MongoDB, Inc. (MDB) Q1 2027 Earnings Call Transcript
Access earnings results, analyst expectations, report, slides, earnings call, and transcript.
Overview
The earnings call summary and Q&A session reveal strong financial metrics, optimistic guidance, and strategic investments in AI. The positive outlook for Atlas consumption, AI-native partnerships, and federal market expansion suggests growth opportunities. Although the guidance is prudent, the company's strategy to attract AI-native companies and its flexible architecture for AI workloads are promising. The acquisition of Clarity and focus on federal business further enhance growth prospects. Overall, these factors indicate a likely positive stock price movement in the short term.
Key Financial Performance
Total Revenue $688 million, up 25% year-over-year. The growth was driven by Atlas, which grew 29.4% year-over-year, including a record $117 million year-over-year dollar growth. This acceleration was attributed to strong enterprise customer use cases and early AI deployments.
Atlas Revenue Grew 29.4% year-over-year, contributing to 75% of total Q1 revenue. This growth was driven by enterprise customer use cases, early AI deployments, and momentum with frontier labs and AI-native companies.
EA & Other Revenue Grew 13% year-over-year. The growth was driven by existing customers across industries, particularly in finance and technology, expanding their on-prem footprint to support traditional and AI applications.
Non-GAAP Operating Margin 18%, up from 16% in the year-ago period. The improvement was primarily due to strength in revenue, mainly driven by Atlas.
Net ARR Expansion Rate 121%, up from 119% a year ago. This reflects ongoing momentum across the customer base.
Customer Count 67,700 customers, up by 2,500 sequentially and from 57,100 in the year-ago period. Growth was driven primarily by Atlas.
Atlas Customers Generating $100,000+ in ARR 45% of these customers are leveraging 2-or-more features of the platform, up from 37% in the year-ago quarter. This growth was largely driven by Vector and text Search adoption.
Operating Cash Flow $202 million, up from $110 million last year. The increase was driven by strong operating profit and seasonally higher cash collections.
Free Cash Flow $198 million, up from $106 million last year. The increase was attributed to strong operating profit and seasonally higher cash collections.
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Operating Highlights
AI capabilities: MongoDB is integrating AI capabilities into its platform, including Vector Search and Voyage embeddings, to enhance real-time system intelligence. Automated Voyage AI embeddings entered public preview this quarter.
MongoDB 8.3: The new version delivers up to 45% more reads, 35% more writes, and 15% more ACID transactions over the previous version without requiring application code changes.
AI adoption: AI adoption of MongoDB technologies is accelerating, with MCP server usage growing significantly and Voyage customers more than doubling quarter-over-quarter.
Customer growth: MongoDB added 2,500 customers in Q1, reaching over 67,700 customers, with strong growth in enterprise and AI-native companies.
Revenue growth: Total revenue reached $688 million, up 25% year-over-year, driven by Atlas, which grew 29.4% year-over-year.
Operating margin: Non-GAAP operating margin was 18%, exceeding guidance.
Strategic platform positioning: MongoDB is increasingly being adopted as a strategic platform rather than for individual workloads, as seen with customers like Zoom and Adobe.
U.S. federal vertical expansion: Acquired Clarity Business Solutions to strengthen its U.S. federal vertical, adding $10 million in services revenue annually.
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Risk or Challenges
Market Conditions: The transcript highlights the dependency on Atlas consumption growth and EA revenue, which are subject to market conditions and customer adoption rates. Any slowdown in these areas could adversely impact revenue growth.
Competitive Pressures: The company faces competition from other database and AI platform providers, which could impact its ability to win new business and maintain its market position.
Regulatory Hurdles: The transcript mentions the importance of compliance with regulatory mandates on data residency, which could pose challenges in certain markets.
Economic Uncertainties: Economic factors such as cost at scale and capacity challenges are noted as potential risks that could affect customer adoption and operational costs.
Strategic Execution Risks: The company’s ability to execute on its AI and emerging product strategies, including investments in AI capabilities and go-to-market initiatives, is critical. Failure to deliver on these could impact growth.
Supply Chain Disruptions: Although not explicitly mentioned, the dependency on cloud infrastructure providers and hybrid environments could pose risks if there are disruptions in these supply chains.
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Guidance & Outlook
Atlas Revenue Growth: Atlas revenue growth is expected to be approximately 26% for Q2 fiscal '27. Full-year growth expectation for Atlas has been raised to a range of 23% to 25%, an increase of 200 basis points.
EA and Other Revenue Growth: EA and other revenue growth is expected to be approximately 20% for Q2 fiscal '27. Full-year expectations for EA and other revenue have been raised to mid-single-digit growth, with revenue expected to be approximately flat during the second half of the year due to tougher comparisons.
Total Revenue Growth: Total revenue for Q2 fiscal '27 is expected to be in the range of $729 million to $734 million, equating to 23% to 24% year-over-year growth. Full-year revenue is expected to be in the range of $2.92 billion to $2.96 billion, representing 19% to 20% growth.
Operating Margin Expansion: Operating margin is expected to expand by 100 to 150 basis points in fiscal '27. Non-GAAP income from operations for Q2 is expected to be in the range of $152 million to $156 million, with a full-year range of $571 million to $591 million, targeting a Rule of 40 performance at the high end of the outlook.
AI and Emerging Products: Investments will focus on enhancing AI capabilities, including Vector Search and Voyage, and expanding EA's product value with new and advanced features, including native AI functionality.
Go-to-Market Investments: Investments include building out presence in Japan, strengthening the U.S. federal vertical, and increasing quota-carrying headcount, marketing programs, and developer awareness.
