Run AI training on your own GCP GPUs

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

Connect your GCP account

vl connect gcp        # service account JSON + project ID

VaultLayer uses a service account scoped to your project to provision GPUs. Credentials are used only to run your jobs and never printed in logs.

Run on your GCP GPUs

vl run --byoc python train.py

The job provisions on your Google Cloud project, checkpoints to your GCS 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 if no credentials are set. Because the compute is billed by Google Cloud under your account, there is no per-run charge from VaultLayer.

Why run on your own GCP

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

Frequently asked questions