Weaviate
by Community
Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance an
OSS
Weaviate
Added 1 June 2026
Overview
Weaviate is an open-source vector database written in Go that stores objects alongside their vector embeddings. It combines vector similarity search with structured filtering and SQL-like queries, built for cloud-native deployment with fault tolerance and horizontal scaling.
Best for
Best for
Teams building production search systems who need open-source control and can manage infrastructure.
Use cases
- Semantic search over document collections with metadata filtering
- Hybrid retrieval combining vector similarity and keyword matching
- Building RAG pipelines with persistent vector storage
Notes
Weaviate is an open-source vector database written in Go that stores objects alongside their vector embeddings. It combines vector similarity search with structured filtering and SQL-like queries, built for cloud-native deployment with fault tolerance and horizontal scaling.
16,258 stars on GitHub. Last updated 2026-06-01. Licensed BSD-3-Clause.
Use cases
- Semantic search over document collections with metadata filtering
- Hybrid retrieval combining vector similarity and keyword matching
- Building RAG pipelines with persistent vector storage
Pros
- Open-source with active community (16k+ stars)
- Native support for both vector and structured queries without separate systems
- Cloud-native architecture with built-in replication and failover
Cons
- Requires operational overhead to deploy and maintain versus managed services
- Learning curve for query syntax and configuration compared to simpler vector stores
- Performance tuning needed for large-scale deployments
Indexed from awesome-llmops and enriched against its public facts.
Pros
- Open-source with active community (16k+ stars)
- Native support for both vector and structured queries without separate systems
- Cloud-native architecture with built-in replication and failover
Cons
- Requires operational overhead to deploy and maintain versus managed services
- Learning curve for query syntax and configuration compared to simpler vector stores
- Performance tuning needed for large-scale deployments
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