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Shareholder Return Plan
Share Repurchase Program: During Q1, we allocated $100 million towards share repurchases and $58 million to settle taxes on employee RSUs.
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Key Q&A
Q:Do you feel like we're approaching the point where agentic workloads start to genuinely move the needle on consumption?
A:Chirantan Desai stated that it is still early for agentic workloads to significantly impact consumption. However, he expressed confidence in MongoDB's readiness to scale for production-level agentic workloads, citing encouraging signs from large financial services and healthcare companies.
Q:Should we expect huge swings on Atlas revenue for the quarter ahead?
A:Michael Berry explained that Atlas revenue has become more predictable as it has grown larger. He noted that Q1 consumption came in better than expected, and Q2 guidance is consistent with the framework of the past two quarters. He emphasized that the company remains prudent in its guidance.
Q:Is the Atlas guide being impacted by other factors we should keep in mind?
A:Chirantan Desai mentioned that while there are small seasonal changes quarter-on-quarter, there are no significant year-over-year changes in seasonality for Atlas. The Q2 guidance reflects strong Q1 consumption.
Q:What is the opportunity for AI native with MongoDB as customers scale their businesses?
A:Chirantan Desai shared an example of ElevenLabs, an AI-native company, which switched to MongoDB due to performance issues with their previous platform. He highlighted MongoDB's ability to handle operational data, search, and vector search in a single platform, making it suitable for AI-native companies and enterprises preparing for AI workloads.
Q:What are you seeing in terms of data consolidation and its impact on Atlas and EA?
A:Chirantan Desai observed more modernization acceleration rather than data consolidation. Customers are moving to Atlas to scale for AI workloads and are benefiting from MongoDB's capabilities like Search and Vector Search, which reduce the need for ETL processes.
Q:Are there any changes we need to be aware of with the new hires on the go-to-market side?
A:Michael Berry stated that there are no expected changes for the remainder of fiscal '27. Territory planning and quotas are already set, and any potential tweaks will be considered next year.
Q:How does MongoDB's go-to-market strategy need to evolve to address AI-native companies?
A:Chirantan Desai acknowledged that this is a work in progress. MongoDB is leveraging its self-serve motion to attract AI-native companies and is determining the right point to intervene with field representatives. The company is making improvements as it learns.
Q:Were there any large deals in Q1 that could explain the flat second half guidance for EA?
A:Michael Berry clarified that the flat second-half guidance is primarily due to a strong Q4 in fiscal '26. He emphasized that the company remains prudent in guiding based on current visibility.
Q:What is the catalyst for the acquisition of Clarity and the focus on the federal business?
A:Chirantan Desai and Michael Berry highlighted the significant opportunity in the federal sector, driven by the need to manage unstructured data. The acquisition of Clarity and the upcoming FedRAMP high certification will enable MongoDB to better serve federal customers and expand its presence in this market.
Q:What drove the sequential increase in NRR?
A:Michael Berry attributed the increase to platform adoption, the move upmarket, and a focus on large enterprises. He noted that Atlas, which has a higher NRR than the company average, was a key driver.
Q:Is MongoDB working with multiple frontier labs, and what are the mission-critical workloads?
A:Chirantan Desai confirmed that MongoDB is working with multiple frontier labs. He mentioned that these labs are leveraging MongoDB for various mission-critical workloads, although specific details were not disclosed.
Q:How will the LangChain partnership and the expansion of the platform capture the AI opportunity?
A:Chirantan Desai explained that the LangChain partnership simplifies the integration of MongoDB as the data layer for agentic workloads. He also highlighted the roles of the two CPOs in focusing on foundational and emerging products to accelerate innovation and capture AI opportunities.
Q:Did the incremental portion of the two large deals announced last quarter ramp during Q1?
A:Michael Berry stated that the deals are multiyear and include future growth opportunities. The impact of these deals was reflected in Q1 results, and they will continue to contribute over time.
Q:Which part of the stack will create the most value in the AI opportunity?
A:Chirantan Desai emphasized MongoDB's architecture as a key enabler for AI workloads, particularly its flexibility with no schema and native JSON. He also highlighted the importance of real-time intelligence and embeddings for accurate data retrieval.
Q:What drove the strong RPO and cRPO performance in Q1?
A:Michael Berry attributed the performance to existing enterprise customers, with a focus on driving incremental ARR through new workloads and expansions. Chirantan Desai praised the go-to-market team's execution in Q1.
Q:Review of Unclear Management Responses
A:Management avoided providing specific details about the mission-critical workloads of frontier labs, citing agreements with these labs. Additionally, while they acknowledged the work in progress for evolving the go-to-market strategy for AI-native companies, they did not provide concrete plans or timelines.
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Earnings Word Cloud
The most frequently occurring keywords in this quarter's earning call
AI agent
AI product
Agent
Chief Product
Endor
LangChain
MongoDB platform
Nugget
Number
Pablo
Product Officer
application top
conversation
core workload
cost
customer
decision
deployment
developer agent
dimension
end
enterprise
frontier lab
layer AI
layer example
line
memory
mission
native
production
scale
schema
security
shift
support
technology
transaction
year
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The earnings call summary and Q&A session reveal strong financial metrics, optimistic guidance, and strategic investments in AI. The positive outlook for Atlas consumption, AI-native partnerships, and federal market expansion suggests growth opportunities. Although the guidance is prudent, the company's strategy to attract AI-native companies and its flexible architecture for AI workloads are promising. The acquisition of Clarity and focus on federal business further enhance growth prospects. Overall, these factors indicate a likely positive stock price movement in the short term.
MDB Report
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