MongoDB's Q3 2026 Earnings Call: Contradictions Emerge on AI Integration, Non-Atlas Growth, and Revenue Guidance

Date of Call: December 1, 2025

Financials Results

• Revenue: $628.3M, up 19% year-over-year and above the high end of guidance
• EPS: $1.32 per diluted share, compared to $1.16 in the year-ago period
• Gross Margin: 74%, down from 77% in the year-ago period
• Operating Margin: 20% non-GAAP operating margin, compared to 19% in the year-ago period

Guidance:

• Q4 revenue $665M–$670M (21%–22% YOY); non‑GAAP operating income $139M–$143M (~21% margin); non‑GAAP EPS $1.44–$1.48 (86.5M diluted).
• FY'26 revenue $2.434B–$2.439B (21%–22%); non‑GAAP operating income $436.4M–$440.4M (~18% margin); non‑GAAP EPS $4.76–$4.80 (86.7M diluted).
• Atlas expected ~27% revenue growth in Q4; non‑Atlas growth expected to be upper single‑digits in Q4; Q3 non‑Atlas outperformance partly due to multiyear deals (~2/3 of beat).
• Free cash flow conversion expected to exceed 100% for FY'26; reiterating long‑term model targets (100–200 bps margin expansion; 80%–100% FCF conversion).

Business Commentary:

• Revenue Growth and Atlas Acceleration:
• MongoDB reported total revenue of $628.3 million for Q3, up 19% year-over-year and exceeding the high end of guidance.
• 

• The growth was driven by strong performance in Atlas, which accelerated to 30% year-over-year growth, now representing 75% of total revenue.

• Customer Additions and Expansion:

• MongoDB ended the quarter with over 62,500 customers, adding 2,600 in Q3, reflecting a 65% year-to-date growth.

• The increase in customers was primarily driven by the strong performance of Atlas, which had over 60,800 customers at the end of Q3.

• Operating Margin Expansion:

• MongoDB delivered non-GAAP operating income of $123.1 million, reflecting a 20% non-GAAP operating margin.

• The company significantly outperformed on operating margins, benefiting from strong revenue growth and prudent expense management.

• Future Guidance and AI Potential:

• For fiscal '26, MongoDB raised its full-year outlook, with expected revenue growth of 21% to 22%.
• The outlook reflects strong Atlas growth and strategic investments in AI technologies, positioning MongoDB as a foundational platform for AI workloads.
  

  Sentiment Analysis:

  Overall Tone: Positive

  • "we exceeded all of our guidance ranges and are increasing our full year outlook across the board." Atlas accelerated to 30% YOY; total revenue $628.3M up 19% YOY; non‑GAAP operating income $123.1M (20% margin). Management raised Q4 and FY'26 guidance and reiterated long‑term model.

Q&A:

• Question from Sanjit Singh (Morgan Stanley): In your first weeks, what are the quick wins and longer‑term changes to make MongoDB the foundational data platform for AI?
  Response: Start with Voyage embeddings and reranking as low‑hanging wins, then move customers to Atlas Vector Search and Atlas as the real‑time operational store; customers are early but show scalable potential.

• Question from Sanjit Singh (Morgan Stanley): Can you characterize the quality of workloads added this year and how they shape growth next year?
  Response: Growth is being driven by large customers expanding existing workloads across the U.S. and EMEA; focus is on larger customers getting bigger and growing longer rather than year‑by‑year cohorts.

• Question from Matthew Martino (Goldman Sachs): What themes are customers emphasizing in your conversations?
  Response: Customers prioritize application modernization and multi‑cloud migration (a multi‑year effort); AI experiments are early but complement modernization, and AI‑native firms adopt MongoDB because relational alternatives don't scale for unstructured data.

• Question from Matthew Martino (Goldman Sachs): With op margins ~200 bps shy of the midterm framework, what's the reinvestment philosophy looking into fiscal '27?
  Response: Company will continue investing (engineering, sales, marketing), some spending shifted into Q4/FY27; margin expansion is expected to continue and will be driven primarily by revenue growth while OpEx grows for reinvestment.

• Question from Karl Keirstead (UBS): Is the core strength fundamentally driven by modernization or already getting an AI tailwind?
  Response: Core strength is driven by modernization of unstructured/semi‑structured workloads; AI may accelerate modernization but is not the sole driver—AI teams depend on agile core data platforms.

• Question from Karl Keirstead (UBS): Why provide more definitive Atlas guidance now—transparency or greater predictability at scale?
  Response: Both: Atlas is larger and more forecastable so management is providing more visibility, but they remain prudent because Q4 holiday seasonality can be unpredictable.

• Question from Raimo Lenschow (Barclays): How will you increase West Coast developer engagement versus the Postgres narrative?
  Response: Reinvested in the Bay Area ('reclaim the Bay') with more feet on the street, events and startup engagement; relaunching .local in San Francisco on Jan 15 and CEO will personally engage developers and founders.

• Question from Raimo Lenschow (Barclays): For fiscal '27, should we anchor on ARR for non‑Atlas? What's the expected growth?
  Response: While not guiding FY'27, current view points to non‑Atlas full‑year revenue growth around mid/low single digits (~4%).

• Question from Brad Reback (Stifel): Are new customers ramping faster for net new workloads and why?
  Response: Yes—faster onboarding is driven by improvements in MongoDB 8.x releases and self‑serve friction removal, enabling quicker adoption, though initial cohort revenue remains small.

• Question from Brad Reback (Stifel): What is the M&A philosophy for MongoDB?
  Response: Preference for organic growth; opportunistic M&A for adjacent technologies or teams that accelerate the roadmap—Voyage AI cited as a strategic example.

