Milvus vs faiss
Two of the top projects in Vector databases, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
Milvus
Distributed vector database with dense, sparse and hybrid search at scale
faiss
C++ library for similarity search and clustering of dense vectors
Verdict
Milvus ranks higher, 80 to 64. Of the 5 score parts we measure for both, Milvus leads on 4, faiss on none and 1 is even.
- AdoptionMilvus leads by 8 points, 78 to 70.
- FreshnessEven at 100.
- MaintenanceMilvus leads by 1 point, 84 to 83: close to even.
- Easy to runMilvus leads by 34 points, 67 to 33: a wide gap.
- Agent-readyMilvus leads by 70 points, 70 to 0: a wide gap.
Pick Milvus if you are
Teams needing billion-scale vector search on Kubernetes
- Index types HNSW, IVF, FLAT, SCANN, DiskANN plus GPU CAGRA
- Dense, sparse (BM25, SPLADE, BGE-M3) and hybrid search in one collection
Pick faiss if you are
Engineers embedding vector search in their own C++ or Python applications
- Many index types trading off speed, accuracy, memory per vector and training time
- Compressed and binary indexes handle sets that do not fit in RAM uncompressed
| What we compare | Milvus | faiss |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 78, popular | 70, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 84, healthy | 83, healthy |
| Easy to run | 67, easy | 33, some setup |
| Agent-ready | 70, partly | 0, none |
| Facts from GitHub and the README | ||
| Stars | 46.4k | 41.2k |
| License | Apache-2.0 (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Sep 2026 |
| Language | Go | C++ |
| Docker | Yes | No |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Mentioned | Not stated |
Milvus
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
- GPU optional
- Compose
- Compose runs Milvus, MinIO
- Models: any embedding model or service; pymilvus[model] wraps embedding and reranking models
faiss
Faiss is a C++ library with Python/numpy wrappers for nearest-neighbor search over dense vectors using L2 distance, dot product or cosine. It offers exact search, HNSW and NSG graph indexes, and compressed indexes that scale to billions of vectors in memory. Some indexes also run on GPU via CUDA or AMD ROCm, with optional NVIDIA cuVS backends.
Who it is for: Engineers embedding vector search in their own C++ or Python applications
Strengths
- Many index types trading off speed, accuracy, memory per vector and training time
- Compressed and binary indexes handle sets that do not fit in RAM uncompressed
- GPU indexes (CUDA, ROCm) replace CPU ones, single or multi-GPU
- Only hard dependency is a BLAS implementation; MIT licensed
Weaknesses
- Library only: no server, REST API, auth or built-in persistence service
- Vectors are identified by integers; metadata filtering must be built separately
- Choosing and tuning an index type requires understanding the trade-offs
- Builds with cmake; prebuilt packages are listed for Anaconda only
- GPU optional
- Needs BLAS, CUDA or ROCm (optional), NVIDIA cuVS (optional)
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