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About
Faiss (Facebook AI Similarity Search) is an open-source library developed by Meta AI. It is designed for efficient similarity search and clustering of dense vectors, capable of handling datasets from millions to billions of high-dimensional vectors. Faiss serves as a foundational component for various AI and machine learning applications requiring fast and scalable vector similarity search.
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Key facts
- Website
- github.com
- Founded
- 2017
- Business model
- Open Source
Faiss by Meta pricing
Plans as published by Faiss by Meta. Current prices on github.com
Tech stack
Detected on github.com, last scanned 29 Oct 2026. Run your own scan
- Email provider
- Microsoft 365
- Affiliate program
- Self-hosted affiliate
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Frequently asked
Is Faiss by Meta trustworthy and reputable?
StartupHub's Data Trust & Reputation score for Faiss by Meta is 91 out of 100, a trusted rating, based on site security posture, privacy practices, malware and phishing checks, and public reputation signals.
Does Faiss by Meta have an affiliate program?
Yes, Faiss by Meta has an affiliate program. The program runs on Self-hosted affiliate. Register here: github.com/facebookresearch/faiss/affiliates
SEC filings, insider activity, nonprofit links and pay benchmarks for Faiss by Meta:
Company details: from public sources. Funding: no announced round on file. Ownership: not yet claimed by the company. Scores: measured by named tools; each links to its method. Our rules
