Faiss

license

MIT(OSI)

source

public

core-gated

ungated

MIT license body confirmed. The repository is public and unarchived and builds the whole product, and the README describes no paid tier, enterprise edition or license-gated build beside it, so source is public and the core ungated.

• https://github.com/facebookresearch/faiss/blob/main/LICENSE recorded 2026-08-18

  LICENSE file is the verbatim MIT text

• https://api.github.com/repos/facebookresearch/faiss recorded 2026-08-18

  Repo metadata - license spdx_id MIT, private false, archived false, default branch main - for facebookresearch/faiss.

• https://github.com/facebookresearch/faiss/blob/main/README.md recorded 2026-08-18

  README describes a library for similarity search and clustering of dense vectors with algorithms that scale past RAM, written in C++ with complete Python wrappers and GPU implementations of the most useful algorithms, developed at Meta's Fundamental AI Research group, with no paid tier, enterprise edition or license-key-gated build beside it.

15,698,726 PyPI downloads of `faiss-cpu` in the trailing 30 days, which lands in the >10M band of the software usage scale, level 5. `faiss-cpu` is the community-maintained wheel of this library rather than a client for a separate service, so its installs are installs of the product; its PyPI project URL points at facebookresearch/faiss. The vendor's own channel is conda, which publishes no comparable figure, so this understates rather than overstates the total.

• https://pypistats.org/api/packages/faiss-cpu/recent recorded 2026-08-18

  last_month downloads = 15,698,726 for faiss-cpu

Banded on the category feature matrix as single-purpose store or index. Placed two bands below the vespa anchor, on a matrix that bands on how much of the persist-index-retrieve path a product provides itself, at what scale and over how many retrieval modes.

• https://github.com/facebookresearch/faiss/blob/main/README.md recorded 2026-08-18

  README still documents a library for similarity search and clustering of dense vectors with algorithms that scale past RAM, written in C++ with complete Python wrappers and GPU implementations of the most useful algorithms, developed at Meta's Fundamental AI Research group.