Run AI training on your own AWS GPUs

VaultLayer's BYOC model runs your training jobs on your own AWS account — your GPU instances, your S3 buckets, your pricing — while adding orchestration, checkpointing, and recovery on top. Connect once and run your existing script.

Connect your AWS account

vl connect aws        # STS role (recommended) or static access keys

An STS role is the recommended path — VaultLayer assumes a scoped role in your account rather than holding long-lived keys. Credentials are used only to run your jobs and never printed in logs.

Run on your AWS GPUs

vl run --byoc python train.py

The job provisions on your AWS account, checkpoints to your S3 bucket, and auto-resumes from the last step if an instance is interrupted. A BYOC job never routes to a managed pool and fails closed with a hard error if no credentials are set — so it never silently runs somewhere unexpected. Because the compute is billed by AWS under your account, there is no per-run charge from VaultLayer.

Why run on your own AWS

Keep your data, pricing, and account — and put existing AWS commitments to work. See using your cloud credits and reserved GPUs, and the BYOC overview. GCP teams: run training on your own GCP GPUs.

Frequently asked questions