AI Inference Is the New Egress: The Cost Layer Nobody Modeled

You modeled compute scaling. You modeled storage durability. You built egress budgets because you learned — the hard way, or from someone who did — that data movement is never free. You did not model AI inference cost. Neither did most of the industry. Inference just crossed 55% of total AI cloud infrastructure spend in early 2026, surpassing training for the first time. And most of the teams running those workloads are still treating inference like a feature — bolted onto an architecture that was designed for something else entirely. It is not a feature. It is a tax. On every request your system makes. Inference ≠ Training The economics are completely different, and teams keep conflating them. Training is a capital expenditure analog. You rent a large GPU cluster for days or weeks. The bill is large, visible, and bounded. You plan for it. You feel it once and move on. Inference is continuous operational expenditure — every API call, every token, every real-time pipeline invocation add