Evaluation-driven model routing: how to find the Pareto frontier

LLM model selection should be based on a workload-specific Pareto frontier, which retains models that offer meaningful tradeoffs among quality, cost, latency, and potentially reliability after removing candidates that are clearly dominated by others. Evaluations should use representative, versioned production-derived datasets segmented by observable request traits, include difficult edge cases, and apply product-specific quality gates so mandatory correctness failures cannot be offset by strengths elsewhere. Teams can use Braintrust experiments, traces, trials, and consistent uncached configurations to compare model settings, measure cost per successful task, quantify variance, and assess uncertain differences before establishing a frontier for each task segment. Routing policies can then select eligible models through fixed mappings, classifiers, or cascades, but the full policy—including classification, escalation, retries, fallbacks, and downstream costs—should be evaluated against single-model baselines and weighted to reflect real production traffic. Ongoing governance includes CI regression checks, provider failover and separately evaluated model fallbacks, canary deployments, online scoring, dashboards, alerts, and periodic re-evaluation using both stable benchmarks and fresh production samples, allowing teams to detect changes in provider pricing, model behavior, or request patterns.

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