Six Proven Techniques to Cut LLM API Costs in Production

Developers running LLM-powered features in production often face API bills far higher than projected, but several concrete strategies can significantly reduce spend. Prompt-prefix caching—reusing static system prompts and few-shot examples across requests—can slash input token costs by 80–90%, especially in RAG pipelines. Routing tasks by complexity, sending simpler jobs like classification to cheaper models and reserving frontier models for reasoning-heavy work, can cut costs by 40–60% with little quality impact. Batch APIs offer roughly 50% savings over synchronous calls for non-urgent workloads, while hard limits on tokens, retries, and session usage prevent runaway agent loops from causing budget spikes. Because model pricing and quality shift rapidly, teams are advised to reassess their cost-optimization setup on a recurring basis rather than treating it as a one-time decision.