I learned this the hard way on a book platform I'm working on. While semantic search is useful for discovery, we found that prioritizing exact matches is critical. It seems users get pretty frustrated if they type a specific title and get a list of conceptually similar results instead of the actual book. We ended up having to tune the ranking to heavily favor literal string matches over the vector distance to keep people from bouncing.

Especially with small datasets it’s more important to be exact at the expense of a user having to fix a typo.

The main trade-off I found is the RAM footprint on your backend workers. If you run the model locally, every Celery worker needs to load it into memory, so you end up needing much larger instances just to handle the overhead.

With Ada your workers stay lightweight. For a bootstrapped project, I found it easier to pay the small API cost than to manage the infrastructure complexity of fat worker nodes.

Do you want it to run on your CPU, or someone else's GPU?

Is the local model's quality sufficient for your use case, or do you need something higher quality?

(The missing close-apostrophe, and the use of “type” are what really confuse me in the original submission)