SurrealDB is a multi-model database built in Rust designed to unify multiple data models into a single, powerful engine. It combines document, graph, time-series, relational, geospatial and key-value data types into one query language, SurrrealQL, with powerful search and retrieval (vector, full-text, hybrid), and real-time and event-driven capabilities, enabling developers to build applications faster and more efficiently.
Common SurrealDB use cases include AI agents, knowledge graphs, real-time apps (e.g. recommendation engines, fraud detection systems), and any other type of application requiring multiple data types. SurrealDB can also be used as a backend-as-a-service (BaaS) thanks to its support for direct user authentication. Given that it’s a single Rust binary, SurrealDB can also run embedded (in‐app), in the browser (via WebAssembly), in the edge, as single backend node, or in a distributed cluster.
SurrealDB is open source (see the code here on GitHub) and is also available as a cloud managed service through SurrealDB Cloud.
Differentiators
Native multi-model: combines document, graph, time-series, relational, geospatial and key-value data models natively into SurrealQL, without workarounds or added complexity.
AI native: purpose-built for AI and context-aware applications with integrated search and retrieval (vector, full-text, hybrid) that blend semantic, graph, and relational intelligence.
Real-time and event-driven: built-in real-time subscriptions, event triggers, and streaming updates power reactive, real-time experiences - no need for extra layers like Kafka.
Powerful developer experience: SurrealQL is intuitive and combines the best ideas from SQL, NoSQL and graph within a single native syntax. Start schemaless and then make your schema as strictly defined as you like.
Rust-powered performance: high efficiency, memory safety, type safety and concurrency with a single Rust binary.
Native ACID compliance: guarantees immediate data consistency. Opt in to eventual consistency in certain cases if desired.
Deployment flexibility: single Rust binary and storage/compute separation allow SurrealDB to run embedded (in‐app), in the browser (via WebAssembly) or as a traditional back-end in a single node or in a highly-scalable distributed cluster.
Secure by design: built-in security with RBAC, record-level permissions, fine-grained access controls, JWT authentication, multi-tenant isolation and built-in compliance (SOC 2, ISO 27001) keep data protected by default.
SurrealDB is being used at scale in production by large organisations. To list a few, SurrealDB is used by:
Samsung Ads, for knowledge graphs in advertising analytics.
SiteForge, to reduce its development cycle, queries, and backend API usage.
Verizon, for its generative AI assistant utilized by field technicians.
Tencent, for infrastructure monitoring, having consolidated 9 tools into one.
PolyAI, for low-latency, customer-controlled RAG across voice AI experiences.
More companies and an overview of the benefits provided by SurrealDB can be found in our enterprise case studies page.
Use cases
SurrealDB is ideal for any application, in particular data intensive applications that require multiple data systems, such as:
AI agents: building Generative AI systems leveraging a single unstructured and structured data layer with vector, graph and real-time capabilities for RAG, Graph RAG, and agent memory.
Knowledge graphs: turning unstructured data into structured, queryable data with a flexible multi-model approach including support for graph relationships.
Real-time analytics: fraud detection systems, recommendation engines and log analytics.
Embedded & edge computing: SurrealDB is a single lightweight Rust binary and can be embedded in industrial environments, run in-memory or in browser.
Backend-as-a-Service: with support for end-user authentication, SurrealDB can also be used as a BaaS for web applications, if desired.
