Use your cloud credits and reserved GPUs for training
Funded teams often sit on cloud credits, reserved instances, or committed-use discounts that go underused — because turning that raw capacity into a reliable training platform is work. VaultLayer runs on your own account, so those commitments apply directly, and adds the reliability layer on top.
How it works
VaultLayer is BYOC-first: vl connect your cloud, then vl run --byoc python train.py provisions on your own account. The compute is billed by your cloud provider — so your credits, reserved instances, and committed-use discounts apply automatically, and there is no per-run charge from VaultLayer.
Stop leaving capacity on the table
Credits expire and reservations bill whether or not you use them. Running training through VaultLayer on your own account turns that committed capacity into finished training runs, without you building provisioning and recovery tooling. Connect AWS or GCP to start.
Reliability on top
Your capacity, made dependable: VaultLayer checkpoints as the job runs and resumes from the last step on interruption — so a reclaimed reserved instance or spot node doesn't cost you the run.
Frequently asked questions
Do my cloud credits apply when training through VaultLayer?
Yes. With BYOC, jobs run on your own cloud account and are billed by your provider, so your credits, reserved instances, and committed-use discounts apply as usual. VaultLayer adds no per-run GPU charge.
Which clouds can I bring?
AWS, GCP, and Azure accounts, plus GPU clouds like Lambda Labs, RunPod, and Vast.ai. Connect them with vl connect.
Keep every training job moving.
VaultLayer is in invite-only early access for teams running real GPU workloads.
Get early access