What is the best way (cheapest / fastest option) to train an model on massive dataset (400GB+, 100m rows x 200 columns)?
I don't know many boosting packages but I've been using XGboost for a while now and the biggest tabular dataset I've had was more than 40 times smaller than yours. The training took 2-3 days.
In my experience training time is worse than linear with the size of the data even though it highly depends on the data itself and the hyperparameters you chose. My guess is your training would be very (too) long.
If you really want to use XGboost, you should train on GPU, it seems to me that you are looking at cloud providers, I know google offers managed training of XGboost on GPU, others surely do as well.
With that amount of data I think you should consider using deep learning. You could maybe use tabnet which is a great model developed for tabular data by Google AI. It is easy to try using pytorch for instance.