VaultLayer vs Modal
Modal is a serverless compute platform where you express work as Python functions using its SDK and run them on Modal's infrastructure. VaultLayer takes the training command you already have and runs it on your own cloud or elastic GPUs, focused specifically on training reliability.
At a glance
| VaultLayer | Modal | |
|---|---|---|
| Programming model | Wraps your existing command — no SDK or decorators | Define functions with Modal's SDK / decorators |
| Where it runs | Your own cloud (BYOC) plus external GPU capacity | Modal's managed infrastructure |
| Focus | Training & fine-tuning reliability | General-purpose serverless compute (incl. inference, jobs, web) |
| Checkpoint & resume | Automatic across interruptions | You implement recovery in your function code |
| Code changes | None — run the script as-is | Restructure your job into a Modal app |
When each fits
Modal is a strong choice if you want a flexible serverless platform for many kinds of Python workloads and you're happy to express your jobs in its programming model.
VaultLayer fits teams whose problem is specifically training and fine-tuning that needs to finish: keep your existing script and cloud, and let the control plane handle checkpointing and resume with no rewrite.
Frequently asked questions
Do I have to rewrite my training code for VaultLayer?
No. Unlike Modal's function/SDK model, VaultLayer wraps the command you already run — vl run python train.py — so your PyTorch, JAX, or Hugging Face script runs unchanged.
Can I run training on my own cloud?
Yes. VaultLayer is BYOC-first: it runs on your own cloud account or GPU contract, while Modal runs jobs on Modal's own infrastructure.
Keep every training job moving.
VaultLayer is in invite-only early access for teams running real GPU workloads.
Get early access