PostgreSQL

Postgres stands out thanks to its rock‑solid reliability and rich feature set—think advanced indexing, robust JSON support, and strong concurrency. It’s open source, highly scalable, and keeps our data queries speedy without locking us into proprietary models. Alternatives Considered: MySQL: Popular, but we preferred Postgres’s deeper JSON and indexing capabilities. MongoDB: Excellent for flexible schemas, but we needed the reliability of SQL. MariaDB: Similar to MySQL, but Postgres’s community and extension ecosystem won us over.

I prefer PostgreSQL because it's just such an incredibly robust and versatile relational database that I trust. The huge advantage, especially for AI and search stuff lately, is the pgvector extension. It lets me store and run similarity searches on vector embeddings right inside my main relational database, totally avoiding the complexity of managing a separate vector database system. It feels like having the best of both worlds in one reliable platform.

PGVector's native vector operations with PostgreSQL's ACID compliance works perfectly. We're storing millions of embeddings with sub-millisecond similarity search using cosine distance. The async connection pooling handles concurrent connections, while the vector indexing makes our query super fast. Having vector search, full-text search, and relational data in one database eliminates the complexity of managing multiple data stores.