 1-1 on your telephone. You will then hear an automated message advising your hand is raised. To withdraw your question, please press star 1-1 again. I would now like to hand the conference over to Jeff Lubrich, Mongo's Vice President of Investor Relations. You may begin.

Thank you, Operator. Good afternoon, and thank you for joining us today to review MongoDB's first quarter fiscal 2027 financial results, which we announced in our press release issued after the close of market today. Joining me on the call today are CJ Desai, President and CEO of MongoDB, and Mike Berry, CFO of MongoDB. 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 EA and other business and multi-year license revenue, the long-term opportunity of AI, our financial guidance, and underlying assumptions in our investments and growth opportunities in AI. These statements are subject to a variety of risks and uncertainties, including the results of operations and financial conditions that could cause actual results to differ materially from our expectations. For a discussion of material risks and uncertainties that could affect our actual results, please refer to the risks described in our annual report on Form 10-K for the year ended January 31, 2026, filed with the SEC on March 11, 2026. Any forward-looking statements made on this call reflect our views only as of today, and we undertake no obligation to update them except as required by law. Additionally, we will discuss non-GAAP financial measures on this conference call. Please refer to the tables in our earnings release on the investor relations portion of our website for a reconciliation of these measures to the most directly comparable GAAP financial measures. With that, I'd like to turn the call over to C.J.

