mcpinfo
Managing and optimizing AWS infrastructure costs is a critical challenge for organizations of all sizes. Traditional cost analysis approaches often involve the following:
Amazon Q Developer CLI with the Model Context Protocol (MCP) offers a revolutionary approach to AWS cost analysis. By using generative AI through natural language prompts, teams can now generate detailed cost estimates, comparisons, and optimization recommendations in minutes rather than hours, while providing accuracy through integration with official AWS pricing data.
In this post, we explore how to use Amazon Q CLI with the AWS Cost Analysis MCP server to perform sophisticated cost analysis that follows AWS best practices. We discuss basic setup and advanced techniques, with detailed examples and step-by-step instructions.
Amazon Q Developer CLI is a command line interface that brings the generative AI capabilities of Amazon Q directly to your terminal. Developers can interact with Amazon Q through natural language prompts, making it an invaluable tool for various development tasks.
Developed by Anthropic as an open protocol, the Model Context Protocol (MCP) provides a standardized way to connect AI models to different data sources or tools. Using a client-server architecture (as illustrated in the following diagram), the MCP helps developers expose their data through lightweight MCP servers while building AI applications as MCP clients that connect to these servers.
The MCP uses a client-server architecture containing the following components:
As announced in April 2025, the MCP enables Amazon Q Developer to connect with specialized servers that extend its capabilities beyond what’s possible with the base model alone. MCP servers act as plugins for Amazon Q, providing domain-specific knowledge and functionality. The AWS Cost Analysis MCP server specifically enables Amazon Q to generate detailed cost estimates, reports, and optimization recommendations using real-time AWS pricing data.
To implement this solution, you must have an AWS account with appropriate permissions and follow the steps below.
Before you can start analyzing costs, you need to set up your environment with Amazon Q CLI and the AWS Cost Analysis MCP server. This section provides detailed instructions for installation and configuration.
Amazon Q Developer CLI is available as a standalone installation. Complete the following steps to install it:
q --versionq loginBefore using the AWS Cost Analysis MCP server with Amazon Q CLI, you must install several tools and configure your environment. The following steps guide you through installing the necessary tools and setting up the MCP server configuration:
pip install pandocpip install uvuv python install 3.10~/.aws/amazonq/mcp.json file: {
"mcpServers": {
"awslabs.cost-analysis-mcp-server": {
"command": "uvx",
"args": ["awslabs.cost-analysis-mcp-server"],
"env": {
"FASTMCP_LOG_LEVEL": "ERROR"
},
"autoApprove": [],
"disabled": false
}
}
}
Now, Amazon Q CLI automatically discovers MCP servers in the ~/.aws/amazonq/mcp.json file.
The AWS Cost Analysis MCP server provides several powerful tools:
These tools work together to help you create accurate cost estimates that follow AWS best practices.
Let’s verify that everything is working correctly by generating a simple cost analysis:
q chatPlease create a cost analysis for a simple web application with an Application Load Balancer, two t3.medium EC2 instances, and an RDS db.t3.medium MySQL database. Assume 730 hours of usage per month and moderate traffic of about 100 GB data transfer. Convert estimation to a PDF format.t to trust it. Amazon Q should generate and display a detailed cost analysis. Your output should look like the following screenshot.~/.aws/amazonq/mcp.json file.The AWS Cost Analysis MCP server supports several configuration options to customize your cost analysis experience:
Now that our environment is set up, let’s create more cost analyses.
In this section, we walk through the process of creating AWS cost analysis reports using Amazon Q CLI with the AWS Cost Analysis MCP server.
When you provide a prompt to Amazon Q CLI, the AWS Cost Analysis MCP server completes the following steps:
This process happens seamlessly, so you can focus on describing what you want rather than how to create it.
AWS Cost Analysis reports typically include the following information:
Let’s create a cost analysis for a simple serverless application. Use the following prompt:
Create a cost analysis for a serverless application using API Gateway, Lambda, and DynamoDB. Assume 1 million API calls per month, average Lambda execution time of 200ms with 512MB memory, and 10GB of DynamoDB storage with 5 million read requests and 1 million write requests per month. Convert estimation to a PDF format.
Upon entering your prompt, Amazon Q CLI will retrieve pricing data using the get_pricing_from_web or get_pricing_from_api tools, and will use generate_cost_report with awslabscost_analysis_mcp_server.
