Most AI applications start simple.
A developer chooses one model provider, gets an API key, connects an SDK, writes a few prompts, and ships the first version.
That works well in the beginning.
But once an AI product starts growing, the model layer becomes much more complicated.
Different tasks need different models. Some requests need strong reasoning. Some need lower cost. Some need fast response time. Some need long context. Some need vision. Some need better performance in local languages. Some need fallback when the primary provider is slow or unavailable.
