Key features
Core capabilities each platform advertises.
Data not available
- Open-source vector database
- Hybrid search (vector + keyword)
- Multi-modal support
- GraphQL and REST APIs
- Kubernetes-native deployment
Redis provides vector similarity search as part of its in-memory data platform. Redis Vector Search enables real-time semantic search with sub-millisecond latency, supporting HNSW and FLAT indexing algorithms. Ideal for applications requiring both traditional caching and vector search in a single data layer.
Weaviate is an open-source vector database that combines vector search with structured filtering and generative capabilities. It supports multiple vectorization modules, hybrid search (combining BM25 and vector search), and built-in integrations with LLMs for retrieval-augmented generation. Weaviate offers both self-hosted and managed cloud deployments, with a GraphQL API that makes it easy to query complex data structures.
Core capabilities each platform advertises.
What each tool does well, and the limitations to keep in mind.
Pros
Cons
Choose Redis Vector if you wantChoose if you want
Choose Weaviate if you wantChoose if you want
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