Bring your own cloud, GPU contracts, reserved instances, or credits. VaultLayer adds the training control plane on top — orchestration, checkpointing, monitoring, and recovery — with external GPU capacity available when you need extra supply.
Run on your own cloud account, reserved instances, credits, or contracted GPU nodes while VaultLayer handles the training reliability layer.
When a GPU is reclaimed, crashes, or becomes unhealthy, VaultLayer detects the failure and resumes from the last checkpoint on available compute.
We handle provisioning, monitoring, checkpoint sync, and resume logic so ML engineers stay on model work instead of maintaining DevOps glue.
# one-time setup
pip install vaultlayer && vaultlayer init
# run any training script — that's it
vl run python train.pyWraps any PyTorch / JAX / HuggingFace training script unchanged.
Route jobs to connected cloud, reserved, or contracted GPUs in your environment.
Use external GPU capacity for overflow, experiments, or urgent jobs when your own fleet is full.
VaultLayer checkpoints, monitors, and resumes failed jobs from the last saved state.
"The babysitting tax. Renting H100s directly from RunPod / Lambda / Voltage Park, I'd lose 20–30 minutes per session to wrapper work around the actual training: hunting for a region with capacity, paying for idle minutes while the pod boots and the image pulls, wiring my own checkpoint sync to S3/R2 so a provider interruption wouldn't wipe out an hour of progress, and writing fragile resume logic because providers just hand you a fresh pod after a crash. … The fine-tune finished cleanly — adapter saved, no leftover pod to clean up, no manual sync, no babysitting."
VaultLayer detects it and your training resumes from where it left off — on your connected BYOC compute first, or external GPU capacity when that path is configured. No lost progress, no restart from scratch.
No SDK, no decorators, no framework to adopt. vl run python train.py wraps the command you already use — your PyTorch / JAX / Hugging Face script runs unchanged.
Yes. BYOC is part of VaultLayer: you keep your compute relationship, pricing, and cloud account, while VaultLayer adds the training control plane on top — job orchestration, checkpointing, monitoring, and recovery.
No. VaultLayer is BYOC-first for teams with cloud credits, reserved instances, or GPU contracts. Elastic GPU capacity is available when you need supply quickly.
Both. External GPU capacity can route to available GPUs automatically or pin a specific class, for example vl run --gpu H100 python train.py. BYOC teams can route jobs to connected capacity in their own environment.
For BYOC, you keep paying your cloud or GPU provider directly and pay VaultLayer for the orchestration and reliability layer. For external GPU capacity, you see the GPU quote before a run starts and pay for actual run time plus VaultLayer. In beta, we scope pricing with each team before setup.
You can — but checkpointing is only one piece. Teams still end up maintaining provisioning scripts, health checks, storage sync, retry logic, and dashboards for every training workflow. VaultLayer packages that reliability work into one control plane.
Yes. Each job runs on its own provisioned GPU with credentials scoped to that job — you're not sharing a container with other customers, and no one can access your data (or you theirs).
Any standard training script — PyTorch, JAX, Hugging Face / TRL, Axolotl, and similar. If it runs with python train.py, it works. VaultLayer is focused on training and fine-tuning, not real-time inference serving.
VaultLayer is in invite-only early access. Request an invite and we'll scope your BYOC pilot directly with your team. External capacity pilots can include trial credits when useful.
VaultLayer is in private beta for funded AI startups, research labs, and ML builders running real GPU workloads. Request access and we'll scope your BYOC setup, with elastic GPU capacity available when you need it.
Questions? rahuljain@vaultlayer.cloud