I'm no authority on the subject, but FWIW I tried quite a bit to make various bayesian methods work for me. I never found them to outperform equivalent frequentist (point estimate) methods.

Modelling uncertainty sounds nice and sometimes is a goal in itself, but often at the end of the day you need a point estimate. And then IME all the priors, flexible models, parameter distributions, just don't add anything. You could imagine they do, with a more flexible model, but that is not my experience.

But then, PPL is just so much harder. The initial premise is nice - you write a program with some unknown parameters, you have some inputs and outputs, and get some probabilistic estimates out. But in practice it is way more complex. It can easily and silently diverge (i.e. converge to a totally wrong distribution), and even plain vanilla bayesian estimation is a dark art.