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 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
Is managed GPU training more expensive than self-hosting?
With VaultLayer's BYOC model, compute is still billed by your own cloud under your contract, so there is no per-run GPU charge — you pay for the reliability layer instead of building and operating it yourself.
Can managed training run on my own cloud?
Yes. VaultLayer is BYOC-first: managed orchestration and recovery run on top of your own cloud account, reserved instances, or GPU contract.
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