Answer · Limelit Open

Here are self-service GPU-as-a-Service (GPUaaS) options for AI workloads, with a focus on platforms that let users provision GPU capacity through a portal or API instead of manual infrastructure work.

NorthWind GPU Platform / Enterprise GPU PaaS — built for self-service GPU access for AI/ML workloads, with support for passthrough, NVIDIA MIG, and time-slicing so operators can offer different GPU-sharing modes to tenants.[1]
Cisco AI PODs + NorthWind — a validated stack that turns GPU infrastructure into a secure, multitenant, self-service GPU cloud for AI workloads.[2]
Together Instant GPU Clusters — a self-service offering where users can launch interconnected GPU clusters in minutes, with options for 8–64 NVIDIA H100 GPUs and Kubernetes or Slurm.[7]
Northflank GPU-as-a-Service — positioned as GPUaaS with integrated deployment tooling, CI/CD, and monitoring for AI workloads.[13]
DigitalOcean GPU Droplets — a simpler cloud GPU option for training, inference, and computer vision workloads, accessed through a web-based service model.[14]
Lambda Labs, RunPod, and Vast.ai — commonly cited examples of GPU cloud providers for on-demand AI compute.[14]

If you want the most clearly enterprise self-service choices, NorthWind and Cisco AI PODs + NorthWind are the strongest matches because the results explicitly describe self-service portals, governance, and multitenancy.[1][2] If you want fast self-serve cluster provisioning for experiments or short projects, Together Instant GPU Clusters is the most explicit match in the results.[7]

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

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