Again this is dependent on your implementation. I think the on-the-fly augmentation generally works better. No dataset is repeatedly trained means less for it to overfit on. Augmentation is generally small perturbations of the original set.
If you wanted to you could augment ahead of time before training so that you have 1 set of transformed samples or design your augmentations such that they are not random across epochs.