FAISS
This entry runs no foundation model. Every finding below is quoted to the vendor’s own document, or marked not disclosed where the vendor publishes nothing.
Facebook AI Similarity Search — high-performance vector database for indexing and querying dense embeddings at billion-scale, essential for repository-wide semantic code retrieval
No foundation model. FAISS does not run one, so this criterion does not apply and is excluded from the grade rather than counted against it. A C++ library with Python bindings implementing nearest-neighbour search and clustering algorithms over dense vectors; it ships index structures and quantization code, not any foundation model or learned weights. Embeddings must be produced by some other tool and handed to Faiss. Established from the product’s own public documentation and what it does. If that is out of date, tell us at right of reply.
Not yet assessed. We publish a sovereignty position only where the vendor documents one — we do not infer it from a domain or a company name.
Vannus records, from the vendor's own published documents, the legal entity a customer contracts with, the country that entity sits in, and the governing law of its terms — and whether the vendor runs its own model or resells someone else's. Each finding is quoted to its source and dated, or marked not disclosed where the vendor publishes nothing. No paid placements — affiliate status is walled off from the record, enforced by a test in the build. See the methodology →