Managed fine-tuning platform
Onrup vs OpenPipe
Should I use Onrup or OpenPipe?
OpenPipe is the better choice if your training data is production traffic and you want the capture step handled. Onrup is the better choice once you have the data and the questions become cost control, model choice and whether the candidate actually beats the incumbent.
Capture production traffic from a large model, then train a small one to replace it on that exact distribution.
Where they differ
| Onrup | OpenPipe | |
|---|---|---|
| Data capture | You bring the dataset | Proxy captures production traffic automatically |
| Model catalogue | 43 models across 13 families | A focused set for distillation |
| Billing | Per GPU-second against an authorised limit | Per training and inference token |
| Evaluation gate | Blinded, blocking, before deploy | Comparison tooling, not blocking |
| Objectives | Supervised, preference and reinforcement learning | Focused on supervised distillation |
On price
Priced per training and inference token by model rather than by GPU-hour; no comparable H100 hourly rate is published.
Rather than estimate a comparable figure, we leave it blank. Their pricing page, checked 2026-08-06.
The longer answer
OpenPipe answers one question extremely well: I have a working prompt on an expensive model and I want a cheap model that does the same thing. Their proxy sits in front of the large model, captures the traffic, and turns it into a training set without you building a capture pipeline. That step is real work and they have removed it.
Once the dataset exists the questions change. Which base model, at what size, on what GPU class, at what cost, and does the result actually beat what it is replacing on held-out cases. That is the part we are built around, and it is where a blinded gate against the incumbent matters more than capture convenience.
They are also narrower by design — the objective is supervised distillation, and preference tuning and reinforcement learning are outside the frame. If your task needs a reward function, that is a hard boundary.
The two are complementary more than competitive. Capturing traffic with one and training with the other is a perfectly sensible arrangement.
Where OpenPipe wins
The capture-then-train workflow is the best answer in this set to "I have a working prompt and a big bill". If your training data is really your production logs, they have removed more of that specific work than anyone.
Choose them if
- Your dataset is production request and response logs
- You are distilling a working prompt into a smaller model
- You want the capture step handled rather than building it
On ownership
Weights on Onrup are downloadable from every finished run and publishable to a model hub in one call. On OpenPipe: Trained weights are downloadable.
Frequently asked questions
Do you capture production traffic?
No. You bring a dataset. If capture is the hard part of your problem, OpenPipe solves it more directly than we do.
Can I use their captured data here?
Yes. The export is conversation-shaped and converts directly into a trainer-ready format.
Which is cheaper?
It depends on shape. Their per-token model is simpler at small scale; GPU-second billing is cheaper on large datasets and on steady serving. There is no single answer that survives contact with real numbers.
Researching OpenPipe alternatives more broadly? →
Last verified 6 August 2026. OpenPipe figures come from their own pricing page on the date checked.
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