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Weaviate

Open-source vector database for AI applications

Quick Verdict

4.6/5

Rating

110

Reviews

freemium

Pricing

Open-source vector database for AI applications

4.6(110 reviews)freemiumFounded 2026
free tieropen sourceapi access

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.

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Frequently Asked Questions

What is Weaviate?

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.

Is Weaviate free?

Weaviate has a free tier with limits. Paid plans unlock more usage and features. See pricing above for the current plans.

Who is Weaviate for?

Teams building production RAG systems or semantic search who need self-hosted infrastructure and control over embeddings.

What are the main benefits of Weaviate?

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.

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