MEMO FROM THE FUTURE: MONGODB
CEO Edition
BOARD STRATEGY SESSION June 2030
TO: MongoDB Board of Directors
FROM: Dev Ittycheria, CEO
DATE: June 2030
SUBJECT: MongoDB as the Database for AI Applications
OPENING
MongoDB dominates NoSQL databases for modern applications. As enterprises build AI applications, they need databases optimized for AI workloads: flexible schema (for evolving training data), vector search (for embeddings), and real-time analytics.
MongoDB is positioned to be the default database for AI applications. This memo proposes a strategic focus on AI-centric database features and positioning.
THE REALITY
Current state: - MongoDB revenue: $1.3 billion (growing 25% YoY) - Customers: 25,000+ - ARR: $1.3 billion - Cloud adoption: 60% of revenue (growing) - Market position: Dominant in NoSQL
Opportunity: - AI applications need flexible, scalable databases - Vector search becoming critical (embeddings for LLMs) - MongoDB can become the default database for AI workloads - This is massive TAM expansion
WHERE WE ARE
Today: - Leading NoSQL platform - Strong in modern applications (startups, tech companies) - Growing in enterprise (slow but steady) - Vector search capabilities launching
THE OPPORTUNITY
Opportunity 1: AI-Optimized Database Features
The play: Build database features optimized for AI workloads.
How: - Native vector search (find similar embeddings, critical for RAG) - Time-series optimization (training data patterns) - Real-time feature engineering (ML features computed in database) - Integration with popular ML frameworks (PyTorch, TensorFlow)
Estimated impact: - Become default database for AI applications - Pricing premiums for AI-specific features - TAM expansion from traditional applications to AI applications (10x market) - NRR improvement (customers use more features)
Timeline: 12-18 months
Opportunity 2: AI-Native Developer Experience
The play: Build developer experience optimized for AI developers (different from traditional app developers).
How: - Native support for popular ML libraries - Built-in feature store capabilities - Monitoring and observability for ML workloads - Seamless integration with data science workflows
Estimated impact: - Attract data scientists as users (new audience) - Easier adoption (reduced friction for AI developers) - Premium pricing for AI-optimized experience
Timeline: 18-24 months
Opportunity 3: Enterprise AI Data Platforms
The play: Position MongoDB as the core database for enterprise AI data platforms.
How: - Partner with enterprise AI/ML platforms - Build integrations with popular tools (Databricks, MLflow, Hugging Face) - Serve as system of record for AI applications - Provide governance and compliance for AI workloads
Estimated impact: - Large enterprise deals ($500K-2M contracts) - High NRR (enterprise expands as they adopt AI) - Strategic positioning in enterprise AI stacks
Timeline: 12-18 months
MY RECOMMENDATION
Pursue all three. Together they position MongoDB as "the database for AI applications."
EXECUTION PLAN
Phase 1: AI-Optimized Features (2030-2032)
- Launch vector search and AI-specific features
- Build ML framework integrations
- Establish as default database for AI workloads
Phase 2: Developer Experience (2032-2034)
- Build AI-native developer experience
- Attract data scientists as users
- Expand beyond traditional developers
Phase 3: Enterprise AI Platforms (2031-2035)
- Build partnerships with enterprise AI platforms
- Land large enterprise AI deals
- Revenue reaches $3-4 billion
FINANCIAL IMPLICATIONS
By 2035:
- Revenue: $3.0-4.0 billion (from $1.3B today, 25-30% CAGR)
- Gross margins: 80-82% (up from 78% today)
- Operating margins: 30-35% (up from 20% today)
- NRR: 125%+ (AI customers expand)
- Customer mix: 40% AI, 60% traditional
Stock target: $400-500 per share by 2035 (from $250 today).
Dev
Confidential — Board of Directors Only