A practical framework for AI API budgets, token limits, routing rules, alerts, quotas, and circuit breakers that prevent surprise production costs.

How to define and enforce model capability contracts for tools, JSON output, context limits, vision, streaming, retries, and safe multi-provider routing.

How to run practical AI API failover drills covering timeouts, rate limits, invalid output, provider outages, routing, idempotency, and recovery metrics.

How to make AI API workflows safe under retries: idempotency keys, request state, streaming recovery, tool side effects, and billing reconciliation.

A practical guide to evaluating AI models in production: accepted-result rate, latency, retries, fallback share, and cost per accepted result.

A 100-episode retrospective on AI API gateways: model access, pricing, routing, validation, observability, content workflows, and the reliability lessons that matter in production.

Why AI API pricing should be measured by cost per accepted result, including retries, validation, fallback routing, latency, and human repair.

A practical architecture guide for reliable AI API gateways: deadlines, retries, token budgets, streaming, validation, fallback routing, and observability.

A practical guide to measuring AI API reliability beyond HTTP 200: incomplete outputs, retries, validation, fallback routing, and cost per accepted result.

A practical benchmark-driven comparison of Kimi K3 and GLM-5.2 across university-level math, physics, and production Python, with a focus on correctness, output completeness, latency, and workflow routing.