We are open-sourcing pgANN – an ANN (approx nearest neighbor) approach with a PostgreSQL backend. The key differentiator between pgANN and the rest (FAISS, Annoy,NearPy etc) is:

1. this enables “online” learning i.e. doesn’t require retraining with every CRUD, and
2. works with extremely large datasets, since it’s not held in RAM like the others

We use it internally to QA images and find it consistently provides sub-second query performance with a few million rows of vectors on a 32GB.8 vcpu Ubuntu box and can reasonably be expected to scale-up with normal pgsql scaling techniques. We invite the community to give this a try and share feedback.