Reducing LLM Costs: Best Practices and Techniques

Short summary

LLM inference costs accumulate through system prompts, context bloat, and unconstrained outputs. Cost optimization is an architecture problem: match pricing models to context patterns, compress prompts, route queries by complexity, and enforce structured outputs. Open-source models on flat-rate platforms eliminate token-penalty coupling, letting teams optimize for accuracy over cost metrics.

• •Compress prompts and cache context to reduce token consumption
• •Route simple queries to fast models, escalate complex ones to reasoning specialists
• •Structured generation and flat-rate pricing remove token-penalty coupling

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