Azure Deployment Note
The foundational models for time series developed by Nixtla can be deployed directly to Azure subscriptions. This guide explains how to quickly start using TimeGEN-1 as an Azure AI endpoint. If you currently use the nixtla library, the Azure deployment works as a drop-in replacement—simply adjust the client parameters (endpoint URL, API key, and model name).

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Deploying TimeGEN-1

Time Gen on the Azure Portal

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Using the Model

After you have successfully deployed TimeGEN-1 and ensured you have permission to access its endpoint, you can interact with the model as you would with a standard Nixtla endpoint.

Ensure you have your deployment URL and API key ready before proceeding.

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How to Use

1

Install the Nixtla Client

Nixtla Client Installation
pip install nixtla

This installs the official Nixtla Python client library so you can make forecast requests to your Azure AI endpoint.

2

Set Up Your Environment

Make sure you have the following environment variables properly configured:

  • AZURE_AI_NIXTLA_BASE_URL

  • AZURE_AI_NIXTLA_API_KEY

3

Initialize the Nixtla Client

Nixtla Client Initialization
import os
from nixtla import NixtlaClient

base_url = os.environ["AZURE_AI_NIXTLA_BASE_URL"]
api_key = os.environ["AZURE_AI_NIXTLA_API_KEY"]
model = "azureai"

nixtla_client = NixtlaClient(
    api_key=api_key,
    base_url=base_url
)

Here, we create a new client instance using your Azure endpoint URL and API key.

4

Make a Forecast Request

Forecast Request Example
# Example forecast call; replace "..." with your actual parameters
nixtla_client.forecast(
    ...,
    model=model,
)

Replace the ellipsis (…) with your specific forecasting parameters and then call the endpoint to get predictions.

Key Concept: Drop-In Replacement

Because TimeGEN-1 on Azure uses the same API structure as the Nixtla library, you only need to switch out the base URL, API key, and model name. Your workflow remains unchanged.

Key Concept: Seamless Integration

Deploying TimeGEN-1 to Azure allows you to leverage Azure’s scalability, security, and management tools directly for your time series forecasting needs without altering core application logic.

Tip: Remember that you can use any Azure-supported authentication or security measures to further protect your endpoint, such as Azure Key Vault for managing secrets or role-based access control for restricting usage.