1. Throttling Limits for AI APIs with Azure API Management

    Python

    When you need to apply throttling limits to APIs, specifically for AI APIs or any other types, Azure API Management (APIM) is an excellent choice. It allows for the management, transformation, integration, and security of API services. Throttling, in particular, is important as it can help protect your APIs from being overwhelmed by too many requests at once, which might either slow down or disrupt the service for all users.

    In the context of Pulumi and Azure, you can manage such configurations using the azure-native package. We will use the azure-native.apimanagement.ApiManagementService resource to create an instance of the Azure API Management service, then define a policy with azure-native.apimanagement.Policy to set the throttling rules.

    In Azure APIM, you can define policies at different scopes such as global, product, API, and operation. The policies are composed of a series of statements that are executed sequentially on the request or response of an API. To set up throttling limits, we'll need to write policies in XML format because that's the language Azure API Management uses for defining policies.

    Here's how you can create an API Management instance and apply a policy to set rate limits:

    import pulumi import pulumi_azure_native as azure_native # Configuration for the API Management Service api_management_name = 'ai-api-management-service' resource_group_name = 'my-resource-group' # Create an instance of API Management Service api_management_service = azure_native.apimanagement.ApiManagementService('aiApiManagementService', resource_group_name=resource_group_name, service_name=api_management_name, publisher_name='Your Publisher Name', publisher_email='publisher@email.com', sku=azure_native.apimanagement.SkuDescriptionArgs( name="Consumption", capacity=0 # The Consumption tier is serverless and does not require a capacity ), location="West US" ) # Define the policy for throttling xml_policy_content = """ <policies> <inbound> <base /> <rate-limit calls="10" renewal-period="60" /> <!-- The `rate-limit` policy enforces that no more than 10 calls can go through within a 60-second window. You can adjust `calls` and `renewal-period` as per your requirements. --> </inbound> <backend> <base /> </backend> <outbound> <base /> </outbound> <on-error> <base /> </on-error> </policies> """ # Apply the policy at the global level - All APIs under this APIM instance will inherit this policy global_policy = azure_native.apimanagement.Policy('globalPolicy', resource_group_name=resource_group_name, service_name=api_management_name, value=xml_policy_content, format='xml' # The format of the policy content is XML ) pulumi.export('apiManagementServiceId', api_management_service.id)

    In this program:

    • We first establish an instance of the Azure API Management Service, which acts as a container for your APIs.
    • Next, we create a global policy for the management service. This policy contains an XML configuration string where we define a rate-limit policy, allowing only a certain number of calls per time period across all APIs.
    • The pulumi.export line is used to output the ID of the newly created API Management Service, which could be useful for reference in external scripts or for further Pulumi deployments.

    The rate-limit inbound policy is what controls throttling. In the example above, it only allows 10 calls per minute. You might want to adjust this value depending on the expected load and the robustness of the backend systems that the APIs interact with.

    Remember that the actual implementation of these policies depends on your specific requirements; you can fine-tune the policy to manage other aspects of the API traffic. With Pulumi, the changes you make to the code are translated into actual cloud infrastructure changes, giving you the power to manage infrastructure through code.


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