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

> Source: <https://dev.to/zny10289/i-spent-50-on-llm-api-calls-then-optimized-to-0-487c>
> Published: 2026-05-20 07:50:04+00:00

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?