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Apache YuniKorn vs GPUStack

Apache YuniKorn

yunikorn.apache.org

GPUStack

gpustack.ai

Side by side

FigureApache YuniKornGPUStack
Price fromFreeFree
Free planYesYes
Free trial——
Open source——
WebYesYes
Windows—Yes
macOS—Yes
Linux—Yes
APIYesYes
Self-hostedYesYes
Plans published11
Spec score · GPU Cluster Management Software8.1 (#6)8.3 (#4)
DeploymentThe project recommends deploying YuniKorn to an existing Kubernetes cluster using its Helm chart and Helm 3 or later.It supports on-premise, Kubernetes, and multi-cloud GPU deployments, and the maker states that air-gapped deployments are supported.
IntegrationsYuniKorn exposes scheduling metrics via Prometheus, and the project provides instructions for monitoring with Prometheus and Grafana.Listed integrations include OpenWebUI, LangChain, n8n, Dify, RAGFlow, Claude Code, OpenClaw, Docker, Podman, Kubernetes, Helm, Prometheus, and Grafana.
SupportUsers can ask for help in the YuniKorn Slack #yunikorn-user channel or use the user mailing list.The contact page offers personalized demos and architecture, deployment, and scaling guidance, and says replies typically arrive within one business day.

Plans

Apache YuniKorn

  • Apache YuniKornFree

Apache YuniKorn pricing →

GPUStack

  • GPUStack Community EditionFree

GPUStack pricing →

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.

Visit yunikorn.apache.org

GPUStack

GPUStack is an open-source GPU cluster manager for deploying, governing, and scaling AI models across on-premise, cloud, or hybrid infrastructure. It provides OpenAI-compatible and Anthropic-compatible inference endpoints, with support for engines such as vLLM, SGLang, llama.cpp, TensorRT-LLM, and MindIE. Users can browse models from Hugging Face and ModelScope or supply local files, with automated compatibility checks. GPUStack can provision GPU instances with lifecycle controls, partitioning, SSH key injection, Jupyter access, and persistent S3 or NFS storage. Deployment options include on-premise, Kubernetes, and multi-cloud, including air-gapped deployments. The maker lists support for accelerators from NVIDIA, AMD, Ascend, T-head, Hygon, MetaX, Moore Threads, Cambricon, and Iluvatar. Security controls include RBAC, multi-tenancy, SSO, scoped API keys, IP allowlisting, token quotas, and usage analytics. GPUStack Community Edition is free. Worker nodes support Linux only; Windows users are directed to consider WSL2, and macOS worker nodes are not supported.

Visit gpustack.ai