VaultLayer vs SkyPilot
SkyPilot and VaultLayer both run training jobs across multiple clouds, but they sit at different layers. SkyPilot is an open-source framework you self-host and operate; VaultLayer is a managed control plane that handles reliability — checkpointing, monitoring, and resume — for you.
At a glance
| VaultLayer | SkyPilot | |
|---|---|---|
| Model | Managed control plane (hosted) | Open-source framework you run yourself |
| Job submission | vl run python train.py wraps your existing command | YAML task spec you author and maintain |
| Checkpoint & resume | Built in, automatic, health-gated | You implement recovery and checkpoint sync |
| Failure recovery | Automatic cross-provider resume from last checkpoint | Retries available; resume logic is up to your code |
| Operate the system | Nothing to run — hosted | You run and maintain it |
| BYOC | BYOC-first; jobs stay on your cloud, no per-run charge | Runs on your clouds (open-source, free) |
When each fits
SkyPilot is a strong choice if you want a free, open-source tool and your team is happy to operate it and own the recovery, checkpointing, and monitoring logic yourselves.
VaultLayer fits teams that want training jobs to finish without building or running that reliability layer: you connect a cloud, run your existing script, and VaultLayer handles checkpoint-and-resume, health monitoring, and cross-provider recovery as a managed service.
Frequently asked questions
Is VaultLayer open source like SkyPilot?
No. SkyPilot is an open-source framework you self-host and operate. VaultLayer is a managed, hosted control plane — there is nothing to run, and checkpointing and resume are built in.
Do I write YAML specs with VaultLayer?
No. VaultLayer wraps your existing command — vl run python train.py — instead of a task spec you author and maintain.
Keep every training job moving.
VaultLayer is in invite-only early access for teams running real GPU workloads.
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