LLM Fallback Routing for Production Stability

Описание и применение

Автор описывает переход от использования одной дорогой модели LLM для всех задач к архитектуре с маршрутизацией на основе задач. Это позволило снизить затраты, повысить стабильность и избежать проблем с доступностью и задержками в продакшене.

Задача

Using a single expensive LLM for all tasks leads to latency spikes, timeouts, cost drift, and availability issues in production, especially when the model is overloaded or throttled.

Результат

By implementing task-specific LLM routing with conditional fallbacks, the author achieved better stability, reduced costs, and improved reliability in production workflows, avoiding the pitfalls of a single-model architecture.

Рабочий процесс

• Identify distinct workloads: planning, extraction, review, and background jobs.
• Assign primary models based on task requirements (e.g., reasoning quality for planning, structured output for extraction).
• Implement routing using tools like OpenRouter or Portkey to manage provider selection.
• Configure fallback policies to trigger only on specific status codes (e.g., 429, 503) rather than general errors.
• Route non-urgent, async tasks to batch APIs (e.g., Gemini Batch) to reduce costs.
• Ensure prompt and schema compatibility across primary and fallback models to prevent output drift.
• Log the final provider and model for every step to aid debugging and cost analysis.

Доказательства

The real upgrade was not a fancier model. It was routing.

Источник

Portkey lets you define fallback chains and trigger them only for specific status codes like 429 or 503.

Источник

Batch pricing is 50% cheaper than the standard path.

Источник