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by Ryan Lindsey · 2026-07-02

Migrating from Anthropic Claude to ZCX can significantly reduce your inference costs without compromising on performance. This guide will walk you through the process of migrating in a single afternoon, covering the route-through adapter setup, regression testing, and the cutover plan. By the end of this guide, you'll be able to leverage ZCX's cheaper token pricing and dedicated NVIDIA GB10 silicon for your language model inference needs.

Route-Through Adapter Setup

To migrate from Anthropic Claude to ZCX, you'll first need to set up a route-through adapter. This adapter will allow you to seamlessly transition your current infrastructure to ZCX's OpenAI-compatible API. Below is a basic example of how you might implement this adapter in Python:

import requests

# Replace with your ZCX API key
zcx_api_key = 'your_zcx_api_key'

# ZCX API endpoint
zcx_api_url = 'https://zcx.zctechnologies.org/v1/chat'

# Function to send a request to ZCX
# This function mirrors the Anthropic Claude API call structure
# and translates it to ZCX's API format

def send_to_zcx(prompt):
    headers = {
        'Authorization': f'Bearer {zcx_api_key}',
        'Content-Type': 'application/json'
    }
    data = {
        'model': 'qwen2.5:32b',
        'messages': [{'role': 'user', 'content': prompt}],
        'max_tokens': 150
    }
    response = requests.post(zcx_api_url, headers=headers, json=data)
    return response.json()

# Example usage
response = send_to_zcx('What is the capital of France?')
print(response['choices'][0]['message']['content'])

Regression Testing Strategy

Before you cut over to ZCX, it's important to conduct a thorough regression test to ensure that the output from ZCX matches your expectations. This involves comparing the responses from ZCX with those from Anthropic Claude for a set of predefined prompts. You can automate this process with a simple script that records the differences between the two outputs:

# Example regression test script

# List of prompts to test
prompts = ['What is the capital of France?', 'Explain quantum computing.']

# Function to compare Claude and ZCX responses
# This is a simplified example and should be expanded for real-world use
def compare_responses(claude_response, zcx_response):
    # Compare the responses here
    # For simplicity, we'll just print them
    print(f'Claude Response: {claude_response}')
    print(f'ZCX Response: {zcx_response}')

# Iterate over prompts and compare responses
for prompt in prompts:
    claude_response = send_to_claude(prompt)  # Assume this function exists
    zcx_response = send_to_zcx(prompt)
    compare_responses(claude_response, zcx_response)

Cutover Plan

Once you've verified that the route-through adapter works and that the regression tests pass, you can proceed with the cutover plan. This involves switching your production environment to use ZCX's API instead of Anthropic Claude's. The cutover should be scheduled for a time when the impact on your users will be minimal, such as during off-peak hours. Ensure that you have a rollback plan in case any issues arise during the transition.

Conclusion

By following this guide, you can migrate from Anthropic Claude to ZCX and take advantage of our more cost-effective token pricing. ZCX offers a range of plans, from the Starter plan at $99/month for 1.5M tokens to the Business plan at $1999/month for 60M tokens, all with dedicated NVIDIA GB10 silicon. To get started, sign up for a prepaid LLM credit line at https://zcx.zctechnologies.org#plans.