Vast.ai 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 Vast.ai 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 Vast.ai does not close: Vast.ai is usually the cheapest raw dollar-per-hour on the market, because hosts bid against each other and you absorb the variance in reliability. Aquanode supports Vast.ai among other providers, so the honest framing is not us-versus-them on price: it is that an environment built on a Vast machine can be snapshotted and moved off it.
To be fair, Vast.ai's real strength is real: Raw price. A host-bid marketplace with interruptible instances will beat brokered on-demand capacity on the low end, and that is by design.
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 Vast.ai
Fine-Tuning alternatives to other clouds
