KAI Scheduler
KAI Scheduler: A self-hosted scheduler for teams managing complex GPU workloads on Kubernetes. Ranked #7 of 21 in GPU Cluster Management Software by our editors (8.5/10); pricing: Open source; best for kubernetes teams needing advanced GPU policies.
At a glance
- Editor score8.5 / 10
- PricingOpen source
- Best forKubernetes teams needing advanced GPU policies
- Paid fromFree
- Deployment modelSelf_hosted
- Facts checked22 Sep 2026
Where it wins
- Hierarchical queues, quotas, priorities, and fairness policies
- Supports gang scheduling, elastic workloads, preemption, and GPU sharing
- Topology-aware placement with Prometheus GPU allocation metrics
Where it doesn't
- Requires self-hosted deployment in a Kubernetes cluster
- Advanced policies may be more than small teams need
- GPU sharing spans several configuration approaches
Our verdict on KAI Scheduler
KAI Scheduler is an open-source, Kubernetes-native scheduler for teams running AI and machine-learning workloads across GPU clusters. It supports interactive notebooks, distributed training, inference, batch jobs, and other workload types. The scheduler is designed for cloud or on-premises Kubernetes environments and can run alongside other schedulers installed in a cluster. Its focus is broad GPU workload coordination rather than a single job type.
Queue and workload controls are the central strength. KAI Scheduler provides hierarchical queues with quotas, limits, priorities, and fairness policies, along with workload prioritization and preemption. Batch and gang scheduling help coordinate distributed workloads, while elastic workloads can use configurable minimum and maximum replica counts. Topology-aware scheduling adds placement controls for GPU workloads, and Prometheus metrics expose queue GPU allocation and usage. These capabilities fit teams that need to manage shared capacity across varied workloads and enforce scheduling policies at cluster level.
The platform ecosystem is centered on Kubernetes, with integrations for NVIDIA GPU Operator, Karpenter, ArgoCD, and Ray. GPU sharing is supported through time slicing, MPS, and MIG, giving operators several ways to divide or partition GPU capacity. KAI Scheduler is distributed as self-hosted open-source software for installation in a Kubernetes cluster, so it suits teams prepared to operate Kubernetes infrastructure and define their own scheduling model. Teams with complex, shared GPU environments should consider it; smaller deployments seeking a simpler scheduling layer may prefer a narrower tool.
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KAI Scheduler pricing
KAI Scheduler fact sheet
| Free plan | Not verified |
|---|---|
| Paid from | Free |
| Deployment model | Self_hosted |
| Workload scheduling | Both |
| Kubernetes support | Yes |
| Quota controls | Yes |
| GPU utilization metrics | Yes |
| Cloud GPU support | Yes |
| Deployment | Self-hosted |
| Support | Community, Docs, Tickets |
| Built for | Mid-market, Enterprise (editorial estimate) |
| Integrations | 5 integrations: Kubernetes, NVIDIA GPU Operator, Karpenter, ArgoCD, Ray |
| Pricing | Open source |
| Website | kai-scheduler.dev |
| Facts checked | 22 Sep 2026 |
KAI Scheduler integrations
KAI Scheduler lists 5 integrations on its own site.
- Kubernetes
- NVIDIA GPU Operator
- Karpenter
- ArgoCD
- Ray
Alternatives to KAI Scheduler
- Backend.AIA broad GPU cluster platform for teams managing shared, multi-tenant AI infrastructure.9.4
- NVIDIA Mission ControlEnterprise GPU cluster management with orchestration, observability, and automated recovery.9.2
- NVIDIA Run:aiA broad Kubernetes-native GPU control plane for training, inference, and shared infrastructure.9.0
See all KAI Scheduler alternatives →
KAI Scheduler vs the competition
- KAI Scheduler vs Backend.AI
- KAI Scheduler vs NVIDIA Mission Control
- KAI Scheduler vs NVIDIA Run:ai
- KAI Scheduler vs dstack
- KAI Scheduler vs Volcano
- KAI Scheduler vs Apache YuniKorn
Compare KAI Scheduler with any tool side by side →
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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