Cut your LLM API bills by 50%+ — without sacrificing performance.
A lightweight, open-source toolkit to optimize AI API costs by intelligently switching between models, measuring efficiency, and avoiding surprise bills.
Built for developers tired of:
- 💸 Skyrocketing LLM API costs at scale
- 🤯 Headaches comparing model performance vs. price
- ⚖️ Tough choices between "cheap but weak" vs. "powerful but expensive" models
When building AI SaaS, chatbots, or agents, we all face the same problem:
How do I use the best model for the job — without blowing my budget?
This repo gives you practical, code-first ways to:
- Estimate costs before you run large workloads
- Test smaller/cheaper models for simple tasks
- Measure prompt efficiency and token waste
- Build auto-switching logic to pick the cheapest viable model
No more guessing. No more $200+ surprise bills.
This repo includes:
- 📜 Production-ready example scripts for calling LLM APIs (OpenAI, Anthropic, Gemini, etc.)
- 🧪 Cost-performance experiment templates to test model tradeoffs
- 💡 Actionable ideas to optimize token usage and reduce waste
- 🔌 Seamless integration with Synstar’s unified API (optional)
- Clone the repo
git clone https://github.com/[your-username]/ai-cost-optimizer.git cd ai-cost-optimizer