pgvector
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.
Open-source PostgreSQL extension adding vector similarity search — stores embeddings alongside relational data and queries them with standard SQL.
No foundation model. pgvector does not run one, so this criterion does not apply and is excluded from the grade rather than counted against it. Postgres extension providing vector storage, distance operators, and approximate nearest neighbor indexes; it stores embeddings produced elsewhere and generates none itself, so no foundation model is involved. Reviewed from the vendor’s own documentation: raw.githubusercontent.com ↗ If that is out of date, tell us at right of reply.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.
Taken from the vendor’s own published material. Vannus does not hold these reports and has not reviewed their scope or dates — ask the vendor for the current report before relying on any of them.
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 →