Pinecone
Managed vector database for AI applications
Open-Source Projects
Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
High-performance vector database built for AI at scale
Milvus is a distributed vector database that powers AI applications by efficiently organizing and searching billions of vectors with real-time updates and hardware acceleration.
Milvus is a high-performance vector database designed to handle massive-scale AI workloads. Written in Go and C++, it efficiently organizes and searches vast amounts of unstructured data—text, images, and multi-modal information—by storing and querying vector embeddings alongside scalar metadata.
Milvus features a fully-distributed, Kubernetes-native architecture that separates compute from storage, enabling horizontal scaling to handle tens of thousands of concurrent queries across billions of vectors. Hardware acceleration for CPU and GPU delivers best-in-class search performance. The platform supports multiple deployment modes: distributed clusters for production scale, Standalone for single-machine setups, and Milvus Lite for lightweight Python development.
Developers choose Milvus for its comprehensive feature set: support for major vector index types (HNSW, IVF, DiskANN, SCANN), hybrid search combining dense and sparse vectors for semantic and full-text search, flexible multi-tenancy with fine-grained access control, and hot/cold storage tiering for cost optimization. Real-time streaming updates keep data fresh, while RBAC, TLS encryption, and user authentication ensure enterprise-grade security. Trusted by startups and enterprises alike, Milvus powers RAG systems, recommendation engines, semantic search, and multimodal AI applications.
When teams consider Milvus, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Retrieval-Augmented Generation (RAG)
Build AI assistants that retrieve relevant context from billions of documents in real-time to generate accurate, grounded responses with hybrid search combining semantic and full-text retrieval.
Multimodal Semantic Search
Enable users to search across text, images, and multi-modal content using dense and sparse vector embeddings, with metadata filtering for precise results at scale.
Recommendation Systems
Power personalized recommendations by efficiently querying user and item embeddings across millions of products or content items with sub-second latency.
Enterprise Knowledge Management
Deploy secure, multi-tenant vector search with RBAC and TLS encryption, allowing different teams to search their own data while maintaining compliance and access control.
Milvus offers three deployment modes: distributed clusters on Kubernetes for production scale, Standalone mode for single-machine deployments, and Milvus Lite for lightweight local development via pip install. Zilliz Cloud provides fully managed options including Serverless, Dedicated, and BYOC.
Milvus uses a distributed architecture that separates compute and storage, allowing horizontal scaling by independently adding query nodes for read-heavy workloads and data nodes for write-heavy workloads. It supports replicas for fault tolerance and can handle tens of thousands of concurrent queries.
Milvus supports all major vector index types including HNSW, IVF, FLAT (brute-force), SCANN, and DiskANN, with quantization-based variations and memory-mapped options. It also supports GPU indexing like NVIDIA CAGRA for hardware acceleration.
Yes, Milvus natively supports hybrid search combining dense vectors for semantic search and sparse vectors for full-text search using BM25 or learned sparse embeddings like SPLADE and BGE-M3. Both vector types can be stored in the same collection with custom reranking functions.
Milvus implements mandatory user authentication, TLS encryption for all network communications, and Role-Based Access Control (RBAC) for fine-grained permissions. These features ensure enterprise-grade security and protect sensitive data from unauthorized access.
Project at a glance
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