Answer · Limelit Open

Here are self-service GPU-as-a-Service (GPUaaS) options for AI workloads that let you provision GPU capacity on demand through a portal, API, or automated workflow:

| Product | What it offers | Best fit |
|---|---|---|
| NorthWind GPUaaS / GPU Cloud / GPU PaaS | Self-service GPU provisioning via UI, API, or CLI; multi-tenant governance; support for full passthrough, NVIDIA MIG, and time-slicing; built for training, fine-tuning, inference, and RAG workloads[1][7][18] | Enterprises, cloud providers, and platform teams building an internal or external GPU cloud |
| Together AI Instant GPU Clusters | Self-service GPU clusters deployed in minutes; configs up to 64 NVIDIA H100 GPUs per cluster; Kubernetes or Slurm options[6][8] | Teams that want fast cluster spin-up for distributed training or inference |
| Uvation GPU as a Service | Access to AI-ready GPU capacity for training, fine-tuning, and inference without building the full infrastructure stack themselves[4] | Organizations wanting managed GPU capacity with less ops burden |
| Quali Torque GPUaaS | Governed GPU environments provisioned in minutes, reserved in advance, scaled dynamically, and decommissioned automatically[10] | Platform engineering teams needing lifecycle automation and governance |
| Cisco AI PODs + NorthWind | Turns Cisco AI PODs into a secure, self-service, multitenant GPU cloud for AI workloads[2] | Enterprises standardizing on Cisco infrastructure |
| Modal GPU cloud | Self-service GPU access without hyperscaler-style quota workflows for ordinary deployments[20] | Developers seeking fast access without waitlists |

A few practical notes: self-service GPUaaS is especially useful for model training, fine-tuning, distributed ML, inference APIs, and RAG; some platforms emphasize governance and tenancy controls, while others focus on speed and developer self-service[1][7][18][20].

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Find self-service GPU-as-a-Service solutions for AI workloads.

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