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

Great. Thank you, CJ, and good afternoon to everyone on the call. I will start by reviewing our first quarter fiscal 27 financial performance before moving on to our outlook for the second quarter and the remainder of the fiscal year. I will be discussing both GAAP and non-GAAP results. As CJ highlighted, we delivered a strong quarter that exceeded all of our guidance ranges, and we are raising our outlook across the board for fiscal 27. Before diving into details, I want to highlight three key takeaways from the quarter. First, Atlas growth remains strong, with a four-straight quarter of year-over-year growth above 29%. Second, EA growth remains durable as we continue to grow both Atlas and EA. And third, our business model continues to deliver operating margin and cash flow expansion. Looking at the top line in more detail, total revenue in the first quarter reached $688 million, representing 25% year-over-year growth compared to 22% growth in the year-ago quarter. Turning to our product breakdown, Atlas consumption was stronger than expected in the quarter, and revenue grew by more than 29% year-over-year and exceeded our guidance. This is the fifth straight quarter of year-over-year dollar growth in Atlas, adding a record $117 million in the quarter. Atlas now accounts for approximately 75% of total Q1 revenue, up from 72% in the year-ago quarter. Our main growth driver continued to be the strength and use cases that established enterprise customers with momentum across the financial services, technology, and media industries in Q1. Smaller but accelerating growth drivers included early AI deployments with many of these same enterprise customers and momentum with frontier labs and AI native companies. We experienced particular strength in North America that was driven by our larger customers, although our self-serve business also performed well in the period. This ongoing momentum across our customer base is reflected in our total company net ARR expansion rate, which was 121% for the quarter compared to 119% a year ago. Turning to EA and other revenue, which encompasses the metrics we previously referred to as non-Atlas, we saw solid results, with revenue growing 13% year over year. This strength was driven by existing customers across all types of industries, particularly in the finance and technology verticals, where customers continue to expand their on-prem footprints to support both traditional and AI applications. EA and other ARR, which normalizes for duration impacts, grew approximately 11% year-over-year. Moving down to P&L, total non-GAAP gross margins of 74.5% expanded by approximately 40 basis points year-over-year, and were approximately 100 basis points below the fourth quarter. Subscription gross margins finished at 77.1%, approximately 60 basis points below the first quarter fiscal 26, and 170 basis points lower than the fourth quarter. The quarter-over-quarter variances were driven mainly by product mix between Atlas and EA, as well as the normal seasonality impact to margins in the first quarter of the fiscal year. Moving to profitability, I'd like to start by noting that we had our second quarter in a row of GAAP profitability, which is a great trend. Non-GAAP income from operations came in at $123 million, yielding an operating margin of 18% compared to 16% in the year-ago period. We are very pleased with our operating margin results, which benefited primarily from strength and revenue driven mainly by Atlas. First quarter non-GAAP net income was $112 million, which translates to $1.32 per share, based on 85.3 million diluted shares outstanding. This compares the net income of $86 million, or $1 per share, on 86.3 million diluted shares outstanding in the year-ago period. Our remaining performance obligations, which we define specifically as obligations for contracts with a duration greater than 12 months, stayed relatively consistent quarter-over-quarter and ended the period at $1.46 billion. This represents year-over-year growth of 88%, with the current portion growing at 69%. Customer ads grew by 2,500 sequentially, bringing the total customer count to 67,700, which is up from 57,100 in the year-ago period. The growth in our total customer count is being driven primarily by Atlas, which had 66,400 customers at the end of the first quarter compared to 55,800 in the year-ago period. Within Atlas, we saw a strong quarter of voyage customer additions, reflecting early but encouraging demand for our AI embedding capabilities. We feel good about the momentum we are seeing with new customers, and please keep in mind this metric will fluctuate from quarter to quarter. We closed out Q1 with 2,895 customers with at least $100,000 in ARR, representing 16% year-over-year growth. Revenue growth from this cohort was strong and outpaced total company revenue growth consistent with our move up market. Furthermore, we continue to see strong Atlas platform adoption. Of our Atlas customers generating at least $100,000 in ARR, 45% are leveraging two or more features of our platform, which is up from 37% in the year-ago quarter, driven largely by vector and text search adoption. Moving on to the balance sheet and cash flow, we ended the first quarter with $2.4 billion in cash, cash equivalents, and short-term investments. During Q1, We allocated $100 million towards share repurchases and $58 million to settle taxes on employee RSUs. Operating cash flow for the quarter was $202 million versus $110 million last year, and free cash flow was $198 million versus $106 million last year. Our cash flow results were driven primarily by strong operating profit and seasonally higher cash collections. Before moving on to guidance, I am pleased to share that we have acquired Clarity Business Solutions. As we have discussed previously, we are strategically increasing our investment in the U.S. federal vertical, and this acquisition is a key component of that strategy. Clarity has been a trusted partner of ours since 2021, providing specialized support and professional services for highly classified workloads within the U.S. government. We have held a small equity stake in Clarity for some time, and this acquisition brings into MongoDB the deep domain expertise and high-level security clearances required to further accelerate our U.S. federal vertical. Financially, This transaction represents approximately $10 million in services revenue annually at roughly break-even profitability, and these impacts are already reflected in our updated guidance. Now I'd like to share some of the assumptions driving our Q2 outlook and provide some additional detail into how we're thinking about the rest of fiscal 27. To begin, as I mentioned earlier, we continue to see strong and consistent at-risk growth. This performance is driven primarily by strength in core workloads, as well as early AI tailwinds from both enterprise and AI native customers. We are encouraged by the continued strength in Atlas and feel good about the business entering the second quarter, where we expect Atlas revenue growth of approximately 26%. This strength is not only driving our second quarter fiscal 27 outlook, but is also giving us confidence to raise our full-year growth expectation to a range of 23% to 25%, an increase of 200 basis points. As we said last quarter, we would like to remind you that as Atlas has gotten larger, it has become more predictable and less sensitive to revenue movements with any individual customer or cohort. With this in mind, we would encourage you to not expect large swings versus guidance for the current quarter as changes in consumption inter-quarter only have a modest impact on revenue within the period. Given Atlas is a consumption-based product, there is more room for variability as we go further out in the year. For EA and other, we have line of sight into a very strong Q2 and expect to see revenue growth of approximately 20%. This reflects our expectations for continued ARR momentum, as well as the timing of several large multi-year deals with existing customers. The continued momentum highlights the strategic importance of EA to some of our largest customers. Given our current momentum, balanced against the timing of certain deals and a more difficult Q4 compare, we are raising our full-year expectations for EA and other revenue to mid-single-digit growth in fiscal 27. This implies that EA and other revenue will be approximately flat during the second half of the year, again due to the comfort compares from the second half of fiscal 26. While we remain optimistic regarding our ability to grow our EA and other revenue over the long term, it remains difficult to predict the duration of our EA deals, so we only include deals in our forecast that have either closed or have a high probability of closing to limit the risk of a negative surprise. Turning to profitability, we remain committed to driving both revenue growth and operating margin expansion. And we now expect to expand operating margin by 100 to 150 basis points in fiscal 27. We will achieve this expansion while investing in key growth initiatives across both products and go-to-market. Our product investment is focused around enhancing our AI capabilities, which includes vector search and voyage, and expanding EA's product value with new and advanced features, including native AI functionality. Our go-to-market investments include building out our presence in Japan, as well as strengthening our U.S. federal vertical, highlighted by our acquisition of Clarity Business Solutions. We will also continue to invest in quota-carrying headcount, marketing programs, and developer awareness. Now let's shift to how that translates to guidance for Q2 and fiscal 27. For Q2, we expect revenue of $729 million. to $734 million, which equates to 23 to 24% year-over-year growth. We expect non-GAAP income from operations to be in the range of $152 to $156 million for an operating margin of approximately 21% at the high end of guidance. We expect non-GAAP net income per share to be in the range of $1.58 to $1.61 based on 86.3 million diluted shares outstanding. For fiscal 27, we expect revenue to be in the range of $2.92 to $2.96 billion, representing full-year revenue growth of 19 to 20%. We expect non-GAAP income from operations of $571 to $591 million for an operating margin of of approximately 20% at the high end of guidance. With the combination of 20% revenue growth and 20% operating margin, we are targeting a Rule of 40 performance at the high end of our outlook. We expect non-GAAP net income per share to be in the range of $5.95 to $6.14, based on 86.7 million diluted shares outstanding. Note that the non-GAAP net income per share guidance for the second quarter and fiscal 27 assumes a non-GAAP tax provision of 20%. In closing, I also want to thank all of the MongoDB employees for staying focused and executing very well in Q1. We are very pleased with our Q1 results and remain highly confident in the long-term opportunity ahead for MongoDB. We are optimistic regarding our growth prospects and will continue to invest responsibly to drive long-term shareholder value. With that, operator, we're now ready to take questions.

