Faiss
NewA library for efficient similarity search and clustering of dense vectors.
Data collected ·ChatGPT, Perplexity, Google AI Mode·coverage 100%·next run
- Faiss ranks #13 of 30 in Vector Databases with an AI visibility score of 5.4 (SlateIndex, Nov 2, 2026).
- Faiss is most visible on Google AI Mode (8.3) and least on ChatGPT (3.7).
- AI engines name Faiss in 6 of the 30 Vector Databases buyer prompts SlateIndex tracks.
- github.com is the source AI engines cite most in answers about Faiss, with 5 citations.
About
Company details for Faiss
Faiss is a library for efficient similarity search and clustering of dense vectors. It is written in C++ with complete Python wrappers and can search sets of vectors that may not fit in RAM. Some of its algorithms run on the GPU, and it can store indexes on disk. It runs inside the calling process and is not a hosted service or a full vector database. It ranks #13 of 30 in Vector Databases on SlateIndex and has a visibility score of 5.4 in the latest weekly run.
Its differentiator: an open-source library of published similarity search algorithms with GPU implementations, rather than a hosted database.
- Website
- faiss.ai
- Categories
- Vector Databases
- Founded
- 2017
- Company type
- Division of Meta Platforms
- Deployment
- Self-hosted library that runs inside the calling process on a local machine.
Read from arxiv.org, g2.com, github.com, faiss.ai, engineering.fb.com · refreshed Nov 2, 2026.
AI visibility
Latest weekly run · Nov 2 · next run Nov 9
Visibility score trend
Weekly runs over the last 60 days, with up to 7 competitors. Click a name to hide or show its line.
Changes appear after the second weekly run in Vector Databases. Pinecone leads the category at 71.8%.
Media
Website and product screenshots for Faiss
AI mentions & citations
Recent answers that name Faiss
What features matter most in a vector database?
Metadata Storage and Filtering: A major differentiator from raw vector indexes (like FAISS) is the ability to attach scalar metadata to vectors and perform hybrid queries (filtering by attributes like date, user ID, or…
What are common mistakes when buying a vector database?
Confusing an In-Memory Library with a Production Database: Adopting a barebones, in-memory vector library (like a local FAISS index) for a multi-tenant production app instead of a full-featured vector database.
What is the difference between a vector database and MLOps Platforms?
How do companies store and search embeddings without a dedicated tool?
Embedded libraries: For smaller or static datasets, libraries such as FAISS or Annoy keep an index inside the application process or on disk—no separate server is required.
Who makes the most used vector database?
FAISS, pgvector, ChromaDB and Qdrant are major players.
How do companies store and search embeddings without a dedicated tool?
How do companies store and search embeddings without a dedicated tool?
Flat Files and Custom In-Memory Arrays (FAISS / NumPy / Scikit-Learn):
What are the best Vector Databases tools in 2026?
FAISS – a vector similarity search library rather than a complete database.
Products & features
What Faiss sells
Faiss
Open-source C++ library with Python wrappers for similarity search and clustering of dense vectors, with some algorithms on the GPU.
faiss-cpu
CPU package for installing Faiss through Conda or PyPI.
faiss-gpu
Package providing CUDA-enabled Faiss indexes, installed through Conda.
Strengths engines cite
- No separate server needed for small or static datasets
Target market & buyers
Developers and machine learning engineers building search and retrieval applications.
Funding & recent developments
Funding
| Latest known round | Total raised |
|---|---|
| — | — |
Recent developments
- Dec 2025Productfaiss-cpu 1.13.2 released
The PyPI faiss-cpu package lists version 1.13.2, released December 24, 2025, after 1.13.0 in November 2025.
- Oct 2025ProductThe Faiss Library paper published
A paper titled The Faiss Library has a publication date of 13 October 2025.
- Aug 2025ProductFaiss v1.12.0 stable release
Wikipedia lists v1.12.0 as the stable release of Faiss.
- May 2025ProductMeta and NVIDIA integrate cuVS GPU algorithms into Faiss
NVIDIA cuVS GPU algorithms were integrated into Faiss, and Faiss 1.10.0 officially included them.
Competitive landscape
How Faiss compares with its top three alternatives
Faiss most often appears alongside Pinecone, PostgreSQL, Qdrant in AI answers.
| Brand | Visibility | Share of voice | vs last week | Starting price |
|---|---|---|---|---|
| Faiss | 5.4% | 1.7% | New | Pricing not available |
| Pinecone | 71.8% | 14% | New | Free plan |
| PostgreSQL | 49% | 12.6% | New | Pricing not available |
| Qdrant | 60.5% | 13.4% | New | Free plan |
| Weaviate | 45.2% | 11.7% | New | Free plan |
What AI answers say
Sentiment and themes from the answers that mention this brand
Positive 12% · Neutral 63% · Mixed 25% of 8 engine answers that mention Faiss
Praised for
- No separate server needed for small or static datasets
Criticised for
- Barebones in-memory library rather than a full-featured database
- Lacks metadata storage and filtering for hybrid queries
Cite as: SlateIndex, “Faiss AI visibility profile”, https://slateindex.ai/brand/faiss, data of Nov 2, 2026. Licence: CC BY 4.0.