Your LLM token bill just exploded 3X. Again.
You’re not alone. I’ve watched teams burn through $50k/month on LLM inference costs that could’ve been $15k, if they’d known these 5 strategies.
The brutal truth? Most of your token spend is waste. You’re:
Shipping entire codebases when you need 3 functions
Paying to remind the LLM of its job 1,000+ times daily
Running LLMs for tasks a regex could handle in milliseconds
After optimizing production LLM systems and working with teams in the field, I’ve identified the exact patterns that slash costs without cutting features.
The 5 strategies that cut our costs by 60%:
- ⚡ Targeted context retrieval (dependency graphs > full docs)
- 🎯 System prompt optimization (38% token reduction)
- 🧪 A/B testing prompts like code
- 💾 Smart caching + batching (avoid my serverless disaster)
- 🔍 Ruthless LLM necessity audits
Read the full breakdown with step-by-step implementation guides →
If you’re shipping production AI, this will save you thousands. Possibly tens of thousands.
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