Milvus
High-performance, cloud-native vector database built for scalable AI applications
Founded
2019
Starting Price
Free
Category
Developer Tools
Last updated April 27, 2026
About Milvus
Milvus is an open-source vector database designed for similarity search and AI applications at scale. Built for high-dimensional vector data, it supports billion-scale vector search with millisecond latency, making it ideal for retrieval-augmented generation (RAG), recommendation systems, image retrieval, and anomaly detection.
Pros & Cons
Pros
- Exceptional performance at billion-scale vector search with millisecond latency
- Fully open source under Apache 2.0 with active community and 35K+ GitHub stars
- Supports all major vector index types with GPU acceleration for maximum flexibility
- Cloud-native distributed architecture scales horizontally for production workloads
- Rich SDK ecosystem with official clients for Python, Java, Go, and Node.js
Key Features
Billion-Scale Vector Search
Process and search billions of vectors with millisecond latency using optimized ANN algorithms
Multiple Index Types
Supports HNSW, IVF, FLAT, SCANN, DiskANN, and quantization-based index variations for different use cases
GPU Acceleration
GPU-accelerated index building and search via Nvidia CUDA and cuVS library with CAGRA algorithm
Hybrid Search
Combine vector similarity search with scalar filtering for precise multi-modal queries
Hot/Cold Storage Tiering
Store frequently accessed data in memory or SSD for performance while keeping cold data on cost-effective storage
Multi-Language SDKs
Official SDK clients for Python, Java, Go, and Node.js, with community C# SDK from Microsoft
Cloud-Native Architecture
Kubernetes-native distributed architecture with horizontal scaling and high availability
Pricing
Open Source
Free
- Self-hosted deployment
- Apache 2.0 license
- All core features
- Community support
- All index types supported
Best For
Retrieval-Augmented Generation (RAG)
Store and search document embeddings to provide context for LLM responses, improving accuracy and reducing hallucinations
Recommendation Systems
Build product, content, or media recommendation engines by finding similar items based on vector representations
Image & Video Retrieval
Power visual search applications by indexing image and video embeddings for similarity-based retrieval
Semantic Search
Enable natural language search across documents, knowledge bases, and enterprise data using semantic embeddings
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