I choose between Django and Litestar these days.

I wish I had a hard and fast rule for the choice, but it's something like:

- Litestar if I know with certainty (a) the thing I'm building is and always will be a tiny service and (b) I probably won't have to evolve the backing data schema much. Wrap an ML model? Litestar. Have a small database I want to expose via API, read-mostly? Litestar.

- Django if I know (a) I'm going to be building on this over a longer timeframe, (b) I have complex user/group auth needs, (c) my data schema will likely go through substantial evolution, or (d) I will need an admin UI in the early goings, either for the managing team or for customer support.

I have plenty of SQLAlchemy+Alembic+Litestar backends but I vastly prefer the creature comforts of Django's ORM + migrations; it hits that 80% sweet spot so very well. SQLalchemy is both raw power and, often, far too much friction.

I used to choose between Django and Flask, but Litestar -- while still young -- seems to capture most of the key things I liked about core Flask but in a more modern package. (Maybe Quart, aka async Flask, will ultimately win the day; not sure.)

It's not targeted at the use case, but I wonder if Datasette will replace my need for the Django admin UI at least in some limited set of cases.

These days, most of my front-ends are React+Typescript+Vite.

(FWIW I personally avoid FastAPI entirely. I appreciate the impact it had getting the python community to think about (a) async (b) proper use of type hints in the web context and (c) the need for OpenAPI support, etc. but there are a lot of footguns and so many issues still opened that its (amazing, lovely) single maintainer probably won't get to. SQLModel -- aka the merging of Pydantic + SQLAlchemy ORM models -- is an even bigger trap, IMO.)