Model routing directs AI requests to different models based on task requirements. It analyzes incoming requests and selects the optimal model considering complexity, cost constraints, and quality needs. For businesses, this means paying for GPT-4 only when needed while handling routine tasks with cheaper models. Without routing, you either overpay or underperform.
You are paying GPT-4 prices for tasks that GPT-3.5 handles just fine.
Simple extractions cost the same as complex reasoning tasks.
Your AI costs scaled linearly when your value did not.
Not every task deserves your most expensive model. Route intelligently.
OPTIMIZATION LAYER - Match each task to the model it deserves.
Model routing sits between your application and your AI providers. It analyzes each incoming request and directs it to the most appropriate model. Simple classification? GPT-3.5. Complex reasoning? GPT-4. Time-sensitive extraction? A fast local model.
The goal is optimization without sacrifice. You want the cheapest model that delivers acceptable quality for each task. Routing makes this automatic instead of hardcoded. As models improve and pricing changes, your routing logic adapts.
Model routing is not about cutting corners. It is about matching resources to requirements. Every task has a complexity ceiling. Exceeding it wastes money without improving results.
Model routing applies a universal resource allocation pattern: match the tool to the task. The same logic appears anywhere resources vary in capability and cost.
Classify the incoming request. Evaluate available resources by capability and cost. Select the resource that meets requirements at lowest cost. Monitor outcomes to refine classification.
Pick a task, choose a model manually, then toggle to automatic routing to see the difference.
Explicit decision trees
Define rules based on task type, input characteristics, or user tier. If task is classification, use GPT-3.5. If task requires reasoning, use GPT-4. Simple, predictable, easy to debug.
ML-powered model selection
Train a small classifier to predict which model will succeed at each task. The classifier learns from historical outcomes which tasks need which capabilities. Routes based on predicted success probability.
Try cheap first, escalate if needed
Start with the cheapest model. If confidence is low or output quality fails validation, escalate to a more capable model. Optimistic approach that minimizes cost for easy tasks.
Answer a few questions to determine the best routing strategy for your situation.
How diverse are your AI tasks?
A customer sends "How do I reset my password?" The system needs to classify the ticket category. Without routing, this hits GPT-4 at $0.03. With routing, the classifier detects this is simple extraction and routes to GPT-3.5 at $0.0015. Same result, 95% savings.
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This component works the same way across every business. Explore how it applies to different situations.
Notice how the core pattern remains consistent while the specific details change
You assume long prompts need expensive models and short prompts can use cheap ones. But a 50-word math problem requires GPT-4 while a 500-word text extraction works fine with GPT-3.5. Length and complexity are not correlated.
Instead: Route based on task type and required capabilities, not input characteristics. Classify the task first, then select the model.
Your router sends classification tasks to GPT-3.5. GPT-3.5 has an outage. All classification fails even though GPT-4 could handle it. You saved money until the system stopped working entirely.
Instead: Design routing with fallback chains. If the primary model fails or is overloaded, escalate to alternatives. Accept higher cost over complete failure.
You route 80% of tasks to the cheap model and celebrate the savings. But you never checked if quality degraded. Users are getting worse results and you have no visibility because you only track costs, not outcomes.
Instead: Pair cost tracking with quality monitoring. Sample outputs for human review. Track user feedback by model. Savings are only real if quality stays acceptable.
Model routing is an intelligent layer that analyzes each AI request and directs it to the most appropriate model. Instead of sending everything to one model, routing considers task complexity, latency requirements, and cost constraints to select the optimal model. A classification task might go to GPT-3.5 while a complex reasoning task goes to GPT-4.
Implement routing when you have diverse AI tasks with different complexity levels and your costs or latency matter. If every request is similar and you need maximum quality regardless of cost, single-model is fine. But most production systems have a mix of simple and complex tasks where routing delivers significant savings without quality loss.
The biggest mistake is routing based only on input length rather than task complexity. A short prompt can require deep reasoning while a long prompt might be simple extraction. Another mistake is not having fallback logic when the primary model fails or is overloaded. Always design routing with graceful degradation.
Start by categorizing your tasks by complexity. Simple tasks like classification, extraction, and formatting work well with smaller models. Complex tasks requiring reasoning, creativity, or domain expertise need larger models. Build a decision tree based on task type, then refine with quality monitoring.
Model routing typically reduces costs by 40-70% without quality degradation. The savings come from sending routine tasks to cheaper models. If 60% of your requests are simple and you route them to a model that costs 1/20th as much, you save significantly while reserving premium models for tasks that need them.
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Choose the path that matches your current situation
You use one model for everything
You use different models but selection is hardcoded
You have routing but want to improve it
You have learned how to direct AI requests to optimal models. The natural next step is building fallback chains for resilience and monitoring the quality of routed outputs.