It's basically just a higher abstraction layer for PyTorch. It's completely separate but works in tandem with PyTorch.

I use LightningModules (analogous to torch.nn.Module) as basically decorators over ordinary PyTorch models. So you have your model class, and then you create a LightningModule which is instantiated with said model, where you implement ex. what optimizers and schedulers you use, how your training, evaluation and testing goes, what metrics you track and when etc.

But once you're done with R&D you can just use ordinary PyTorch as-is, that's why I like it. It doesn't make setting stuff up for production different in any way, but it makes plenty of stuff during R&D effortless. It has some smelly parts but IMO they're not a dealbreaker, just take a day or two to learn it.