emma Technologies Tackles AI's Governance Gap with Unified Platform

LUXEMBOURG – May 13, 2026 – As enterprises race to deploy artificial intelligence, many are discovering a chaotic and costly side effect: a sprawling, ungoverned "Wild West" of AI infrastructure. Addressing this growing crisis, emma Technologies today announced a major expansion of its cloud operations platform, designed to bring order to the chaos by integrating the full stack of AI infrastructure under a single, unified governance framework.

The new capabilities aim to manage GPU compute, cross-cloud networking, observability, and inference deployment, extending the company's existing multi-cloud management solution to the highly specialized and often fragmented world of production AI.

The Widening AI Governance Chasm

The push for AI has led to an unprecedented investment in specialized hardware, particularly Graphics Processing Units (GPUs). Yet, this hardware gold rush has created a significant "Day-2 governance problem." While companies are successful in acquiring GPU capacity, they struggle to manage, optimize, and secure it effectively once it's running.

Industry analysis confirms the urgency of the situation. Research from ECI Research and theCUBE Research indicates that 76% of organizations are already running GPU workloads, establishing high-performance computing as a new baseline for enterprise infrastructure. Despite this, many are hitting a wall.

"Our research confirms that despite unprecedented investment in AI infrastructure, organizations continue to encounter bottlenecks related to data movement, orchestration and utilization efficiency,” noted Paul Nashawaty, Practice Lead and Principal Analyst at the research firms. He added that this confirms "GPU capacity alone is insufficient for production AI, and that governance platforms like emma are essential to bridging that gap."

This gap manifests as a series of critical business challenges. Infrastructure teams are often forced to provision GPU resources manually for each cloud provider, using fragmented tools and disparate security policies. Costs spiral out of control, with underutilization rates for expensive GPUs often exceeding 50%, and unexpected data egress fees appearing on massive cloud bills with little warning. For many, the infrastructure intended to accelerate AI innovation has become a significant bottleneck.

Unifying the Unruly Stack

emma Technologies is positioning its new offering as a direct answer to this fragmentation. Instead of adding another standalone MLOps tool or single-cloud solution to an already-complex tech stack, the company has integrated AI infrastructure management directly into its existing cloud operations platform.

"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," said Dmitry Panenkov, founder and CEO, emma Technologies. "These capabilities bring GPU workloads into the platform that already governs everything else — same policies, same visibility, same operational model."

The integrated capabilities function as a connected stack:

• GPU Virtual Machines and Managed Kubernetes: This allows engineering teams to provision GPU-powered VMs and container clusters using the same automated policies and guardrails that apply to their standard compute workloads.
• GPU Observability: By providing a single dashboard for monitoring performance and utilization across different cloud environments, the platform eliminates the need to jump between multiple provider-specific interfaces, offering a holistic view of the entire AI infrastructure landscape.
• Cross-Cloud AI Networking: Leveraging emma's private network backbone, the platform facilitates secure and low-latency connections for AI workloads distributed across different cloud providers, reducing reliance on the public internet and its associated costs and complexities.
• Inference Workflows: The platform provides governed deployment templates for inference, the process of running live data through a trained AI model. This standardizes deployments and prevents teams from having to build and secure inference pipelines from scratch for every new model.

This approach aims to empower cloud engineering and DevOps teams to extend their existing operational models to AI, rather than forcing them to learn and manage an entirely new set of tools and processes.

Calculating the True Cost of Ungoverned AI

Beyond the operational headaches, the lack of governance carries significant financial implications. The high price of advanced GPUs means that any underutilization represents a substantial waste of capital. Industry studies have shown that without careful management, GPU utilization can hover below 50%, effectively doubling the cost of compute resources.

Furthermore, the complexity of distributed AI systems, which often require moving large datasets between different clouds or from on-premise data centers, can lead to exorbitant data transfer fees. These "hidden costs" can derail project budgets and undermine the return on investment for AI initiatives. Research suggests that a majority of AI projects fail to meet their expected ROI, often due to a failure to integrate them into well-managed, cost-effective operational workflows.

By enforcing cost and security policies automatically and providing clear visibility into resource consumption, a unified governance platform promises a path toward financial accountability in AI. It allows organizations to move from reactive firefighting—analyzing a shocking cloud bill at the end of the month—to proactive optimization, ensuring that expensive AI resources are used efficiently and aligned with business objectives.

A Neutral Layer in a Competitive Market

The market for AI infrastructure is crowded and rapidly evolving. Hyperscale cloud providers like Amazon Web Services, Microsoft Azure, and Google Cloud offer powerful GPU instances and a suite of AI services. At the same time, a new class of specialized, AI-native cloud providers like CoreWeave has emerged, offering highly optimized environments for large-scale training and inference.

emma Technologies is positioning itself not as a direct competitor to these infrastructure providers, but as an essential agnostic management layer that sits on top of them. The platform's core value proposition is its vendor-neutral stance, enabling companies to pursue a true multi-cloud strategy without being locked into a single provider's ecosystem. This allows an organization to run a training workload on a specialized cloud for cost-effectiveness, then deploy the resulting inference model on a hyperscaler's edge network to be closer to users, all while maintaining consistent governance, security, and visibility from a single platform.

This strategy of extending an existing, proven governance model into the AI domain reflects a deliberate choice to provide stability in a turbulent market. As Panenkov stated, "We're not chasing the AI wave. We're extending the answer we already had." This focus on integration and consistency aims to transform AI from a high-stakes, experimental endeavor into a manageable, scalable, and value-driven component of enterprise technology.