I looked at Prophet a few months ago because we needed a fire-and-forget library similar to 'auto.arima' (R "forecast" package) for Python, but no good candidates existed.

However I found Prophet to be computationally a little heavier than auto.arima because it uses "stan" (Bayesian) underneath, which in turn uses an MCMC type approach and has quite a few dependencies. We needed fast model retraining as well, and at the time, it didn't seem like that was something it excelled at. (might have changed, I'm not sure)

I ended up putting together a simple ensemble forecast model class with "statsmodels" which automatically selected/averaged the best models over a collection of model types via heuristics and cross-validation. It works ok, but I'm still waiting for someone to port the R auto.arima over to Python. (I tried rpy, which in theory should have worked, but I struggled with the impedance mismatch)