1. Budget Management for AI Workloads with Azure Consumption


    Creating and managing budgets for AI workloads in Azure is essential to ensure that your costs are in control and predictably managed. You can use the Pulumi Azure Native provider to create a budget for your AI services, which allows you to set cost thresholds and receive notifications when these are approached or exceeded.

    The Azure Native provider by Pulumi offers different resources to help you manage your costs:

    • Budget - This resource is used to create a budget for various scopes like subscription, resource groups, or management groups.
    • ReportByBillingAccount - While not directly a budget, this resource can provide customized billing reports.
    • CapacityReservationGroup - This is useful for reserving capacity for your AI services which might have a predictable load and to potentially save costs with reserved instances.
    • CommitmentPlan - This is specifically tied to the Cognitive Services and allows tracking and managing your spend on these resources.

    Let's walk through an example where we create a budget for a subscription scope using the Budget resource within Azure Native provider. The goal will be to set a monthly budget amount. If the cost surpasses 80% of the allocated budget amount, we should receive notifications.

    In this example, you will see how to:

    1. Create a budget for a subscription.
    2. Set a filter to target the budget to specific resources or services, like AI services in this case.
    3. Define notifications for when certain thresholds are reached.

    Here's a Python program using Pulumi for creating such a budget:

    import pulumi import pulumi_azure_native as azure_native # Replace these variables with appropriate values budget_amount = 1000.0 # Budget amount in your currency subscription_id = 'your-subscription-id' # Azure Subscription Id contact_emails = ['your-email@example.com'] # List of emails for budget notifications # Create an Azure Budget for AI Workloads budget = azure_native.consumption.Budget( "budget", scope=f"/subscriptions/{subscription_id}", amount=budget_amount, time_period={ "startDate": "2023-01-01T00:00:00Z", # Set the start date of the budget "endDate": "2024-01-01T00:00:00Z" # Set the end date of the budget }, notifications={ "EmailContacts": { "enabled": True, "operator": "GreaterThan", # Notify when cost exceeds the threshold "threshold": 80, # Threshold set at 80% of the budget "contactEmails": contact_emails, "thresholdType": "Actual" # Type of threshold (Actual or Forecasted) } }, filters={ # Filter to focus on Machine Learning or AI service costs "tags": { "name": "service", # Example tag that might be used to identify AI services "values": ["ai", "machinelearning"] # Specify the values associated with AI workloads }, "resources": [ # List the resource instances or not scope if not limiting to specific ones ] }, time_grain="Monthly", # Budget is evaluated monthly ) # Export the budget ID to be available once the Pulumi program is deployed pulumi.export('budget_id', budget.id)

    What this code does:

    • It starts by importing necessary Pulumi modules.
    • The budget amount, IDs, and emails are defined for easier configuration.
    • The Budget resource is created with a scope of a subscription. Adjust the scope as needed for management groups or resource groups.
    • A time period is defined for the budget's effectiveness, with start and end dates.
    • Notifications are set up to alert when spending reaches 80% of the budget.
    • Filters are in place to focus on specific resource tags related to AI workloads.
    • The time_grain is set to "Monthly" to monitor monthly spending, as is typical with cloud budgets.
    • Finally, the program exports the budget_id which lets you identify and track the budget once it's deployed.

    Each time this Pulumi program is deployed, it will ensure that the budget defined is either created or updated to match the specified parameters. With Pulumi's real-time feedback, you will know immediately if the deployment succeeded or not.

    This Pulumi program would help manage the costs of AI workloads by initially setting a budget cap and monitoring it with notifications. This process is just one part of a larger, prudent cloud cost management strategy.