Build a Benchmark Harness to Test Cheaper AI Models Before Going Live

AI product teams risk hidden costs when switching to open-weight models that perform well in demos but fail in production with issues like JSON drift, missing citations, and noisy tool calls. A benchmark harness offers a structured way to evaluate candidate models against real product tasks before routing live user traffic to them. The harness typically includes a task catalog, scoring rules, cost and latency tracking, and routing recommendations tailored to a product's specific requirements. Generic leaderboards fall short because they ignore product-level details such as prompt style, schema needs, latency budgets, and domain-specific facts. The approach is especially relevant now as open-weight model adoption accelerates and engineering teams face growing pressure to manage AI infrastructure costs without sacrificing reliability.