Azure OpenAI Setup in Turbo360
  • 12 Jun 2026
  • 2 Minutes to read
  • Dark
    Light
  • Download PDF

Azure OpenAI Setup in Turbo360

  • Dark
    Light
  • Download PDF

Article summary

Introduction

Turbo360 supports AI-powered features through Azure OpenAI integration. This integration enables capabilities such as AI-generated summaries and resource analysis within Cost Analyzer.

The setup experience differs based on the selected deployment model:

  • SaaS: AI capabilities are available by default and do not require additional configuration.
  • Private hosting: AI capabilities require a customer-managed Azure OpenAI resource to be configured.

Private hosting deployment

For private deployments, Turbo360 requires an external Azure OpenAI connection. Users must create and configure their own Azure OpenAI resource and provide the required connection details within Turbo360.

Prerequisites

Before configuring Azure OpenAI in Turbo360, ensure the following prerequisites are available:

  • An active Microsoft Azure subscription
  • Azure OpenAI resource created in Azure portal
  • Access to Azure OpenAI API keys and endpoint details
  • A deployed model in Azure OpenAI
  • Required permissions to manage Azure resources

Create Azure Open AI resource

Step 1: Create the resource

  1. Sign in to the Azure portal.
  2. Search for Azure OpenAI and select Create.
    Azure Open AI.png
  3. Provide the required details such as:
  • Subscription
  • Resource Group
  • Region
  1. Complete the resource deployment.

Step 2: Deploy a Model

The deployment types compatible with Turbo360 include: Global Standard, Standard, and DataZone Standard.

  1. Open the Azure OpenAI resource.
  2. Click Overview -> Explore Foundry portal option to deploy generative AI models.
    Access foundry.png
  3. Go to Shared resources -> Deployments in the Microsoft Foundry portal.
  4. Click + Deploy model -> Deploy base model to deploy the required model for Turbo360 AI operations.
    Deploy base model.png
  5. Choose the desired model and click Confirm to initiate the deployment.
    Available models.png

Step 3: Retrieve connection details

  1. Once the deployment is complete, click on the deployed model from the Model deployments screen available in the Microsoft Foundry portal
  2. The following details can be found here:
  • API key
  • Endpoint url
  • Deployment name

Deployment details.png

Configure Azure OpenAI in Turbo360

Step 1: Open Connection Settings

  1. In Turbo360, open the Cost Analyzer module.
  2. Navigate to Settings > OpenAI connection.
    Open AI connection.png

Step 2: Create a Connection

  1. Enter the Azure OpenAI connection details:
  • Endpoint URL
  • API key
  • Deployment name
    OpenAI configuration.png

Step 3: Validate the Connection

  1. Click Validate to verify the provided details.
  2. Confirm the validation is successful.

Step 4: Save Configuration

  1. Click Save to complete the Azure OpenAI connection setup.

Verify the Integration

After completing the configuration, verify the Azure OpenAI integration by generating an AI summary in Cost Analyzer.

  1. Open the Cost Analyzer module and navigate to the Analysis, Recommendations, (or) Schedules section. Access the Cost intelligence view for the required resource.
  2. Open the AI Agents tab within the view.
  3. Select any available AI Agent and click Generate.
  4. Confirm that the AI-generated response is returned successfully.

Available AI Agents in Cost Analyzer

The AI Agents tab displays the list of available AI-powered agents in Cost Analyzer after successful Azure OpenAI configuration. Users can select and run the required agent based on their analysis use case.

AI Agents.png

Troubleshooting

IssueResolution
Invalid API keyVerify the API key from Azure portal
Invalid endpointConfirm the endpoint URL is correct
Deployment not foundVerify deployment name configured in Azure OpenAI
Validation failedCheck authentication details and network accessibility

Security best practices

  • Store API keys securely and avoid exposing them in source code.
  • Restrict Azure access permissions where possible.
  • Rotate credentials periodically based on internal security policies.

Was this article helpful?