An AI routing policy chooses among eligible model services for a specific task. Its quality depends on clear requirements, representative evaluation, and a shared request budget. Read the guide below to design selection and recovery as one coherent policy.
Begin with simple rules that can be inspected. Eliminate models that do not meet data, capability, or permission requirements before comparing speed and cost. Decide how the application handles a timeout before streaming starts and a failure after partial output is already visible.
Connect these ideas with the AI routing overview. If the question spans several parts of the system, the guide directory offers a sequence through the related decisions.
A useful LLM router chooses among eligible models, budgets the entire request, and measures successful outcomes. Build a practical policy for deadlines, retries, streaming, evaluation, and gradual rollout without hiding quality failures behind lower request prices.