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This post asks engineers to share their experiences with unexpected cost spikes when running AI models in production and offers advice on optimizing costs and setting up guardrails to avoid budget overruns.
Explains why agent API bills grow quadratically with context length because each turn re-reads the full history, and shares practical techniques like expiring tool results, shrinking tool schemas, and compacting context to cut costs.