Model Routing - Choosing the Right AI Model per Request

Model routing is the practice of dynamically sending each incoming request to the model that answers it best - and often most cheaply while still being sufficient. Instead of using the same, usually expensive, model for every task, a routing layer decides per request which model is used.

The reasoning is economic: many requests - simple summaries, classifications, formatting - can be handled by a small, cheap model at the same quality as a large one. Only a portion of tasks truly needs the most capable model. Whoever ignores this difference pays top price for every trivial request.

Model routing is therefore a central building block of LLM cost optimization: it noticeably lowers costs without broadly degrading answer quality.

Variants of Model Routing

• Request-level routing: A central proxy receives each request and forwards it individually to the fitting model. The routing decision is made anew for every request.
• Task-level routing: Requests are separated by task type - simple tasks run on a small model, complex ones on a strong model. The mapping follows the nature of the task, not the individual request.
• Cascade / escalation: A cheap model answers first. Only if the result is insufficient - for example low confidence or a failed check - is the request escalated to a stronger model. Most requests stay with the cheap model.
• Semantic routing: The request is classified by its content (e.g. code, translation, analysis) and directed to a model specialized in or suited for it.

Practical Implementation

In practice, model routing is often implemented via an AI gateway - a central mediation layer between the application and the various model providers. The gateway knows the available models, applies the routing rules, and forwards the request to the chosen model. This keeps the routing logic in one place instead of reimplementing it in every application.

Benefits

• Lower costs: A large share of requests moves from expensive to cheap models without users noticing a difference.
• Maintained quality: Demanding requests still reach the strong model - the routing decision protects output quality where it matters.
• Flexibility: New or cheaper models can be integrated centrally without changing the applications themselves.

The Trade-off

Model routing is not a free lunch. To ensure that offloading to cheaper models does not quietly lower quality, two things are needed: robust routing logic that makes the right assignment, and continuous quality measurement that reveals when a cheaper model performs worse on certain requests. Without this measurement, you save in the wrong place - the savings on the bill then stand against a silent degradation of results.

Model Routing at Elasticbrains

At Elasticbrains we build routing layers that distribute requests to the fitting model based on data - with measurable quality control instead of gut feeling. See how this lowers ongoing costs on our service page AI Cost Optimization.

Further Resources

• Glossary: LLM Cost Optimization, AI Gateway, LLM
• Service: AI Cost Optimization