Efficiency Wins the Inference Era explores how enterprises can optimize AI performance, control costs, and maintain data sovereignty as AI adoption accelerates
Core42, a G42 company specializing in AI infrastructure, sovereign cloud, and digital transformation, has released a new whitepaper examining how organizations can scale artificial intelligence deployments while maintaining control over cost, performance, and governance. Titled Efficiency Wins the Inference Era, the report explores the changing economics of AI inference as enterprises move beyond pilot projects and embed AI into everyday operations.
The whitepaper argues that traditional cost metrics, such as the price of individual AI tokens, do not provide a complete understanding of production AI expenses. Instead, organizations should measure tokens per second per dollar, a metric that evaluates how much useful output a system delivers relative to the speed and cost of generating that output. According to Core42, this approach helps businesses focus on sustainable AI outcomes rather than simply minimizing token costs.
“The quoted price of a token provides only a partial picture of AI economics. What matters is how much useful output a system delivers, how quickly it reaches users, and how efficiently organizations can scale AI into production while maintaining governance and control.”
As AI adoption increases, organizations face growing computational demands driven by larger workloads, more complex prompts, faster response expectations, and the rise of agentic AI systems that can trigger multiple model interactions for a single task. These factors make infrastructure efficiency a critical consideration for enterprises seeking to scale AI economically.
The report also emphasizes the importance of workload-aware architecture. Rather than running all AI workloads on a single model or infrastructure stack, organizations should match workloads to the most suitable combination of models and hardware. Core42’s Compass platform is designed to support this approach by routing workloads across diverse silicon environments, including NVIDIA, AMD, Qualcomm, and Cerebras technologies.
Beyond performance and cost optimization, the whitepaper highlights the growing importance of governance, security, and sovereignty. Core42 argues that organizations must manage AI efficiency alongside data residency, access controls, compliance requirements, and usage monitoring to ensure responsible AI deployment.
According to the company, Compass combines centralized visibility into AI usage and costs with security controls, encryption, budget management, in-country data residency capabilities, and more than 170 security policies.
With enterprises increasingly moving AI into production environments, Core42 concludes that long-term success will depend on balancing scalability, operational efficiency, and governance, making inference economics a central consideration in the next phase of enterprise AI adoption.