Thank you. Ladies and gentlemen, as a reminder to ask the questions, please press star 1-1 on your telephone. Then wait for your name to be announced. To withdraw your questions, please press star 1-1 again. Please stand by while we compile the Q&A roster. We ask that you limit yourself for one question and one follow-up. Our first question comes from the line of Matt Martino with Goldman Sachs. The line is open.

Yeah, awesome. Thanks for taking the questions, guys. CJ, maybe to start with you, the agentic conversation seems to have really shifted even over the past three months from proof of concept into real production deployments. And Mongo's put a lot of work into the platform to meet that moment with the Langchain partnership and the performance upgrades to the core database. I think as those pieces come together, do you feel like we're approaching the point where agentic workloads start to genuinely move the needle on consumption, or is the bigger inflection still ahead of us? I'd love to get your thoughts there.

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

That's great to hear. Thanks for the thoughts there, CJ. And then, Mike, for you, you made a comment, I think, not to expect huge swings on Atlas revenue for the quarter ahead. Can you unpack that comment a bit? Should we take that as expect a beat magnitude similar to what we saw this quarter or something different?

Thanks. Yeah. Thank you for the question, Matt. So as it relates to guidance, we think it's important that our guidance reflects the true strength of the underlying business and feel there's room to do that while still being prudent. As Atlas has gotten bigger, it has become more predictable and has become less sensitive to movements from individual customers or cohorts. Coming off a strong Q1 where consumption came in better than expected, we're guiding Q2 consistent with the framework of how we've guided the past two quarters. To put that in context, in Q4, consumption came largely in line with our expectations, and in Q1, it came in a little better, which you can see reflected in our results versus guidance. The strength in Atlas this quarter allowed us to roll the beat and raise guidance for the full year, and then, of course, that revenue drove higher profitability in EPS. For the full year, given Atlas is a consumption-based product, there's a little more room for variability as we go further out from the year. So, we've not changed our philosophy on EA. We will always guide conservatively due to the uncertainty around the timing of the deals. So, hopefully, that gives you the context of the framework in terms of how we guide a Q2. Thanks, Mike. Very clear.

Thank you. Thanks, Brian. Our next question comes from the line of Ryan McWill with Wells Fargo. The line is open.

Thanks for the question. Mike, you're getting to another strong 2Q for Atlas against the strong performance you had last year. Is this how we should think about the seasonality for the Atlas biz going forward, or is this Atlas guy being impacted by other factors we should keep in mind?

Yeah, so... Thanks for the question, Ryan. So as we got to Q2, a lot of that was coming off of a strong Q1 in terms of consumption. And as we've talked about, Ryan, as the business gets a little bit bigger, there's always some small seasonal changes. But on a year-over-year basis, I wouldn't expect significant changes. Now, quarter-on-quarter, certainly it does change a little bit. But year-over-year, I wouldn't expect much change in the seasonality.

Excellent. And then for CJ, I'd like to hear about the opportunity for AI natives with Mongo as those customers really start to scale their own businesses. Are there use cases for large AI natives that maybe make more sense for Mongo? And I guess for the quarter itself, like, you know, how can we think about the contribution from AI natives to Atlas? Thank you.

