Choosing one AI model for every request is simple, but it often creates unnecessary cost or poor latency. A short classification task does not need the same model as a difficult coding explanation. A routing layer can choose deliberately, as long as it is observable and easy to override.

The short answer

Route by workload and risk. Use a smaller model for predictable, low-risk tasks; use a stronger model when quality or reasoning matters; and keep a fallback for provider failures. Measure cost per successful task, p95 latency, retries, and reviewer acceptance.

Start with workload classes

Useful classes include classification, extraction, summarization, interactive chat, coding, background research, and safety review. Give each class a quality threshold, time limit, and maximum budget. The router should receive the class as a deliberate application signal rather than trying to infer everything from the prompt.

This also makes testing easier. A change to the coding route should not silently change the model used by a billing workflow. Keep route decisions in configuration and include the selected route in traces.

What the router should measure

Record model, provider, region, input tokens, output tokens, queue time, total duration, retry count, tool calls, and final status. Add a task-specific quality signal. For extraction, validate the schema. For code, run tests. For support, record whether the user accepted or corrected the answer.

Run a representative 2-hour test with normal traffic, long context, provider timeout, and cancellation. The cheapest model on a clean example may be the most expensive on a failure-heavy workload.

Avoid the common routing mistakes

Do not route solely by advertised token price. Do not send every failure to the strongest model without a budget. Do not hide provider changes from the user-facing trace. Do not let a retry create a duplicate write operation.

Use a circuit breaker when a provider’s error rate rises. Let the route degrade gracefully, perhaps by returning a partial answer or placing background work into a queue. A good router protects the product from both cost spikes and vendor outages.

Sources and limits

OpenAI’s multi-cloud and silicon update shows why large AI platforms use several infrastructure paths. The Amazon partnership and Cerebras partnership illustrate different serving constraints. These announcements do not prescribe a route for your application; your own traces and evaluations must decide.

Routing is useful when it is boring: explicit rules, measurable outcomes, safe fallbacks, and no mystery about which model handled the request.

A practical router shape

Keep the router separate from the model client. The router selects a route, the client executes it, and the evaluator records whether the result met the task contract. This separation lets the team change routing rules without rewriting every feature that uses a model.

Use a small rules table at first. It can contain workload, maximum latency, quality threshold, preferred model, fallback model, and budget. Add learned routing only after the team has enough labeled outcomes to explain why the learned choice is better.

Run a 2-hour test with each workload class and deliberately disable the preferred provider once. A route that looks correct in a diagram but cannot fail over is not a resilient route.

Keep humans in the loop for high-risk routes

For payments, account changes, security decisions, and customer-facing claims, the router should choose a safe workflow rather than only a stronger model. Require validation and approval where the consequence is expensive. A model selector is not a policy engine.

Write a short route decision into the trace. It should say which rule matched, which model was selected, and why a fallback was used. That explanation makes a cost spike or quality regression diagnosable.

The router should fail closed for sensitive actions and fail soft for low-risk background work. That difference belongs in the product design, not only in a model prompt.

Run this policy test for 2 hours before launch.