Build real-time apps faster
with the world's most powerful
multi-model database

Zero maintenance. Zero hassle.

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One platform,
unlimited possibilities

Multi-model: for all types of data

Handle relational, document, graph, time series, key value, vector and search, in one place.

Simplicity that scales

Scale compute and storage separately, from 0 to 100s of nodes and back to 0, without sharding.

Superior developer experience

SurrealQL is intuitive and easy to learn, blending SQL familiarity with modern flexibility.

AI made easy

Build generative AI apps with vectors, knowledge graphs, and in-database ML capabilities.

Transactional and analytical

Built for data intensive applications and real-time analytics.

Advanced security

Built-in access controls, granular table, row and field-level permissions.

Query the way you want without switching between databases

Learn about SurrealQL

SQL

SELECT p.name, pr.name, pr.price
FROM person p
INNER JOIN order o ON p.id = o.person_id
LEFT JOIN product pr ON o.product_id = pr.id

SurrealQL

SELECT name, ->order->product.{name, price}
FROM person

MQL

db.review.aggregate([
    {
        "$match": {
            "_id": "01FXNDZ2C893992S408TX4MYYQ"
        }
    },
    {
        "$lookup": {
            "from": "person",
            "localField": "person",
            "foreignField": "_id",
            "as": "person"
        }
    },
    {
        "$project": {
            "_id": 0,
            "rating": 1,
            "review_text": 1,
            "person.name": 1,
            "person.email": 1
        }
    }
])

SurrealQL

SELECT
    rating,
    review_text,
    person.name,
    person.email
FROM review:01FXNDZ2C893992S408TX4MYYQ

Cypher

MATCH (p:Person)-[:ORDER]->(pr:Product)
RETURN p.name, pr.name, pr.price

SurrealQL

SELECT name, ->order->product.{name, price}
FROM person

Built by developers, loved by developers

Explore our case studies

"I think it's going to change how we query databases."

Anup Jadhav

Director, Salesforce

"It's fast, small, can run on browser, edge and cloud. It can handle large data. It can run in-memory like Redis or disk like SQLite and it can run multi-cluster. It can sync between cloud and edge. And to top of it off, it can also run as a graphdb."