Managed fine-tuning platform
Onrup vs Fireworks AI
Should I use Onrup or Fireworks AI?
Fireworks is the better choice for light or spiky traffic, because serving a fine-tuned adapter at base-model token rates means an idle model costs nothing. Onrup is the better choice at steady volume, where GPU-hour pricing is cheaper, and where you want spend authorised before it is spent.
Inference, fine-tuning and evaluation behind one API, with fine-tuned adapters served at the same per-token rate as the base model.
Where they differ
| Onrup | Fireworks AI | |
|---|---|---|
| Serving a fine-tune | Your own endpoint, per GPU-second | Same per-token rate as the base model |
| Idle cost | Zero at scale-to-zero; hourly when warm | Zero — you pay per token only |
| Dedicated H100 | $2.99 per GPU-hour serving | $7.00 per GPU-hour |
| Evaluation gate | Blinded, blocking, before deploy | Evaluation offered, not blocking |
| Spend control | Reserved against a limit before the lease | Usage-based with limits |
On comparable hardware
Fireworks AI publishes $7 per H100 80GB hour. Our serving rate for the same class is $2.99, which makes theirs 2.3× the rate. Total cost is the rate multiplied by wall time, so this ratio is the starting point of a comparison rather than the end of one.
LoRA supervised fine-tuning is $0.50 per 1M training tokens up to 16B, $3.00 for 16–80B and $6.00 for 80–300B; preference tuning is double the supervised rate at every tier. On-demand H100 and H200 are both $7.00 per GPU-hour. Source, checked 2026-08-06.
The longer answer
Fireworks made a genuinely good decision that shapes this whole comparison: a fine-tuned adapter serves at the same per-token rate as the base model it was trained from. That means a model nobody is calling costs nothing, which removes the single most common source of waste in fine-tuning — the endpoint somebody spun up and forgot.
The arithmetic flips as traffic becomes steady. A per-token rate has to cover the provider’s cost of keeping capacity available at your peak; once you are using that capacity most of the time, paying for it directly is cheaper. Their own dedicated pricing acknowledges this, and at $7.00 per H100-hour against our $2.99 the gap is large.
Beyond price, the difference is where the checks sit. Fireworks offers evaluation as a capability you can use. On Onrup it is a gate: the candidate is scored blinded against the model it would replace, and a fail means the deploy does not happen. That is a stronger guarantee and a less flexible one, which is the trade.
For a team whose usage is a few thousand requests a day across several fine-tunes, Fireworks is very likely cheaper and simpler. Run the arithmetic on your own numbers rather than taking either of our words for it.
Where Fireworks AI wins
Serving a fine-tuned adapter at base-model token rates is a genuinely good deal, and it means a low-traffic fine-tune costs nothing to keep available. If your traffic is spiky and light, per-token beats per-hour and they are the cleanest expression of that model.
Choose them if
- Your endpoint traffic is light or very spiky
- You want fine-tuning, inference and evals from one vendor with no GPU sizing
- You are serving many adapters against one popular base model
On ownership
Weights on Onrup are downloadable from every finished run and publishable to a model hub in one call. On Fireworks AI: Adapters can be exported; the serving stack is theirs.
Frequently asked questions
At what traffic does GPU-hour pricing beat per-token?
It depends on your average tokens per request and model size, but the crossover is generally where an endpoint would be busy more than roughly a fifth of the time. Below that, per-token is usually cheaper; above it, reserved capacity usually is.
Does Fireworks let me download my weights?
Adapters can be exported. The serving stack is theirs, so what moves is the adapter rather than a running deployment — which is true of us as well.
Can I gate a Fireworks model with your evaluation?
Not directly. The gate compares a candidate against a baseline on endpoints it controls, so both models need to be reachable within the same run.
Researching Fireworks AI alternatives more broadly? →
Last verified 6 August 2026. Fireworks AI figures come from their own pricing page on the date checked.
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