MongoDB opened fiscal 2027 with a strong Q1, posting revenue of $688 million up 25% year-over-year and beating the high end of guidance, with Atlas growing 29.4% to a record $117 million in net new and its fourth straight quarter above 25%. Non-GAAP operating margin reached 18% with a second consecutive quarter of GAAP profitability, prompting a raised full-year outlook and a federal-focused acquisition of Clarity Business Solutions. Management highlighted accelerating AI and agentic adoption, including MongoDB emerging as a memory layer for AI agents, while core enterprise workloads remained the primary growth driver.
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, Chief Financial Officer 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 31st, 2026, filed with the SEC on March 11th, 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 CJ.
Thank you, Jess, and thank you all for joining us today. I continue to spend a lot of time working with a wide range of customers, from AI natives and digital natives to large enterprises and public sector organizations. This customer-driven focus is to deliver meaningful outcomes for MongoDB. The process I follow is tightly linked, so each part strengthens the others. Number one, engage directly with C-suite leaders to elevate MongoDB from a technical decision to a strategic platform commitment. Number two, surface new pipeline by helping customers connect their most pressing modernization and AI opportunities to power what MongoDB can uniquely solve. Number three, feed what I learn directly into our product and technology teams to accelerate our customer-driven innovation roadmap. These conversations reinforce my conviction in both what we have built and the scale of the opportunity ahead. That opportunity has two dimensions.
The first is core workloads, where large customers run their most demanding mission-critical workloads on MongoDB across on-prem, public clouds, and hybrid environments. The second is AI, where enterprises, digital natives, Frontier Labs, and AI natives alike are moving agentic applications into production and choosing MongoDB as the data platform to power them. As you heard from other software companies, these two opportunities are not distinct and in fact reinforce each other. Enterprises are starting to build agentic 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 25%. EA and other, previously referred to as non-Atlas, grew 13% year-over-year. We delivered a non-GAAP operating margin of 18% above the high end of the guidance. We ended the quarter with over 67,700 customers, adding 2,500 customers in Q1, growing year-over-year and quarter-over-quarter. AI adoption of MongoDB technologies across our customer base continues to accelerate. MCP server usage is growing significantly. Voyage customers have more than doubled quarter-over-quarter, and vector search adoption is far outpacing overall company growth. Let me walk through each dimension of our opportunity.
Across my conversations with customers, one ship stands out. MongoDB is starting to become a strategic platform decision in addition to a workload by workload evaluation. This is driven by a powerful combination of our platform technology fundamentals, high performance at scale, the ability to run anywhere, and AI capabilities that are fully integrated in a single data platform. Zoom is a clear example of that. Zoom, a global leader in AI-powered workplace collaboration, runs MongoDB Enterprise Advanced as a unified data platform for Zoom Meetings, Zoom Phone, Zoom Contact Center, and Zoom Virtual Agent deployed across dozens of clusters globally to deliver low latency, highly available communications at scale. By standardizing these workloads on MongoDB, Zoom gains a cloud-agnostic hybrid deployment model that runs anywhere their business requires. This simplifies the previously polyglot data estate, 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. 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, Endor Labs is an AI-native application security platform protecting over 7 million applications across both human-written and AI-generated code.
Endor selected Atlas as its default database to support 225% year-over-year revenue growth. Endor uses Atlas and Atlas Search to power its mission-critical security workflows, including Ori, 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, write 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 context at the right time. Adobe's Journey Agent is a clear example. A composite multimodal AI agent that unifies Adobe's marketing suite and orchestrates end-to-end customer journeys for their global B2C user base with MongoDB as the agent's long-term memory and reasoning layer. Adobe leverages the MongoDB platform, Atlas Search, and Atlas Vector Search together to power the sub-100-millisecond hybrid search the agent needs to act in real time. To be clear, our results today are driven primarily by core workloads.
We are seeing real and growing momentum from AI and agentic workloads, and believe MongoDB is purpose-built to be generational data platform for the agentic era. Built natively into the platform, MongoDB's innovations in the core database, embeddings, and vector capabilities are moving us beyond a system of record to becoming the real-time system of intelligence. That shift comes down to five core strengths. Number one, MongoDB is architecturally built for AI in two key ways. First, our flexible schema is uniquely suited to how applications get built in the agentic era. A growing share of software is now created through prompt-driven development, natural language iteration rather than line-by-line authorship.
Whether the prompt comes from a developer or an agent, the shape of the application shifts with each prompt and a rigid relational schema becomes a tax on every iteration, compromising agility. In addition, LLMs are the lingua franca for AI, and they speak in unstructured, document-shaped data, the exact form MongoDB was built around. We have been compounding both advantages for 15 years, well before the current AI wave gave them a tailwind. Second, MongoDB is a transactional high-performance data platform built for how agents actually work. Agents don't behave like traditional applications. They read, write, and act continuously across multiple simultaneous threads with a one 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 asset 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. 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 reranker 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. They need a platform that doesn't force a choice. Fifth, MongoDB is embedded in the tools developers and agents actually use to build agentic applications. LangChain is the world's most widely adopted agent framework with over 1 billion downloads. We delivered 10+ native integrations with LangChain for vector search, hybrid retrieval, semantic caching, and agent memory. We recently announced that MongoDB Checkpointer for LangSmith deployment, which collapses what used to be a dedicated 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 Claude Code marketplace, where we are already seeing strong early traction with developers. Wherever agents are built, MongoDB is already there. Executing on this opportunity requires a world-class team. On the product side, we recently announced two CPO appointments. Ben Cefalo, a longtime MongoDB leader, is now Chief Product Officer for Core Products, overseeing Atlas and Enterprise Advanced. Pablo Stern-Plaza, who is based in San Francisco, joined as Chief Product Officer for AI and Emerging Products with responsibility for our AI product portfolio and our strategic relationships with top AI native and frontier customers. Over the years, Pablo has worked for many software companies in technical roles, helping scale their product lines into meaningful, thriving businesses.
Great. Thank you, CJ. Good afternoon to everyone on the call. I will start by reviewing our first quarter fiscal 2027 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. We are raising our outlook across the board for fiscal 2027. Before diving into details, I want to highlight three key takeaways from the quarter. First, Atlas growth remains strong, with the fourth straight quarter of year-over-year growth above 29%. Second, EA growth remains durable as we continue to grow both Atlas and EA. 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 in use cases at 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 2026 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 in 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 adds 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 upmarket.
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 Search and Text Search adoption. Moving on to the balance sheet and cash flow, we ended the first quarter with $2.4 billion in 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 2027. To begin, as I mentioned earlier, we continue to see strong and consistent Atlas 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 2027 outlook, but is also giving us confidence to raise our full-year growth expectation to a range of 23%-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 2027. This implies that EA and other revenue will be approximately flat during the second half of the year, again, due to the tougher compares from the second half of fiscal 2026.
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 risks 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 2027. 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.