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

Cost Optimization Is Not a Model-Picker Problem

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 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, project configuration, and v0.2.4 release.

The Useful Design Choice: Analysis Before Automation

The interesting part is not a headline saving. It is converting a bill into a reviewable hypothesis:

• Collect a bounded, sanitized sample of real calls.
• Identify call groups that are candidates for a cheaper model.
• Run a task-specific regression suite against those groups.
• Automate routing only for groups that pass.

That sequence is boring in the best way. It creates an artifact an engineering team can challenge, reproduce, and roll back.

A Local Capture Still Has Security Boundaries

Frugon also includes frugon capture. Its capture implementation 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.

Where It Fits

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 and public repository are linked for further review.