One difference between the two projects is that pg_vectorize does not run the embedding or chat models on the same host as postgres, rather they are always separate. The extension makes http requests to those models, and provides background workers that help with that orchestration. Last I checked, PostgresML extension interoperates with a python runtime on same host as postgres.

PostgresML has a bunch of features for supervised machine learning and ML Ops...and pg_vectorize does not do any of that. e.g. you cannot train an XGboost model on pg_vectorize, but PostgresML does a great job with that. PGML is a great extension, there is some overlap but architecturally very different projects.