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

Thank you. Our next question comes from the line of Raymond Linschow with Barclays. Your line is open.

Thank you. Congrats from me as well. CJ, on that note, you're meeting a lot of customers at the moment. The one theme that comes up in the industry a lot around data is that people realize with AI, your data needs to be consolidated and cleaner. So what are you seeing in terms of that kind of consolidation move towards the Mongo and maybe just talk to how that's kind of impacting Atlas and EA? And then I have one follow-up from Mike.

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

Okay, perfect. Makes sense. Sounds exciting. And then, Mike, one for you, like with the two new hires on the go-to-market side, I know it's now we're now in Q2, but any changes we need to be aware of there, or what are you thinking there in terms of impact on the organization this year?

Yeah, so thanks for the question. As we talked about going into Q1, we felt very confident in terms of making sure that there was not going to be any disruption. So from a territory planning quota, all of that stuff, those are all out. We don't expect there to be any changes in the year. As you know, making changes to comp plans during the year is always fraught with issues. Ryan's done a great job so far. He'll get his arms around the organization, maybe some tweaks next year. We'll see what he wants to do. But I wouldn't expect any significant changes for the remainder of fiscal 27. Okay, perfect.

Thank you. Thank you.

Thank you.

Our next question comes from the line of Etai Chistrom with Oppenheimer and Company. Your line is open.

Hey, guys. Congrats on a good quarter. CJ, I wanted to get your perspective on the AI natives. In what way do you think your go-to-market needs to evolve to address them differently? Is there a need to address them differently in a go-to-market effort?

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

Fantastic. And then for you, Mike, great numbers again. Two small things. Well, first on the EA comments on the second half when you talked about the flat year-over-year in the second half. I'm just wondering, were there any large yields? I talked about large multi-year yields in the quarter. Was there any movement from richer quarters into 2Q that have made that?

That could also explain the... Translates to guidance for Q2 and fiscal 27. For Q2, we expect revenue of $729 to $734 million, which equates to 23% to 24% year-over-year growth. We expect non-GAAP income from operations to be in the range of $152 to $156 million for an operating margin of approximately 21% at the high end of guidance. We expect non-GAAP net income per share to be in the range of $1.58 to $1.61 based on 86.3 million diluted shares outstanding. For Fiscal 27, we expect revenue to be in the range of $2.92 to $2.96 billion, representing full-year revenue growth of 19 to 20 percent. We expect non-GAAP income from operations of $571 to $591 million for an operating margin of approximately 20 percent at the high end of guidance. With the combination of 20% revenue growth and 20% operating margin, we are targeting a rule of 40 performance at the high end of our outlook. We expect non-GAAP net income per share to be in the range of $5.95 to $6.14 based on 86.7 million diluted shares outstanding. Note that the non-GAAP net income per share guidance for the second quarter and fiscal 27 assumes a non-GAAP tax provision of 20%. In closing, I also want to thank all of the MongoDB employees for staying focused and executing very well in Q1. We are very pleased with our Q1 results and remain highly confident in the long-term opportunity ahead for MongoDB. We're optimistic regarding our growth prospects, and we'll continue to invest responsibly to drive long-term shareholder value. With that, operator, we're now ready to take questions.

Thank you. Ladies and gentlemen, as a reminder to ask the questions, please press star 11 on your telephone, then wait for your name to be announced. To withdraw your question, please press star 11 again. Please stand by while we compile the Q&A roster. We ask that you limit yourself for one question and one follow up. Our first question comes from the line of Matt Martino with Goldman Sachs. Your line is open.

Yeah, awesome. Thanks for taking the questions, guys. CJ, maybe to start with you, the agentic conversation seems to have really shifted even over the past three months from proof of concept into real production deployments. And Mongo's put a lot of work into the platform to meet that moment with the Langchain partnership and the performance upgrades to the core database. I think as those pieces come together, do you feel like we're approaching the point where agentic workloads start to genuinely move the needle on consumption or is the bigger inflection still ahead of us? Love to get your thoughts there.

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

That's great to hear. Thanks for the thoughts there, CJ. And then, Mike, for you, you know, you made a comment, I think, not to expect huge swings on Atlas revenue for the quarter ahead. Can you unpack that comment a bit? Should we take that as expect a beat magnitude similar to what we saw this quarter or something different?

