Google Colab Alternatives for Fine-Tuning
Adapting an existing base model to your own data, typically LoRA or a full fine-tune over hours.
What fine-tuning actually needs
Why people look past Google Colab for this
Base weights are re-downloadable but slow; the adapter checkpoints and the dataset you spent time cleaning are not something you want to re-derive. 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 fine-tuning wants
Live per-GPU rates from Aquanode's marketplace. Refreshes hourly.
How you run it here today
Run it today with the seeded lora-finetune job recipe — a real, publicly-imaged container Aquanode can queue directly, not a template we're promising to build later.
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
Fine-Tuning alternatives to other clouds
