GPUStack vs KAI Scheduler
GPUStack
gpustack.aiKAI Scheduler
kai-scheduler.devSide by side
| Figure | GPUStack | KAI Scheduler |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | — |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · GPU Cluster Management Software | 8.3 (#4) | 7.4 (#13) |
| Deployment | It supports on-premise, Kubernetes, and multi-cloud GPU deployments, and the maker states that air-gapped deployments are supported. | The project provides production Helm installation, source build, and ArgoCD GitOps installation options for Kubernetes. |
| Integrations | Listed integrations include OpenWebUI, LangChain, n8n, Dify, RAGFlow, Claude Code, OpenClaw, Docker, Podman, Kubernetes, Helm, 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. |
| Security | The site lists RBAC, multi-tenancy, OIDC/SAML/AD-LDAP SSO, scoped API keys, IP allowlisting, token quotas, and usage analytics. | Security vulnerabilities should be reported through GitHub Security Advisories rather than public GitHub issues. |
| Support | The contact page offers personalized demos and architecture, deployment, and scaling guidance, and says replies typically arrive within one business day. | 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
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.
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.