LLM Cost Optimisation — Reduce API Spend Without Quality Loss

I will audit your LLM API usage and implement a cost optimisation programme — covering prompt token reduction without quality loss, semantic caching for repeated or similar queries, model routing (using cheaper models for simple queries and frontier models only for complex ones), batch processing for non-real-time tasks, and a cost monitoring dashboard giving you real-time visibility into spend by feature, user, and prompt version. LLM API costs that are not actively managed scale linearly with usage regardless of whether that usage is generating business value — organisations running LLM applications at scale without cost optimisation consistently overspend by 40-70% relative to what a well-optimised implementation would cost.

The optimisation covers token usage audit by endpoint and prompt, prompt compression techniques, semantic caching implementation, model routing configuration, batch API usage for non-latency-sensitive tasks, and a cost monitoring dashboard with per-feature cost attribution.