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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

 VaultLayerAWS SageMaker
Where jobs runYour own cloud (AWS, Azure, GCP, or any provider) and GPU clouds, plus external capacityAWS only
Code changesvl run train.py wraps your existing commandAdapt to SageMaker estimators/SDK and container conventions
Checkpoint & resumeAutomatic, built in, cross-providerConfigurable via managed spot training + checkpoint S3 paths you set up
Lock-inPortable across clouds (BYOC)AWS ecosystem
Compute billingBilled by your own cloud under BYOC; no per-run chargeAWS 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 own AWS, Azure, GCP, or any-provider account, 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 train.py — instead of SageMaker estimators, SDK calls, and container conventions.

Keep every training job moving.

Sign up, install the CLI, and submit your first training job in minutes — on your own cloud or elastic GPU capacity.

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Evaluating for a team, or bringing your own cloud or GPU contract? Talk to us — you'll reach the founder directly.