Why Model Routing Backfires and How to Build Agents That Don’t Burn Your Budget
Blog post from JFrog
Model routing in AI systems aims to optimize costs by using cheaper models for routine tasks and reserving advanced models for complex reasoning, potentially reducing inference costs by 40-85%. However, incorrect implementation, such as switching models mid-session in multi-turn agent workflows, can increase costs due to the loss of prompt cache savings, as each model's cache is specific and does not carry over when a model switch occurs. Effective strategies include minimizing turns per task, delegating tasks early, and using subagent patterns to maintain model diversity without cache invalidation. Summarizing before switching models and locking session configurations at the start are recommended practices to avoid cost penalties. Tools like JFrog Boost can help manage context bloat by compacting terminal output, reducing unnecessary token processing and maintaining prompt cache efficiency, ultimately supporting sustainable agent economics by treating token efficiency as a core constraint.
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