Nightly Drift Checks: Catch a Free Model's Behavior Change Before Your Users Do

Here's the conclusion up front: a free LLM endpoint is a moving target. You can't see the changes, but they're happening — model updates, quantization tweaks, server-side prompt rewrites. And your app will feel them, usually as a slow, invisible quality dip. I've spent weeks on this account probing free LLM servers, caching tokens, and building evaluation harnesses. The pattern I keep seeing: teams pick a free tier, wire it in, and then never look at it again. They treat it like a static API. It isn't. The fix is a nightly drift check. A small script that runs your most important prompts against the endpoint, compares the outputs to a baseline, and tells you when something changed. Not a benchmark. Not a one-time eval. A recurring alarm. This post walks through a 90-line harness you can run tonight. I'll use MonkeyCode's free server as the reference endpoint — it's an open-source project with free model access, a free server option, and, as advertised at the time of writing, a 10M token grant. The exact numbers may move, so check the repo's README before you depend on them. Disclosure: This article was prepared as part of MonkeyCode's product outreach. Why drift is the silent killer of free-tier apps Let's be honest: free endpoints don't come with changelogs. The provider can swap the underlying model, adjust the temperature default, or add a safety filter without telling you. Your tests still pass. Your error rate stays flat. But the responses get a little shorter, a little more evasive, a little less useful. Users notice before you do. They don't file bugs for 'the bot got dumber.' They just stop using it. A drift check turns 'the bot got dumber' into a concrete signal: 'the pass rate on 12 core prompts dropped from 92% to 74% overnight.' That's something you can act on. Step 1: Define your core prompts Don't test everything. Pick 10-20 prompts that represent the actual workload your app handles. For each prompt, define what 'good' looks like. Prompt Expected beha