Context, made atomic

Documents, graphs, vectors, time-series, and relational data as native primitives in a single ACID transaction. No plugins, no bolt-ons, no frankenstack.

Native multi-model engine

Documents, graphs, vectors, full-text search, time-series, and relational data - fully unified in one database and query language. No plugins or bolt-ons.

Explore data models

AI data layer

Built for context, memory, search, and retrieval - combining vectors, graphs, full-text search, and relational joins in one engine.

Explore AI use cases

Deploy anywhere, scale horizontally

The same API runs across in-memory, embedded, and edge deployments to self-hosted or fully managed cloud.

Explore deployment options

Real-time and event-driven

Built-in real-time subscriptions, event triggers, and streaming updates to power reactive, real-time experiences.

Explore real-time features

Enterprise security and compliance

RBAC, record-level permissions, JWT auth, multi-tenant isolation, and SOC 2 / ISO 27001 compliance built in.

Explore security features

Developer tools

Surrealist visual IDE, WebAssembly plugins, and SurrealQL - the best of SQL, NoSQL, and graph querying in one syntax.

Explore developer tools

MCP server

Connect AI tools directly to SurrealDB through the Model Context Protocol. Queries, schema exploration, graph traversal, and vector search as MCP tool calls.

Explore MCP integration

High-performance, reliable core

Built in Rust with full ACID guarantees. Memory-safe, thread-safe, and crash-resilient by design.

Explore performance

See all features

01 |HOW IT WORKS

Multi-model query architecture

One query flows through documents, graphs, vectors, and time-series - resolved from a single transactional snapshot.

Get started

SurrealDB SDK

Postgres-compatible client

GraphQL client

SurrealDB

RPC protocol

PostgreSQL wire protocol

GraphQL endpoint

SurrealQL parser

ANSI-SQL parser

GraphQL parser and schema generator

SurrealDB executor

Execution logical plan

Execution physical plan

Document processing

Key-value engine data storage and retrieval

02 |DEPLOY ANYWHERE

Single binary, any device

In-memory, embedded, edge, WASM, or cloud - the same engine runs everywhere.

Deploy now

Agent or application

Python, C, C++, Rust, Java, or JavaScript runtime

In-process native communication

SurrealDB

Query engine

Storage engine(Local disk or block storage)

Periodic storage data compaction
and compacted data storage

Local disk

03 |HOW SURREALDB COMPARES

Every other agent stack is a frankenstack

Document store, graph database, vector database, cache, message broker, memory middleware - glued together and prayed over. SurrealDB replaces all of it.

vs. Postgres

Postgres bolts on vectors with pgvector and graphs with recursive CTEs. SurrealDB makes every model native - no extensions, no workarounds.

Compare with Postgres

vs. MongoDB

MongoDB is a document store. Graph traversal, vector search, and temporal queries require separate systems. SurrealDB unifies them.

Compare with MongoDB

vs. Neo4j

Neo4j is a graph database. Documents, vectors, and time-series require separate infrastructure. SurrealDB does it all in one engine.

Compare with Neo4j

vs. Vector databases

Pinecone, Chroma, and Weaviate store embeddings. SurrealDB stores embeddings alongside their entities, relationships, and temporal context.

Compare with vector databases

vs. Memory middleware

Mem0 layers memory on top of fragmented databases. Spectron builds memory directly into SurrealDB - one transaction, no consistency gaps.

Compare with memory middleware

vs. Agent databases

HelixDB and HydraDB internalise a frankenstack. SurrealDB provides true multi-model unification with ACID across all data types.

Compare with agent databases

04 |BENCHMARKS

Benchmarked in the open

SurrealDB tested against the leading key-value, embedded, relational, document, and graph databases - the same workload, the same harness, the same hardware.

Key-value

Redis-class throughput with full database semantics.

View results

Embedded

Embedded performance with full SurrealQL on the same disk format.

View results

Relational

SQL workloads against Postgres and MySQL.

View results

Document

Document workloads against MongoDB and ArangoDB.

View results

Graph

Graph workloads against Neo4j and ArangoDB.

View results

View all benchmarks

Features

Explore the full set of capabilities built into SurrealDB - from multi-model querying and vector search to real-time subscriptions and record-level permissions.

Explore features