VentureBeat AI | April 30, 2026

As enterprises move from AI experimentation into production deployment, the primary cost driver has shifted from foundation model training to the infrastructure required to run thousands of concurrent inference workloads at scale, with agentic AI as the accelerant. Inference costs per token have dropped by roughly an order of magnitude over the past two years, driven by model efficiency improvements and competitive pressure among cloud providers. However, total costs are rising due to the Jevons paradox: when a resource becomes cheaper to use, consumption tends to increase faster than the price drops. Consumption has risen more than 100X while cost per token dropped by nearly 10X. Production agentic AI introduces a workload profile that traditional enterprise infrastructure was not designed to handle — unpredictable, high-frequency bursts of short inference requests that place new demands on networking and storage. The response emerging among infrastructure vendors is a move toward tightly integrated, validated full-stack platforms designed specifically for production AI workloads. Nutanix's Agentic AI solution, built on the Nutanix AHV hypervisor, represents one approach to this problem, with NVIDIA topology-aware enhancements that automatically optimize how GPUs, CPUs, memory, and DPUs are allocated to virtual machines.

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