MongoDB's Q2 2026 Earnings Call: Contradictions Emerge on AI Adoption, Atlas Growth, and Consumption Trends

The above is the analysis of the conflicting points in this earnings call

Date of Call: August 26, 2025

Financials Results

• Revenue: $591M, up 24% YOY and above the high end of guidance
• EPS: $1.00 per diluted share, up from $0.70 in the prior year
• Gross Margin: 74%, compared to 75% in the prior year
• Operating Margin: 15%, compared to 11% in the prior year

Guidance:

- FY26 revenue expected at $2.34B–$2.36B (raised by $70M)- FY26 non-GAAP operating income $321M–$331M; operating margin up to 14% at the high end (prior 12.5%)- FY26 non-GAAP EPS $3.64–$3.73 on 87.4M diluted shares; assumes 20% tax- Atlas growth implied mid-20s% in H2- FY26 non-Atlas subscription revenue down mid-single digits (improved from high-single-digit decline); multiyear license headwind now ~$40M (vs ~$50M)- Q3 revenue $587M–$592M; non-GAAP operating income $66M–$70M; EPS $0.76–$0.79 on 87.7M shares- Q3: non-Atlas down low-20% YOY; operating margin lower than Q2 due to mix and opex timing

Business Commentary:

Revenue and Atlas Growth:* - MongoDB reported revenue of $591 million for Q2, up 24% year-over-year and above the high end of their guidance. - Atlas revenue grew 29% year-over-year, representing 74% of total revenue. - The growth was driven by strong consumption in Atlas and broad-based strength, particularly in larger customers in the U.S.

• Customer Additions and Expansion:
• MongoDB added over 5,000 customers in the last two quarters, ending the quarter with over 59,900 customers.
• The growth in the total customer count was primarily driven by Atlas, which now has over 58,300 customers compared to 49,200 in the year-ago period.

• This increase is attributed to the acquisition of new customers and existing customers deploying workloads on Atlas for the first time.

• Operating Margin and Financial Performance:

• Non-GAAP operating income was $87 million for a 15% non-GAAP operating margin, up from 11% in the year-ago period.
• The company ended the quarter with $2.3 billion in cash and operating cash flow of $72 million, reflecting strong operating profit and higher cash collections.

• This performance was driven by Atlas growth, revenue outperformance, and disciplined spending, leading to margin improvement.

• AI Integration and Market Adoption:

• MongoDB is emerging as a standard for AI applications, with many recently added customers building AI applications, underscoring its value proposition for AI infrastructure.
• The enterprise segment is in early stages of adopting AI, with activity primarily centered around employee productivity tools and packaged ISV solutions.
• The integration of vector search and embedding models positions MongoDB well for future AI applications in the enterprise segment.

  Sentiment Analysis:

  • Revenue up 24% YOY; Atlas revenue grew 29% YOY and is 74% of total. Non-GAAP operating margin rose to 15% from 11% last year. Q2 non-GAAP EPS was $1.00 vs $0.70 prior year. Strong cash flow: $72M operating, $70M free cash flow. Guidance raised: FY26 revenue to $2.34B–$2.36B and operating margin high end to 14%. Management highlighted durable growth, upmarket wins, and improving efficiency.

  Q&A:

