Weaviate

Weaviate is a vector database that stores and indexes data based on semantic meaning, not just keywords. It powers AI applications like recommendation engines, image search, and chatbots.

What it does

Ingest text, images, or other data, and Weaviate converts them into vector embeddings. You query by meaning—find similar documents, products, or images. Supports hybrid search combining vector and keyword methods. Runs on-premise or in the cloud.

Best for

• Developers building AI search or recommendation systems
• Teams needing semantic similarity for large datasets
• Companies deploying RAG (retrieval-augmented generation) pipelines

Strengths

• Open-source with active community
• Hybrid search (vector + keyword) out of the box
• Built-in vectorization modules (OpenAI, Cohere, Hugging Face)
• Scalable to billions of objects
• GraphQL and REST APIs

• Vector search — Find similar items by semantic meaning
• Hybrid search — Combine vector and keyword for better results
• Built-in modules — Integrate with OpenAI, Cohere, Hugging Face
• Scalable — Handles billions of objects with sharding
• GraphQL API — Query with flexible filters and aggregations
• Self-hosted or cloud — Deploy on your infrastructure or use Weaviate Cloud