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Shadeform 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 Shadeform 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 Shadeform does not close: Shadeform solves the same first half of the problem we do: one API across many GPU clouds instead of an account per vendor. Where we diverge is the second half: finding capacity is not the same as being able to take your environment with you when you move to it.
To be fair, Shadeform's real strength is real: Broad first-party cloud coverage with a single API, and a clean deployment story for teams that mainly need capacity aggregation.
Live rates for the GPUs fine-tuning wants
| GPU | From (per GPU/hr) |
|---|---|
| H100 | $2.19 |
| A100 | $1.16 |
| L40S | $0.869 |
| RTX 4090 | $0.374 |
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 Shadeform
Fine-Tuning alternatives to other clouds
Related reading: H100 pricing and specs, H100 vs H200, A100 vs H100, H100 and H200: SXM vs NVL vs PCIe, and The best GPUs for AI, ranked.
