Developer Claims 60% LLM Cost Cut by Fixing Bloated Prompts and Adding Caching

A software developer found that their OpenAI API costs were growing three times faster than revenue despite only 40% user growth, prompting a week-long investigation into spending patterns. Analysis of thousands of API calls revealed four main culprits: redundant system prompts, lack of semantic caching, unnecessarily large context windows, and no per-feature cost visibility. Trimming a bloated 89-token support prompt down to 18 tokens while preserving the same model behavior was cited as one quick fix. The developer also built a tool called Tokoscope, which wraps existing LLM clients to automatically score prompts for waste, rewrite inefficient ones, and enable semantic caching. The piece is partly a product promotion, though the underlying optimization techniques described are widely recognized practices in LLM cost management.