KAI Scheduler vs Koordinator
KAI Scheduler
kai-scheduler.devKoordinator
koordinator.shSide by side
| Figure | KAI Scheduler | Koordinator |
|---|---|---|
| Price from | — | — |
| Free plan | — | — |
| Free trial | — | — |
| Open source | — | — |
| Linux | — | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 0 |
| Spec score · GPU Cluster Management Software | 7.4 (#13) | 7.5 (#10) |
| License | The public GitHub repository identifies the project as open source and lists an Apache-2.0 license. | The Koordinator project is licensed under the Apache License, Version 2.0. |
| Purpose | 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. | Koordinator is a QoS-based scheduling system for efficient orchestration of microservices, AI and big-data workloads on Kubernetes. |
In their own words
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.
Koordinator
Koordinator is a free, self-hosted QoS scheduling system for Kubernetes microservices, AI, big-data, audio, and video workloads. It adds priority and quality-of-service controls so different pods and workload types can share nodes. Application profiling supports resource overcommitment while preserving QoS guarantees, and fine-grained isolation targets latency-sensitive work alongside batch jobs. Customizable policies cover Web Service, Spark, Presto, TensorFlow, and PyTorch. QoSManager tunes pod isolation using profiling, interference detection, and SLO settings. Koord-Queue supports TFJob, PyTorchJob, Spark, Argo Workflow, Ray, and native Kubernetes Jobs, with Kueue AdmissionCheck compatibility. YARN Copilot connects Hadoop YARN jobs with Kubernetes pods. Koordinator works alongside kube-scheduler, using control-plane and node components without invasive native changes. Helm 3.5 or newer and Kubernetes 1.18 or newer are required.