Show HN: A tool to optimize AI model cost without benchmark score trade-offs

Category: ai-ml

Tags: llm, cost-optimization, benchmarking, data-envelopment-analysis, model-selection

Score: 4.3/10 (Innovation: 5, Technical: 4, Documentation: 4, Utility: 4)

A blog-style analysis applying data envelopment analysis (DEA) and free disposal hull methods to compare AI model API costs against benchmark performance, identifying cheaper model blends and settings that match or beat pricier options. It is interesting as a methodological application of academic DEA techniques to practical LLM cost optimization, though it is a static analysis page rather than a reusable tool.

Target audience: ML engineers and AI product teams evaluating model cost-performance trade-offs

Repository: https://jeff-kline.github.io/posts/same-ai-results-for-less/index.html

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