As companies increasingly adopt generative AI, they encounter challenges related to managing costs effectively. With a rising demand for generative AI applications cutting across various projects and business segments, accurately tracking and allocating expenses is becoming more intricate. Organizations must prioritize their generative AI expenditures based on their business impact and importance while ensuring transparency across different customer and user segments. This clarity is vital for establishing appropriate pricing for generative AI services, implementing chargebacks, and creating usage-based billing systems.
Without a scalable method for controlling expenses, companies risk unplanned usage and budget overruns. Relying on manual monitoring of spending and periodic adjustments to usage limits can lead to inefficiencies and human error, potentially resulting in overspending. While tagging is supported for a range of Amazon Bedrock resources—including provisioned models, custom models, agents, and model evaluations—there was a previous limitation regarding tagging on-demand foundation models. This gap has complicated cost management for generative AI projects.
To tackle these issues, Amazon Bedrock has introduced a feature that allows organizations to tag on-demand models and monitor related costs. Businesses can now label all Amazon Bedrock models with AWS cost allocation tags, aligning usage with specific organizational frameworks such as cost centers, business units, and applications. For effective budget management, organizations can utilize services like AWS Budgets to set tag-based budgets and alerts, helping them monitor usage and receive notifications for anomalies or when predefined thresholds are reached. This scalable approach reduces inefficient manual processes, mitigates the risk of unnecessary spending, and ensures that high-priority applications are adequately supported. Improved visibility and control over AI expenditures empower organizations to optimize their generative AI investments and drive innovation.
Introducing Application Inference Profiles for Better Control
Amazon Bedrock has recently unveiled cross-region inference, which enables automatic routing of inference requests across AWS Regions. This feature utilizes system-defined inference profiles (set by Amazon Bedrock), which consolidate different model Amazon Resource Names (ARNs) from various Regions under a single model identifier. While this enhances model usage flexibility, it lacks support for attaching custom tags for tracking and expense management across workloads and tenants.
To bridge this gap, Amazon Bedrock has now introduced application inference profiles, a new feature that allows organizations to apply custom cost allocation tags to track and manage their on-demand model expenses and usage. This feature enables organizations to create unique inference profiles for Bedrock’s base foundation models, adding metadata tailored to specific tenants, which streamlines resource allocation and cost monitoring across diverse AI applications.
Creating Application Inference Profiles
Application inference profiles enable users to define personalized settings for inference requests and resource management. These profiles can be established in two ways:
- Single model ARN configuration: Directly create an application inference profile using a single on-demand base model ARN for quick setup with a selected model.
- Copy from system-defined inference profile: Duplicate an existing system-defined inference profile to create a new application inference profile, inheriting configurations like cross-region inference capabilities for better scalability and resilience.
The application inference profile ARN follows this format, where the inference profile ID is a unique 12-digit alphanumeric string generated by Amazon Bedrock upon profile creation:
arn:aws:bedrock:<region>:<account_id>:application-inference-profile/<inference_profile_id>
Understanding the Differences Between Profile Types
The main difference between system-defined and application inference profiles lies in their type attribute and resource specifications within the ARN namespace:
- System-defined inference profiles: These profiles have a type attribute of SYSTEM_DEFINED and use the inference-profile resource type. They are designed for cross-region and multi-model capabilities but are managed centrally by AWS.
- Application inference profiles: These profiles possess a type attribute of APPLICATION and utilize the application-inference-profile resource type. They are user-defined, granting more granular control and flexibility over model configurations, and allowing organizations to tailor policies with attribute-based access control (ABAC) using AWS Identity and Access Management (IAM). This improves IAM policy authoring for more secure and efficient management of Amazon Bedrock access.
These distinctions are crucial when integrating with Amazon API Gateway or other API clients to ensure accurate model invocation, resource allocation, and workload prioritization. Organizations can apply customized policies based on profile type, enhancing control and security over distributed AI workloads.
Implementing Application Inference Profiles for Cost Management
Consider an insurance company looking to enhance its customer service through generative AI. The organization identifies opportunities to automate claims processing, deliver personalized policy recommendations, and improve risk assessment for clients across different regions. However, to achieve this vision, the organization must establish a solid framework for efficiently managing its generative AI workloads.
The journey begins with the insurance provider developing application inference profiles tailored to its various business units. By assigning AWS cost allocation tags, the organization can effectively monitor and track spending patterns within Bedrock. For instance, the claims processing team may create an application inference profile with tags like dept:claims, team:automation, and app:claims_chatbot. This tagging structure helps categorize costs and assess usage against budgets.
Users can manage and utilize application inference profiles through Bedrock APIs or the boto3 SDK:
- CreateInferenceProfile: Initiates a new inference profile, allowing users to configure its parameters.
- GetInferenceProfile: Retrieves details of a specific inference profile, including its configuration and current status.
- ListInferenceProfiles: Lists all inference profiles available within the user’s account, offering an overview of the created profiles.
- TagResource: Allows users to attach tags to resources effectively.
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