
High-performance, cloud-native vector database built for scalable AI applications
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.
Process and search billions of vectors with millisecond latency using optimized ANN algorithms
Supports HNSW, IVF, FLAT, SCANN, DiskANN, and quantization-based index variations for different use cases
GPU-accelerated index building and search via Nvidia CUDA and cuVS library with CAGRA algorithm
Combine vector similarity search with scalar filtering for precise multi-modal queries
Store frequently accessed data in memory or SSD for performance while keeping cold data on cost-effective storage
Official SDK clients for Python, Java, Go, and Node.js, with community C# SDK from Microsoft
Kubernetes-native distributed architecture with horizontal scaling and high availability
Store and search document embeddings to provide context for LLM responses, improving accuracy and reducing hallucinations
Build product, content, or media recommendation engines by finding similar items based on vector representations
Power visual search applications by indexing image and video embeddings for similarity-based retrieval
Enable natural language search across documents, knowledge bases, and enterprise data using semantic embeddings
Built-in data replication and WAL (write-ahead log) for fault tolerance and disaster recovery
Identify outliers and anomalies in datasets by detecting vectors that deviate from normal patterns

AI-powered SQL client that turns natural language into database queries