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Apache YuniKorn

Free#6 of 21 in GPU Cluster Management Software

Apache YuniKorn: Deep Kubernetes GPU scheduling with queues, quotas, fairness, and reservations. Ranked #6 of 21 in GPU Cluster Management Software by our editors (8.6/10); pricing: Open source; best for open-source Kubernetes GPU scheduling.

8.6/10Editor score

Explore YuniKorn

Apache YuniKorn8.6 Explore YuniKorn

At a glance

Editor score
8.6 / 10
Pricing
Open source
Best for
Open-source Kubernetes GPU scheduling
Paid from
Free
Deployment model
Self_hosted
Facts checked
22 Sep 2026

Where it wins
- Application-aware scheduling with hierarchical queues and quotas
- Gang scheduling, reservations, preemption, and resource fairness
- NVIDIA GPU scheduling, time-slicing, web UI, and REST APIs
Where it doesn't
- Requires self-hosted deployment into Kubernetes clusters
- Workload scheduling is focused on batch processing
- GPU tooling integrations center on the NVIDIA ecosystem

Our verdict on Apache YuniKorn

Apache YuniKorn is an open-source Kubernetes scheduler for multi-tenant batch, data, and machine-learning workloads. It is designed for teams managing shared Kubernetes clusters that need application-aware placement, queue-based resource allocation, workload fairness, and GPU scheduling. The project is distributed as source code and container images, with a central web interface for viewing cluster, queue, and application resource usage.

Its Kubernetes ecosystem fit is a central strength. YuniKorn integrates with Kubernetes, the NVIDIA Device Plugin, and the NVIDIA GPU Operator. The published capabilities also cover cloud and on-premises use cases, REST APIs, and scheduler metrics, giving platform teams several ways to connect scheduling activity with cluster operations and external tooling. This makes it a practical fit where Kubernetes is already the control plane and NVIDIA GPU workloads are part of the environment.

Scheduling depth is the main reason to consider YuniKorn. It supports FIFO, fair, state-aware, and priority-based job ordering, alongside hierarchical queues, resource quotas, queue capacity limits, gang scheduling, reservations, preemption, and resource fairness. NVIDIA GPU workload scheduling and GPU time-slicing extend that control to machine-learning and other GPU-backed jobs. Choose YuniKorn when open-source, self-hosted scheduling depth matters more than a hosted service. Teams seeking a standalone scheduler outside Kubernetes or a broader workload model than batch scheduling should look elsewhere.

Your product? Our editors can test Apache YuniKorn in depth and publish a review, labelled as paid · from $149/mo →

Apache YuniKorn pricing

Plans Open sourceFree Free to use — no paid tier required for the core job.

See plans on yunikorn.apache.org

Apache YuniKorn fact sheet

Free plan	Not verified
Paid from	Free
Deployment model	Self_hosted
Workload scheduling	Batch
Kubernetes support	Yes
Quota controls	Yes
GPU utilization metrics	Not verified
Cloud GPU support	Yes
Deployment	Self-hosted, Cloud
Platforms	Web
Support	Community, Docs
Built for	Mid-market, Enterprise (editorial estimate)
Integrations	3 integrations: Kubernetes, NVIDIA Device Plugin, NVIDIA GPU Operator
Pricing	Open source
Website	yunikorn.apache.org
Facts checked	22 Sep 2026

Apache YuniKorn integrations

Apache YuniKorn lists 3 integrations on its own site.

Kubernetes
NVIDIA Device Plugin
NVIDIA GPU Operator

Alternatives to Apache YuniKorn

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Apache YuniKorn vs the competition

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Featured on iTechGuides

Apache YuniKorn is listed in our GPU Cluster Management Software directory. Add the badge to your site — it links back to this page.

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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

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Last updated 7 Oct 2026 · How we research and update