VaultLayer makes affordable, interruptible GPUs finish. When a GPU crashes or gets reclaimed mid-run, your job auto-resumes from its last checkpoint — all the savings, none of the lost work. Start a job, walk away, come back to a finished model.
Run vl run python train.py and the lowest-cost available GPU wins — across every provider we support. No portals, no price-shopping, no lock-in.
When a GPU dies mid-run — reclaimed, crashed, or gone — VaultLayer detects it, shuts it down cleanly, and resumes your training from where it left off — on the same provider or a different one, whichever is available.
No SDK to import, no decorators, no framework to adopt. Your PyTorch or JAX script doesn't know it's being managed. vl run wraps the command you already have.
# one-time setup
pip install vaultlayer && vaultlayer init
# run any training script — that's it
vl run python train.pyWraps any PyTorch / JAX / HuggingFace training script unchanged.
Routed to the lowest-cost available provider with capacity, in seconds.
Your training progress is saved to secure storage as the job runs, so no work is ever lost.
A failed GPU triggers a re-provision and resume from the last checkpoint — same provider or another, whichever comes back fastest.
"The babysitting tax. Renting H100s directly from RunPod / Lambda / Voltage Park, I'd lose 20–30 minutes per session to wrapper work around the actual training: hunting for a region with capacity, paying for idle minutes while the pod boots and the image pulls, wiring my own checkpoint sync to S3/R2 so a spot preemption wouldn't wipe out an hour of progress, and writing fragile resume logic because providers just hand you a fresh pod after a crash. … The 10-epoch fine-tune finished in 9 minutes 23 seconds for $0.48 — clean exit, adapter saved, no leftover pod to clean up, no manual sync, no babysitting."
VaultLayer detects it and your training resumes from where it left off — on the same provider or another, whichever's available. No lost progress, no restart from scratch.
No SDK, no decorators, no framework to adopt. vl run python train.py wraps the command you already use — your PyTorch / JAX / Hugging Face script runs unchanged.
Both. By default VaultLayer routes to the best available GPU at the lowest current rate. Want a specific one — say, to run faster? Pin it: vl run --gpu H100 python train.py.
You pay for the GPU at the current market rate, and you always see the quote before a run starts — billed on actual run time, no surprises. Rates move with supply and demand, so VaultLayer shops for the best available price each run.
You can — but you'll spend time hunting for capacity, paying for idle boot minutes, wiring checkpoint sync, and writing resume logic for every run. VaultLayer does all of that automatically, so you start a job and walk away.
Yes. Each job runs on its own provisioned GPU with credentials scoped to that job — you're not sharing a container with other customers, and no one can access your data (or you theirs).
Any standard training script — PyTorch, JAX, Hugging Face / TRL, Axolotl, and similar. If it runs with python train.py, it works. VaultLayer is focused on training and fine-tuning, not real-time inference serving.
VaultLayer is in invite-only early access. Request an invite and we'll email you a token plus $25 in free credits.
VaultLayer is in private, invite-only beta. Drop your email and we'll send you an invite as soon as a spot opens up — no marketing drips.
Questions? rahuljain@vaultlayer.cloud