Apache YuniKorn
5.9#11 in gpu cluster management softwareFree planFree planWebAPISelf-hosted
VisitApache YuniKorn vs dstack
What both publish
| Fact | Apache YuniKorn | dstack |
|---|---|---|
| Deployment | The project recommends deploying YuniKorn to an existing Kubernetes cluster using its Helm chart and Helm 3 or later. | The server can run on a laptop or another environment with access to the cloud and on-prem clusters being used. |
| Integrations | YuniKorn exposes scheduling metrics via Prometheus, and the project provides instructions for monitoring with Prometheus and Grafana. | Listed backend types include AWS, Azure, GCP, Kubernetes, Slurm, Runpod, Nebius, Lambda, and other cloud providers. |
| Purpose | Apache YuniKorn is a resource scheduler for running batch, data, and machine learning workloads on Kubernetes. | dstack is an open-source orchestration layer for AI workloads on heterogeneous accelerators, including GPU clouds, Kubernetes, VMs, and bare-metal clusters. |
| Support | Users can ask for help in the YuniKorn Slack #yunikorn-user channel or use the user mailing list. | The documentation directs users to report issues on GitHub and ask questions in the dstack Discord server. |
Apache YuniKorn plans
- Apache YuniKornFree
dstack plans
- dstack OSSFree
- dstack Sky GPU MarketplacePriced on request
Pick Apache YuniKorn if…
YuniKorn suits teams running batch, data, or machine learning workloads on Kubernetes that need queue-based resource controls and scheduling policies. It is also relevant to users and developers interested in using or contributing to the project.
Pick dstack if…
dstack suits teams orchestrating AI workloads across different accelerator and infrastructure environments. It is also suited to users who want to configure workloads with YAML and manage resources through a CLI or HTTP API.