Optimizing GCP Costs for AI Inference with Billing Reports

Question

Pulumi · Accepted Answer

To optimize costs for AI inference services in Google Cloud Platform (GCP), you'll want to monitor your spending and usage effectively. The billing reports and budgets in GCP allow you to keep track of your cloud expenses and to set thresholds for your spending, so you can take action if the costs exceed certain amounts.

Here we will use Pulumi to create a budget in GCP for monitoring the costs associated with AI inference services. We will leverage the `gcp.billing.Budget` resource, which will help us to keep an eye on the GCP spending and send notifications when certain thresholds are met.

### Explanation

We will create a GCP budget using Pulumi, which entails the following steps:

1. **Setting up the Budget**: This involves identifying the services (like AI services) that you want to monitor, deciding on the amount of cost you're willing to allocate, and specifying the project ID to which this budget applies.

2. **Defining the Threshold Rules**: These rules will determine at what percentage of the budget notifications should be sent out.

3. **Specifying Notification Channels** (optional): You can set up notification channels if you want to receive alerts when your costs approach the budget limits.

4. **Configuring the Budget Filters**: Filters enable you to target specific resource usage. For example, you can filter by resources, labels, or even by the type of credit.

Now let’s go through the process using Pulumi to manage GCP costs by setting up a budget for AI Inference services.

### Pulumi Program

```python
import pulumi
import pulumi_gcp as gcp

# Set up the GCP budget for AI inference services
ai_budget = gcp.billing.Budget("ai-inference-budget",
    # Define the amount for the budget
    amount={
        "specified_amount": {
            "currency_code": "USD",
            "units": "1000" # Budget set for $1000 USD
        }
    },
    # Specify the GCP budget to be applied on a particular billing account 
    billing_account="your-billing-account-id",
    # Define the budget display name
    display_name="AI Inference Services Budget",
    # Threshold rules define at what percentage of the budget you should be alerted.
    threshold_rules=[
        {"threshold_percent": 0.5},  # Notify at 50% of budget
        {"threshold_percent": 0.9},  # Notify at 90% of budget
        {"threshold_percent": 1.0}   # Notify at 100% of budget
    ],
    # Set up the budget filter for AI inference services
    budget_filter={
        # In this case, we will monitor all the projects.
        # You could, for example, filter by specific projects or AI services by specifying project IDs or services.
        "projects": ["projects/your-project-id"],
        # Filter by services, in this case, filter by services related to AI and ML. 
        # You'll need to replace 'your-service-id' with the actual service ID for the AI-related services you're utilizing.
        "services": ["services/your-service-id"] 
    },
    # Notification setup to send budget alerts to Pub/Sub topic (optional)
    all_updates_rule={
        "pubsub_topic": "projects/your-project-id/topics/your-topic-id",
        "schema_version": "1.0" # Use the latest schema version
    }
)

# Export the budget name
pulumi.export("budget_name", ai_budget.display_name)
```

This code block creates a budget constraint on Google Cloud Platform, specifically for monitoring the costs of AI inference services. It defines the expected spending amount and the rules for when alerts should be triggered. It also sets a filter to narrow down the costs to a particular project and service ID related to AI.

Replace `"your-billing-account-id"`, `"your-project-id"`, `"your-topic-id"`, and `"your-service-id"` with your respective GCP billing account ID, project ID, Pub/Sub topic ID for notifications, and the service ID for the AI inference services you're using.

This budget will help you monitor your GCP costs, and you can adjust the settings as necessary based on your organization's specific needs and usage patterns. With proper budget monitoring, you can ensure that your spending on AI Inference services in GCP is optimized and under control.