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

For self-service GPU-as-a-Service (GPUaaS) aimed at AI/ML workloads, these are strong options to evaluate as of August 2026:

• Lambda Cloud — One of the cleanest fits for self-service AI compute. Users can launch NVIDIA B200, H100, A100, and GH200 instances in minutes, including 1-, 2-, 4-, and 8-GPU configurations. Self-serve, first-come availability and no egress fees make it attractive for model training and experimentation. (lambda.ai)

lambda.ai

• Runpod — Developer-oriented GPU cloud with both dedicated GPU “Pods” and serverless GPUs. Particularly useful for inference, fine-tuning, image generation, and workloads needing scale-to-zero/serverless economics. (runpod.io)

runpod.io

• CoreWeave — More infrastructure-heavy and enterprise-oriented, but offers on-demand GPU instances plus Kubernetes and managed/dedicated inference. Strong candidate for larger training and production inference deployments. (coreweave.com)

coreweave.com

• Vast.ai — A self-service GPU marketplace rather than a conventional centralized cloud. Hosts compete on pricing, so GPU prices and infrastructure quality vary. It also offers serverless GPU execution for inference. Particularly compelling when minimizing compute cost is the priority. (docs.vast.ai)

vast.ai

• DigitalOcean Paperspace — Conventional self-service GPU VMs provisioned through a console/API/CLI. Current options range from older workstation GPUs through A100-80GB and H100, with hourly billing; compute billing stops when the VM is powered off. (docs.digitalocean.com)

docs.digitalocean.com

For a shortlist, I'd start with Lambda for straightforward AI training, Runpod for developer/serverless workloads, CoreWeave for production-scale infrastructure, and Vast.ai for price-sensitive workloads. Paperspace is worth considering when you want a more conventional VM experience.

Key buying criteria are GPU availability (especially H100/H200/B200), multi-GPU/NVLink networking, provisioning/API support, persistent storage, egress charges, serverless/scale-to-zero support, SLAs, and SOC 2/enterprise security requirements.