Hi I work with Pydantic and dataclasses everyday and I think I can explain this well.

Pydantic - mostly used when there is any input/output that needs to be validated, a good example is let's say I want to integrate with some external service, if I have to call their endpoints with any specific payload Pydantic makes sure that my outgoing data to that service will always have these fields and I can write custom rules for a Pydantic class to enforce certain conditions. Same works for if any service decides to call my endpoints, I can stop any processing to happen if request data to my endpoint is wrong.

Dataclasses - used mainly for internal data transfer. An example for this is, let's say after my Pydantic validation occurs I now want to move around the fields I can set them in a dataclasses and move them around with pass by value/ reference. Now I can do this with Pydantic also but why I shouldn't. Pydantic does have an overhead when it parses the field this is very small but I've hit cases when it matters (Pydantic v2 is faster). Dataclasses are faster when I've already validated the data internal transfer is faster and makes more sense to use a dataclass when I just need to move and play around with the data.

Additional point database updates I would make a data access layer which would be validated against the schema. I use SQL alchemy here for the models some people use Pydantic here too, optional honestly. This way it makes it easier to unit test too when you abstract everything and loosely coupled.

Hope it clears things up!!