Weaviate
An open-source vector database, Weaviate stores objects and vectors and supports vector search.
About Weaviate
Weaviate is described in its repository as an open-source, cloud-native vector database that stores both objects and vectors. It combines vector similarity search with structured filtering, keyword filtering, retrieval-augmented generation (RAG) and reranking in a single query interface. Data can be vectorized automatically at import through integrated models such as OpenAI, Cohere and HuggingFace, or supplied as pre-computed embeddings. Deployment options include Docker, Kubernetes and Weaviate Cloud, with installation docs also covering AWS and GCP; production features mentioned include multi-tenancy, replication and RBAC authorization.
Key features
- Stores objects and vectors with combined vector search and structured filtering
- Automatic vectorization at import via OpenAI, Cohere, HuggingFace and others
- Option to import pre-computed vector embeddings instead
- RAG and reranking handled in a single query interface
- Deployment via Docker, Kubernetes or Weaviate Cloud
- Built-in multi-tenancy, replication and RBAC for production use
Best for: Developers building semantic search, RAG, image search, recommendation, chatbot or content-classification systems that need vector similarity combined with filtering.
Good for
- Combines vector similarity search with filtering in one query interface
- Supports either automatic vectorization through integrated model providers or self-provided embeddings
- Production-oriented features such as multi-tenancy, replication and RBAC authorization
Watch out for
- No pricing information is stated in the source material
- Automatic vectorization depends on third-party model providers such as OpenAI, Cohere and HuggingFace
Weaviate at a glance
- Category
- Development › APIs & Databases
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
Guides for APIs & Databases
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Weaviate FAQ
How can Weaviate be deployed?
Documented options include Docker, Kubernetes and Weaviate Cloud, with further deployment options such as AWS and GCP covered in the installation docs.
Does Weaviate generate embeddings automatically?
It can vectorize objects at import using integrated models such as OpenAI, Cohere and HuggingFace, or you can import your own pre-computed vector embeddings.
What are common use cases?
RAG systems, semantic and image search, recommendation engines, chatbots and content classification.
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