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

Here are practical, self-service GPU-as-a-Service (GPUaaS) options for AI training/inference, ranging from instant on-demand GPU clouds to platform software for building your own GPUaaS:

Instant on-demand GPU clouds (developer self-service, pay-as-you-go):

• Together AI –

Instant Clusters are essentially designed to emulate the user experience of conventional cloud infrastructure while handling the specific demands of AI workloads.

Recent updates include improved autoscaling features, the ability to extend reserved infrastructure dynamically and support for infrastructure-as-code tools Skypilot and Terraform.
It also validates hardware before use —
it performs hardware checks, stress tests and inter-node communication validations before making clusters available.
(siliconangle.com)

• RunPod, Lambda Labs, Vast.ai, Paperspace (by DigitalOcean), CoreWeave, Crusoe Cloud — these are well-known self-service marketplaces/clouds where you can spin up H100/A100/L40S GPU pods or clusters in minutes via web console or API, with per-second/hour billing, no long-term contracts, and container-based deployment (Docker/Kubernetes). Good for training, fine-tuning, and inference without managing hardware.

Enterprise/self-service platform layers (for building internal or provider-hosted GPUaaS):

• NorthWind GPU PaaS – lets enterprises and neoclouds turn raw GPU infrastructure into a catalog-based, self-service offering.

GPU as a Service (GPUaaS) delivers on-demand access to GPU compute through APIs or self-service portals, similar to how cloud platforms deliver CPU-based infrastructure, enabling on-demand, self-service access to GPU resources instead of provisioning clusters manually.
It even packages NVIDIA Run:ai:
GPU Clouds can deliver NVIDIA Run:ai as a managed service with self-service provisioning, ensuring customers receive a fully configured environment automatically.
(northwind.co)

• Red Hat OpenShift AI – a Kubernetes-based approach:

a multi-user GPUaaS solution where achieving true GPUaaS requires dynamic allocation based on workload demand, so GPUs are more quickly reclaimed to minimize idle time
, using tools like Kueue and KServe for multi-tenant scheduling. (redhat.com)

Recommendation: For quick, no-ops access to GPUs, start with Together AI, RunPod, or Lambda Labs. For building a governed, multi-tenant internal GPUaaS platform, evaluate NorthWind or Red Hat OpenShift AI on top of your own hardware.