Qdrant 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.
Qdrant
Rust vector database with payload filtering, REST and gRPC
faiss
C++ library for similarity search and clustering of dense vectors
Verdict
Qdrant ranks higher, 66 to 64. Of the 5 score parts we measure for both, Qdrant leads on 1, faiss on 2 and 2 are even.
- Adoptionfaiss leads by 12 points, 70 to 58.
- FreshnessEven at 100.
- Maintenancefaiss leads by 2 points, 83 to 81: close to even.
- Easy to runQdrant leads by 17 points, 50 to 33: a wide gap.
- Agent-readyEven at 0.
Pick Qdrant if you are
Developers wanting a filter-heavy vector store with gRPC
- Dense, sparse and multivector (ColBERT) search with RRF and DBSF fusion
- Quantization cuts RAM up to 97 percent; on-disk storage and io_uring
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 | Qdrant | faiss |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 58, popular | 70, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 81, healthy | 83, healthy |
| Easy to run | 50, easy | 33, some setup |
| Agent-ready | 0, none | 0, none |
| Facts from GitHub and the README | ||
| Stars | 35.1k | 41.2k |
| License | Apache-2.0 (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Sep 2026 |
| Language | Rust | C++ |
| Docker | Yes | No |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Mentioned | Not stated |
Qdrant
Qdrant is a Rust vector database exposing REST (OpenAPI 3.0) and gRPC on port 6333 for storing points (vectors plus JSON payload) and searching with dense, sparse and multivector (ColBERT) embeddings, rich payload filters and hybrid fusion (RRF, DBSF). It adds quantization, on-disk storage, sharding and replication, multitenancy, GPU-accelerated indexing and a web UI. Qdrant Edge runs the same engine embedded in-process.
Who it is for: developers wanting a filter-heavy vector store with gRPC
Strengths
- Dense, sparse and multivector (ColBERT) search with RRF and DBSF fusion
- Quantization cuts RAM up to 97 percent; on-disk storage and io_uring
- REST with OpenAPI 3.0 spec plus gRPC; six official clients
- Sharding and replication with zero-downtime collection resize
Weaknesses
- Default docker run has no auth and binds all interfaces
- GPU acceleration covers indexing only; search runs on CPU
- Qdrant Edge embedded mode is Python and Rust only
- Sharding and tenant isolation require upfront design
- GPU optional
- Docker
- Models: any embedding model; dense, sparse and late-interaction (ColBERT) vectors
- port 6333
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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