Case studies and use cases

Generative AI: SurrealDB unifies structured and unstructured data, with vector search, graphs, and transactional updates in a single query, ideal for RAG, agent memory, and real-time LLM apps.

Knowledge Graphs: Model rich relationships with native edges, graph traversals, and live queries. Perfect for dynamic ontologies, identity graphs, and explainable AI.

Real-Time Analytics: Fraud detection systems, recommendation engines, and log analytics solutions leveraging real-time capabilities, time-series data, and event driven archtiectures. Build reactive dashboards and alert systems without middleware.

Embedded Systems: Lightweight Rust binary that delivers multi-model capabilities, full ACID, ML inference, and real-time sync, perfect for mobile, IoT, and field deployments.

OLTP & BaaS: SurrealDB simplifies backends with multi-model ACID transactions, secure auth, and schemaless or schemafull design, all without glue code.

Finance & Fintech: combine graph-based fraud detection, audit trails, and scoring models to power fast, secure, compliant fintech apps.

Defence & Aerospace: deploy at the edge or in hardened clusters. SurrealDB supports mission-critical telemetry, versioned state, and role-based access.

Gaming & Entertainment: track players, sessions, matches, and items in one real-time engine. Reactive queries enable multiplayer games and live leaderboards.

Energy & Manufacturing (IoT): store time-series data, monitor devices, and embed ML for predictive maintenance, all in a single store from edge to cloud.

Retail & E-commerce: personalise feeds and offers with vector search, user graphs, and real-time signals.

Healthcare: model patients, records, and sensors with field-level security and time-travelled queries, built for data integrity and compliance.

Multi-model data support: Use a single database to handle relational, graph, document, time-series, and vector data — ideal for modern applications with evolving data needs.

Real-time processing: Built-in subscriptions and triggers enable reactive architectures and real-time experiences.

Graph-native capabilities: Model and query complex relationship networks without requiring separate graph database infrastructure.

Vector search for AI: Integrated support for vector embeddings enables retrieval-augmented generation (RAG), semantic search, and recommendation systems.

Multi-tenant support: True isolation and role-based access control (RBAC) allow easy scaling of SaaS platforms.

Reduced latency for complex graph queries across user-device interactions.

Supported dynamic audience segmentation at scale.

Consolidated multiple data sources into a unified SurrealDB backend.

Stored product embeddings and user interaction data in SurrealDB’s hybrid document and vector model.

Powered semantic search and personalised recommendations in real time.

Enabled rapid iteration on machine learning pipelines without re-architecting storage.

Simplified system architecture → one database instead of separate stores for time-series, graph, and document data.

Achieved scalable ingestion of millions of events per second.

Gained deep observability via SurrealDB’s flexible query language and graph features.

Replaced multiple backend services with SurrealDB’s multi-model database.

Achieved high-concurrency handling and reduced system latency.

Established a scalable foundation for future growth and AI integration.

Case studies

Product features

Data models documentation