Engineering Team Cuts LLM API Costs by 60% Using Caching and Token Monitoring

A software engineering team shared how they reduced their large language model API costs by 60% on production AI projects by systematically identifying and addressing cost drivers. They found that the bulk of expenses came from repetitive input tokens — such as repeated system prompts and retrieved documents — rather than output tokens. The team built middleware to log token counts and estimated costs for every LLM call, enabling data-driven decisions instead of guesswork. Their single biggest saving came from implementing semantic caching, which returns stored responses for queries that are similar in meaning rather than only identical in wording. The approach, documented with code examples for Django projects, prioritizes measuring usage first before attempting any optimization.