The AI bottleneck isn't ambition. It's complexity.
Every new cloud your team approves for AI infrastructure adds more overhead — another provisioning workflow, another networking challenge, another compliance gap. Your AI roadmap isn't waiting on model access. It's waiting on platform engineering capacity you've already used up.
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GPU provisioning is one sprint. Cross-cloud networking is another. Governance and audit readiness is a third. In practice, these projects run in parallel, compete for the same platform engineers, and block each other at every integration point.
It's an architectural problem.
When each GPU cloud you operate has its own provisioning API, its own VPC configuration, and its own compliance surface, your platform team becomes the integration layer. They spend capacity on plumbing that generates no product value.
91%
of middle market firms are using generative AI — but over half feel only "somewhat prepared" for the infrastructure requirements. RSM 2025 AI Survey
86%
of enterprises expect AI infrastructure budgets to more than triple over the next three years. Deloitte AI Infrastructure Survey 2025
59%
of organizations report bandwidth issues with AI workloads — up from 43% the year prior. Flexential State of AI Infrastructure
emma connects your approved GPU clouds into a single control plane. Four integrated capabilities — GPU compute at two levels, integrated observability, cross-cloud networking, and governed inference deployment.
GPU compute
Provision GPU VMs across AWS, GCP, Azure, emma, and Nebius. GPU-enabled managed Kubernetes (mK8s) across AWS, Azure, and GCP. Two levels of abstraction, one governance model.
GPU monitoring
GPU observability at both VM level and mk8s cluster level. Zero agents. Zero setup. GPU metrics on mk8s including power, temperature, and clock.
Cross-cloud AI networking
On-demand connectivity between GPU workloads across providers through emma's networking backbone. Training data on one provider, inference on another — connected, not cobbled.
Inference workflows
Reusable, governed templates for deploying AI inference environments on GPU VMs. Self-service with guardrails. Deploy in minutes, not hours.
One conversation. GPU provisioning, networking, and governed inference as a single operating model.
Book a 45-minute demo with an emma infrastructure engineer. No slides. No positioning deck. Infrastructure, running.
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