Allocating costs for large language models with Azure Cost Management

Question

Pulumi · Accepted Answer

Azure Cost Management is a suite of tools provided by Microsoft that allows organizations to monitor, allocate, and optimize their cloud spend across various services within Azure. In the context of allocating costs for large language models, you would typically use the cost management services to ensure that the resources you're utilizing for these models are within budget and efficiently managed.

One of the relevant resources from Azure Cost Management that can help with this is the CostAllocationRule resource from azure-native.costmanagement. This resource allows you to configure rules that allocate costs from a shared resource, such as a machine learning service where your large language models might be running, to different departments or projects based on defined criteria.

Below is a Pulumi program in Python that illustrates how to define a cost allocation rule for an Azure service. This rule will specify how costs should be distributed across different departments or projects. I'll include comments throughout the code to explain each step in detail.

import pulumi
import pulumi_azure_native as azure_native

# Define a cost allocation rule.
cost_allocation_rule = azure_native.costmanagement.CostAllocationRule("costAllocationRule",
    rule_name="allocateCostsForLanguageModels",
    properties=azure_native.costmanagement.CostAllocationRulePropertiesArgs(
        status="Active",  # The status of the rule, "Active" indicates the rule is currently being applied
        details=azure_native.costmanagement.CostAllocationRulePropertiesDetailsArgs(
            sourceResources=[  # Define the source resources whose costs you want to allocate
                azure_native.costmanagement.CostAllocationRulePropertiesDetailsSourceResourceArgs(
                    name="ServiceName",  # The name of the service, e.g., Azure Machine Learning
                    values=["<your-resource-id>"],  # The specific resource ID of your service
                    resourceType="Microsoft.MachineLearningServices/workspaces",  # The type of the resource
                ),
            ],
            targetResources=[  # Define the target resources where you want to allocate costs
                azure_native.costmanagement.CostAllocationRulePropertiesDetailsTargetResourceArgs(
                    name="Department",  # The criteria for allocation, e.g., by department
                    values=[
                        azure_native.costmanagement.CostAllocationRulePropertiesDetailsTargetResourceValueArgs(
                            name="ResearchAndDevelopment",  # Name of the department
                            percentage=60,  # Percentage of costs to allocate to this department
                        ),
                        azure_native.costmanagement.CostAllocationRulePropertiesDetailsTargetResourceValueArgs(
                            name="Marketing",  # Name of the department
                            percentage=40,  # Percentage of costs to allocate to this department
                        ),
                    ],
                    policyType="Fixed",  # The allocation method, "Fixed" or "Proportional"
                    resourceType="Department",  # The type of the target resource
                ),
            ],
        ),
        description="Allocation of costs for language models",  # A description of the rule
    ),
    billingAccountId="<your-billing-account-id>",  # The ID of the billing account where the rule applies
)

pulumi.export("costAllocationRuleId", cost_allocation_rule.id)  # Export the ID of the cost allocation rule

Each resource and attribute is clearly explained in the code comments. The CostAllocationRule resource allows you to specify the criteria for which costs should be allocated from a source (like a shared Azure Machine Learning service) to targets such as departments, with a fixed or proportional method and by certain percentages. You would replace placeholders like <your-resource-id> and <your-billing-account-id> with actual values from your Azure environment.

Once this rule is applied, costs associated with the large language models running on specified resources will be distributed according to the defined percentages among the departments or projects. This makes it easier to manage budgets and ensures that each department is accountable for its share of the cloud costs.