Layer 4 · Batch scheduling
Kubernetes batch scheduling
Add-ons giving Kubernetes the queueing and gang-scheduling behaviour AI workloads need and plain Kubernetes does not provide.
Evaluated from public documentation, architecture material and source code. No vendor contact.
What it solves
Lets you keep one platform for everything while still queueing work, enforcing quotas between teams, and starting all workers of a distributed job together.
Who it suits
Teams with platform engineering capability who would rather extend Kubernetes than operate a second scheduler beside it.
What to watch out for
This is a set of components, not a product. It works well with platform engineers on the team and is painful without them.
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
Run:ai
L4 · GPU pooling and fractional sharing
A Kubernetes-based orchestration layer that pools accelerators across teams and allocates fractions of one to a workload.
- Fit
- Shared clusters where several teams compete for fixed capacity.
- Catch
- Assumes you already run Kubernetes, and run it well.
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 Kubernetes batch scheduling is the right choice?
Describe your setup and constraints. We will tell you whether it fits, and what else you should be looking at.