Google Colab Alternatives for Training
Training a model from scratch or continuing a pre-training run, usually for days rather than hours.
What training actually needs
Why people look past Google Colab for this
The checkpoint is the cheap part to move; the built environment, the dataset cache and the exact CUDA and framework versions around it are what take a day to rebuild. That is exactly the gap Google Colab does not close: Colab is the best free way to touch a GPU and a fine notebook environment. It is also the sharpest example of the problem we exist for: Google's own FAQ states that GPU types vary over time, resources are not guaranteed, and idle VMs are deleted, so the environment you built is gone and you rebuild it next session.
To be fair, Google Colab's real strength is real: A genuinely free tier, zero setup, and a notebook interface that is the right tool for teaching, prototyping and sharing work.
Live rates for the GPUs training wants
Live per-GPU rates from Aquanode's marketplace. Refreshes hourly.
How you run it here today
Run it today as a Pod: the console's own box workload card preselects the right template for this job.
Restoring an environment requires a snapshot that already exists. Stopping a deployment yourself captures it on the way out, so you can bring it back later on any provider. A provider-side termination is different: it is only recoverable if you had already switched automated snapshots on for that deployment, and it costs you the work since the last one. Automated snapshots are opt-in, nothing runs until you start it, and with none running there is nothing to restore.
More alternatives pages
Other workloads on Google Colab
Training alternatives to other clouds
