New: Run AI anywhere your cloud runs
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emma | Product Launch | May 2026 | 8 min read
91% of mid-market companies are using generative AI (RSM 2025 Middle Market AI Survey). More than half feel only "somewhat prepared" to run it.
That’s why we're launching purpose-built AI infrastructure capabilities: GPU compute provisioning across five providers, GPU Managed Kubernetes across three hyperscalers, cross-cloud AI networking, and governed inference workflow templates — unified under one platform, one governance model, one control plane.
As AI moves from pilot to production, operationalizing AI is becoming a challenge because the infrastructure needed to run those models at scale is fragmented, manual, and provider-locked.
Getting a best-fit GPU today means navigating a different console per cloud, different quota processes, different driver configurations, and a separate cost dashboard for each. Connecting GPU workloads across providers requires manual network engineering that teams with limited platform engineering headcount simply can't absorb. Standing up an inference environment still means stitching together runtimes, models, dependencies, and ports from scratch — every time, for every team.
"The AI bottleneck isn't ambition — it's infrastructure."
The numbers reflect this. More than half of organizations experience difficulties across their entire AI stack (S&P Global Voice of the Enterprise: AI & ML Infrastructure, 2025), and about 85% of organizations misestimate AI costs by more than 10%, and nearly a quarter are off by 50% or more (Benchmarkit & Mavvrik, 2025 State of AI Cost Management). Only 49% say their networks can support the bandwidth and latency that AI workloads actually require (Broadcom 2026 State of Network Operations). And with 89% of businesses now running across more than one cloud provider (Scalr, via Clarifai), the need for cross-cloud AI tooling isn't a niche edge case. It’s the default operating reality for most engineering teams.
emma's AI offering is built around four capabilities that work as a system.
GPU Compute
Source and provision GPU-backed VMs across AWS, Azure, GCP, Nebius, and emma's own cloud — from one wizard. Orchestrate at scale with GPU multi-cloud Kubernetes (mk8s) — fully managed, GPU-accelerated clusters across AWS, Azure, and GCP. Preconfigured ML images eliminate driver setup, and all GPU resources are provisioned, monitored, and governed from emma’s unified control plane.
Gain complete GPU observability across both VMs and mk8s — built directly into the platform. Track GPU utilization, vRAM usage, and performance in real time, with no agents, no external tooling, and no context switching. From individual GPU VMs to multi-cloud Kubernetes clusters, teams get the visibility needed to operate AI workloads with confidence.
Connect AI workloads across providers through emma's multi-cloud networking backbone. Low-latency, high-bandwidth — built in. Egress costs become predictable.
Governed, reusable templates for deploying inference environments. Platform teams define and publish; application teams self-serve in minutes. RBAC and instance limits built in.
Together, these capabilities eliminate the barriers that consistently stall AI adoption and operationalization: fragmented GPU access, lack of observability, manual cross-cloud networking, and the absence of governed, repeatable inference deployment.
It's that GPU compute is portable, connected, and governed across providers — without requiring a platform engineering team per cloud. The networking backbone is the same infrastructure that powers emma's platform for all distributed workloads, now extended to AI. Inference workflow templates give platform teams the control surface they need without forcing application teams through tickets or shell scripts.
For European organizations, there's an additional dimension: emma's Luxembourg base with its own data center and on-demand GPU capacity, and ISO 27001 and SOC 2 certifications mean AI workloads can be provisioned where data residency and compliance requirements demand.
can now provision GPU VMs with preconfigured ML drivers across five providers from one wizard — without navigating per-provider quota processes or configuring drivers by hand.
get a governed workflow library they can publish to their teams, replacing per-team scripting with repeatable, RBAC-enforced inference templates.
get cross-provider GPU cost attribution at the VM-level — in one view, across every provider.
At the leadership level, the value is different in kind but adjacent in direction: an AI strategy that depends on infrastructure your team can actually provision, connect, and govern, without building a platform engineering function per cloud.
emma's AI capabilities provision and connect the compute layer; they don't replace your ML platform, experiment tracker, or model registry. SageMaker, Vertex AI, and Azure ML continue to run the workloads they're built for. emma provisions and connects the GPU infrastructure those platforms depend on — across providers — and gives you unified cost visibility across all of it.
*This product launch is also currently scoped.
The companies that will move fastest on AI are the ones with infrastructure that doesn't slow them down. GPU compute that requires a new workflow per provider, cross-cloud networking that's manual and brittle, and inference deployment that gets reinvented per team are solvable engineering problems. They shouldn't be the bottleneck.
emma's AI capabilities are available now. GPU compute, cross-cloud connectivity, and governed inference workflows — from one control plane, across five clouds.
Not more siloed clouds. Making them work as one.
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Download the solution brief to see how emma’s AI capabilities work.
