Database Technology

Vector databases store high-dimensional embeddings and enable similarity search for AI applications including retrieval-augmented generation (RAG), semantic search, and recommendation systems. PostgreSQL extensions (pgvector, pgvectorscale, pgai) add vector search, AI embedding management, and semantic querying to standard PostgreSQL databases. Serverless databases automatically scale compute and storage based on demand with consumption-based pricing, eliminating capacity planning. Apache Iceberg provides an open table format that unifies streaming, batch, and AI workloads without vendor lock-in. Apache Kafka processes trillions of messages daily with 2ms latency for real-time data streaming. Distributed SQL (CockroachDB) provides globally consistent transactions across multiple regions with automatic failover.

Supabase serves 4 million developers with PostgreSQL-as-a-service including pgvector for AI vector search, real-time subscriptions, and authentication. Apache Kafka processes real-time data streams for 80% of Fortune 500 companies at 2ms latency. pgvector enables RAG applications by storing and searching AI embeddings directly within PostgreSQL, eliminating the need for separate vector databases. CockroachDB provides globally distributed SQL for applications requiring consistent transactions across continents. Snowflake's CrunchyData acquisition and Postgres service enables analytical customers to run operational PostgreSQL alongside their data warehouse. DuckDB provides embedded analytical processing with 146% year-over-year growth.

The global DBMS market reached $84-161 billion in 2025 (Gartner's broader definition at $161 billion growing 18.4% YoY). DBaaS reached $23.8 billion at 19.9% CAGR to $59.1 billion by 2030. Serverless databases reached $18.2 billion at 24.1% CAGR. Databricks raised $1.6 billion at $38 billion valuation. CockroachDB valued at $5 billion. Supabase valued at $5 billion with 4 million developers. Snowflake acquired CrunchyData for $250 million.