The Patterns That Stop AI Pipelines from Burning Money

Production AI systems fail differently than test environments, often cascading into expensive disasters when error handling is naive. Without circuit breakers, exponential backoff, and fallback chains, a single rate-limit error can trigger infinite retries—costing hundreds of dollars in minutes. This article provides the specific patterns that have proven to reduce LLM-related errors from 4% to under 0.1%, transforming AI reliability from a testing afterthought into a production reality.