Apache YuniKorn vs KAI Scheduler
Apache YuniKorn
yunikorn.apache.orgKAI Scheduler
kai-scheduler.devSide by side
| Figure | Apache YuniKorn | KAI Scheduler |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · GPU Cluster Management Software | 8.1 (#6) | 7.4 (#13) |
| Deployment | The project recommends deploying YuniKorn to an existing Kubernetes cluster using its Helm chart and Helm 3 or later. | The project provides production Helm installation, source build, and ArgoCD GitOps installation options for Kubernetes. |
| Integrations | YuniKorn exposes scheduling metrics via Prometheus, and the project provides instructions for monitoring with Prometheus and Grafana. | The project states it is natively integrated with KubeRay for Ray workloads on Kubernetes and describes integrations with Grove and Dynamo for complex multi-component workloads. |
| License | The software is licensed under the Apache License 2.0. | The public GitHub repository identifies the project as open source and lists an Apache-2.0 license. |
| Purpose | Apache YuniKorn is a resource scheduler for running batch, data, and machine learning workloads on Kubernetes. | KAI Scheduler is a Kubernetes-native scheduler for AI workloads that optimizes GPU allocation across data processing, training, and inference while keeping resource shares fair across teams. |
| Queues | Hierarchical resource queues provide fine-grained resource controls for tenants and can map to an organization’s structure. | Hierarchical queues support quotas, limits, priorities, and borrowing resources across teams. |
| Support | Users can ask for help in the YuniKorn Slack #yunikorn-user channel or use the user mailing list. | The project directs users to GitHub issues for bugs, feature requests, or technical help, and also lists CNCF Slack, community calls, and a mailing list. |
Plans
In their own words
Apache YuniKorn
Apache YuniKorn is a resource scheduler for batch, data, and machine learning workloads on Kubernetes. It is a Kubernetes-compatible alternative to the default scheduler and is described as transparent to existing Kubernetes applications. Hierarchical queues provide fine-grained tenant controls that can reflect an organization’s structure. Scheduling features include gang scheduling, FIFO and Fair job ordering, resource fairness, reservations, and preemption. A web UI centralizes cluster capacity, utilization, and application information. YuniKorn exposes metrics through Prometheus, and project guidance covers monitoring with Prometheus and Grafana. The project recommends deployment to an existing Kubernetes cluster using its Helm chart and Helm 3 or later. The software is free under the Apache License 2.0. Releases include source tarballs, which must be built before use, and prebuilt Docker images. The project advises checking release artifacts for tampering with GPG or SHA-512. Help is available through the YuniKorn Slack #yunikorn-user channel or the user mailing list.
KAI Scheduler
KAI Scheduler is a self-hosted, open-source Kubernetes scheduler for allocating GPUs to AI workloads such as data processing, training, and inference. It supports interactive notebooks through multi-node distributed training in one cluster and is designed for large GPU clusters and high-throughput workloads. Hierarchical queue trees provide quotas, limits, priorities, and borrowing between teams. Gang scheduling places distributed workloads together when resources are available, while elastic workloads change replica counts within configured minimum and maximum values. GPU sharing includes time slicing, MPS, and MIG. Other scheduling controls include topology-aware placement, time-based fairshare, and priority-based preemption. KAI documents integrations with Ray on Kubernetes and Grove. It runs on cloud or on-premises infrastructure, including Karpenter environments. Installation uses a production Helm chart, source builds, or ArgoCD, with a running Kubernetes cluster, Helm CLI, and NVIDIA GPU Operator required for GPU requests. The Apache-2.0 project is also a CNCF sandbox project.