Google Colab is launching a new paid tier, Pay As You Go, giving anyone the option to purchase additional compute time in Colab with or without a paid subscription. This grants access to Colab’s  powerful NVIDIA GPUs and gives you more control over your machine learning environment.

Colab is fully committed to supporting all of our users whether or not they pay for additional compute, and our free-of-charge tier stays in its current form. Today's announcement reflects additions to paid options only.

Colab helps you accomplish more with machine learning

Google Colab is the easiest way to start machine learning. From the Colab notebooks powering TensorFlow's tutorials and guides to Deepmind's AlphaFold example, Colab is helping the world learn ML and share the results broadly, democratizing machine learning.

Colab Pay As You Go further expands the potantial for using Colab. Pay As You Go allows anyone to purchase more compute time with Colab, regardless of whether or not they have a monthly subscription. Customers can use this feature to dramatically increase their usage allotments of Colab over what was possible before. Try it out at colab.research.google.com/signup

Previously, Colab's paid quota service throttled compute usage to smooth out quota exhaustion over the entire month of a subscription to ensure a paid user would be able to access Colab compute as much as possible over their month's subscription: we didn't want users to fully exhaust their quota on day one and spend the rest of the month frustrated by lack of access to runtimes. Now with Pay As You Go, we are relaxing usage throttling for all paid users (though this will remain the case for users in our free of charge tier).

Paid users now have the flexibility to exhaust compute quota, measured in compute units, at whatever rate they choose. As compute units are exhausted, a user can choose to purchase more with Pay As You Go at their discretion. Once a user has exhausted their compute units their Colab usage quota will revert to our free of charge tier limits.

Increasing your power with NVIDIA GPUs

Paid Colab users can now choose between a standard or premium GPU in Colab, giving you the ability to upgrade your GPU when you need more power. Standard GPUs are typically NVIDIA T4 Tensor Core GPUs, while premium GPUs are typically NVIDIA V100 or A100 Tensor Core GPUs. Getting a specific GPU chip type assignment is not guaranteed and depends on a number of factors, including availability and your paid balance with Colab. If you want guaranteed access to a specific machine configuration, we recommend purchasing a VM on GCP Marketplace.

When you need more power, select premium GPU in your runtime settings: Runtime > Change runtime type > GPU class > Premium. Premium GPUs will deplete your paid balance in Colab faster than standard GPUs.

Colab is the right choice for ML projects

Colab is the right choice for your machine learning project: TensorFlow and many excellent ML libraries come pre-installed, pre-warmed GPUs are a click away, and sharing your notebook with a collaborator is as easy as sharing a Google doc. Collaborators can access runtimes with GPU accelerators without need for payment. Pay As You Go makes Colab an even more useful product for any ML project you're looking into.