The problem with GraphQL is on the front end. Suddenly, the FE team becomes responsible for understanding the entire data model, which resources can be joined together, by what keys, and what is actually performant vs what isn't.

Instead of doing a simple GET /a/b/1/c and presenting that data structure, they now need to define a query, think about what resources to pull into that query etc. If the query ends up being slow, they have to understand why and work on more complex changes than simply asking the BE team for a smaller response with a few query params.

I hit this problem when contemplating exposing the API of the application I work on to customers, to be used in their automation scripts.

We quickly realized that expecting them to learn our data model and how to use it efficiently would be much more complicated than exposing every plausible use-case explicitly. We could do this on the "API front-end" by building a set of high-level utilities that would embed the GraphQL queries, but that would essentially double much of the work being done in the front-end (and more than double if some customers want to use Python scripting while others want JS and others want TCL or Perl).

So, we decided that the best place to expose those high-level abstractions is exactly the REST API, where it is maximally re-usable.