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How to Switch from OpenAI API to a Cheaper OpenAI-Compatible Endpoint

Published by XiDao API Blog

If your AI app already uses the OpenAI SDK, the easiest way to reduce cost is not rebuilding your stack. It is switching to an OpenAI-compatible endpoint that lets you keep most of your existing code while giving you access to more model choices and lower operating cost.

In this guide, we will show the practical migration path, what to watch out for, and how to switch with minimal engineering effort.

Why developers switch

• API cost starts growing faster than expected
• One model provider is not enough for all use cases
• You want fallback options when a model is slow or unavailable
• You need to test cheaper models without rewriting your app

What “OpenAI-compatible” actually means

• You keep using familiar request formats
• You keep using the OpenAI SDK in many common cases
• You mainly change the base_url and API key
• You can route requests to multiple available models from one endpoint

The fastest migration path

1. Replace the base URL
2. Replace the API key
3. Update the model name if needed

Python example

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_XIDAO_API_KEY",
    base_url="https://api.xidao.online/v1"
)

response = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Write a short launch post for my AI app."}
    ]
)

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

Node.js example

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.XIDAO_API_KEY,
  baseURL: "https://api.xidao.online/v1"
});

const response = await client.chat.completions.create({
  model: "gpt-5.4-mini",
  messages: [
    { role: "system", content: "You are a helpful assistant." },
    { role: "user", content: "Summarize this customer ticket in 3 bullet points." }
  ]
});

console.log(response.choices[0].message.content);

What you gain from switching

1. Lower cost pressure

If your app is growing, even small savings per request matter.

2. Easier model experimentation

You can test different models for different workloads without reworking your integration layer.

3. Better production flexibility

Many teams want one endpoint for premium quality, cheaper fallback, and routing flexibility.

Recommended rollout strategy

1. Local test
2. Internal traffic
3. Partial production traffic
4. Optimize by use case