Managed fine-tuning platform
Onrup vs Hugging Face
Should I use Onrup or Hugging Face?
Hugging Face is the better choice if you want everything in one ecosystem and the Hub is already central to your work. Onrup is the better choice if you want the fine-tuning path opinionated, costed and gated — and we publish to the Hub anyway.
The model hub itself, plus AutoTrain for training and Inference Endpoints for serving. The centre of gravity of the open-weight world.
Where they differ
| Onrup | Hugging Face | |
|---|---|---|
| Ecosystem | Focused on one workflow | The centre of the open-weight world |
| Training | 40 validated templates, cost-forecast per run | AutoTrain, billed as underlying compute |
| Serving | Your endpoint, per GPU-second | Inference Endpoints, per instance-hour |
| Evaluation gate | Blinded, blocking, before deploy | Not part of the product |
| Publishing | One call, with a generated model card | Native — it is their Hub |
On price
Inference Endpoints are billed per hour by instance type across several cloud providers, so there is no single H100 rate to quote. AutoTrain is billed as underlying compute rather than per training token.
Rather than estimate a comparable figure, we leave it blank. Their pricing page, checked 2026-08-06.
The longer answer
It would be strange to argue against Hugging Face here. Every base model in our catalogue is identified by its Hub repository, publishing a finished model means pushing it to the Hub, and the formats this whole industry uses are the ones the Hub standardised. We are a client of that ecosystem, not a competitor to it.
The difference is opinionatedness. AutoTrain and Inference Endpoints are general tools that will do many things; ours is a narrow path that does one thing with the decisions already made. Which you want depends on whether those decisions are ones you would enjoy making.
The concrete additions are cost authorisation before a run starts, a per-run cost meter, and a blinded evaluation gate between training and deploy. None of those exist in the Hub workflow, and all of them are the sort of thing teams build themselves and then maintain badly.
If your work is already organised around the Hub and you have the capacity to make the training decisions yourself, staying put is entirely reasonable.
Where Hugging Face wins
Gravity. The models, the datasets, the leaderboards and the community are all there already, and publishing to the Hub is where a fine-tune becomes visible to anyone else. We publish to the Hub too, which should tell you how we rate it.
Choose them if
- You want the whole workflow inside one ecosystem
- Public model publishing and community visibility is the point
- You are already paying for Hub infrastructure
On ownership
Weights on Onrup are downloadable from every finished run and publishable to a model hub in one call. On Hugging Face: Everything lives in a repo you control.
Frequently asked questions
Can I publish my fine-tune to the Hub from Onrup?
Yes, in one call, to a public or private repository, with a model card generated from the run — base model, dataset reference, hyperparameters, final metrics, GPU class and wall time — which you can then edit.
Do you use Hub models as bases?
Yes. Every model in the catalogue is identified by its Hub repository, so what you fine-tune is exactly what the Hub publishes.
Is my data uploaded to the Hub?
No. Datasets stay in your own tenant storage. Publishing pushes model weights and a card, and nothing else, and only when you ask it to.
Researching Hugging Face alternatives more broadly? →
Last verified 6 August 2026. Hugging Face figures come from their own pricing page on the date checked.
Start with the free tier
A magic link creates your account, your tenant and your first API key. No card until you ask for compute.