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 If that is out of date, tell us at right of reply.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 publishes a nine-dimension trust framework — data sovereignty, training privacy, compliance posture, operational resilience, exit portability, and more. The heaviest criterion asks whether the tool builds its own AI or resells someone else's model; where the vendor discloses it, the grade cites the vendor's own documentation. No paid placements — scoring is walled off from affiliate revenue. See the methodology →