SurrealDB AI

SurrealDB is a native multi-model database designed for AI agents, built to streamline the tech stack, accelerate development, and reduce complexity with a unified architecture. It natively integrates documents, graphs, vectors, and other data models, and offers flexible deployment options to serve developers and organizations building scalable AI-powered applications.

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AI-native databasemulti-model databaseSurrealDBvector databaseAI agent data layerunified data queryingcloud-native databasereal-time data synchronization

Features of SurrealDB AI

Unified multi-model engine with native support for documents, graphs, vectors, full-text search, time-series and relational data

Built-in vector, graph, semantic and relational intelligence, providing AI agents with integrated context, memory and retrieval capabilities

ACID transactions and a persistent single data source to ensure operation consistency and contextual continuity

Flexible deployments from in-memory / embedded / edge devices to self-hosted or cloud

Granular access control and auditing to manage data security

Real-time data synchronization, subscriptions, and event triggers for building dynamic, responsive apps

Direct secure connections from client devices, simplifying frontend-to-data layer interactions

Operate using the unified SurrealQL query language to simplify the tech stack and query complexity

Use Cases of SurrealDB AI

For developers building generative AI applications, storing and retrieving vectorized data to support Retrieval-Augmented Generation (RAG)

Organizations building knowledge graphs or complex relationship analytics can leverage its native graph data model for efficient queries

For real-time data streams and event-driven applications, leveraging its real-time sync and subscription features

Teams looking to simplify their backend stack by replacing multiple data stores with a single database

As a unified data layer in microservices architectures to reduce data movement and context loss between services

When building highly scalable backend services, leveraging its horizontally scalable clustering deployment

In embedded or edge computing scenarios, a lightweight yet feature-rich database solution

FAQ about SurrealDB AI

QWhat is SurrealDB AI?

SurrealDB AI is a native multi-model database designed for AI agents, built to simplify the tech stack through a unified architecture and support modern AI application development.

QWhat data models does SurrealDB support?

It natively supports documents, graphs, time-series, vectors, relational, geospatial, and key-value data models, with all operations accessible via the SurrealQL query language.

QHow does SurrealDB deploy?

It supports deployment modes from in-memory and embedded to edge devices, self-hosted, or cloud, and also offers a fully managed database service called Surreal Cloud.

QIs SurrealDB suitable for RAG systems?

Yes. Its built-in vector and full-text search features are optimized for context-aware applications and are commonly considered for RAG-based database architectures.

QIs there a cost to use SurrealDB?

There are free tiers, free trials, and tiered pricing; costs depend on the chosen service and deployment model.

QWhat data security features does SurrealDB offer?

Granular access control, auditing, row-level access control, and JWT authentication help manage data security.

QHow does SurrealDB perform and scale?

Built with Rust, it supports from single-node to distributed clusters, with no sharding required for horizontal scaling, optimized for vector computation and multi-agent workloads.

QWhat distinguishes SurrealDB from traditional databases?

Its native unified multi-model architecture integrates multiple data models and features in a single product, reducing architectural fragmentation and simplifying development and operations.

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