Agent ops & evalsFree→ Claude / ChatGPT
LLM Cost Optimization Review
Find the biggest savings in an agent's usage without hurting quality.
What you get: Ranked savings opportunities with expected impact and quality-risk checks.
Review the cost profile of this LLM-powered system and find savings without hurting quality.
Usage data (per feature or agent: calls, avg input tokens, avg output tokens, model, cache hit rate): {{usage_data}}
Current monthly spend: {{monthly_spend}}. Quality targets: {{quality_targets}}.
Rank opportunities by (monthly saving × confidence ÷ effort): model routing and tiering, prompt caching of stable prefixes, context trimming and compaction, tool-result truncation, output length controls, response caching, batch/async processing, step and loop limits, and eliminating redundant calls. For each give: the expected saving range, how to implement, the quality risk, and the exact eval you must run before shipping it. Highlight any anomalies in the data that look like bugs (e.g. loops or oversized contexts). Do not assume savings numbers are guaranteed.