Colab is where most people first run something on a GPU and it has a specific and maddening flaw. The environment does not persist. You install your packages, download your data, get everything working, close the tab, and the next day it is gone. Anyone who has learned deep learning through Colab has spent hours reinstalling the same dependencies.
Lightning AI's central idea is a persistent workspace that behaves like a machine you own, and that single decision fixes the most tedious part of learning on cloud hardware. Persistence is the feature and the documentation explains it well. Your files stay, your installed packages stay, your environment is where you left it. That sounds mundane and it changes the experience substantially, because you can work on something over several weeks without the setup tax on every session.
For anyone working through a course or building a project across evenings, this is the difference between progress and repeated setup. Hardware switching without rebuilding is the second good idea. Develop on a cheap machine, switch to a GPU when you need to train, switch back when you are debugging code that does not need one. The documentation covers this clearly and the workflow it enables is genuinely better than the alternatives, where changing hardware usually means recreating your environment somewhere else.
It also encourages a habit worth having, which is not paying for a GPU while you are editing code. The free tier is meaningful. Monthly credits sufficient for real work rather than a token demonstration means you can complete a substantial learning project without paying, and the documentation is upfront about what the credits cover. Compared with platforms whose free tier exists only to show you the interface, this is a more honest offering.
The PyTorch Lightning documentation underneath is strong and worth separating from the platform. The framework's material on structuring training code, handling distributed training and managing the training loop is well established and good. If you learn nothing else from this ecosystem, the discipline that Lightning imposes on training code organisation is worth having, and it transfers to any project regardless of where you run it. The platform documentation is less mature than the framework documentation and the gap is visible.
Studios, environment configuration and the deployment features are covered less thoroughly, with occasional gaps where you work something out by experiment. That is normal for a newer product and it does mean you feel the difference when you move from framework documentation to platform documentation within the same session. Credit consumption is easy to lose track of. Different machine types consume at different rates and a session left running while you take a break is still consuming.
The documentation explains the mechanics accurately, and accuracy is not the same as making the running total obvious while you work. Check your usage regularly rather than at the end of the month, particularly during long sessions where it is easy to forget what hardware you are on. The Studios concept takes adjustment. It is not quite a notebook, not quite a virtual machine, and understanding what persists, what is shared and how the pieces relate takes longer than the documentation implies.
Once it clicks, the model is good. Before it clicks there is a period of mild confusion that better conceptual documentation would shorten. The platform habits question is worth raising honestly. Working here teaches you a workflow that exists on this platform, and if you later move to a raw cloud GPU or a company cluster, some of what you learned does not apply.
The framework knowledge transfers completely, the platform knowledge does not. That is true of every managed environment and it is worth knowing so you can distinguish between the two while learning. Four point zero for a platform that fixes the most annoying limitation of the free alternatives, documented adequately and backed by genuinely good framework documentation. The platform material lags the framework material, credit tracking demands attention, and for someone who has hit Colab's limits and does not want to become an infrastructure engineer this is the most comfortable option available.