Weaviate
by Weaviate · weaviate.io ↗
Open-source vector database for semantic search, retrieval-augmented generation, and hybrid search.
vector-database open-source retrieval
- Category
- Platforms & Infrastructure
- Business model
- Open (open weights/source)
- Availability
- Global
- Launched
- 2019
- Record updated
- 2026-07-30
- License
- BSD-3-Clause
- Founded
- 2019
- Headquarters
- NL
- Canonical URL
- https://globalaiproductindex.com/products/weaviate/
Overview
Weaviate is an open-source vector database used for semantic search, retrieval-augmented generation (RAG), and hybrid keyword-plus-vector search. It stores objects with their vector embeddings, integrates with popular model providers for vectorization, and exposes GraphQL and REST APIs. The database is free to self-host under BSD-3-Clause, with a paid managed cloud offering.
Key features
- Vector, keyword, and hybrid search in one engine
- Built-in vectorizer modules for popular model providers
- GraphQL and REST APIs with client libraries
- Horizontal scaling, replication, and multi-tenancy
Use cases
- Retrieval-augmented generation pipelines for LLM apps
- Semantic search over documents and product catalogs
- Recommendation and similarity-matching systems
Pricing
Weaviate is free to self-host (BSD-3-Clause); Weaviate Cloud is a paid managed service priced by usage and tier.
Frequently asked questions
Is Weaviate free?
Yes, self-hosting Weaviate is free under the BSD-3-Clause license; the managed Weaviate Cloud service is paid.
What is Weaviate used for?
Weaviate stores vector embeddings for semantic search, hybrid search, and retrieval-augmented generation in AI applications.
Does Weaviate create embeddings?
Weaviate can vectorize data through built-in modules that call popular embedding providers, or accept vectors you supply.
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Sources
This record was last reviewed on 2026-07-30.
Machine-readable record: /api/products/weaviate.json · Spot an error? Suggest a correction