Build a Multi-Model AI Router in 50 Lines of Python
The article presents a simple multi-model AI router in 50 lines of Python that can route requests to different AI models based on complexity, reducing API costs by 60% while maintaining quality.
Why it matters
This approach can significantly reduce API costs for AI-powered applications without sacrificing quality, making AI more accessible to developers.
Key Points
- 1The router classifies each request and sends it to the appropriate AI model based on complexity
- 2Three tiers of models are used - fast/cheap, mid-tier, and powerful/expensive
- 3Classification rules are defined to determine which tier to use based on keywords in the request
- 4The router can cut API costs by 60% or more while maintaining quality where it matters
Details
The core idea is to use different AI models for different task complexities. Simple tasks like formatting JSON or translating text can be handled by a fast and cheap model ($0.15/1M tokens), while more complex tasks like architecture reviews or security audits require a powerful but expensive model ($15/1M tokens). The router classifies each request using a set of predefined keywords and sends it to the appropriate model tier automatically. This allows the application to optimize costs while maintaining high-quality responses where needed. The full router implementation is just 50 lines of Python code, making it easy to integrate into any AI-powered application.
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