Red Hat Summit 2026: Token Cost Is an Architecture Problem
What Shipped
A practical economic framework for controlling AI costs: per-task cost tracking, right-sized models, multi-provider optionality, model lifecycle control, and self-hosted inference. These are architectural controls, not procurement negotiations.
What's Next
Map current AI spend against the five cost dimensions: task-level tracking, model sizing, provider mix, lifecycle management, and inference topology.
Context
The tokenomics breakout laid out the brutal economics: $7.6T estimated AI CapEx over six years, token input costs that vary 27x, output costs that vary 500x, and hosted models that can retire with 45 days notice. Inference is the dominant cost no matter where the model lives.
Decision
Treat tokenomics as a first-class engineering discipline. Design systems that can track usage by task, select the right model for the workload, maintain provider optionality, and preserve model lifecycle control — including the option to self-host optimized inference.
Outcome
Organizations that build token economics into their architecture move from experimental AI to sovereign AI — where the economics, governance, and runtime are under their control, not their vendor's.
The Judgment Call
Most teams treat AI cost as a finance problem — track the vendor bill and negotiate rates. That misses the architectural levers that actually move cost: context windows, model right-sizing, cold starts, inference topology, and lifecycle management.
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