VaultLayer vs Slurm
Slurm is the open-source scheduler behind many on-prem GPU clusters — powerful, but you operate the cluster and write job scripts. VaultLayer is a managed, cloud-elastic control plane: no cluster to run, your existing command instead of sbatch scripts, and checkpoint-and-resume built in.
At a glance
| VaultLayer | Slurm | |
|---|---|---|
| Model | Managed cloud control plane | Self-operated cluster scheduler |
| Where it runs | Your cloud / elastic GPUs (BYOC) | Your own, often on-prem, cluster |
| Job submission | vl run python train.py | sbatch job scripts |
| Checkpoint & resume | Built in, automatic | You implement it |
| Operate it | Nothing to run — hosted | You install and maintain Slurm + the cluster |
| Elasticity | Scale to cloud capacity on demand | Fixed to your cluster's size |
When each fits
Slurm is a strong fit if you run a fixed, owned GPU cluster and want a battle-tested HPC scheduler you operate yourself.
VaultLayer fits teams that want cloud-elastic training without operating a scheduler: submit your existing command, scale to available GPU capacity, and get checkpoint-and-resume without building it.
Frequently asked questions
Is VaultLayer a Slurm replacement?
For cloud-based training, effectively yes — it's a managed control plane with no cluster to operate. Teams running a fixed on-prem cluster may keep Slurm; teams that want cloud-elastic GPUs without running a scheduler use VaultLayer.
Do I write sbatch scripts with VaultLayer?
No. VaultLayer wraps your existing command — vl run python train.py — instead of sbatch job scripts, and handles provisioning and recovery for you.
Keep every training job moving.
VaultLayer is in invite-only early access for teams running real GPU workloads.
Get early access