While it is true that Bayesian inference is very powerful in that it allows one to introspect and view effects of the model's parameters on the outcome, it is equally as good at predicting the outcome variable as well. It just depends on what you want to get out of it. In fact you get more information about your outcome variable from Bayesian Inference as it is a distribution.

I'm not saying it is better than DL by any means, as DL can scale much better. Just that I don't think it's necessary to pigeonhole Bayesian inference to just predicting the parameters. In my opinion the "fundamental difference of focus" is just a personal decision, not something inherent to the method.