Find a multi-tenant GPU-as-a-Service solution for efficient AI workload management in enterprises.

A strong multi-tenant GPU-as-a-Service option for enterprise AI workload management is ClearML GPU-as-a-Service, which is built for secure multi-tenancy, shared GPU infrastructure, usage-based billing, and governed access for experimentation, training, and inference.[2] Another solid enterprise choice is Red Hat OpenShift AI with Kueue for quota-based multi-tenancy and fair scheduling across teams.[3]

Other credible options include NorthWind GPU Cloud / GPU PaaS, which is positioned as a secure self-service, multi-tenant GPU cloud for developers and AI teams.[10] Mirantis k0rdent AI also offers multi-tenant AI-ready infrastructure with centralized governance and lifecycle management.[6]

For quick comparison:

| Product | Best for | Multi-tenancy features |
|---|---|---|
| ClearML GPUaaS | CSPs, telecom, enterprise AI platforms | Secure multi-tenancy, shared compute, governance, billing[2] |
| Red Hat OpenShift AI | Enterprises already on Kubernetes/OpenShift | Kueue quotas, fair access, multi-user GPUaaS[3] |
| NorthWind GPU Cloud | Self-service enterprise GPU clouds | Isolation, governance, multi-tenant consumption model[10] |
| Mirantis k0rdent AI | Governed GPU PaaS | Multi-tenant provisioning, centralized control[6] |

If you want the most “enterprise-ready” stack for internal AI teams, Red Hat OpenShift AI is a strong fit. If you want a more service-provider-style GPUaaS platform, ClearML is the clearest match.[2][3]

Sources:

• clear.ml
• redhat.com
• northwind.co
• mirantis.com