Optimizing OpenAI API Integration Costs Without Sacrificing Reliability
A practical framework for AI and MLOps engineers to measure, control, and forecast OpenAI API costs in production, covering token accounting, model selection, caching, batching, and observability with concrete thresholds and fallback patterns.