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

Multi-Tenant GPU-as-a-Service Options for Enterprise AI

Several enterprise-grade platforms currently address this need, each with different strengths:

1. Red Hat OpenShift AI – Built on Kubernetes with the Kueue scheduler, it's designed specifically for shared GPU clusters.
OpenShift AI's built-in Prometheus/Grafana stack allows administrators to create custom dashboards to track GPU utilization, broken down per-tenant, per-project, and per-GPU, with metrics feeding back to refine quotas and fair-sharing policies enforced by Kueue.
It handles
inference via frameworks like KServe and vLLM, and distributed training via KubeFlow Training and KubeRay
. Link: redhat.com

2. ClearML GPU-as-a-Service – A datacenter-grade enterprise solution
offering secure multi-tenancy, granular resource allocation management and policies, dynamic fractional GPUs, governance, and real-time reporting on usage
. Link: clear.ml

3. Mirantis k0rdent AI – Focuses on governance at scale, providing
secure access across multiple teams with hard multi-tenancy, RBAC, authentication, and built-in observability
for regulated industries like finance and healthcare. Link: mirantis.com

4. Cisco AI PODs + NorthWind GPU PaaS – Combines infrastructure and software for a turnkey solution, delivering
a secure, fully operational, self-service, multi-tenant GPU Cloud platform that enables SKU-based provisioning, GPU slicing, quota enforcement, and AI workload catalogs
. Link: cisco.com

5. hosted.ai – A neocloud stack good for GPU resellers/providers, offering
multi-org and multi-user management for GPU, CPU, storage and network resources, with project isolation, resource quotas per tenant and reseller/sub-tenancy RBAC
. Link: hosted.ai

Recommendation: If you're already on Kubernetes/OpenShift, Red Hat OpenShift AI is the most mature open-standard choice. For turnkey hardware+software with less DIY, Cisco AI PODs + NorthWind is strong. For pure MLOps-centric teams, ClearML integrates workload orchestration with training pipelines. All support GPU slicing (MIG) and quota-based isolation—key for cost-efficient multi-tenant utilization.