Pinecone
Managed vector database for AI applications
Milvus
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.
Overview
Built for AI at Scale
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.
Architecture and Performance
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.
Enterprise-Ready Capabilities
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.
Highlights
Distributed architecture with horizontal scaling for billions of vectors
Hardware acceleration (CPU/GPU) and support for HNSW, IVF, DiskANN, SCANN indexes
Hybrid search combining dense vectors, sparse vectors, and full-text (BM25)
Real-time streaming updates with flexible multi-tenancy and RBAC security
Pros
- Scales horizontally with Kubernetes-native architecture for high availability
- Hardware acceleration and GPU indexing deliver best-in-class performance
- Supports hybrid search, metadata filtering, and multiple vector index types
- Enterprise security with RBAC, TLS encryption, and user authentication
Considerations
- Distributed mode requires Kubernetes expertise for optimal deployment
- Learning curve for configuring index types and tuning performance
- Resource-intensive for large-scale deployments with billions of vectors
- Advanced features like multi-tenancy and hybrid search add complexity
Managed products teams compare with
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.
Fit guide
Great for
- AI teams building RAG systems, semantic search, or recommendation engines at scale
- Enterprises requiring fine-grained access control and data security compliance
- Applications needing real-time vector search on billions of embeddings
- Organizations wanting flexible deployment from local development to cloud production
Not ideal when
- Small projects with minimal vector data that don't require distributed architecture
- Teams without Kubernetes experience deploying large-scale clusters
- Use cases requiring traditional relational database features as primary functionality
- Prototypes needing the simplest possible setup without performance optimization
How teams use it
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.
Tech snapshot
Go59%
Python20%
C++19%
Shell1%
Groovy1%
C1%
Frequently asked questions
What deployment options does Milvus support?
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.
How does Milvus handle scaling for billions of vectors?
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.
What vector index types does Milvus support?
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.
Can Milvus perform both semantic and full-text search?
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.
What security features does Milvus provide?
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
Active- Stars
- 42,311
- Watchers
- 42,311
- Forks
- 3,771
LicenseApache-2.0
Repo age6 years old
Last commit2 days ago
Primary languageGo
Last synced yesterday