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emma Technologies Closes the Governance Gap in AI Infrastructure
LUXEMBOURG, May 13, 2026 (GLOBE NEWSWIRE) -- emma Technologies, provider of the emma cloud operations platform for distributed infrastructure, today launched new capabilities that bring the full stack of AI infrastructure - GPU compute, observability, cross-cloud networking and inference deployment - in its platform that already manages and governs cloud-native workloads.
"With 76% of organizations already running GPU workloads, making high-performance parallel computing a baseline infrastructure requirement, the need for unified governance frameworks that extend to AI infrastructure, as emma now provides, has never been more urgent,” said Paul Nashawaty, Practice Lead and Principal Analyst, ECI Research and theCUBE Research. “Our research confirms that despite unprecedented investment in AI infrastructure, organizations continue to encounter bottlenecks related to data movement, orchestration and utilization efficiency, confirming that GPU capacity alone is insufficient for production AI, and that governance platforms like emma are essential to bridging that gap."
Enterprise AI has a Day-2 governance problem: infrastructure either becomes an accelerator for AI or a bottleneck. Most enterprises are on the wrong side of that right now. The shortage is not simply hardware; it is knowing where the AI infrastructure needs to be and can be deployed in a governed, economical, reproducible infrastructure that scales consistently across clouds.
GPU and AI infrastructure are now production infrastructure but most enterprises are still provisioning manually per cloud with governance that is fragmented across the individual hardware stacks and costs that don't appear in any dashboard until the cloud bill arrives.
Standalone, single-cloud GPU tools and MLOps platforms add a new operational layer. emma adds AI infrastructure capabilities into a governance platform that already exists so engineering teams extend what they already run rather than implement new tech. By integrating these new capabilities in its cloud operations platform, emma brings compute and data together wherever they need to be — cross-cloud, under existing governance, optimized to the performance, sovereignty and cost demands of each workload.
emma's new capabilities work as a connected stack:
- GPU Virtual Machines and GPU Managed Kubernetes provisions infrastructure at VM and cluster level under the same governance policies that apply to every other workload on the platform.
- GPU Observability provides a single interface for both environments, not through separate provider dashboards, so performance is visible without switching contexts.
- Cross-Cloud AI Networking connects workloads across providers over emma's private backbone, cutting the manual configuration and egress costs that come with public internet routing.
- Inference Workflows provide governed deployment templates, so each new model does not require building inference from scratch.
"GPU infrastructure has been operating outside the governance frameworks that apply to everything else in the enterprise. That's not sustainable when it's running production AI. These capabilities bring GPU workloads into the platform that already governs everything else — same policies, same visibility, same operational model. We're not chasing the AI wave. We're extending the answer we already had," said Dmitry Panenkov, founder and CEO, emma Technologies.
About emma
emma enables a world where organizations run their infrastructure on their own terms — across any cloud, any region, any provider — without complexity, fragmentation or compromise. emma is a cloud operations platform that provides unified access, deployment and management across distributed infrastructure. Companies use emma to eliminate operational fragmentation, control costs at infrastructure boundaries and maintain governance across environments without vendor lock-in. For more information, go to: https://www.emma.ms/.
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