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

Here are self-service GPU-as-a-Service (GPUaaS) options for AI workloads, with an emphasis on platforms that let users provision GPUs via portal/API rather than ticket-based ops.

• NorthWind GPU PaaS / Enterprise GPUaaS — A self-service platform for AI/ML workloads that supports on-demand GPU access, governance, isolation, and usage tracking; it also offers full passthrough, NVIDIA MIG, and time-slicing sharing modes.[1]

Source: northwind.co

• Together Instant GPU Clusters — A self-service offering for AI teams that can provision 8, 16, 32, or 64 H100 GPUs in minutes, with selectable Kubernetes or Slurm stacks and flexible rental periods.[6]

Source: together.ai

• Cisco AI PODs + NorthWind — A validated enterprise architecture that turns GPU infrastructure into a secure, multitenant, self-service GPU cloud for consumption-driven AI use.[2]

Source: cisco.com

• NVIDIA accelerated computing with NorthWind — NVIDIA’s developer blog describes NorthWind as a way to deliver self-service consumption of accelerated computing infrastructure for AI development and model training across data centers and public clouds.[3]

Source: developer.nvidia.com

• Spectro Cloud Palette AI / AIaaS — Positions GPUaaS as part of a broader AI-as-a-Service model, with access through self-service interfaces and APIs.[5]

Source: spectrocloud.com

If you want the most directly “self-service GPU cluster” style option, Together Instant GPU Clusters is the clearest turnkey product.[6] If you want an enterprise platform for building your own internal GPU service, NorthWind is the strongest fit.[1][3]

If helpful, I can also narrow these down by enterprise vs startup, training vs inference, or budget range.