Do Not Add an LLM Router Until You Can Defend Each Downgrade

A developer released Frugon, an MIT-licensed CLI tool that analyzes OpenAI-compatible request/response logs to help teams decide which LLM calls can be downgraded to cheaper models without sacrificing quality. The tool generates a reviewable hypothesis for cost savings by comparing candidate models and estimating costs, rather than applying blanket downgrades. Frugon also includes a capture mode that logs requests locally for analysis.

When an LLM bill grows, the tempting answer is a blanket model downgrade. That is usually the wrong first move. A support classification, a retrieval rewrite, and a complex planning step do not have the same failure cost. The useful question is: which calls have evidence that they can be cheaper? Frugon https://github.com/Rodiun/frugon is an MIT-licensed, Python 3.10+ CLI that analyzes OpenAI-compatible request/response JSONL logs. Its stated purpose is to compare candidate models, estimate costs, and propose a split between calls that might move and calls that should remain on the baseline. See the README https://github.com/Rodiun/frugon/blob/main/README.md , project configuration https://github.com/Rodiun/frugon/blob/main/pyproject.toml , and v0.2.4 release https://github.com/Rodiun/frugon/releases/tag/v0.2.4 . The interesting part is not a headline saving. It is converting a bill into a reviewable hypothesis: That sequence is boring in the best way. It creates an artifact an engineering team can challenge, reproduce, and roll back. Frugon also includes frugon capture . Its capture implementation https://github.com/Rodiun/frugon/blob/main/src/frugon/capture.py runs a local HTTP server, forwards OpenAI-compatible completion requests to the configured upstream, and stores canonical JSONL records locally. This deserves normal production scrutiny. Logs can contain prompts and responses; retention and redaction are still your responsibility. Optional quality measurement uses your own provider keys, so it is a real provider-boundary decision, not a free offline proof. The source also validates upstream schemes and strips sensitive headers on cross-origin redirects—good defensive detail, but not a substitute for your own review. Frugon looks most useful for teams that already retain compatible call logs and need a defensible downgrade shortlist. It is less useful if you have no representative task set, because a cost estimate cannot prove output quality. Not tested / not run: this is a public-documentation and source review only. I did not install, execute, benchmark, or validate Frugon in a production workload. The project’s MIT license https://github.com/Rodiun/frugon/blob/main/LICENSE and public repository are linked for further review.