SurrealDB vs. Postgres

Architecture

Monolithic relational engine. Extensions bolt additional capabilities onto a core not designed for multi-model execution.

Distributed, multi-model database with a decoupled query and storage layer. Efficient as single node or distributed.

Models

Relational database with JSON, full-text search, and vector support via extensions. No native graph model.

Native multi-model: document, relational, graph, key-value, time-series, vector, full-text search, and geospatial.

Relationship querying

Relationships expressed through foreign keys and joins. Cost increases with join depth.

First-class relationships traversed directly without joins. No join planning or join indexes required.

Scale

Scaling beyond a single node requires manual primary/replica coordination. Large deployments commonly require manual sharding.

Horizontally scalable for both reads and writes. Designed to eliminate the need for sharding.

Schema evolution

Schema changes on large tables are disruptive and risky. Migrations often require maintenance windows.

Schema-flexible by design. Start schemaless and incrementally enforce schema without downtime.

Pricing

Infrastructure costs grow with index count, replicas, and sharding.

Unified engine reduces the need for multiple specialised systems. Costs scale linearly with usage.