Managed vs self-hosted GPU training

Every team running GPU training picks a point on a spectrum: build and operate the infrastructure yourself, or use a managed control plane that handles reliability for you. The right answer depends on how much of your time you want spent on training operations versus model work.

Self-hosted: maximum control, maximum upkeep

Self-hosting — directly on cloud VMs, a Slurm cluster, or an open-source tool you operate — gives you full control. The cost is ongoing: you own provisioning, capacity hunting, checkpoint sync, health checks, retry and resume logic, and the dashboards around them, for every framework and provider you use.

Managed: jobs that finish, nothing to run

A managed control plane like VaultLayer takes the training-operations layer off your plate. You connect a cloud, run your existing script with vl run python train.py, and the platform handles provisioning, monitoring, checkpointing, and resume. There is no system for you to operate.

How to choose

• Choose self-hosted if operating training infrastructure is core to your team and you want to own every layer.
• Choose managed if you want training and fine-tuning jobs to finish reliably without building recovery glue — while still keeping your own cloud and contracts via BYOC.

Frequently asked questions