16 guides
AI cost governance guide for teams using multiple LLM APIs. Learn how to attribute spend by feature, set model routing rules, control RAG and agent budgets, run weekly bill reviews, and create launch checklists that keep AI API costs predictable.
Learn when AI API output tokens cost more than input, how to estimate output-heavy workflows, and strategies to control response length without sacrificing quality.
Compare prompt caching ROI under different TTL windows, compute break-even hit rates, and decide when caching saves real money versus when it adds complexity without meaningful cost reduction.
Learn how to choose the right AI model by balancing capability, cost, and use case. Compare reasoning vs text models, fast vs slow inference, and make cost-conscious decisions.
Advanced strategies for optimizing AI Agent costs including tool call management, context pruning, model routing, and session optimization with practical budgeting checks, cost drivers, validation steps, and examples for production AI teams.
Use this prompt caching budget checklist to estimate cacheable input, hit rate, dynamic variables, tool schemas, retry behavior, and measured savings before relying on cached token discounts.