You should receive an output giving a detailed cost breakdown based on the prompt along with optimization recommendations.
The generated cost analysis shows the following information:
Multi-tier architectures separate applications into functional layers (presentation, application, and data) to improve scalability and security. This example analyzes costs for implementing such an architecture on AWS with components for each tier:
Create a cost analysis for a three-tier web application with a presentation tier (ALB and CloudFront), application tier (ECS with Fargate), and data tier (Aurora PostgreSQL). Include costs for 2 Fargate tasks with 1 vCPU and 2GB memory each, an Aurora db.r5.large instance with 100GB storage, an Application Load Balancer with 10
This time, we are formatting it into both PDF and DOCX.
The cost analysis shows the following information:
When deploying containers on AWS, choosing between Amazon ECS with Amazon Elastic Compute Cloud (Amazon EC2) or Fargate involves different cost structures and management overhead. This example compares these options to determine the most cost-effective solution for a specific workload:
Compare the costs between running a containerized application on ECS with EC2 launch type versus Fargate launch type. Assume 4 containers each needing 1 vCPU and 2GB memory, running 24/7 for a month. For EC2, use t3.medium instances. Provide a recommendation on which option is more cost-effective for this workload. Convert estimation to a HTML webpage.
This time, we are formatting it into a HTML webpage.
The cost comparison includes the following information:
Let’s explore some real-world architecture patterns and how to analyze their costs using Amazon Q CLI with the AWS Cost Analysis MCP server.
Ecommerce platforms require scalable, resilient architectures with careful cost management. These systems typically use microservices to handle various functions independently while maintaining high availability. This example analyzes costs for a complete ecommerce solution with multiple components serving moderate traffic levels:
Create a cost analysis for an e-commerce platform with microservices architecture. Include components for product catalog, shopping cart, checkout, payment processing, order management, and user authentication. Assume moderate traffic of 500,000 monthly active users, 2 million page views per day, and 50,000 orders per month. Ensure the analysis follows AWS best practices for cost optimization. Convert estimation to a PDF format.
The cost analysis includes the following key components:
Modern data analytics platforms need to efficiently ingest, store, process, and visualize large volumes of data while managing costs effectively. This example examines the AWS services and costs involved in building a complete analytics pipeline handling significant daily data volumes with multiple user access requirements:
Create a cost analysis for a data analytics platform processing 500GB of new data daily. Include components for data ingestion (Kinesis), storage (S3), processing (EMR), and visualization (QuickSight). Assume 50 users accessing dashboards daily and data retention of 90 days. Ensure the analysis follows AWS best practices for cost optimization and includes recommendations for cost-effective scaling. Convert estimation to a HTML webpage.
The cost analysis includes the following key components:
If you no longer need to use the AWS Cost Analysis MCP server with Amazon Q CLI, you can remove it from your configuration:
~/.aws/amazonq/mcp.json file.awslabs.cost-analysis-mcp-server” entry.This will prevent the server from being loaded when you start Amazon Q CLI in the future.
In this post, we explored how to use Amazon Q CLI with the AWS Cost Analysis MCP server to create detailed cost analyses that use accurate AWS pricing data. This approach offers significant advantages over traditional cost estimation methods:
As you continue exploring AWS cost analysis, we encourage you to deepen your knowledge by learning more about the Model Context Protocol (MCP) to understand how it enhances the capabilities of Amazon Q. For hands-on cost estimation, the AWS Pricing Calculator offers an interactive experience to model and compare different deployment scenarios. To make sure your architectures follow financial best practices, the AWS Well-Architected Framework Cost Optimization Pillar provides comprehensive guidance on building cost-efficient systems. And to stay at the cutting edge of these tools, keep an eye on updates to the official AWS MCP servers—they’re constantly evolving with new features to make your cost analysis experience even more powerful and accurate.
submitted by /u/OrangeFluffyCatLover [link] [comments]
As language models support larger and larger context sizes, evaluating their ability to make effective…
San Francisco-based CTGT, a startup focused on making AI more trustworthy through feature-level model customization,…
Whether you need a tent, sleeping pad, rain jacket, or new pack, REI’s Independence Day…
You can find the workflow by scrolling down on this page: https://comfyanonymous.github.io/ComfyUI_examples/flux/ submitted by /u/comfyanonymous…
Machine learning practitioners spend countless hours on repetitive tasks: monitoring model performance, retraining pipelines, data…