MilvusDB

Open-source vector database built for scalable similarity search and AI applications. Handles unstructured data like text, images, and audio by storing embeddings and running approximate nearest neighbor search at billion scale, with persistent storage and a cloud-native distributed architecture.

Trusted by enterprises such as Roblox, Poshmark, Otter.ai, Palo Alto Networks, Chegg, Shell, and Salesforce for production workloads. Milvus distinguishes itself from ANN libraries by providing full database features: durable storage, hybrid queries combining vector similarity with metadata filters, and built-in scalability from laptops to distributed clusters without external orchestration.

Key capabilities:

• Approximate nearest neighbor search with HNSW and IVF index types for low-latency queries at scale
• Hybrid search pairing vector similarity with scalar filters for production use cases
• Distributed architecture with compute-storage separation, sharding, and microservices for horizontal scaling
• Billion-scale vector storage with persistent durability and multi-tenant support
• Official SDKs for Python, Go, Node.js, and Java, plus REST and gRPC APIs
• Runs standalone or distributed with options for on-premises or cloud deployment

Teams use it for semantic search, RAG pipelines, and retrieval-augmented Q&A by storing document embeddings and querying by similarity. Other common patterns include product and avatar search, recommendation systems, fraud detection over behavioral embeddings, and enterprise document search for regulatory compliance or internal knowledge bases.