VaultLayer vs AWS SageMaker
Both run managed training jobs, but Amazon SageMaker is tied to AWS and its own SDK, while VaultLayer is cloud-agnostic and wraps the training command you already have. The decision usually comes down to whether you want to standardize on AWS or keep your compute portable.
At a glance
| VaultLayer | AWS SageMaker | |
|---|---|---|
| Where jobs run | Your own clouds (AWS, GCP, Azure) and GPU clouds, plus external capacity | AWS only |
| Code changes | vl run python train.py wraps your existing command | Adapt to SageMaker estimators/SDK and container conventions |
| Checkpoint & resume | Automatic, built in, cross-provider | Configurable via managed spot training + checkpoint S3 paths you set up |
| Lock-in | Portable across clouds (BYOC) | AWS ecosystem |
| Compute billing | Billed by your own cloud under BYOC; no per-run charge | AWS training instance pricing |
When each fits
SageMaker is a natural fit if your stack is already all-in on AWS and you want a deeply integrated, AWS-native training service and are happy to adopt its SDK and conventions.
VaultLayer fits teams that want training to be reliable without rewriting for one vendor — run your existing script on your own cloud or GPU contract (including AWS), keep it portable, and get checkpoint-and-resume without wiring it yourself.
Frequently asked questions
Is VaultLayer locked to AWS like SageMaker?
No. SageMaker training runs on AWS only. VaultLayer is BYOC and cloud-agnostic — it runs on your AWS, GCP, or Azure account, on GPU clouds, or on external capacity, with no rewrite for a single vendor.
Do I have to use the SageMaker SDK with VaultLayer?
No. VaultLayer wraps your existing command — vl run python train.py — instead of SageMaker estimators, SDK calls, and container conventions.
Keep every training job moving.
VaultLayer is in invite-only early access for teams running real GPU workloads.
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