  • Question from Sanjit Kumar Singh (Morgan Stanley): What drove the strong sequential Atlas dollar adds and acceleration in Q2?
  • Response: Upmarket strategy is yielding larger, faster-growing workloads; increased adoption of search/vector search; and robust self-serve customer adds contributed.
  • Question from Sanjit Kumar Singh (Morgan Stanley): Update on salesforce operations and GTM strategy after recent changes?
  • Response: Sticking with the move upmarket for enterprise and leveraging self-serve for SMB; execution is improving without abandoning self-serve.
  • Question from Raimo Lenschow (Barclays): Why is self-serve accelerating despite the move upmarket?
  • Response: Data-driven experiments (e.g., targeting SQL developers, office hours) are working; self-serve motion has become more sophisticated and effective.
  • Question from Raimo Lenschow (Barclays): Does the non-Atlas cohort imply improvement next year?
  • Response: Too early to say; outlook depends on how Q3 multiyear activity plays out this year.
  • Question from Tyler Maverick Radke (Citi): How much did AI use cases contribute to Atlas strength?
  • Response: AI-native cohort is growing but was not a material contributor; Q2 growth came from core customers and workloads.
  • Question from Tyler Maverick Radke (Citi): Any progress on relational migration/app modernization velocity?
  • Response: Building tooling (including AI-driven) and adding leaders; promising long-term opportunity, with more detail at Investor Day.
  • Question from Jason Noah Ader (William Blair): Impact of Databricks Lakebase and Linux Foundation DocumentDB efforts and differentiation?
  • Response: OLTP is AI’s strategic high ground; MongoDB’s JSON architecture plus integrated search/vector is advantaged; hyperscalers’ approach underscores need for real JSON and Mongo’s balanced open-source model.
  • Question from Jason Noah Ader (William Blair): Why do many AI startups start on Postgres?
  • Response: Founders default to familiar tools; at scale, Postgres JSONB hits performance limits, prompting migrations; MongoDB is educating and investing in the startup community.
  • Question from Michael Joseph Cikos (Needham): What drove Atlas consumption trends and large-customer strength?
  • Response: Consumption growth was similar to last year with a strong May; larger U.S. customers expanded; go-to-market changes helped, and Q1 was a softer compare.
  • Question from Michael Joseph Cikos (Needham): Was multiyear outperformance due to early renewals?
  • Response: No pull-forwards; broader multiyear activity across customers; FY26 multiyear headwind reduced to ~$40M from ~$50M.
  • Question from Aleksandr J. Zukin (Wolfe Research): Where is AI workload momentum and when will it materially impact growth?
  • Response: AI interest is high but enterprise adoption is early; Mongo’s JSON + search/vector + embedded embeddings differentiates; material impact will grow as custom AI apps scale.
  • Question from Aleksandr J. Zukin (Wolfe Research): How are you balancing growth investments with margin expansion?
  • Response: Revenue growth is the primary margin lever; disciplined reallocation to ROI, enabling both growth and expanding margins.
  • Question from Kasthuri Gopalan Rangan (Goldman Sachs): Are AI startup wins like DevRev a leading indicator for enterprises?
  • Response: Startup traction is encouraging, but Q2 growth was driven by large-enterprise workloads; upmarket and self-serve motions are both contributing.
  • Question from Brad Robert Reback (Stifel): Outlook for EA (non-Atlas) growth and on-prem vs cloud positioning?
  • Response: Enterprises are adopting hybrid; MongoDB’s portability across on‑prem and clouds is a key advantage for EA and Atlas mix.
  • Question from Ittai Kidron (Oppenheimer): GTM to capture AI beyond generic enterprise/self-serve split?
  • Response: Self-serve customers are transitioned to direct sales as they scale; enterprise AI remains early with mostly low-stakes use cases today.
  • Question from Ittai Kidron (Oppenheimer): Any change in multiyear mix rationale for non-Atlas?
  • Response: No; customers use multiyear to align strategy and lock pricing given the stickiness of data.
  • Question from Sitikantha Panigrahi (Mizuho): When will AI adoption inflect and contribute materially?
  • Response: Progress is gradual; enterprises need higher output quality, security, reliability, and scalability; agent workloads will intensify usage over time.
  • Question from Bradley Hartwell Sills (BofA Securities): Where are R&D investments focused?
  • Response: Core platform performance (8.0/8.1) and expansion areas like search/vector/streaming; more details at Investor Day.
  • Question from Bradley Hartwell Sills (BofA Securities): Did focusing on higher-quality workloads drive the upside?
  • Response: Yes; larger, more durable workloads in strategic accounts were a major driver of growth.
  • Question from Rishi Nitya Jaluria (RBC Capital Markets): MongoDB’s role in a future multi-agentic world?
  • Response: JSON model fits real-world state, integrated search/vector enables hybrid retrieval, and MongoDB can serve as agents’ memory and orchestration layer.