I don't follow point 2, batch processes can definitely take advantage of scaling on demand. You can spin up a huge cluster, only pay for it for an hour while training, and then spin down to just what you need to serve requests for your already trained model.

Of course it depends on how much data you're training on, and how up-to-date you need your model to be.