As someone who sells both of these services, I can only add that it depends, and if you have a good dataset, it's trivial to write either one.
But once you start having to account for noise or seasonality or autoregression or dynamic weights or non linear kernel spaces, pure SQL really starts to fall down on the job.
But once you start having to account for noise or seasonality or autoregression or dynamic weights or non linear kernel spaces, pure SQL really starts to fall down on the job.