Apache YuniKorn
5.9#11 in gpu cluster management softwareFree planFree planWebAPISelf-hosted
VisitApache YuniKorn vs Backend.AI
Backend.AI
6.3#4 in gpu cluster management softwareFree planFree planWebAPILinuxSelf-hosted
VisitWhat both publish
| Fact | Apache YuniKorn | Backend.AI |
|---|---|---|
| Deployment | The project recommends deploying YuniKorn to an existing Kubernetes cluster using its Helm chart and Helm 3 or later. | Backend.AI can be deployed on-premises, in public cloud environments, or in a hybrid setup. |
| Intended users | The community page identifies users and developers interested in using or contributing to YuniKorn as audiences for its community meetings. | Lablup describes its audience as ranging from small teams with a single GPU to institutions running clusters of thousands. |
| Purpose | Apache YuniKorn is a resource scheduler for running batch, data, and machine learning workloads on Kubernetes. | Backend.AI is an AI infrastructure platform for building, training, and serving models and running parallel computing jobs. |
| Support | Users can ask for help in the YuniKorn Slack #yunikorn-user channel or use the user mailing list. | Lablup invites organizations to contact its team for help planning AI clusters and scaling production workloads. |
Apache YuniKorn plans
- Apache YuniKornFree
Backend.AI plans
- Open-sourceFree
- Try-outFree
- Enterprise (cloud)Priced on request
- Enterprise (on-premises)Priced 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 Backend.AI if…
Backend.AI suits teams or institutions running GPU workloads, from single-GPU home use to larger clusters. It also fits organizations seeking on-premises, cloud, hybrid, or air-gapped deployment options.