The Hidden Costs of AI

What Tokens Hide

Enterprise AI is priced in tokens, which makes the math feel clean and simple. But tokens hide what you're really paying for. Underneath each token sits model licensing, infrastructure overhead, execution inefficiencies, and cloud margins. These are all bundled together.

Two teams can consume the exact same number of tokens and pay very different amounts. Model selection is part of the reason. The system delivering those tokens is another. One team runs an optimized stack. The other runs on loosely integrated infrastructure with idle compute, inefficient coordination between layers, and poor hardware utilization.

Why Costs Climb at Scale

At a small scale, AI costs feel manageable. As usage grows — more requests, more users, more workflows — costs rise in ways that are hard to predict. Most organizations assume those rising costs are primarily driven by frontier models themselves. Model choice matters, but most of the cost increase comes from somewhere less visible: how efficiently infrastructure executes and delivers AI at scale.

Agentic AI makes these execution challenges more visible and more expensive. Multi-step reasoning, tool calls, retries, and long-running context windows multiply token usage in ways that traditional applications never did. Agents make the cost curve steeper and the inefficiencies underneath them more expensive.

As costs rise, organizations are starting to treat token usage as a measurable line item, monitoring consumption across teams. That's a useful start, but it only tells part of the story. Knowing how much you're using doesn't tell you how efficiently you're using it.

Throughput and Utilization

Execution efficiency is an increasingly important driver of AI cost as organizations scale usage — meaning how much output you generate from a given amount of compute.

Systems that keep compute consistently active and efficiently scheduled produce more output at lower cost. Inefficient systems require more compute to produce the same result, which costs more. This is well understood in high-performance computing, and the same dynamics apply to AI inference.

The Economics of Model Choice

Model choice is becoming an important determinant of AI cost. Much of the enterprise market still defaults toward the largest and most expensive closed models, even when the workload doesn't require that level of capability — but this is changing. Research from MIT and Georgia Institute of Technology shows that lower-cost open-weight models now reach roughly 80–90% of the performance of leading closed models on most enterprise workloads. The gap in cost is often far greater than the gap in results.

"You don't need a Ferrari to go to the corner store for a carton of milk."

Enterprises continue to choose closed platforms for reasons that extend beyond raw model performance, including trust, familiarity, perceived safety, and operational convenience. As open models continue to improve, organizations are increasingly evaluating whether those premiums remain justified for every workload.

Optimize the System, Not Just the Model

Reducing AI cost requires looking beyond models alone and considering the systems that deliver them. That means optimizing how AI is delivered: better routing and execution, higher throughput, maximum utilization, and less infrastructure overhead.

When those things are optimized, the economics change. The task is the same. The outputs are functionally equivalent. The cost structure is different, and performance gets more predictable as you scale.

The future of AI efficiency will not be driven by models alone, or infrastructure alone, but by how intelligently the two are paired and delivered together. Organizations that optimize both the model layer and the system layer will dramatically reduce cost without sacrificing capability.