Answer · Limelit Open

A strong enterprise option is NVIDIA Run:ai, which provides a secure, scalable multi-tenant control plane for AI Platform-as-a-Service and is designed for enterprises and service providers that need isolated, policy-driven GPU access with centralized control.[17] If you want a more complete platform for shared GPU infrastructure, Red Hat OpenShift AI is also positioned for multi-user GPUaaS with quotas and fair-sharing via Kueue.[4]

| Product | Why it fits multi-tenant GPUaaS | Best for |
|---|---|---|
| NVIDIA Run:ai | Multi-tenant control plane, policy-driven isolation, centralized orchestration[17] | Enterprises needing strong governance and utilization optimization |
| Red Hat OpenShift AI | Kubernetes-based GPUaaS with quotas and fair access across teams/projects[4] | Enterprises already using OpenShift/Kubernetes |
| ClearML GPU-as-a-Service | Secure multi-tenancy, shared compute, usage-based billing, white-labeled AIaaS[3] | CSPs, telecoms, and internal enterprise platform teams |
| NorthWind GPU Cloud | Secure, multi-tenant, revenue-ready GPU cloud platform[5][15] | Hybrid/on-prem GPU service delivery |
| Spectro Cloud PaletteAI | Namespace isolation, RBAC, SSO, quotas, self-service guardrails[7] | Enterprise platform teams enabling self-service AI |

For efficient AI workload management, the key features to look for are tenant isolation, GPU quota enforcement, fair scheduling, self-service provisioning, and billing/metering.[4][7][11][12] Among the options above, Run:ai is the most directly aligned with enterprise multi-tenancy, while ClearML is especially attractive if you need a turnkey GPUaaS offering with billing and white-label delivery.[3][17]

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

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