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Paperspace 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 Paperspace 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 Paperspace does not close: Paperspace is now part of DigitalOcean, and its persistent-machine model is closer to ours than most of this list: a Paperspace machine does keep its disk. The gap is that the machine is the unit: it lives in one account, on one vendor's hardware, and cannot be re-materialised somewhere cheaper.
To be fair, Paperspace's real strength is real: Persistent machines with attached storage that survive a stop: the core habit of keeping your environment is already there.
Live rates for the GPUs training wants
| GPU | From (per GPU/hr) |
|---|---|
| H100 | $2.19 |
| H200 | $3.95 |
| B200 | $8.79 |
| A100 | $1.21 |
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 Paperspace
Training 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.
