This is one of the reasons I keep gravitating back to local models even when the closed API ones are technically stronger.
I had a production pipeline running on a major closed API for about four months. Stable, tested, working. Then one day the outputs started drifting. Not breaking errors, just subtle behavioral changes. Format slightly different, refusals on things it used to handle fine, confidence on certain task types quietly degraded.
No changelog. No notification. Support ticket response was essentially "models are updated periodically to improve quality." There is no way to pin to a specific checkpoint. You signed up for a service that reserves the right to change what the service does at any time.
The thing that gets me is how normalized this is. If a database provider silently changed query behavior between versions people would lose their minds. But with LLMs everyone just shrugs and says yeah that happens.
Local models are not always as capable but at least Llama 3.1 from six months ago is the same model today. I can version control my actual inference stack. I know exactly what changed when something breaks.
Not saying local is always the answer. For some tasks the capability gap is too large to ignore. But the hidden cost of closed APIs is that you are renting behavior you do not own and they can change the terms at any time.
Anyone else hit this wall? How do you handle behavioral regressions in production when you are locked into a closed provider?
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