From Prototype to Production — Cost Control That Scales (Part 4)

As AI systems move from prototype to production, cost behavior changes dramatically due to multi-step model calls, retrieval pipelines, and agentic workflows that create compounding effects. Dashboards alone are insufficient — real-time cost control requires segmented budgets (per user, feature, team, environment), telemetry tracing to identify inefficient workflows, and a centralized control plane that enforces policies consistently. Cost control, guardrails, and routing together form the foundation for sustainable, scalable AI systems.