VaultLayer vs renting GPUs directly
Renting H100s straight from RunPod, Lambda, or Vast.ai is the cheapest-looking option until you count the babysitting tax: hunting for a region with capacity, paying for idle boot minutes, wiring your own checkpoint sync, and writing resume logic because a crash just hands you a fresh pod. VaultLayer wraps those providers and handles that work for you.
What you own, with and without VaultLayer
| Renting directly | With VaultLayer | |
|---|---|---|
| Find capacity | Manually shop regions/providers for a free GPU | Routed to available capacity automatically |
| Cold-boot idle | You pay while the pod boots and the image pulls | Managed provisioning across providers |
| Checkpoint sync | You wire it to S3/R2 yourself | Built in, automatic |
| After a crash | Fresh empty pod; you write resume logic | Auto-resume from the last checkpoint |
| Your contract | Direct with the provider | Keep it — BYOC runs on your account |
Same providers, less glue
VaultLayer isn't a replacement for RunPod, Lambda, or Vast.ai — it runs on top of them. You can connect your own provider account and keep your pricing, or use elastic external capacity through the same CLI. Either way, vl run python train.py handles provisioning, checkpointing, and recovery so you stop maintaining wrapper scripts around every run.
Frequently asked questions
Does VaultLayer replace RunPod, Lambda, or Vast.ai?
No. VaultLayer runs on top of those providers. You can connect your own account (BYOC) and keep your contract, or use external capacity through the same CLI — VaultLayer adds orchestration, checkpointing, and recovery.
Can I keep my existing provider contract?
Yes. With BYOC, the job runs on your connected account and is billed by your provider; VaultLayer adds the reliability layer with no per-run GPU charge.
Keep every training job moving.
VaultLayer is in invite-only early access for teams running real GPU workloads.
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