Layer 4 · GPU pooling and fractional sharing
Run:ai
A Kubernetes-based orchestration layer that pools accelerators across teams and allocates fractions of one to a workload.
Evaluated from public documentation, architecture material and source code. No vendor contact.
What it solves
Stops capacity being locked to one team while another queues, and lets smaller workloads share an accelerator rather than each taking a whole one.
Who it suits
Organisations with a shared Kubernetes cluster and measured utilisation well below what they are paying for.
What to watch out for
It assumes Kubernetes competence you may not have. If operating Kubernetes is itself a strain on the team, that is the first problem to solve — this will not fix it.
Every product here gets one of these. A recommendation without a trade-off is not a recommendation.
Problems this comes up for
Worth comparing against
Kubernetes batch scheduling
L4 · Batch scheduling
Add-ons giving Kubernetes the queueing and gang-scheduling behaviour AI workloads need and plain Kubernetes does not provide.
- Fit
- Organisations already standardised on Kubernetes.
- Catch
- You assemble a scheduler from components rather than buying one.
SkyPilot
L4 · Multi-cloud workload orchestration
An open-source layer that runs the same job on whichever cloud, specialist GPU provider or Kubernetes cluster has capacity.
- Fit
- Teams using more than one compute provider, or wanting to.
- Catch
- You still need accounts and quota with every provider it places work on.
Wondering whether Run:ai is the right choice?
Describe your setup and constraints. We will tell you whether it fits, and what else you should be looking at.