How to Stop Your AI Provider From Holding Your App Hostage

The discourse around who controls AI's future got loud again this week. But while pundits debate trust and governance, I'm staring at a very concrete problem in my codebase: my entire application is hardwired to a single AI provider's API. If they change pricing tomorrow, deprecate a model, or go down for six hours (again), I'm cooked. And if you've built anything with LLM APIs in the last two years, you probably are too. Let's fix that. The Root Cause: Tight Coupling to a Single Provider Here's what most AI integration code looks like in the wild: # This is everywhere. This is the problem. import openai def summarize(text: str) -> str: response = openai.chat.completions.create( model="gpt-4o", messages=[{"role": "user", "content": f"Summarize: {text}"}], temperature=0.3, max_tokens=500 ) return response.choices[0].message.content Every function that touches AI is married to one provider's SDK, model names, response shapes, and quirks. When you have 40 of these scattered across your