Create an inference deployment with authorization

To ensure that only authenticated clients can access your AI models, you must deploy an inference instance with authorization enabled.

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Step 1. Enable authorization

When deploying an AI model, set the auth_enabled option to true. This means an API Key will be automatically generated and linked to the deployment.

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Step 2. Retrieve the API key

Once the deployment is created with authentication enabled, you can retrieve the API Key via the designated API endpoint.

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API request

The API key can be retrieved via this endpoint.

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Step 3. Use the API key for authorization

Once you have retrieved the API Key, include it in your API requests using the X-API-Key header.

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Example using OpenAI python client library

Here’s an example demonstrating how to use the API key for authorization:

from openai import OpenAI



def get_llm_response(message: str) -> str:

    client = OpenAI(api_key=LLM_KEY, base_url=f"{LLM_API}/v1")



    response = client.chat.completions.create(

        model="meta-llama/Llama-3.3-70B-Instruct",

        messages=[

            {"role": "user", "content": message},

        ],

        extra_headers={"X-API-Key": LLM_KEY},

    )

    return response.choices[0].message.content



if __name__ == "__main__":

    print(get_llm_response("Why is the sky blue?"))

To learn more about deploying AI models, refer to our dedicated guide.