How to Use One OpenAI-Compatible API for Multiple AI Models

Modern AI applications rarely depend on only one model.

A team might use one model for reasoning, another for fast classification, and another for multimodal tasks. But integrating every provider separately introduces duplicated work:

• Different authentication methods
• Different request and response formats
• Different SDKs and error structures
• Separate usage dashboards
• More code to maintain

An OpenAI-compatible API gateway can simplify this architecture by exposing one familiar interface for multiple models.

The basic idea

Instead of connecting your application directly to every model provider, your application connects to one gateway:

Your application
       ↓
OpenAI-compatible gateway
       ↓
OpenAI / Claude / Gemini / other models

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Your authentication and request format remain consistent. In many cases, switching models only requires changing the model identifier.

JavaScript example

Install the OpenAI SDK:

npm install openai

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Create a client that points to your gateway:

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.AI_GATEWAY_API_KEY,
  baseURL: process.env.AI_GATEWAY_BASE_URL,
});

async function generateResponse(model, prompt) {
  const response = await client.chat.completions.create({
    model,
    messages: [
      {
        role: "user",
        content: prompt,
      },
    ],
  });

  return response.choices[0].message.content;
}

const result = await generateResponse(
  process.env.AI_MODEL_ID,
  "Explain API gateways in three sentences."
);

console.log(result);

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Set the environment variables using the values provided by your gateway:

AI_GATEWAY_API_KEY=your_api_key
AI_GATEWAY_BASE_URL=your_gateway_base_url
AI_MODEL_ID=your_model_id

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Keeping these values outside the application code makes it easier to change endpoints and models between development and production.

Python example

Install the SDK:

pip install openai

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Then initialize the client with a custom base URL:

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AI_GATEWAY_API_KEY"],
    base_url=os.environ["AI_GATEWAY_BASE_URL"],
)

response = client.chat.completions.create(
    model=os.environ["AI_MODEL_ID"],
    messages=[
        {
            "role": "user",
            "content": "Explain API gateways in three sentences.",
        }
    ],
)

print(response.choices[0].message.content)

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Add a model-routing layer

Avoid scattering model identifiers throughout your code. Keep them in one configuration object:

const models = {
  fast: process.env.FAST_MODEL_ID,
  reasoning: process.env.REASONING_MODEL_ID,
  multimodal: process.env.MULTIMODAL_MODEL_ID,
};

const selectedModel = models.reasoning;

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This lets you change providers without rewriting the rest of the application.

Handle failures consistently

A gateway reduces integration complexity, but production applications still need basic resilience:

async function requestWithRetry(model, prompt, retries = 2) {
  for (let attempt = 0; attempt <= retries; attempt++) {
    try {
      return await generateResponse(model, prompt);
    } catch (error) {
      if (attempt === retries) throw error;

      const delay = 500 * 2 ** attempt;
      await new Promise(resolve => setTimeout(resolve, delay));
    }
  }
}

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For production workloads, also consider:

• Request timeouts
• Rate-limit handling
• Usage and cost monitoring
• Logging without exposing prompts or API keys
• Model fallbacks
• Separate keys for development and production

Why we built APIHubRelay

We built APIHubRelay to provide developers with a unified, OpenAI-compatible gateway for leading AI models.

The goal is straightforward: integrate once, manage access in one place, and make it easier to test or switch models as applications evolve.

The website also supports multiple interface languages for international developers and teams.

You can learn more at:

https://apihubrelay.com/

Before copying the examples into production, replace the gateway URL and model identifiers with the exact values shown in the APIHubRelay documentation or dashboard.

Final thoughts

A unified API does not eliminate every difference between AI models. Models can still have different capabilities, context limits, pricing, and supported parameters.

However, standardizing the connection layer removes a large amount of repetitive integration work. It also gives teams a cleaner foundation for model routing, monitoring, and future provider changes.

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Disclosure: I am involved with APIHubRelay. AI assistance was used to help structure and edit this article, and the final content was reviewed before publication.