Milvus vs Redis Vector (2026)
Updated February 28, 2026
Overview
Milvus is an open-source vector database built for scalable similarity search, capable of handling billions of vectors. Backed by the Zilliz company, Milvus supports multiple index types (IVF, HNSW, DiskANN), GPU-accelerated search, and multi-tenancy. Zilliz Cloud offers a fully managed version with automatic scaling. Milvus is widely used in enterprise deployments requiring high-throughput vector search at scale.
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.
Key features
Core capabilities each platform advertises.
- Billion-scale vector search
- GPU-accelerated indexing
- Distributed architecture
- Multiple index types
- Cloud and self-hosted options
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Extreme scalability handling billions of vectors in distributed environments
- Fully open-source under Apache 2.0 with no vendor lock-in
- Hybrid search combining dense and sparse vectors in single queries
- Rich index flexibility with IVF, HNSW, DiskANN and GPU acceleration
- Managed cloud option reduces DevOps burden with auto-scaling
Cons
- Complex distributed architecture requiring Kubernetes for production
- Steep learning curve for index tuning and deployment configuration
- High memory and storage requirements for large datasets
- No in-place vector updates requiring delete and re-insert workarounds
Pros
- Multi-modal capabilities: vector search, caching, sessions, and messaging in one platform
- Sub-millisecond latency with in-memory data storage
- Multiple deployment options: fully managed cloud, self-managed, or open source
- HNSW and Flat indexing support for flexible search requirements
- Eliminates need for multiple tools, reducing architectural complexity
Cons
- In-memory storage can be expensive at large scale
- Not purpose-built solely for vector search like dedicated vector databases
- May require additional configuration for optimal vector search performance
- Pricing can become complex with usage-based model
Milvus or Redis Vector — which should you choose?
Choose Milvus if you wantChoose if you want
- Billion-vector similarity search
- Large-scale recommendation systems
- Image and video retrieval
- Genomics and scientific computing
- Enterprise-scale RAG systems
Choose Redis Vector if you wantChoose if you want
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