I was just starting to learn about embeddings for a very similar use on my project. Newbie question: what are pros/cons of using an API like gpt Ada to calculate the embeddings, compared to importing some model on Python and running it locally like in this article?

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?