11st of 8 in Vector databases
Milvus
Distributed vector database with dense, sparse and hybrid search at scale
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- Stars
- 46.3k
- License
- Apache-2.0
- Last commit
- Oct 2026
- Last release
- Sep 2026
Overview
Milvus is a distributed vector database (Go and C++, LF AI & Data Foundation) that separates compute and storage on Kubernetes, with a Standalone Docker mode and pip-installable Milvus Lite. It offers HNSW, IVF, FLAT, SCANN and DiskANN indexes, GPU CAGRA, sparse BM25 and learned-sparse vectors for hybrid search, metadata filtering, multi-tenancy, hot/cold storage, auth, TLS and RBAC.
Who it is for: teams needing billion-scale vector search on Kubernetes
Strengths
- Index types HNSW, IVF, FLAT, SCANN, DiskANN plus GPU CAGRA
- Dense, sparse (BM25, SPLADE, BGE-M3) and hybrid search in one collection
- Multi-tenancy at database, collection, partition or partition-key level
- Mandatory auth, TLS and RBAC; Milvus Lite via pip for local dev
Weaknesses
- Distributed mode is Kubernetes-native with several microservices to operate
- No port, RAM or Docker command in the README; install lives in docs
- Zilliz is the major contributor and promotes its managed cloud
- Source build needs Go 1.21+, CMake, GCC 11+ and Python 3.8 to 3.11
What it needs
- GPU optional
- Compose
- Compose runs Milvus, MinIO
- Models: any embedding model or service; pymilvus[model] wraps embedding and reranking models
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| #5 | pgvectorPostgreSQL extension for vector similarity search with HNSW and IVFFlat | 59 out of 100 |
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