AI is getting cheaper to run, but enterprises are spending more on it. As the economics of AI shift from upfront model development towards recurring inference, wider adoption, deeper usage, reasoning models, multimodal workloads, longer context, and autonomous agents are driving consumption faster than falling unit costs can offset it. The result is a new economic challenge: capturing the benefits of greater AI use while keeping consumption aligned with value.
In this report, Ecosystm Principal Advisor Darian Bird examines the forces driving inference consumption, the evolution of AI pricing beyond fixed per-seat models, and the emerging approaches to managing inference economics. Drawing on examples from EY and MetLife, the report explores model selection, inference efficiency, AI workflow design, and AI FinOps, providing insight into how organisations can understand the full cost of AI workloads and connect inference expenditure more closely to the business value generated.
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