Azure AI

Introduction

Mistral AI's open and commercial models can be deployed on the Microsoft Azure AI cloud platform in two ways:

• Pay-as-you-go managed services: Using Model-as-a-Service (MaaS) serverless API deployments billed on endpoint usage. No GPU capacity quota is required for deployment.

• Real-time endpoints: With quota-based billing tied to the underlying GPU infrastructure you choose to deploy.

This page focuses on the MaaS offering, where the following models are available:

• Mistral Large
• Mistral Small
• Mistral NeMo

For more details, visit the models page.

Getting started

The following sections outline the steps to deploy and query a Mistral model on the Azure AI MaaS platform.

Deploying the model

Follow the instructions on the Azure documentation to create a new deployment for the model of your choice. Once deployed, take note of its corresponding URL and secret key.

Querying the model

Deployed endpoints expose a REST API that you can query using Mistral's SDKs or plain HTTP calls.

To run the examples below, set the following environment variables:

• AZUREAI_ENDPOINT: Your endpoint URL, should be of the form https://your-endpoint.inference.ai.azure.com/v1/chat/completions.
• AZUREAI_API_KEY: Your secret key.

cURL

curl --location $AZUREAI_ENDPOINT/v1/chat/completions \
    --header  "Content-Type: application/json" \
    --header "Authorization: Bearer $AZURE_API_KEY" \
    --data '{
  "model": "azureai",
  "messages": [
    {
      "role": "user",
      "content": "Who is the best French painter? Answer in one short sentence."
    }
  ]
}'

Python

This code requires a virtual environment with the following packages:

• mistralai-azure>=1.0.0

from mistralai_azure import MistralAzure
import os

endpoint = os.environ.get("AZUREAI_ENDPOINT", "")
api_key = os.environ.get("AZUREAI_API_KEY", "")

client = MistralAzure(azure_endpoint=endpoint,
                 azure_api_key=api_key)

resp = client.chat.complete(messages=[
    {
        "role": "user",
        "content": "Who is the best French painter? Answer in one short sentence."
    },
], model="azureai")

if resp:
    print(resp)

TypeScript

This code requires the following package:

• @mistralai/mistralai-azure (version >= 1.0.0)

import { MistralAzure } from "@mistralai/mistralai-azure";

const client = new MistralAzure({
    endpoint: process.env.AZUREAI_ENDPOINT || "",
    apiKey: process.env.AZUREAI_API_KEY || ""
});

async function chat_completion(user_msg: string) {
    const resp = await client.chat.complete({
        model: "azureai",
        messages: [
            {
                content: user_msg,
                role: "user",
            },
        ],
    });
    if (resp.choices && resp.choices.length > 0) {
        console.log(resp.choices[0]);
    }
}

chat_completion("Who is the best French painter? Answer in one short sentence.");

Going further

For more details and examples, refer to the following resources:

• Release blog post for Mistral Large 2 and Mistral NeMo.
• Azure documentation for MaaS deployment of Mistral models.
• Azure ML examples GitHub repository with several Mistral-based samples.