SurrealDB vs.
Postgres

Q: Can SurrealDB replace PostgreSQL?

Yes, for many workloads. SurrealDB supports relational queries, ACID transactions and schema enforcement, plus native graph, vector and full-text capabilities. A migration guide is in the documentation.

Q: How does SurrealDB handle relationships compared to PostgreSQL joins?

PostgreSQL expresses relationships through foreign keys and joins, and query cost grows with join depth. SurrealDB traverses relationships directly with graph operators.

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PostgreSQL. Relational at its core, with extensions for the rest.

SurrealDB. Relational, graph, vector, document and temporal data, natively.

One engine. Every model in the core, in a single transaction.

01 |THE DIFFERENCE

Where Postgres reaches, SurrealDB runs natively

Postgres covers much of this through extensions and joins. SurrealDB carries every model in the core, in one transaction.

RELATIONSHIPS

Traversal in place of joins

Postgres reaches a related row through a foreign key and a join, and the cost grows with depth. SurrealDB follows the edge directly, with no join planning and no join index.

Explore graph relations

VECTORS

Embeddings beside the rows

pgvector adds a vector column and an index type to a relational core. SurrealDB stores the embedding with the record and queries it alongside graph, full-text and structured filters in one statement.

Explore vector search

SCALE

Horizontal reads and writes

Postgres scales past one node through primary and replica coordination, and large deployments shard by hand. SurrealDB separates compute from storage and scales both directions without sharding.

Explore the architecture

02 |THE SAME QUERY

SurrealDB vs. Postgres + pgvector

AI and retrieval workloads stretch a relational engine past its design. SurrealDB handles them natively.

Data and relationships

SurrealDB

PostgreSQL

Models

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

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

Relationship querying

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

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

Operations

Architecture

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

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

Scale

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

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

Schema evolution

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

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

Cost

Pricing

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

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

TRUSTED BY

Enterprise teams building on SurrealDB

See more case studies

Three data stores collapsed into one

An AI assistant for 10,000 technicians

Nine backend tools consolidated into one

Low-latency RAG, under the customer's control

45 million recommendation queries a month

Postgres,answered

How is SurrealDB different from PostgreSQL?

PostgreSQL is a relational database that adds capabilities through extensions. SurrealDB unifies relational, document, graph, vector, full-text and temporal models in one engine and one query language.

Can SurrealDB replace PostgreSQL?

How does SurrealDB handle relationships compared to PostgreSQL joins?

THE PLATFORM

Everything an application and its agents know. Five surfaces, one engine.

Database

Document, graph, vector, time-series and relational in one engine.

Five data models as dotted tiles: documents, graph, vector, time-series and relational

Agent Memory

What an agent learns, with its source and its time, in the same engine.

A timeline of remembered facts, each with its source

Cloud

Managed clusters in the regions you choose, scaled on demand.

Clusters in three regions on a world map, each running or scaling

Studio

Query, explore and design the schema from the browser.

MCP

Every model that speaks MCP reaches the database and the memory directly.

Three models connected through MCP to the database and Agent Memory

IN PRODUCTION

Trusted at scale. Samsung, Nvidia, Verizon, Tencent, and Walmart run on SurrealDB.

14,000+

Developers building on SurrealDB Cloud

4M+

Developers building on SurrealDB worldwide

FROM THE TEAMS

SurrealDB gives us a foundation where we can unify semantic search, knowledge graphs, and AI-driven decision making without stitching together multiple systems. Collapsing responsibility into SurrealDB has become our default engineering posture.

Justin Foley

VP of Engineering, Later

GET STARTED

Migrate from Postgres. Documents, graphs and vectors in one ACID engine.

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SurrealDB vs. Postgres