AI Cost Optimization Journey
How I reduced AI API costs by 60% through caching, prompt optimization, and smart model selection.
When AuthorAI first launched, AI API costs were eating into margins. Here's how I optimized.
Initial State: - No caching (every request hit the API) - Long, verbose prompts - Always using GPT-4 for everything - Cost: ~$0.15 per content generation
Optimization Strategies:
1. Response Caching - Cache common content generation patterns - Cache key based on prompt hash - Result: 40% of requests served from cache
2. Prompt Optimization - Reduced prompt length by 30% through iteration - Removed redundant instructions - Used system messages more effectively - Result: 30% token reduction per request
3. Model Selection - GPT-3.5 for simple tasks (summaries, basic content) - GPT-4 for complex tasks (SEO optimization, creative writing) - Result: 50% cost reduction on simple tasks
Final State: - Cost: ~$0.06 per content generation (60% reduction) - Quality maintained through careful testing - Users see faster responses (cached requests)
What I'd change: I wish I had implemented caching from day one. The prompt optimization took time but was worth it. Model selection requires ongoing monitoring to ensure quality doesn't degrade.