Over the past year, I’ve noticed a pretty clear trend: many AI app developers say they are integrating “different models,” but from an engineering perspective, what they really want is for those models to behave like the same API.
The OpenAI-style Chat Completions API has already become a kind of default interface in many projects. Whether the underlying model comes from OpenAI, Claude, Gemini, DeepSeek, or other closed-source or open-source models, the ideal experience for developers is simple: don’t make me rewrite the SDK, don’t make me redesign the message format, and don’t force me to change a bunch of business logic just to switch models.
This is not because developers are lazy. It’s because AI application engineering is already complicated enough.
A serious AI product usually needs to handle much more than the model call itself: prompt management, context length, token costs, retry logic, streaming responses, logs, user quotas, safety filters, evaluation, and monitoring. If every new model requires a different request format, response format, error handling logic, and streaming implementation, the team can quickly get buried in glue code.
So in my view, the popularity of OpenAI-compatible APIs is not necessarily because OpenAI will always be the strongest model provider. It’s because developers need a stable abstraction layer.
