How to cut LLM costs with model routing without hurting quality

Model routing can reduce LLM costs only when lower-priced models meet application-specific quality, safety, relevance, formatting, and latency requirements for distinct query classes. OpenRouter and LiteLLM provide routing mechanisms based on task classification, configured tiers, usage patterns, or adaptive feedback, but reliable production decisions require separate benchmarks using representative traffic, task-specific scorers, and defined quality thresholds. Braintrust supports this process by storing evaluation datasets, comparing baseline and candidate models under consistent conditions, setting release rules that account for both minimum pass rates and allowable declines from baseline performance, and connecting deployed traffic to evaluations through its gateway and tracing tools. The approach emphasizes grouping requests by expected output and failure consequences rather than superficial complexity, monitoring score, cost, and latency after deployment, and automatically reverting to an approved baseline if quality falls below a defined floor. An illustrative support-ticket example shows a mid-tier model maintaining 95% accuracy versus a 96% baseline while reducing classification costs by 80%, whereas a cheaper but less accurate candidate remains excluded.

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