Managed fine-tuning platform
Onrup vs Lamini
Should I use Onrup or Lamini?
Lamini is the better choice if the model has to run on your own hardware or factual accuracy on proprietary data is the central problem. Onrup is the better choice if a managed platform is acceptable and you want a published rate card and a self-serve path.
Enterprise fine-tuning with a focus on factual accuracy and hallucination reduction, deployable on-premises.
Where they differ
| Onrup | Lamini | |
|---|---|---|
| Deployment | Managed, serverless across multiple providers | On-premises available |
| Access | Self-serve from a magic link | Enterprise engagement |
| Pricing | Published rate card per GPU class | By arrangement |
| Focus | General fine-tuning with an evaluation gate | Factual accuracy and hallucination reduction |
| Catalogue | 43 open-weight models | A focused set |
On price
Enterprise pricing by arrangement; no public rate card is published.
Rather than estimate a comparable figure, we leave it blank. Their pricing page, checked 2026-08-06.
The longer answer
Lamini competes on two things we do not offer: running inside your own building, and specific technique work aimed at making a model stop inventing facts about proprietary data.
On-premises is the decisive one. If the model cannot leave your network, most of this comparison set is simply unavailable to you and the field narrows to vendors who ship software rather than services.
The accuracy work is a genuine specialisation. Our position is more general — a blinded gate that measures whether a candidate beats its baseline on your cases, whatever the failure mode — which is broader and less targeted at hallucination specifically.
The other real difference is how you start. A magic link and an API key against an enterprise sales process is a different buying experience, and for small teams that difference decides it.
Where Lamini wins
On-premises and air-gapped deployment, and specific technique work aimed at making a model stop inventing facts. If the model has to run inside your own building, most of this comparison set is simply unavailable to you.
Choose them if
- The model must run on your own hardware
- Factual accuracy on proprietary data is the central problem
- You want an enterprise engagement rather than a self-serve product
On ownership
Weights on Onrup are downloadable from every finished run and publishable to a model hub in one call. On Lamini: On-premises deployment means you hold everything.
Frequently asked questions
Can I run Onrup on my own hardware?
No. Compute is serverless across multiple providers and the control plane is managed. If on-premises is a requirement, Lamini is the honest recommendation.
Do you address hallucination specifically?
Indirectly. Training on refusal examples and gating against a baseline both help, but we do not claim a specialised technique for it in the way they do.
Can I start without talking to sales?
Yes. A magic link creates your account and your first API key, and no card is needed until you request compute.
Researching Lamini alternatives more broadly? →
Last verified 6 August 2026. Lamini 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.