MDB Capital Holdings, LLC (Nasdaq: MDBH) Q2 2025 Earnings Call | 08/28/2025

MongoDB, Inc. Q2 Fiscal Year 2026 Earnings Call Summary

Date: August 28, 2025 Speakers: Dave Edicheria (President and CEO), Mike Berry (CFO), Jess Lubert (VP of Investor Relations), Brian Demio (ICR Moderator)

Financial Performance Overview:

• Q2 FY2026 Results:
  • Revenue: $591M, up 24% year-over-year, exceeding guidance.
  • Atlas Revenue: Grew 29% year-over-year (up from 26% in Q1), representing 74% of total revenue (vs. 71% in Q2 FY2025).
  • Non-GAAP Operating Income: $87M, with a 15% operating margin (vs. 11% in Q2 FY2025).
  • Net Income: $87M or $1.00/share, based on 87M diluted shares (vs. $59M or $0.70/share in Q2 FY2025).
  • Customer Growth: Total customers reached 59,900 (up from 50,700 in Q2 FY2025), with 2,800 added in Q2, including 300 from the Voyage AI acquisition. Atlas customers: 58,300.
  • Net ARR Expansion Rate: ~119%, consistent with recent quarters.
  • Gross Margin: 74% (down from 75% in Q2 FY2025, due to Atlas’s higher revenue share).
  • Cash Flow: Operating cash flow at $72M; free cash flow at $70M (vs. negative $1M and negative $4M in Q2 FY2025).
  • Balance Sheet: Ended with $2.3B in cash, cash equivalents, short-term investments, and restricted cash. Repurchased 930,000 shares for $200M under a $1B authorization.
  • Restructuring: Minor restructuring impacted <2% of employees, with $5M in one-time charges (excluded from non-GAAP results).
• Full-Year FY2026 Guidance (Updated):
  • Revenue: $2.34B–$2.36B (up $70M from prior guidance).
  • Non-GAAP Operating Income: $321M–$331M (up $44M).
  • Non-GAAP Net Income per Share: $3.64–$3.73, based on 87.4M diluted shares.
  • Operating Margin: Raised to 14% at the high end (from 12.5%).
• Q3 FY2026 Guidance:
  • Revenue: $587M–$592M.
  • Non-GAAP Operating Income: $66M–$70M.
  • Non-GAAP Net Income per Share: $0.76–$0.79, based on 87.7M diluted shares.
  • Notes: Expect a low 20% year-over-year decline in non-Atlas revenue due to a strong Q3 FY2025 multi-year deal comparison. Operating margin to dip due to non-Atlas decline and increased R&D/marketing expenses.

Operational and Strategic Highlights:

• Atlas Performance: Accelerated growth to 29%, driven by strong consumption (consistent with prior year), particularly among larger U.S. customers. New workloads and capabilities (e.g., search, vector search) contributed to growth.
• Non-Atlas Performance: Outperformed expectations with 7% ARR growth year-over-year. Benefited from multi-year deals and success in selling incremental workloads to existing Enterprise Advanced (EA) customers. Multi-year deal headwind reduced to $40M for FY2026 (from $50M).
• Customer Dynamics: Added 5,000+ customers over the last two quarters, primarily via Atlas. Direct sales customers dipped to 7,300 (down 200 sequentially), reflecting a shift in go-to-market focus to enterprise clients over mid-market. Customers with ≥$100K ARR grew 17% to 2,564.
• AI and Platform Differentiation:
  • MongoDB is emerging as a key AI infrastructure component, with strong adoption by AI-native startups and early enterprise use cases (e.g., employee productivity tools, autonomous driving platforms).
  • Key wins include an electric vehicle company using Atlas and Vector Search for in-vehicle voice assistants (handling 1B+ vectors) and DevRev’s AgentOS platform for help desk automation.
  • MongoDB’s flexible document model, JSON support, and integrated features (search, vector search, embeddings, stream processing) outperform relational databases like Postgres, which struggle with scalability and unstructured data.
  • Example: Agibank (Brazilian neobank) migrated from Postgres to Atlas, achieving 5x better performance, 90% lower costs, and no outages.
• Go-to-Market Strategy:
  • Continued focus on enterprise customers for high-value workloads, with self-serve channel effectively targeting SMBs and AI-native startups.
  • Investments in developer education (e.g., SQL developer outreach, hackathons) to drive adoption among startups initially choosing familiar databases like Postgres.
  • New leadership and tools (e.g., relational migrator, AI-driven code refactoring) to accelerate legacy app modernization.
• R&D Investments: Focused on platform performance (MongoDB 8.0/8.1 releases), vector search, streaming, and app modernization tools to support AI and enterprise workloads.
• Market Positioning: MongoDB’s enterprise-ready platform serves 70%+ of Fortune 500, major banks, healthcare, and manufacturers. Its ability to handle mission-critical, transaction-intensive workloads (e.g., Deutsche Telekom’s 30M customer platform) and unified architecture reduces complexity compared to patchwork solutions (e.g., Postgres + Pinecone/Elastic).

AI Opportunity and Outlook:

• Current State: AI adoption is early, particularly in enterprises, with most activity in low-stakes use cases (e.g., productivity tools). Startups drive initial AI-native growth, but scaling challenges with relational databases (e.g., Postgres JSON-B limitations) are pushing adoption toward MongoDB.
• Long-Term Potential: MongoDB is well-positioned for agentic AI systems requiring scalability, JSON support, and integrated search/vector capabilities. Investments in embeddings (via Voyage AI) and developer tools aim to capture future AI-driven growth.
• Challenges: Enterprises remain cautious due to AI output quality, security, and scalability concerns. No single inflection point expected; adoption will grow gradually as trust and capabilities improve.

Q&A Highlights:

• Atlas Growth Drivers: Strong sequential growth ($40M+ in Q2) driven by larger, faster-growing enterprise workloads, enhanced capabilities (search, vector search), and robust customer additions. U.S. large customers showed notable strength.
• Go-to-Market Optimization: Shift to enterprise focus and self-serve for SMBs/AI startups is yielding results. No abandonment of early-stage markets; self-serve efficiency allows serving smaller customers effectively.
• Non-Atlas Dynamics: 7% ARR growth reflects steady demand. Multi-year deals in Q2 were widespread, not driven by pull-forwards. Mix of multi-year vs. annual deals remains consistent, driven by pricing stability and data gravity.
• AI Traction: AI-native startups (e.g., DevRev) and select enterprise use cases (e.g., EV company) show promise, but AI was not a material growth driver in Q2. Focus on developer education to counter Postgres adoption among startups.
• Competitive Landscape: MongoDB’s JSON-native architecture and integrated features provide a durable advantage over relational databases and hyperscaler clones (e.g., DocumentDB). Partnerships with hyperscalers remain strong.
• R&D Focus: Investments in platform performance, vector search, and app modernization tools to support AI and enterprise growth. More details to be shared at Investor Day (September 17, 2025).