AI-native architecture
Why you shouldn’t settle for AI as the sixth bullet point.
Why you shouldn’t settle for AI as the sixth bullet point.
Building intelligent applications means battling brittle systems. Your AI ends up starved of real-time context, trapped in a maze of APIs and exports.
Training models on stale, sampled data from warehouses - not live, holistic datasets.
Shipping features to a database, then to Python for inference, then back—adding critical delays.
SurrealDB’s AI-native architecture. SurrealDB embeds machine learning into its core - store vectors, run models, and serve predictions directly alongside your data with millisecond latency.
Run ONNX models inside the database. Predict failures on a factory sensor using local data and ML.
SurrealDB offers vector and full-text search. Store and query vectors natively for AI use cases.
Unify relational, document, and graph data. Combine vector results with structured business data.
Run models where the data lives. No unnecessary network hops.
Iterate on AI workloads without switching tools - use SurrealQL.
Adapt to new use cases without costly changes. Built-in flexibility.
Get started with SurrealDB: the multi-model database for knowledge-intensive applications.
