VaultLayer vs Google Vertex AI
Vertex AI is Google Cloud's managed ML platform — powerful if you're all-in on GCP and adopt its conventions. VaultLayer is cloud-agnostic: it wraps the training command you already run and adds reliability on top, without tying you to a single cloud. For teams weighing GCP, here's how the two compare.
At a glance
| VaultLayer | Vertex AI custom training | |
|---|---|---|
| Where jobs run | Your own cloud (AWS, Azure, GCP, or any provider) + GPU clouds + elastic capacity | Google Cloud only |
| Job definition | vl run train.py — your command, unchanged | CustomJob spec, container/python-package conventions |
| Checkpoint & resume | Automatic, cross-provider | You wire checkpoints to GCS and handle restarts |
| Lock-in | Portable (BYOC) | GCP ecosystem |
| GPU quota | Elastic external capacity when your quota is tight | Bounded by your GCP GPU quota/region |
When each fits
Vertex AI makes sense for teams standardized on GCP who want the integrated Google ML stack (pipelines, model registry, endpoints) and are happy to package jobs its way.
VaultLayer fits teams that want training reliability without single-cloud lock-in: run on your own GCP project, burst to elastic capacity when GPU quota is tight, and keep your script untouched. See also VaultLayer vs SageMaker — the same trade-off on AWS.
Frequently asked questions
Can VaultLayer run training inside my GCP project like Vertex AI does?
Yes. With BYOC, VaultLayer provisions GPUs in your own GCP project via a scoped service account and checkpoints to your own bucket — while staying portable to other clouds.
Do I have to repackage my code as a Vertex CustomJob with VaultLayer?
No. VaultLayer wraps your existing command — vl run train.py — with no job spec, container convention, or SDK adoption required.
Keep every training job moving.
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