Migration Diary: Building a Token Ledger Before You Trust a Free Model Tier

Description and use

Migrated a PR‑summarizer bot from a paid AI endpoint to MonkeyCode’s free tier with a free server, adding token tracking, a stateless worker, and a fallback to paid service.

Problem

A side‑project bot was incurring a $7/month bill on a paid AI endpoint; the team grew, so did the cost. The goal was to eliminate recurring charges by moving inference to a free model tier with a free server, while ensuring reliability and avoiding silent quota exhaustion.

Outcome

The migration eliminated the $7/month bill, providing a 10M token allowance that comfortably covered the bot’s 140K average daily usage. The stateless worker design ensured reliability on free infrastructure, and the fallback mechanism protected against quota exhaustion.

Workflow

• Measure daily token consumption with a log‑parsing script to estimate budget usage.
• Split the bot into a scheduler and a stateless worker that fetches PR diffs fresh each run.
• Deploy the worker to the free server using a minimal shell script and systemd user service.
• Configure the MonkeyCode endpoint via environment variables (BASE_URL, API_KEY, MODEL).
• Implement a wrapper that attempts the free endpoint first and falls back to the paid endpoint on rate‑limit or quota errors, recording fallback usage metrics.

Evidence

The bot summarized PR descriptions for my team's repository. It cost about $7 per month, which felt harmless until the team doubled and the bill doubled with it.

Source

I parsed my old API call logs and built a tiny script that tallied prompt and completion tokens by day, so the 10M budget had a real number to compare against.

Source

The worker received a PR URL, fetched the diff, called the model, and stored the summary in the repository's existing storage layer.

Source