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Understanding AI Cost Drivers
AI costs are driven by four factors: model selection (GPT-4o costs 10x more than GPT-3.5 Turbo), token volume (input and output tokens), feature usage (web search, file analysis cost extra), and user growth. Without visibility into these drivers, optimization is impossible.
The Universal Credit System
Normalizing costs across providers is essential. A universal credit system converts varied provider pricing into a single internal currency. One credit might equal 1,000 GPT-4o input tokens or 10,000 GPT-3.5 tokens, normalized for comparable value.
Department-Level Budgets
Set hard budget caps per department with configurable thresholds. Auto-stop logic prevents overspending. Alert notifications warn department heads before budgets are exhausted. This creates accountability without restricting productive AI usage.
Intelligent Model Routing
Route simple tasks (summarization, formatting) to cheaper models and complex tasks (analysis, coding) to premium models automatically. This can reduce AI costs by 40-60% without impacting output quality.
ROI Measurement
Track AI ROI by comparing AI costs against time saved, quality improvements, and revenue impact. Most enterprises see 3-8x ROI on governed AI platforms within the first quarter.