Thanks. Yeah. Thank you for the question, Matt. So as it relates to guidance, we think it's important that our guidance reflects the truth to strength of the underlying business and feel there's room to do that while still being prudent. As Atlas has gotten bigger, it has become more predictable and has become less sensitive to movements from individual customers or cohorts. Coming off a strong Q1, where consumption came in better than expected, we're guiding Q2 consistent with the framework of how we've guided the past two quarters. To put that in context, in Q4, consumption came largely in line with our expectations, and in Q1, it came in a little better, which you can see reflected in our results versus guidance. The strength in Atlas this quarter allowed us to roll the beat and raise guidance for the full year, and then, of course, that revenue drove higher profitability in EPS. For the full year, given Atlas is a consumption-based product, there's a little more room for variability as we go further out in the year. So we've not changed our philosophy on EA, where we'll always guide conservatively due to the uncertainty around the timing of the deals, So hopefully that gives you the context of the framework in terms of how we guided Q2. Thanks, Mike, very clear.

Thank you. Thanks, Brian. Our next question comes from the line of Ryan McWill with Wells Fargo. The line is open.

Thanks for the question. Mike, you're guiding to another strong QQ for Atlas against the strong performance you had last year. Is this how we should think about the seasonality for the Atlas biz going forward, or is this Atlas guide being impacted by other factors we should keep in mind?

Yeah, so thanks for the question, Ryan. So as we got to Q2, a lot of that was coming off of a strong Q1 in terms of consumption. And as we've talked about, Ryan, as the business gets a little bit bigger, there's always some small seasonal changes, but on a year-over-year basis, I wouldn't expect significant changes. changes. Now quarter on quarter certainly it does change a little bit but year over year I wouldn't expect much change in the seasonality.

Excellent. And then for CJ, I'd like to hear about the opportunity for AI natives with Mongo as those customers really start to scale their own businesses. Are there use cases for large AI natives that maybe make more sense for Mongo? And I guess for the quarter itself, how can we think about the contribution from AI natives to Atlas? Thank you.

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

Thank you. Our next question comes from the line of Raymond Linschow with Barclays. Your line is open.

Thank you. Congrats from me as well. CJ, on that note, you're meeting a lot of customers at the moment. The one theme that comes up in the industry a lot around data is that people realize with AI, your data needs to be consolidated and cleaner. So what are you seeing there in terms of that kind of consolidation move towards the Mongo and maybe just talk to how that's kind of impacting Atlas and EA? And I have one follow-up from Mike.

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

Okay, perfect. Makes sense. Sounds exciting. And then, Mike, one for you, like with the two new hires on the go-to-market side, I know it's now we're now in Q2, but any changes we need to be aware of there or what are you thinking there in terms of impact on the organization this year?

Yeah, so thanks for the question. As we talked about going into Q1, we felt very confident in terms of making sure that there was not going to be any disruption. So from a territory planning quota, all of that stuff, those are all out. We don't expect there to be any changes in the year. As you know, making changes to comp plans during the year is always fraught with issues. Ryan's done a great job so far. He'll get his arms around the organization, maybe some tweaks next year. We'll see what he wants to do. but I wouldn't expect any significant changes for the remainder of fiscal 27. Okay, perfect.

Thank you.

Thank you. Thank you.

Our next question comes from the line of ETAG History with Oppenheimer & Company. Your line is open.

Hey, guys. Congrats on a good quarter. CJ, I wanted to get your perspective on, you know, the AI natives. In what way do you think your go-to-market needs to evolve to address them differently? Is there a need to address them differently in a go-to-market effort?

Yeah. Itai, I'll give you a straightforward answer. This is work in progress. So what we find is that some of these AI-native companies come through our self-serve motion. We constantly watch, you know, we add so many customers through our self-serve motion, and that motion has been working really, really well as – A lot of venture investments have gone into AI-native companies. So post-2023, first I want to acknowledge through our CELSAR motion, we are getting some of these iconic logos that have now become a truly company with 100 billion ARR+. With Ryan now in place, we are figuring it out. What is the right point to intervene and, And that is a work in progress. Okay, what are the characteristics? It's a Tier 1 VC company. Maybe it's not. Like, for example, a customer that grew in Q1, we found out that there was an AI slash robotics company, and they were growing a lot on Atlas, and then our team reached out to them right away. So we see that some of these companies are coming via our self-serve motion, and then, one, when do we intercept and put a field wrap on it. And number two is that how do we scale and focus on that motion because we are a great database for those kind of companies. So work in progress, but we are making definitely improvements as we learn.

Fantastic. And then for you, Mike, great numbers again. Two small things. Well, first on the EA comments on the second half when you talked about a flat year of