Summary
An open-source AI-native database combining vector search, structured filtering, and built-in model integrations.
Description
Weaviate is a vector database that treats objects, not just embeddings, as the unit of storage. Data has a schema with typed properties and cross-references, and vector search happens alongside the structured query capabilities you would expect from a database.
Distinguishing features
- Modules that call models for you. Vectorisation, reranking, and generative steps can run inside the database, so retrieval-and-generate is one query rather than three round trips.
- Hybrid search blending BM25 keyword scoring with vector similarity, tunable per query.
- Cross-references between objects, enabling graph-like traversal alongside similarity.
- Multi-tenancy designed in, with per-tenant isolation and the ability to offload inactive tenants to cheap storage — a real cost lever for SaaS products.
- Compression through product and binary quantisation.
Deployment
Self-host with Docker or Kubernetes under a BSD licence, or use Weaviate Cloud with serverless and dedicated options. Client libraries cover Python, JavaScript, Go, and Java.
Well suited to applications where retrieval needs both semantic similarity and genuine data modelling — product catalogues, knowledge bases, and multi-tenant SaaS.
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