Model Routing

Model routing is the decision of which model to use for each request. In the early days of AI products, you might use a single model. As your product scales and your needs become more sophisticated, you want flexibility: different models for different use cases, cheaper models for latency-insensitive workloads, expensive models for high-value operations.

The simplest routing is by request type. Chat requests go to GPT-4. Code generation requests go to Claude. Summarization goes to a cheaper, faster model. You're routing based on the nature of the task, not based on performance at runtime.

More sophisticated routing makes decisions at runtime. A request comes in. The router evaluates: "Will a fast cheap model likely solve this well enough?" If yes, use the cheap model (saves money). If no, use the expensive model (ensures quality). The router is making cost-quality tradeoffs in real-time.

Routing can also be based on latency. If a user is waiting for a response (interactive), use a fast model (lower latency matters more than cost). If this is a batch job that can run overnight, use a slower, cheaper model (cost matters more than latency).

Routing can route based on specialization. Some models are fine-tuned for specific domains. A document classification task might use a specialized model. An arbitrary question might use a general-purpose model. The router chooses based on whether a specialized solution exists.

The most sophisticated routing combines multiple signals. It estimates: "What's the probability this cheap model will produce an acceptable response?" If high probability, use the cheap model. If uncertain, use a more expensive model. This requires calibration: understanding the actual success rates of different models on your workload.

There's also the scheduling dimension. Some models have quotas (you only have 1 million tokens per day). The router might load-balance across multiple models to stay within quotas. Or it might queue requests when approaching quota limits.

Fallback routing is essential for reliability. If your primary model provider is down or rate-limited, you route to a backup provider. This requires maintaining relationships with multiple vendors and having routing logic that automatically switches.

A/B testing with routing is common. Route 50% of traffic to Model A and 50% to Model B, then compare performance. This gives you confidence that a new model is actually better before fully switching.

The infrastructure for routing is increasingly becoming a competitive advantage. Companies with good routing can dramatically reduce costs (using cheaper models more often while maintaining quality) or improve experience (using the right model for each scenario). Some companies build routing as a business: "We'll route your AI requests across all major vendors and save you 40% on costs."

There's a vendor lock-in angle too. If you're dependent on a single vendor and that vendor goes down or raises prices, you're in trouble. Routing to multiple vendors reduces this risk.