Snapshot
MongoDB, Inc. reported $688M of revenue in Q1 2027, up 25.2% year over year, with diluted EPS of $0.05 and an operating margin of -3.6%.
- Revenue
- $688M
- YoY growth
- +25.2%
- Diluted EPS
- $0.05
- Operating margin
- -3.6%
What management said
- •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.
- •Number two, surface new pipeline by helping customers connect their most pressing modernization and AI opportunities to power what MongoDB can uniquely solve.
- •As you heard from other software companies, these two opportunities are not distinct and in fact reinforce each other.
- •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%.
- •We delivered a non-GAAP operating margin of 18% above the high end of the guidance.
- •Voyage customers have more than doubled quarter-over-quarter, and vector search adoption is far outpacing overall company growth.
- •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.
- •Endor selected Atlas as its default database to support 225% year-over-year revenue growth.
- •After evaluating DynamoDB and DocumentDB, they chose Atlas for its aggregation pipeline, write consistency, and flexible schema.
- •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.
What went well
- •Total revenue was $688 million, up 25% year-over-year, beating the high end of guidance and accelerating from the 22% growth reported in fiscal Q1 of the prior two years.
- •Atlas grew 29.4% year-over-year with a record $117 million in year-over-year growth, now at a $2 billion run rate and its fourth straight quarter of at least 25% growth.
- •EA and other (previously non-Atlas) grew 13% year-over-year, with EA and other ARR up approximately 11%.
- •Non-GAAP operating margin was 18%, above the high end of guidance and up from 16% in the year-ago period, with the second straight quarter of GAAP profitability.
- •Operating cash flow was $202 million versus $110 million last year, and free cash flow was $198 million versus $106 million last year.
- •The company raised its fiscal 2027 outlook across the board, added 2,500 customers to reach over 67,700, and acquired Clarity Business Solutions to expand in the U.S. federal vertical.
What went wrong
- •Total non-GAAP gross margin of 74.5% was approximately 100 basis points below Q4, and subscription gross margin of 77.1% was about 60 basis points below the year-ago quarter and 170 basis points below Q4, driven by Atlas/EA product mix and first-quarter seasonality.
- •Management guided EA and other to roughly flat year-over-year growth in the second half, largely due to a very strong fiscal 2026 Q4 comparison and prudence around multi-year deal timing.
Guidance changes
| Metric | Period | Previous | Current | Change |
|---|---|---|---|---|
| Full-year FY2027 outlook | FY2027 | prior guidance | raised across the board | raised |
| Q2 guidance (Atlas) | Q2 FY2027 | — | guided consistent with the framework of the past two quarters, off a strong Q1 consumption | — |
| EA and other growth | H2 FY2027 | — | roughly flat year-over-year (driven by tough FY2026 Q4 comparison) | — |
Performance breakdown
| Metric | YoY change | Reason |
|---|---|---|
| Total revenue | +25% | Driven by Atlas, beating the high end of guidance and accelerating from 22% in the prior two years' Q1. |
| Atlas revenue | +29.4% | Stronger-than-expected consumption, with strength in use cases at established enterprise customers across financial services, technology, and media, plus early AI deployments and Frontier Labs/AI-native momentum. |
| EA and other revenue | +13% | Existing customers across industries, particularly finance and technology, expanding on-prem footprints for traditional and AI applications. |
| EA and other ARR | +11% | Underlying revenue growth normalized for duration impacts. |
| Operating margin | 18% vs 16% | Benefited primarily from revenue strength, driven mainly by Atlas. |
| Net ARR expansion rate | 121% vs 119% a year ago | Ongoing momentum from platform adoption and move up-market. |
| $100K+ ARR customers | +16% | 2,895 customers, with revenue growth from this cohort outpacing total company growth. |
Earnings call themes & trends
| Topic | Previous mention | Current period | Trend |
|---|---|---|---|
| Atlas momentum | crossed $2 billion run rate in Q4 | record $117 million net new, fourth straight quarter of 25%+ growth, fifth straight quarter of YoY dollar growth | improving |
| AI/agentic adoption | Vector Search customers doubled, Voyage doubled since acquisition | MCP server usage growing significantly, Voyage customers more than doubled QoQ, vector search far outpacing company growth; still early but accelerating | improving/early |
| MongoDB as agent memory layer | — | emerging pattern of customers (e.g., Adobe Journey Agent) choosing MongoDB as transactional long-term memory and reasoning layer for AI agents | emerging |
| Strategic platform decision vs workload-by-workload | workload-by-workload evaluation | MongoDB becoming a strategic platform decision (e.g., Zoom standardizing on EA) | improving |
| U.S. federal vertical | small, under-invested | acquired Clarity Business Solutions, FedRAMP High certification expected this year, large TAM | improving |
| Frontier Labs | — | multiple Frontier Labs selected MongoDB for mission-critical, demanding use cases; early but expanding | emerging |
| Go-to-market leadership | CRO search ongoing | new CRO (Ryan Mac Ban) in place; no significant changes expected for remainder of FY2027 | stable |
Q&A summary
Are we approaching the point where agentic workloads genuinely move the needle on consumption?
CJ Desai said it is still early due to security, governance, and observability requirements, but he is seeing very encouraging signs and the platform is ready; a Fortune 25 firm got excited seeing MongoDB act as both operational data layer and long-term memory.
Should Q2 Atlas guidance imply a beat similar to Q1, and how is seasonality?
Mike Berry said guidance reflects the underlying strength while remaining prudent; Atlas has become more predictable and less sensitive to individual customers, Q1 came in a little better than expected, and Q2 is guided consistent with the past two quarters' framework with more variability further out in the year.
What is the opportunity for large AI natives and their contribution to Atlas?
Desai cited ElevenLabs (now ~$500 million ARR) moving to MongoDB after the data layer choked growth, saying AI natives gain peace of mind scaling on MongoDB; contribution is showing encouraging signs but most growth was still driven by core enterprise workloads getting AI-ready.
Is data consolidation toward MongoDB happening with AI?
Desai said he sees more modernization acceleration (moving to Atlas to be AI-ready) than a consolidation play, plus some elimination of ETL to external search providers and some migration from Postgres into MongoDB given its strength with unstructured data and JSON.
Did Mongo confirm working with multiple Frontier Labs?
Desai confirmed the reference was deliberately plural; multiple Frontier Labs have selected MongoDB for varying mission-critical use cases after trying Postgres alternatives, though specifics are confidential and it remains early with room to expand.
Which part of the AI/agent stack will capture the most value?
Desai stack-ranked MongoDB's flexible native-JSON architecture (suited to changing AI requirements and storing chat conversations as long-term memory) first, followed by real-time intelligence on operational data with embeddings that lower token costs and improve retrieval accuracy.