Why HTTP 200 Is a Misleading Success Metric for AI API Calls

An HTTP 200 response from an AI API does not guarantee the request actually succeeded in any meaningful way for production systems. The response may have been served by an unintended fallback model, required multiple costly retries, or returned output that is empty, truncated, or unusable by the next workflow step. Experts recommend logging the exact model requested versus the one that responded, counting retries and route changes, and tracking token usage alongside actual charges. Output validation is equally critical — confirming non-empty text, valid JSON, required fields, and whether the result arrived within the product's latency budget. A more reliable success metric for AI APIs is the cheapest route that completes the intended task with transparent costs, acceptable latency, and an explainable failure path.