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.
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?