SurrealDB vs. Postgres

SurrealDB 3.0 offers a unified database solution that natively supports vectors, full-text search, and graph traversal, contrasting with PostgreSQL, which relies on extensions and workarounds like pgvector for similar capabilities. While PostgreSQL requires manual joins, indexes, and added complexity for handling AI and retrieval workloads, SurrealDB simplifies operations by directly integrating these features, reducing the need for secondary indexing and manual sharding. This design enables SurrealDB to compress datasets by 70-80%, significantly lowering storage and infrastructure costs while maintaining operational efficiency and scalability. SurrealDB is distributed by design and supports live schema evolution without downtime, offering high availability and fault tolerance. It is particularly suitable for environments where retrieval accuracy, operational efficiency, and scale are critical, as it provides a seamless and cost-effective alternative to traditional relational databases like PostgreSQL.