Weaviate
Open-source vector database for AI applications
Weaviate is a vector database designed for storing and querying AI-generated embeddings at scale. It enables semantic search, recommendation systems, and RAG pipelines without external dependencies.
Weaviate provides a self-hosted or cloud vector database with built-in search capabilities, CRUD operations, and integration with language models. Supports multiple embedding providers, offers hybrid search combining vector and keyword queries, includes data validation schemas, and scales to billions of vectors. Deploys via Docker or Kubernetes with REST and GraphQL APIs.
Pros
- Deploy on-premises or in-cloud for full data control
- Integrate directly with OpenAI, Cohere, and other embedding providers
- Combine vector search with keyword filtering in single queries
- Scale horizontally across clusters for large datasets
Cons
- Requires operational overhead to self-host and maintain
- Smaller ecosystem compared to established vector database alternatives
- Learning curve for GraphQL API and schema configuration
Best For
Teams building production RAG systems or semantic search who need self-hosted infrastructure and control over embeddings.
Pricing
Free
- Core features
- Email support
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