The right GPU cloud depends on four things: the workload (steady inference, bursty inference, or large training runs), the GPU models you need, where your data must live, and how much lock-in you can accept. Many teams combine providers to improve price and availability, and run them behind one control layer so moving capacity is a traffic change, not a migration.

Disclosure: Control Plane publishes this guide and is one of the options listed.

This guide covers ten options, compares CoreWeave, Lambda, and AWS side by side, and gives you a cost formula for deciding whether to rent or build. Every vendor description comes from that vendor’s own pages, linked inline and read on October 22, 2026. Vendor offerings change often, so confirm details before you sign.

TL;DR: What are the best GPU cloud providers for inference and training?

Control Plane is listed first because it is the layer that runs workloads across the others.…