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Documentation Index

Fetch the complete documentation index at: https://gcore.com/docs/llms.txt

Use this file to discover all available pages before exploring further.

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.
InfoThe API Key is only available through this endpoint. Store it securely.

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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?"))
InfoFor Gcore deployments with authorization enabled, the X-API-Key header is mandatory in all API requests.
To learn more about deploying AI models, refer to our dedicated guide.