About Weaviate
Weaviate is an open-source, cloud-native vector database that stores both objects and vectors, enabling semantic search at scale. It combines vector similarity search with keyword filtering, retrieval-augmented generation (RAG), and reranking in a single query interface. Common use cases include RAG systems, semantic and image search, recommendation engines, chatbots, and content classification. Weaviate supports automatic vectorization at import using integrated models (OpenAI, Cohere, HuggingFace, and others) or direct import of pre-computed vector embeddings. Production deployments benefit from built-in multi-tenancy, replication, RBAC authorization, and support for multiple deployment options including Docker, Kubernetes, and cloud services.
Key Features
Pros & Cons
- Open-source with an active community (16.6k+ stars on GitHub)
- Combines vector search and keyword filtering for hybrid search
- Built-in RAG pipeline for generative AI applications
- Scalable and cloud-native with replication and multi-tenancy
- Supports popular embedding model providers (OpenAI, Cohere, HuggingFace)