Engineer Ran Just One LLM Cost Experiment and Found It Sufficient

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A software engineer set out to determine when expensive frontier AI models are truly necessary versus when cheaper alternatives suffice for real work tasks. He designed a 10-experiment evaluation program, complete with a reusable harness called model-compass, to benchmark multiple LLMs across agent use cases like CI diagnostics, code review, and incident triage. The motivation was personal: at home, every API call comes out of his own pocket, making model selection a financial decision rather than a convenience. He ultimately ran only the first experiment, focused on CI log diagnostics, choosing it because it reflected his team's actual daily pain points across multiple codebases and languages. That single experiment yielded enough actionable insight to inform his model-routing decisions without needing to complete the remaining nine.

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