What is model routing? How LLM routers choose the right model

Model routing selects the most suitable AI model, provider endpoint, generation settings, and fallback path for each request by applying eligibility requirements such as context capacity, tool support, regional processing, and budget constraints before evaluating quality, cost, latency, availability, or task type. Routing can be implemented in application code, gateways, or managed services, while unified APIs standardize provider access and gateways add features such as credential management, logging, caching, and failover. Effective policies distinguish model selection from provider selection, log both separately, and account for behavioral differences between endpoints even when requests succeed technically. A typical request lifecycle includes intake of application metadata, optional task or complexity classification, candidate filtering, policy evaluation, execution, fallback after eligible failures, output validation, and detailed decision logging. Reliable routing depends on representative, segment-level evidence measuring task quality, latency distributions, token costs, failures, and changing provider or traffic conditions. Policies should be tested through controlled comparisons, including tuned prompts and statistically meaningful evaluations, then deployed gradually through shadow tests, canaries, A/B tests, predefined rollback triggers, and ongoing drift monitoring. Routing is most valuable when workloads have distinct quality, cost, latency, availability, or data-residency needs, while simpler configurations may be preferable when one model already meets requirements. Braintrust is presented as a platform for evaluating candidate routes using production traces, datasets, experiments, provider failover through its Gateway, and dashboards or alerts that connect routing decisions to quality, cost, and latency outcomes.

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