How we optimized our LLM pipeline to cut token usage by 70%

Short summary

A development team cut LLM API costs by 70% through pipeline optimization rather than downgrading models. Key wins: adding a complexity router to match tasks to appropriate models, pruning verbose prompts, implementing semantic caching, filtering RAG results aggressively, and requesting structured outputs. Token reduction came from smarter architecture around the model, not from the model itself—a pattern increasingly important as LLM apps move to production.

• •Route simple tasks to cheaper models, complex tasks to capable models
• •Aggressively filter RAG results and trim verbose prompts
• •Request structured outputs and use semantic caching to eliminate duplicate requests

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