Intelligent Routing: Choosing the Right Model for Each Request

Not every request needs your most expensive model. A simple "Hello" does not require GPT-4. Intelligent routing automatically selects the right model for each request, optimizing for cost, quality, or speed.

The Problem with One-Model-Fits-All

Most applications send every request to the same model. This creates two problems:

• Overspending on simple tasks: Using a premium model for basic queries like greetings, simple lookups, or formatting tasks.
• Underperforming on complex tasks: Using a cheaper model when quality really matters, leading to poor user experiences.

Intelligent routing solves this by analyzing each request and routing it to the optimal model.

Three Routing Strategies

1. Cost Strategy

Routes to the cheapest model capable of handling the request. Best for high-volume applications where cost is the primary concern.

Use when: Budget is tight, quality requirements are flexible, high request volumes

2. Quality Strategy

Prioritizes response quality while still avoiding overkill on simple tasks. Uses premium models for complex requests, efficient models for simple ones.

Use when: Output quality is critical, user-facing applications, complex reasoning tasks

3. Speed Strategy

Optimizes for the fastest response time. Routes to models with the lowest latency that can still produce acceptable results.

Use when: Real-time applications, chatbots, latency-sensitive UX

How Request Complexity is Determined

Intelligent routing systems analyze requests using several signals:

• • Token count — Longer prompts often indicate more complex tasks
• • Task detection — Identifies if the request is code, math, creative writing, etc.
• • Question complexity — Simple factual vs. multi-step reasoning
• • Domain specificity — General knowledge vs. specialized domain expertise

Example Routing Decisions

Here is how intelligent routing might handle different requests:

Implementation Approaches

Rule-Based Routing

Define explicit rules based on request characteristics:

• • Requests under 50 tokens → Use cheap model
• • Code-related tasks → Use code-specialized model
• • Requests mentioning "analyze" or "compare" → Use premium model

Pros: Simple, predictable, easy to debug
Cons: Requires manual maintenance, may miss edge cases

ML-Based Routing

Use a lightweight classifier to predict optimal routing:

• • Train on historical request/response data
• • Learns patterns humans might miss
• • Continuously improves with more data

Pros: Adapts to your specific use case, handles nuance
Cons: Requires training data, more complex to implement

Key Takeaways

1. Match model to task — Not every request needs your best model
2. Choose your strategy — Cost, quality, or speed based on your priorities
3. Start simple — Rule-based routing is a great starting point
4. Measure and iterate — Track routing decisions and outcomes to optimize