I Spent $50 on LLM API Calls. Then Optimized to $0.

The author reduced their OpenAI API bill from $50 to $0 by optimizing prompts, switching to cheaper models like Claude Haiku and Gemini 1.5 Flash for simple tasks, and implementing a semantic cache to avoid redundant API calls. Key optimizations included restructuring prompts with examples to reduce token usage by 40% and caching repeated user queries so one API call serves multiple users. The author concludes that many high AI API costs stem from unoptimized prompts and model choices rather than inherently expensive features.

The real cost of AI features isn't the subscription — it's the prompts you haven't optimized yet. Two months ago, my OpenAI API bill hit $50. For a side project used by maybe 100 people. The features I was using weren't complex: I was calling GPT-4o mini for everything because it was "cheap enough." But it added up. Same model, better prompts. A well-structured prompt with examples often matches a more expensive model. Before: Categorize this email: "{subject}" After: Categorize this email into one of: urgent, follow-up, spam, newsletter Example: "RE: Meeting at 3pm" → follow-up Example: "Free iPhone " → spam Now categorize: "{subject}" Result: Same model, 40% fewer tokens needed. For categorization and extraction, I switched to: Both handle simple structured extraction tasks at near-zero cost. Repeated questions get cached. If 50 users ask the same question, one API call serves all. Simple semantic cache cache key = hash prompt + first 50 chars of context if cache.exists cache key : return cache.get cache key Not everything needs GPT-4o: After optimization: Start with the cheapest model that works. Optimize prompts before switching models. Add caching before adding more expensive calls. The $50/month problem is usually a $5/month problem you haven't solved yet. What's your biggest AI API expense? Any optimization wins you've found?