Some of the key differences:
- Faster storage. Colab uses Google Drive which is convenient to use but very slow. For example, training datasets often contain a large amount of small files (eg 50k images in the sample TensorFlow and PyTorch datasets). Colab will start to crawl when it tries to ingest these files which is a really standard workflow for ML/DL. It's great for toy projects eg training MNIST but not for training more interesting models that are popular in the research/professional communities today.
- Notebooks are fully persistent. With Colab, you need to re-install everything every time you start your Notebook.
- Colab instances can be shutdown (preempted) in the middle of a session leading to potential loss of work. Gradient will guarantee the entire session.
- Gradient offers the ability to add more storage and higher-end dedicated GPUs from the same environment. If you want to train a more sophisticated model that requires say a day or two of training and maybe a 1TB dataset, that's all possible. You could even use the 1-click deploy option to make your model available as an API endpoint. The free GPU tier is just an entrypoint into a full production-ready ML pipeline. With Colab, you would need to take your model somewhere else to accomplish these more advanced tasks.
- A large repository of ML templates that include all the major frameworks eg the obvious TensorFlow and PyTorch but also MXNet, Chainer, CNTK, etc. Gradient also includes a public datasets repository with a growing list of common datasets freely available to use in your projects.
Those are the main pieces but happy to elaborate on any of this or other questions!