LLM Fallback Routing for Production Stability

说明与用途

作者描述了从对所有任务使用单一昂贵LLM模型转向基于任务的路由架构。这提高了稳定性，降低了成本，并避免了生产环境中的可用性和延迟问题。

问题

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.

结果

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.

工作流

• 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.

证据

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

来源

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

来源

Batch pricing is 50% cheaper than the standard path.

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