Faiss vs LEANN
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A C++ library with Python bindings for fast similarity search over large sets of vectors.
Open source · MIT
Local semantic search over your own files and chats, using far less storage than typical vector databases.
The short answer
Choose Faiss if you need…
- Developers building semantic search or recommendation features over embedding vectors
- Teams working with very large vector collections that need compressed indexes
- Python users who want numpy-compatible similarity search
- Researchers comparing approximate nearest neighbor methods
Choose LEANN if you need…
- Developers building a private RAG prototype on a personal machine
- People who want to semantically search their own files or notes locally
- Users of coding agents who want semantic code search through an MCP service
- Anyone with a large document collection who is short on disk space
Side-by-side
| Faiss | LEANN | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 41.2k | 13.8k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | C++ | Python |
| Last code update | 4 days ago | 4 days ago |
What each one is
Faiss
Faiss is a library for finding vectors that are similar to a query vector, such as searching embeddings for text or images. It can handle collections of vectors large enough that they may not fit in RAM, and it includes tools for evaluation and parameter tuning. It is written in C++ with Python/numpy wrappers, and some algorithms run on GPUs.
LEANN
LEANN is a Python tool that builds a searchable index of your personal documents, such as files, emails, and chat logs, so an AI assistant can find relevant passages before answering. It is a vector database that recomputes embeddings on demand instead of storing them all, which the project says cuts storage by about 97% without accuracy loss. People use it to run private retrieval-augmented generation (RAG) on their own laptop without sending data to a cloud service.
Best for
Faiss
- Developers building semantic search or recommendation features over embedding vectors
- Teams working with very large vector collections that need compressed indexes
- Python users who want numpy-compatible similarity search
- Researchers comparing approximate nearest neighbor methods
LEANN
- Developers building a private RAG prototype on a personal machine
- People who want to semantically search their own files or notes locally
- Users of coding agents who want semantic code search through an MCP service
- Anyone with a large document collection who is short on disk space
Strengths
Faiss
- Designed for efficient search over large sets of dense vectors, with options that trade speed, quality, and memory
- Mostly C++ with complete Python/numpy wrappers and optional GPU support via CUDA or AMD ROCm
- Released under the MIT license and developed primarily at Meta's Fundamental AI Research group
LEANN
- Keeps data on the local machine, with no cloud dependency described in the README
- Claims large storage savings, with a benchmark example of 60M chunks in 6GB versus 201GB
- Works with common tooling including Ollama, LangChain, and LlamaIndex, per the repository topics
Not ideal for
Faiss
- People who want a finished search app with a user interface, since this is a library
- Users with small datasets where a simple brute-force search in another tool is enough, because Faiss adds setup complexity
- Projects that need a managed database with storage, filtering, and updates handled for them, since the README describes only the search and clustering library
LEANN
- Teams needing a hosted, multi-user database, since the README centres on a personal, single-device setup
- Users who need GPU acceleration now, as the README says it is only being considered based on survey votes
- People who want a polished end-user app with no command line, since setup uses uv and Python tooling
Trade-offs
Faiss
- The README describes many index types and trade-offs, so choosing the right one takes some study
- GPU support requires suitable hardware and drivers, and the README does not detail every setup requirement
- Full documentation is on an external wiki and the README is truncated, so some details could not be verified here
LEANN
- Setup involves Python version requirements, uv, and optional build steps for DiskANN that can be fiddly on Linux and macOS
- Some README sections are truncated here, so the full feature set and configuration are not verified
- Accuracy and storage claims come from the project and its paper; independent verification was not part of this review
Getting started
Faiss
Install CPU build from Anaconda (per README link to faiss-cpu)
git clone https://github.com/facebookresearch/faiss.gitLEANN
Install uv
curl -LsSf https://astral.sh/uv/install.sh | shClone the repository
git clone https://github.com/StarTrail-org/LEANN.git leannFrequently asked questions
What is the difference between Faiss and LEANN?
Faiss: A C++ library with Python bindings for fast similarity search over large sets of vectors. LEANN: Local semantic search over your own files and chats, using far less storage than typical vector databases. Faiss suits developers building semantic search or recommendation features over embedding vectors, while LEANN suits developers building a private rag prototype on a personal machine.
Are Faiss and LEANN free?
Faiss: Free and open source. LEANN: Free and open source.
Is Faiss or LEANN more popular?
Faiss has more GitHub stars (41.2k) than LEANN (13.8k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Faiss and LEANN?
Other options people compare them with include LlamaIndex, PageIndex, LightRAG, turbovec, LangGraph.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.