Serverless GPU platform
Onrup vs Anyscale
Should I use Onrup or Anyscale?
Anyscale is the better choice if your training genuinely needs multi-node distribution or you already build on Ray. Onrup is the better choice if your runs fit on one GPU, which most fine-tunes do.
Managed Ray. Distributed training and serving for teams that have outgrown a single machine.
Where they differ
| Onrup | Anyscale | |
|---|---|---|
| Distribution | Single-GPU runs by design | Multi-node distributed training |
| What you write | A dataset and a template choice | Ray applications |
| Where it runs | Serverless compute across multiple providers | Your own cloud account |
| Pricing | Published rate card per GPU class | Platform fee plus your cloud cost |
| Evaluation gate | Blinded, blocking, before deploy | You build it |
On price
Billed as a platform fee on top of underlying cloud instance cost, which varies by the account it runs in; no standalone GPU-hour rate is published.
Rather than estimate a comparable figure, we leave it blank. Their pricing page, checked 2026-08-06.
The longer answer
Ray is the right abstraction for genuinely distributed workloads, and Anyscale is the managed version of it. If a run does not fit on one machine and the parallelism is not trivially simple, that is the tool.
The observation that shapes this comparison is that most fine-tunes do fit on one GPU. Adapter methods and four-bit quantisation between them put models up to around seventy billion parameters within reach of a single card, and single-GPU training avoids an entire category of distributed-systems failure.
So the question is whether you are in the minority that needs distribution. If you are, this comparison favours them decisively. If you are not, a distributed framework is complexity you are paying for and not using.
Running inside your own cloud account is also a real difference, and one that matters when data residency is a constraint.
Where Anyscale wins
Genuine distributed workloads. If a run does not fit on one node and the parallelism is not embarrassingly simple, Ray is the right abstraction and Anyscale is the managed version of it.
Choose them if
- Your training genuinely needs multi-node distribution
- You already build on Ray
- You want to run inside your own cloud account
On ownership
Weights on Onrup are downloadable from every finished run and publishable to a model hub in one call. On Anyscale: Your cluster, your artefacts.
Frequently asked questions
Do you support multi-node training?
No. Runs are single-GPU by design, which is what makes cost forecasting and resumption tractable. Models needing more than one card are trained with quantisation and adapters instead.
What if my model does not fit on one GPU?
Four-bit training brings a 70B model inside 48GB and the largest models in the catalogue inside 80GB. Beyond that, a distributed platform is the honest answer.
Can I run inside my own cloud account?
No. Compute is serverless across multiple providers, which is what the rate card reflects. If running in your own account is a requirement, Anyscale fits it and we do not.
Researching Anyscale alternatives more broadly? →
Last verified 6 August 2026. Anyscale 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.