• Question from Aleksandr Zukin (Wolfe Research): Where will you personally drive the biggest impact in 12–24 months?
  Response: Focus on deepening penetration in Fortune 500/Global 2000 accounts and seeding AI‑native startups via personal relationships and West Coast network.

• Question from Aleksandr Zukin (Wolfe Research): Are AI‑native customers adopting Voyage first, Atlas, or both?
  Response: They often adopt Voyage embeddings first to improve accuracy, then Atlas Vector Search and ultimately Atlas operational DB—management cites customer examples following that path.

• Question from Ryan MacWilliams (Wells Fargo): Are enterprises seeing stronger product velocity from agentic coding?
  Response: Agentic tools accelerate prototyping and iteration, but enterprises require security, durability and governance for production—so production rollouts lag prototypes.

• Question from Ryan MacWilliams (Wells Fargo): On the Q4 Atlas guidance, should we expect results nearer the midpoint or maintain historical guidance conservatism?
  Response: Management is positive on Atlas but remains cautious for Q4 due to holiday seasonality, so guidance is prudent rather than a firm 'pin' forecast.

Contradiction Point 1

AI Integration and Product Positioning

It highlights differing perspectives on MongoDB's integration with AI and the positioning of its product, which could impact strategic decisions and market positioning.

What are the initial steps MongoDB is considering to become a foundational data platform for the AI era? What specific changes or evolutions do you see the company making to adapt to the AI era? - Sanjit Singh (Morgan Stanley)

2026Q3: The opportunity for MongoDB to be that data platform for AI workloads is very real. AI teams are typically separate from the core data team, and MongoDB has all the elements needed to be the right foundational platform. Embedding models and reranking model are where customers can start, then move on to the vector database, and use MongoDB for operational storage. - Chirantan Desai(CEO)

What are MongoDB's initial priorities for the AI era and what strategies will address its challenges? - Sanjit Singh (Morgan Stanley)

20251202-2026 Q3: MongoDB is well-positioned as a foundational platform for AI workloads due to its real-time operational data capabilities and a document model that can handle diverse data. The opportunity is real, but it's early. MongoDB's embedding model and reranking model can be an immediate focus, followed by the vector database and real-time operational store. The aim is to leverage the company's strengths to become a core data platform for AI. - Chirantan Desai(CEO)

Contradiction Point 2

Non-Atlas ARR Growth Expectations

It involves differing expectations for non-Atlas ARR growth, which could impact financial forecasts and investor expectations.

Should non-Atlas be anchored on ARR performance next year? - Raimo Lenschow (Barclays)

2026Q3: We are not guiding for fiscal '27, but the full-year revenue growth of non-Atlas is currently expected to be in the low single digits, likely around 4%. This takes into account multiyear deals. - Michael Berry(CFO)

Should non-Atlas performance be based on ARR growth? - Raimo Lenschow (Barclays)

20251202-2026 Q3: We are focusing on mid-single-digit growth for non-Atlas ARR performance. There is no multiyear headwind expected in fiscal '27, with consistent performance expected from non-Atlas business. - Michael Berry(CFO)

Contradiction Point 3

AI as a Driver for Growth

It involves the perceived impact of AI on MongoDB's growth. The responses suggest differing views on whether AI is a material driver for growth, which is crucial for understanding MongoDB's long-term growth prospects.

What are the recurring themes in customer conversations as you reassess the business? - Matthew Martino (Goldman Sachs)

2026Q3: Customers are experimenting with AI applications, though not at scale yet. MongoDB is well positioned for AI workloads in enterprises. AI-native companies are also becoming customers, and MongoDB is positioned well because alternatives in the relational world do not scale. - Chirantan Desai(CEO)

What portion of Atlas's strength is AI-driven, and are AI workloads contributing significantly to growth? - Tyler Maverick Radke (Citi Group)

2026Q2: The AI cohort is not a material driver of the growth this quarter, with the core business and customer base being the key growth factors. However, the strong adoption of MongoDB by AI-native startups is encouraging. - Dev C. Ittycheria(CEO)

Contradiction Point 4

Atlas Growth and Guidance

It involves the company's expectations and guidance on Atlas growth, which is a key component of their financial performance and investor expectations.

What's causing the recent shift to more detailed guidance for Atlas? - Karl Keirstead (UBS)

2026Q3: MongoDB's guidance for Q4 fiscal 2026 for total revenue is between $327 million and $332 million, an increase of 33% at the midpoint, and our guidance for Atlas revenue growth for the fourth quarter is between 37% and 40%. - Michael Berry(CFO)

What's driving Atlas growth, and how do application types on MongoDB contribute to it? - Sanjit Kumar Singh (Morgan Stanley)

2026Q1: We are increasing our fiscal year 2026 guidance for total revenue to a range of $1.350 billion to $1.360 billion, which implies year-over-year growth of 28% to 29%. - Michael Berry(CFO)

Contradiction Point 5

Non-Atlas Revenue Growth

It involves the company's expectations for non-Atlas revenue growth, which is a critical component of their financial performance and strategic direction.

Should next year's non-Atlas performance be anchored to ARR? - Raimo Lenschow (Barclays)

2026Q3: The full-year revenue growth of non-Atlas is currently expected to be in the low single digits, likely around 4%. This takes into account multiyear deals. - Michael Berry(CFO)

What's driving Atlas's growth, and can you explain it in terms of the applications built on MongoDB? - Sanjit Kumar Singh (Morgan Stanley)

2026Q1: We are increasing our fiscal year 2026 guidance for total revenue to a range of $1.350 billion to $1.360 billion, which implies year-over-year growth of 28% to 29%. - Michael Berry(CFO)