Optimizing AI Workload Costs with Amazon Bedrock Projects: A Comprehensive Guide to Cost Attribution and Management

Introduction

As organizations scale their AI workloads on Amazon Bedrock, understanding what’s driving spending becomes critical…

Understanding Amazon Bedrock Projects and Cost Allocation

In this section, we dive into the mechanics of Amazon Bedrock Projects…

Prerequisites for Implementing Cost Attribution

Make sure you have the essential elements in place…

Defining Your Tagging Strategy

Create an effective tagging strategy to enhance cost tracking…

Step-by-Step: Creating a Project

Learn how to set up your first project…

Associating Inference Requests with Your Project

Best practices for linking inference requests to specific projects…

Activating Cost Allocation Tags

Ensure your project tags feed into the billing system…

Viewing and Analyzing Project Costs

Utilize AWS Cost Explorer to understand your spending…

Conclusion

Leverage Amazon Bedrock Projects to achieve cost accountability and optimization…

About the Authors

Meet the experts behind this invaluable guide…

Managing Costs with Amazon Bedrock Projects: A Guide to Cost Attribution and Analysis

As organizations scale their AI workloads on Amazon Bedrock, the importance of understanding spending patterns cannot be overstated. Efficient cost management requires teams to perform chargebacks, investigate cost spikes, and guide optimization decisions. At the core of this process is cost attribution, enabling organizations to analyze expenses at a granular level.

With Amazon Bedrock Projects, teams can allocate inference costs to specific workloads and analyze them seamlessly using AWS Cost Explorer and AWS Data Exports. In this post, we’ll cover how to set up Projects end-to-end, from defining a tagging strategy to analyzing costs effectively.

How Amazon Bedrock Projects and Cost Allocation Work

A project in Amazon Bedrock serves as a logical boundary that encapsulates workloads such as applications, environments, or experiments. To attribute costs accurately:

1. Attach Resource Tags: Each project requires resource tags that represent various dimensions like Application, Environment, Team, and Cost Center.
2. API Calls: By passing the project ID in API calls, you ensure that the workload costs are associated with the correct project.
3. Activate Cost Allocation Tags: Once the tags are activated in AWS Billing, you can filter, group, and analyze spending in AWS Cost Explorer and AWS Data Exports.

Notes:

• Amazon Bedrock Projects support OpenAI-compatible APIs such as Responses API and Chat Completions API.
• Requests without a project ID will be attributed to the default project in your AWS account.

Prerequisites

Before diving in, ensure you have the following in place for a smooth setup:

• AWS account with access to Amazon Bedrock
• Permission to create projects and manage tags
• Defined tagging strategy

Defining Your Tagging Strategy

Your tagging strategy is crucial in shaping your cost reports. Plan these tags meticulously before creating your first project. A widely adopted approach includes tagging by:

For detailed guidance, refer to Best Practices for Tagging AWS Resources.

Creating a Project

Now that your tagging strategy is defined, it’s time to create your first project. Each project will have its own set of cost allocation tags flowing into your billing data. Here’s how to create a project using the Projects API:

Install Required Dependencies

$ pip3 install openai requests

Create a Project with Tag Taxonomy

Set up your Bedrock API key in the OPENAI_API_KEY environment variable and then run the following code:

import os
import requests

# Configuration
BASE_URL = "https://bedrock-mantle..api.aws/v1"
API_KEY  = os.environ.get("OPENAI_API_KEY")  # Your Amazon Bedrock API key

def create_project(name: str, tags: dict) -> dict:
    response = requests.post(
        f"{BASE_URL}/organization/projects",
        headers={
            "Authorization": f"Bearer {API_KEY}",
            "Content-Type": "application/json"
        },
        json={"name": name, "tags": tags}
    )

    if response.status_code != 200:
        raise Exception(
            f"Failed to create project: {response.status_code} - {response.text}"
        )

    return response.json()

# Create a production project with full tag taxonomy
project = create_project(
    name="CustomerChatbot-Prod",
    tags={
        "Application": "CustomerChatbot",
        "Environment": "Production",
        "Team":        "CustomerExperience",
        "CostCenter":  "CC-1001",
        "Owner":       "alice"
    }
)
print(f"Created project: {project['id']}")

The response will include the project ID and ARN necessary for tracking and permissions.

Associating Inference Requests with Your Project

To maintain clean cost attribution, ensure you specify the project ID in your API calls. For example, when using the Responses API:

from openai import OpenAI

client = OpenAI(
    base_url="https://bedrock-mantle..api.aws/v1",
    project="proj_123",  # ID returned when you created the project
)

response = client.responses.create(
    model="openai.gpt-oss-120b",
    input="Summarize the key findings from our Q4 earnings report."
)
print(response.output_text)

Activating Cost Allocation Tags

Before your project tags appear in cost reports, activate them as cost allocation tags in AWS Billing. This is a one-time setup necessary to link your project tags to the billing pipeline. Note that it may take up to 24 hours for tags to show in AWS Cost Explorer and AWS Data Exports.

Viewing Project Costs

After setting up projects and tagging inference requests, you can dive into AWS Cost Explorer to visualize spending by project.

Steps to Analyze Costs:

1. Open the AWS Billing and Cost Management console and select Cost Explorer.
2. In the Filters pane, expand Service and select Amazon Bedrock.
3. Under Group by, select Tag and choose your key (e.g., Application).

For more granular detail, refer to the AWS Billing documentation for creating Data Exports.

Conclusion

With Amazon Bedrock Projects, organizations can efficiently attribute costs to individual workloads and track spending using familiar AWS tools. By implementing the tagging strategy and patterns outlined in this post, teams can ensure accountability across applications as their workloads continue to grow.

For more information, check out the Amazon Bedrock Projects documentation and the AWS Cost Management User Guide.

About the Authors:

• Ba’Carri Johnson: Sr. Technical Product Manager on the Amazon Bedrock team, leverages AI infrastructure knowledge to drive innovative product solutions.
• Vadim Omeltchenko: Sr. Solutions Architect passionate about enabling AWS customers to innovate in the cloud.
• Ajit Mahareddy: Experienced leader in product management with a focus on AI/ML technologies.
• Sofian Hamiti: Experienced technology leader in AI, dedicated to cultivating talent and driving impactful customer outcomes.

By following this guide, you can take charge of your Amazon Bedrock costs, ensuring optimization and responsible scaling throughout your organization.