Core features and design patterns

SurrealDB supports a powerful combination of data models and features, enabling a wide variety of modern application architectures.

From simple CRUD apps to highly connected graph-driven platforms, real-time event systems, and AI-native workflows, SurrealDB provides a versatile foundation for innovation.

SurrealDB offers unified support for multiple data models - all within a single ACID-compliant engine:

SurrealQL is SurrealDB’s built-in query language - a single, SQL-style language designed to query all supported data models.

It provides a consistent and powerful way to build complex application logic across hybrid data patterns.

This example combines vector similarity search, graph traversal, and document field filtering - a typical hybrid pattern in modern AI-native applications.

With SurrealDB’s multi-model architecture and SurrealQL query language, developers can implement a wide range of powerful design patterns across industries:

Support true isolation of tenant data via namespaces and databases, with fine-grained access control (table- and field-level).
Learn more in the RBAC and Access Control docs.

Built-in support for real-time subscriptions (LIVE SELECT) and triggers enable responsive user experiences and automation.

Model rich relationship networks - such as social graphs, organisational hierarchies, and knowledge graphs - using SurrealDB’s native graph features.

Combine vector search with graph and document data to enable retrieval-augmented generation (RAG), semantic search, and context-aware systems.

SurrealDB’s SurrealML runtime enables in-database model inference, allowing AI pipelines to run closer to the data.