How to Slash AI Workflow Costs with Intelligent Model Routing
A step-by-step guide to using Commonstack's local proxy and smart routing to cut LLM expenses by over 50% while maintaining performance in coding and agentic tasks.
Practical Summary
This workflow shows how to use Commonstack as a local proxy that automatically routes your AI requests (including multi-step agent workflows) to the most cost-effective model from a pool of 50+ options. Benchmarks indicate cost reductions of 53-82% versus using a single premium model like Claude Opus, with near-equal task completion rates on coding benchmarks. The setup involves a drop-in proxy that works with tools like Cursor and Claude Code with minimal configuration.
Why It Matters
For developers and teams building with LLMs, this approach offers a direct path to reduce operational costs without sacrificing output quality. It enables sustainable scaling of AI-powered workflows, particularly for complex agentic tasks, by intelligently selecting models based on the specific requirements of each request step.
Practical Steps to Implement Cost-Efficient AI Routing
1. Set up Commonstack: Sign up for the service and install the local UncommonRoute proxy on your machine. This acts as a transparent middleware for your AI API calls.
2. Configure your tools: In your development environment (e.g., Cursor, Claude Code, or custom scripts), update the API endpoint to point to your local Commonstack proxy instead of directly to a model provider's API.
3. Enable intelligent routing: The proxy's routing logic automatically analyzes each request—including each step in an agentic workflow—and selects the most suitable and cost-effective model from its integrated pool (which includes models like GLM 5.2, Kimi K2.7 Code, and Qwen 3.7 plus).
4. Monitor savings and performance: Track your API usage through Commonstack's dashboard. The referenced SWE-bench Verified testing shows that for coding tasks, this routing can reduce costs by 53-82% compared to using a top-tier model for all requests, while solving a comparable number of problems.
5. Scale agentic workflows: Use this setup to build and run multi-agent systems more sustainably. The intelligent routing ensures that complex steps can use specialized models when needed, while simpler steps use more economical ones, optimizing the overall cost-performance ratio.