M4A

Weaviate

Open-source vector database with hybrid search and GraphQL API

freemiumproductionopen-sourcegraphqlhybrid-searchself-hostedmanaged
Visit WebsiteGitHub

Memory Types

semantic, contextual, graph

Integrations

langchain, llamaindex, openai, cohere, huggingface, palm


Overview


Weaviate is an open-source vector database that combines vector search with the power of knowledge graphs. Founded in 2019, Weaviate has grown into one of the most popular vector database solutions, offering both self-hosted and cloud-managed options. It features a unique GraphQL API, built-in vectorization modules, and hybrid search capabilities.


The platform stands out for its flexible architecture that allows combining vector similarity search with traditional filtering and graph-like connections between objects. Weaviate's modular design lets you plug in different vectorization models and choose between various backends for optimal performance.


Key Features


  • **Hybrid Search**: Combines vector and keyword search with BM25
  • **GraphQL API**: Flexible querying with GraphQL instead of SQL
  • **Built-in Vectorization**: Integrated embedding models (optional)
  • **Multi-Tenancy**: Native support for isolating tenant data
  • **Replication & Sharding**: Horizontal scaling and high availability
  • **Generative Search**: Built-in RAG capabilities with LLM integration
  • **Schema Flexibility**: Dynamic schema with automatic type inference
  • **Modular Architecture**: Pluggable vectorizers and storage backends

    • When to Use Weaviate


    Weaviate is ideal for:

  • Teams wanting open-source flexibility with managed option
  • Applications requiring hybrid search (vector + keyword)
  • Multi-tenant SaaS applications with data isolation needs
  • Projects benefiting from GraphQL query flexibility
  • Organizations requiring on-premise deployment
  • Applications with complex data relationships and knowledge graphs

    • Pros


  • Open-source with active community
  • Both self-hosted and managed cloud options
  • Excellent hybrid search capabilities
  • GraphQL provides powerful, flexible queries
  • Strong multi-tenancy support
  • Built-in vectorization simplifies architecture
  • Good performance at scale
  • Comprehensive documentation

    • Cons


  • GraphQL learning curve for SQL-oriented developers
  • More complex setup than some alternatives
  • Cloud pricing can be expensive
  • Performance tuning requires deep knowledge
  • Smaller ecosystem than established databases
  • Resource-intensive for large deployments

    • Pricing


  • **Open Source**: Free, self-hosted
  • **Sandbox**: Free cloud tier with limits
  • **Serverless**: Pay-as-you-go starting at $25/month
  • **Enterprise**: Custom pricing with SLAs and support

    • ← Back to Frameworks