Train like you hired an infra team.

Seed-stage teams run real fine-tuning on VaultLayer without an ML-ops hire: your training code on your own cloud accounts and GPU credits, checkpointed and monitored, resuming after preemption without anyone watching.

Skip the infra hire

Provisioning, checkpoint sync, failure recovery, and teardown are the platform's job — not your first engineer's. vl run python train.py wraps the command you already use, so your team trains models instead of writing rsync loops and resume logic. No SDK, no decorators, no framework to adopt.

Spend the cloud credits you already have

Bring your own cloud — AWS, Azure, GCP, or any provider — and put the credits and committed-use discounts your accelerator gave you to work on real training. BYOC runs on your own account under a flat monthly contract with no VaultLayer per-run charge; your cloud bills you directly for compute. See using your cloud credits and the BYOC overview.

You talk to the person who built it

No support tiers, no ticket queue. Email the founder and you reach the person who wrote the scheduler. For a seed-stage team, that is the difference between filing a ticket and fixing it on a call.

What a startup actually runs on it

A typical run looks like this: you start a fine-tune on your own cloud, a spot instance gets reclaimed mid-run, and the job resumes from its last checkpoint on fresh hardware — adapter saved, no leftover pod to clean up, no manual sync, nobody babysitting. That is the whole point: the job finishes even when the hardware doesn't.

Frequently asked questions