emma

Idle GPUs are the most expensive thing in your data center. Score your AI infrastructure across compute, cost, data, governance, and operations — 15 questions, about 3 minutes, immediate results. → Where do you actually stand?

Most infrastructure teams can tell you what their AI stack costs. Far fewer can tell you what it's returning. The 2026 gap isn't compute. It's control: GPU utilization per workload, capacity that takes three weeks to hand over, egress bills, models in production with no rollback path.

Four bands: Exposed, Reactive, Managed, Ready. Most infrastructure leaders running AI workloads land in Reactive — the stack works, but they're firefighting utilization, cost, or governance rather than controlling them. 15 questions tells you which band you're in and the four things to fix in order. → Get your readiness score

GPUs are cheap somewhere else — but your data isn’t there, and the egress to feed your training jobs adds up fast. emma wires frontier GPUs to wherever your data already lives, over a private backbone at $0.05/GB — 58% lower than public egress. No migration, no replatforming.

Your ML team needs GPUs they can afford — today, not after a quota ticket. emma puts B300, H200 and H100 capacity live across every major cloud, billed by the second. First instance the same day. No waitlist, no lock-in.

Frontier GPU capacity that scales with you and stays cost-efficient as usage ramps. Run across AWS, Azure, GCP and bare metal on one contract — switch providers anytime, no replatforming. Commit to capacity and we add free months on top, stacked with spot pricing.