head to head · open source

llama_index vs faiss

llama_index has 52,206 GitHub stars, 8,165 forks, 770 open issues and last shipped yesterday. faiss has 40,929 stars, 4,526 forks, 327 open issues and last shipped yesterday. llama_index leads on adoption by 28% (52,206 vs 40,929 stars). llama_index is written in Python under MIT; faiss is written in C++ under MIT. llama_index has attracted 16% as many forks as stars, faiss 11%. faiss was the more recently maintained of the two, and both are self-hostable with no licence fee.

Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.

llama_index ★ 52K faiss ★ 41K category AI & Machine Learning

← all 13541 open source comparisons

Side by side

llama_index faiss
GitHub stars ★ 52K ★ 41K
License MIT MIT
Written in Python C++
Last push 2026-09-18 2026-09-18
Forks ⑂ 8.2K ⑂ 4.5K
Self-hosting Yes Yes
Data ownership Your server Your server

pick llama_index if

  • You weight community size — 52K stars and counting
  • You want the MIT license terms
  • Your stack matches Python
  • You value the larger contributor base for long-term maintenance

full llama_index profile →

pick faiss if

  • You want the faiss feature set and don't need the biggest community
  • You prefer the MIT license terms
  • Your stack matches C++
  • You evaluated both and faiss fits your workflow better

full faiss profile →

About llama_index

LlamaIndex is an MIT licensed, open source Python framework for building agentic applications — retrieval augmented generation systems, agents and multi agent workflows — on top of private documents and data, and it is aimed at AI engineers and teams who need to connect large language models to their own sources of context.

read the full llama_index overview →

About faiss

Faiss is an MIT licensed C++ library for efficient similarity search and clustering of dense vectors, with complete Python and NumPy wrappers, developed primarily at Meta's Fundamental AI Research group, and it is intended for engineers and researchers who need nearest neighbour search over vector sets that range from small collections to sets that possibly do not fit in RAM.

read the full faiss overview →

More in AI & Machine Learning

OpenClaw ★ 390K Hermes Agent ★ 247K Ollama ★ 181K Dify ★ 156K Open WebUI ★ 152K langchain ★ 147K

Related comparisons

ollama vs llama-index openclaw vs ollama ollama vs llama-cpp ollama vs vllm ollama vs gpt4all ollama vs localai open-webui vs gpt4all llama-cpp vs vllm openclaw vs hermes-agent openclaw vs opencode openclaw vs n8n openclaw vs open-webui openclaw vs lobechat openclaw vs anythingllm hermes-agent vs opencode hermes-agent vs n8n openclaw vs dify openclaw vs multica openclaw vs langgraph n8n vs dify dify vs langflow dify vs open-webui dify vs langchain dify vs ponytail

More Machine Learning Infrastructure projects

Compare either of these against the rest of the Machine Learning Infrastructure field.

llama_index vs Ollama llama_index vs llama.cpp llama_index vs vllm llama_index vs GPT4All llama_index vs LocalAI llama_index vs PageIndex llama_index vs Langfuse llama_index vs cognee llama_index vs taipy llama_index vs dagster llama_index vs zvec llama_index vs langchain4j

Frequently asked questions

Is llama_index or faiss more popular?

llama_index has 52,206 GitHub stars and faiss has 40,929. llama_index has the larger community by that measure.

Are llama_index and faiss free?

Both are open source. llama_index is licensed under MIT and faiss under MIT. Neither carries a licence fee.

What is the difference between llama_index and faiss?

llama_index is written in Python and faiss in C++. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.

Which should I choose, llama_index or faiss?

Choose llama_index if you want the larger community (52,206 stars) or its MIT licence terms. Choose faiss if its feature set, stack or MIT licence fits better. Both are self-hostable.