How to switch AI models without rewriting your app

A developer from TokenBay demonstrates how to switch AI models without rewriting application code by using an OpenAI-compatible API gateway. The approach allows developers to keep the familiar OpenAI SDK style while routing requests to different model families through a single endpoint. Changing the base URL and API key enables testing of models like Claude, Gemini, and DeepSeek with minimal code changes.

Most AI apps start with one model provider. That is usually the right choice. For a first version, you want one SDK, one API key, one billing page, and one model name. Simple is good when you are trying to ship. But once the product grows, the model decision gets more complicated. You may want to test another model because: The annoying part is that switching models is often not just changing a string. It can mean adding another SDK, another API key, another request format, another dashboard, and another set of provider-specific edge cases. That gets messy quickly. A first version might look like this: python python from openai import OpenAI client = OpenAI api key="YOUR OPENAI API KEY", response = client.chat.completions.create model="gpt-4o-mini", messages= { "role": "user", "content": "Write a short onboarding message for a developer tool." } , print response.choices 0 .message.content This is clean and totally fine. But if you later want to compare Claude, Gemini, DeepSeek, or another model family, you may not want to rewrite your AI integration around each provider. One practical option is to use an OpenAI-compatible API gateway. Your app keeps using the OpenAI SDK style, but the gateway lets you route requests to different model families through one endpoint. I work on the TokenBay team, so the example below uses TokenBay. The general idea applies to any OpenAI-compatible gateway. python python from openai import OpenAI client = OpenAI base url="https://api.tokenbay.com/v1", api key="YOUR TOKENBAY API KEY", response = client.chat.completions.create model="gpt-5.4-mini", messages= { "role": "user", "content": "Write a short onboarding message for a developer tool." } , print response.choices 0 .message.content The main change is just: python base url="https://api.tokenbay.com/v1" api key="YOUR TOKENBAY API KEY" That is the useful part. You keep the familiar OpenAI client shape, but you are no longer wiring every provider separately. Once your app uses an OpenAI-compatible endpoint, testing another supported model can be as simple as changing the model name. python response = client.chat.completions.create model="claude-sonnet-4.6", messages= { "role": "user", "content": "Write a short onboarding message for a developer tool." } , Or: python response = client.chat.completions.create model="gemini-2.5-pro", messages= { "role": "user", "content": "Write a short onboarding message for a developer tool." } , The point is not that every model behaves the same. They do not. The point is that your business logic should not need to change every time you want to compare models. For a real app, I would keep the base URL, API key, and model name in environment variables: bash LLM BASE URL=https://api.tokenbay.com/v1 LLM API KEY=YOUR TOKENBAY API KEY LLM MODEL=gpt-5.4-mini Then your application code can stay stable while you test different models. Change config, redeploy, compare results. Very boring. Very useful. This setup is useful if you are: It does not magically solve model selection. You still need to test output quality, latency, pricing, context length, and reliability. But it does make the integration layer much simpler. A gateway is not always the right choice. Direct provider integration may be better if: That is a fair tradeoff. The point is not "always use a gateway." The point is this: If you are going to test multiple models anyway, your app should not need a rewrite every time. TokenBay is an OpenAI-compatible API gateway for accessing models such as GPT, Claude, Gemini, DeepSeek, and others through one endpoint and one API key. It includes: If you want to test this pattern, you can try TokenBay here: Try TokenBay https://www.tokenbay.com/?utm source=devto&utm medium=community content&utm campaign=week1 free content https://www.tokenbay.com/?utm source=devto&utm medium=community content&utm campaign=week1 free content Current launch offer: I would love feedback from builders: