Snowflake and Databricks Spend $1.25B on PostgreSQL for AI Dominance
Snowflake acquires Crunchy Data for $250M and Databricks buys Neon for $1B in a strategic push to control AI agent infrastructure through PostgreSQL. These deals highlight the growing importance of database technology in powering next-gen AI applications.
Key Acquisitions
-
Snowflake → Crunchy Data ($250M)
- Focus: Enterprise-ready PostgreSQL for government and large businesses
- Revenue: $30M+ annually
- Expected close: Next few weeks
- Announced at Snowflake Summit 2025
-
Databricks → Neon ($1B)
- Focus: Serverless PostgreSQL optimized for AI agents
- Customers: 18,000+
- Founded: 2021, raised $129.6M prior
Strategic Motivations
Databricks' AI-First Approach
- Over 80% of Neon databases are created by AI agents
- CEO Ali Ghodsi: "Every customer wants to leverage agents"
Snowflake's Enterprise Play
- Targets regulated industries and government use cases
- SVP Vivek Raghunathan: "Simplifying how developers build and scale AI apps"
Market Context
- PostgreSQL is now the #1 database (Stack Overflow 2024 survey)
- Handles geospatial, time series, JSON, and vector workloads
- Snowflake previously considered acquiring Neon
Broader Implications
-
AI Infrastructure Race
- Gartner: 33% of enterprise apps will use agentic AI by 2028 (vs. <1% in 2024)
-
SaaS Consolidation
- U.S. software M&A rose to 30.8% in H1 2024
- Record SaaS deals expected in 2025
-
Database as Competitive Edge
- Serverless capabilities critical for AI workloads
- Cost structures must scale precisely with usage
Valuation Differences
-
Neon's $1B price tag reflects:
- Serverless architecture
- 18,000+ developer customers
- Proven AI agent adoption
-
Crunchy Data's $250M valuation driven by:
- Enterprise compliance expertise
- Government contracts
- Battle-tested infrastructure
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About the Author
Dr. Sarah Chen
AI Research Expert
A seasoned AI expert with 15 years of research experience, formerly worked at Stanford AI Lab for 8 years, specializing in machine learning and natural language processing. Currently serves as technical advisor for multiple AI companies and regularly contributes AI technology analysis articles to authoritative media like MIT Technology Review.