LLM Cost Optimization overview

Reviews and optimises spend across LLM API providers — analysing per-model token consumption, identifying prompt-length bloat, evaluating cache-hit rates, testing cheaper model substitutions for low-criticality tasks, auditing retry and fallback policies that inflate costs, and projecting budget burn-rate against forecasts. Produces a cost breakdown dashboard and actionable savings plan. Excludes model fine-tuning work.