Unified multi-model database with SurrealDB

SurrealDB is a genuinely novel approach to database architecture — one that eliminates the need to stitch together separate relational, document, graph, and vector databases. Its multi-model engine lets you store and query data in any paradigm, all within a single database with a unified query language we've found remarkably productive.

We recommend SurrealDB when your application needs more than one data model but you don't want the operational complexity of managing multiple databases. Instead of running PostgreSQL for relational data, Neo4j for graph relationships, and Pinecone for vector search, SurrealDB handles all three — and lets you combine them in a single query. Find all products with similar embeddings that were purchased by friends of the current user: that's one SurrealQL query, not three separate systems to coordinate.

The built-in Row-Level Security is another standout feature. Security policies are defined at the database level using SurrealQL, not scattered across application code. Each user sees only the data they're authorized to access, enforced by the query engine itself — no middleware, no attribute-based access control frameworks, no risk of a forgotten check in a controller.

For AI applications, SurrealDB's native vector support is a game-changer. Store embeddings alongside your source data, query by semantic similarity with filters on any field, and combine vector search with graph traversal or relational joins — all without a separate vector database, synchronization pipeline, or complex architecture. It's a genuinely simpler approach to building AI-powered applications.