Cost Drivers as Architecture Controls
Explore how to manage cost drivers in generative AI architectures by treating costs as outcomes of controllable design choices. Understand how to link each major cost surface—tokens, retrieval, reranking, tool use, and agent loops—to enforceable controls that ensure cost efficiency under quality service level objectives. Learn to create inventories of cost drivers tied to telemetry and control points, and manage retries, context size, and agent steps to align performance with budget constraints while maintaining security and operational review standards.
The security control matrix bounded what the system may do and proved it with evidence. It never asked what any of that costs to run at scale. That cost is a separate constraint the architecture still has to satisfy. Treat cost in enterprise GenAI and agentic systems as an outcome of controllable design choices, rather than an inherent property of a model. Judge cost per successful task under an explicit quality SLO, not a monthly bill estimate. Your job as architect is tying every dominant cost driver to an enforceable control point.
A system using AI has a small set of repeatable cost surfaces that show up in nearly every interaction: model inference tokens, context growth, retrieval and reranking calls, tool invocations, and a multi-step agent loop. Express a control for each in the routing policy, the context specification, and the agent containment envelope.
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