AI Architecture: Switching Vendors Has to Be Possible

The best AI model is not worth much if you cannot switch away from it.

That is what the current compute shortage makes visible. GitHub is pausing new Copilot sign-ups for several individual plans. Anthropic is securing additional TPU capacity with Google and Broadcom. Both point in the same direction: companies are building workflows today on providers whose capacity may become scarce, expensive, or unstable tomorrow.

Many AI architectures still look as if choosing a model were like choosing a software suite. Pick one, integrate deeply, keep it for years.

That will not be enough.

AI has to be built more like a replaceable engine: application at the top, orchestration in the middle, model provider underneath. The driver should not have to notice whether Claude, OpenAI, Gemini, or a specialized model is doing the work. But the architecture has to notice, measure, and switch.

That does not require grand platform rhetoric. Prompts should not be buried deep inside a vendor tool. Tools, permissions, and data access have to stay separate from the model provider. Quality, cost, and latency should be visible per model. And fallbacks should not sit in an emergency slide at the end of the architecture deck, but as real operating logic inside the workflow.

The next AI maturity test will not be who uses the newest model.

It will be who can switch without tearing open operations.