AWS prices estimation utilizing Amazon Q CLI and AWS Value Evaluation MCP

Managing and optimizing AWS infrastructure prices is a crucial problem for organizations of all sizes. Conventional value evaluation approaches typically contain the next:

• Complicated spreadsheets – Creating and sustaining detailed value fashions, which requires vital effort
• A number of instruments – Switching between the AWS Pricing Calculator, AWS Value Explorer, and third-party instruments
• Specialised data – Understanding the nuances of AWS pricing throughout companies and AWS Areas
• Time-consuming evaluation – Manually evaluating totally different deployment choices and eventualities
• Delayed optimization – Value insights typically come too late to tell architectural selections

Amazon Q Developer CLI with the Model Context Protocol (MCP) gives a revolutionary method to AWS value evaluation. By utilizing generative AI by way of pure language prompts, groups can now generate detailed value estimates, comparisons, and optimization suggestions in minutes slightly than hours, whereas offering accuracy by way of integration with official AWS pricing knowledge.

On this submit, we discover the best way to use Amazon Q CLI with the AWS Cost Analysis MCP server to carry out refined value evaluation that follows AWS finest practices. We focus on fundamental setup and superior strategies, with detailed examples and step-by-step directions.

Resolution overview

Amazon Q Developer CLI is a command line interface that brings the generative AI capabilities of Amazon Q on to your terminal. Builders can work together with Amazon Q by way of pure language prompts, making it a useful instrument for varied improvement duties.
Developed by Anthropic as an open protocol, the Model Context Protocol (MCP) offers a standardized technique to join AI fashions to totally different knowledge sources or instruments. Utilizing a client-server structure (as illustrated within the following diagram), the MCP helps builders expose their knowledge by way of light-weight MCP servers whereas constructing AI purposes as MCP purchasers that join to those servers.

The MCP makes use of a client-server structure containing the next parts:

• Host – A program or AI instrument that requires entry to knowledge by way of the MCP protocol, comparable to Anthropic’s Claude Desktop, an built-in improvement atmosphere (IDE), or different AI purposes
• Shopper – Protocol purchasers that preserve one-to-one connections with servers
• Server – Light-weight applications that expose capabilities by way of standardized MCP or act as instruments
• Information sources – Native knowledge sources comparable to databases and file programs, or exterior programs obtainable over the web by way of APIs (internet APIs) that MCP servers can join with

As introduced in April 2025, the MCP permits Amazon Q Developer to attach with specialised servers that stretch its capabilities past what’s potential with the bottom mannequin alone. MCP servers act as plugins for Amazon Q, offering domain-specific data and performance. The AWS Value Evaluation MCP server particularly permits Amazon Q to generate detailed value estimates, reviews, and optimization suggestions utilizing real-time AWS pricing knowledge.

Stipulations

To implement this answer, it’s essential to have an AWS account with applicable permissions and observe the steps under.

Arrange your atmosphere

Earlier than you can begin analyzing prices, it’s worthwhile to arrange your atmosphere with Amazon Q CLI and the AWS Cost Analysis MCP server. This part offers detailed directions for set up and configuration.

Set up Amazon Q Developer CLI

Amazon Q Developer CLI is offered as a standalone set up. Full the next steps to put in it:

1. Obtain and set up Amazon Q Developer CLI. For directions, see Utilizing Amazon Q Developer on the command line.
2. Confirm the set up by working the next command: q --version
   You need to see output much like the next: Amazon Q Developer CLI model 1.x.x
3. Configure Amazon Q CLI together with your AWS credentials: q login
4. Select the login methodology appropriate for you:

Arrange MCP servers

Earlier than utilizing the AWS Value Evaluation MCP server with Amazon Q CLI, it’s essential to set up a number of instruments and configure your atmosphere. The next steps information you thru putting in the mandatory instruments and establishing the MCP server configuration:

1. Set up Panoc utilizing the next command (you’ll be able to install with brew as well), changing the output to PDF: pip set up pandoc
2. Set up uv with the next command: pip set up uv
3. Set up Python 3.10 or newer: uv python set up 3.10
4. Add the servers to your ~/.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 mechanically discovers MCP servers within the ~/.aws/amazonq/mcp.json file.

Understanding MCP server instruments

The AWS Value Evaluation MCP server offers a number of highly effective instruments:

• get_pricing_from_web – Retrieves pricing data from AWS pricing webpages
• get_pricing_from_api – Fetches pricing knowledge from the AWS Value Listing API
• generate_cost_report – Creates detailed value evaluation reviews with breakdowns and visualizations
• analyze_cdk_project – Analyzes AWS Cloud Growth Package (AWS CDK) tasks to determine companies used and estimate prices
• analyze_terraform_project – Analyzes Terraform tasks to determine companies used and estimate prices
• get_bedrock_patterns – Retrieves structure patterns for Amazon Bedrock with value issues

These instruments work collectively that can assist you create correct value estimates that observe AWS finest practices.

Take a look at your setup

Let’s confirm that every thing is working accurately by producing a easy value evaluation:

1. Begin the Amazon Q CLI chat interface and confirm the output reveals the MCP server being loaded and initialized: q chat
2. Within the chat interface, enter the next immediate:Please create a price evaluation for a easy internet utility with an Utility Load Balancer, two t3.medium EC2 situations, and an RDS db.t3.medium MySQL database. Assume 730 hours of utilization per thirty days and average site visitors of about 100 GB knowledge switch. Convert estimation to a PDF format.
3. Amazon Q CLI will ask for permission to belief the instrument that’s getting used; enter t to belief it. Amazon Q ought to generate and show an in depth value evaluation. Your output ought to seem like the next screenshot.
   
   In case you see the price evaluation report, your atmosphere is ready up accurately. In case you encounter points, confirm that Amazon Q CLI can entry the MCP servers by ensuring you put in set up the mandatory instruments and the servers are within the ~/.aws/amazonq/mcp.json file.

Configuration choices

The AWS Value Evaluation MCP server helps a number of configuration choices to customise your value evaluation expertise:

• Output format – Select between markdown, CSV codecs, or PDF (which we put in the bundle for) for value reviews
• Pricing model – Specify on-demand, reserved situations, or financial savings plans
• Assumptions and exclusions – Customise the assumptions and exclusions in your value evaluation
• Detailed value knowledge – Present particular utilization patterns for extra correct estimates

Now that our surroundings is ready up, let’s create extra value analyses.

Create AWS Value Evaluation reviews

On this part, we stroll by way of the method of making AWS value evaluation reviews utilizing Amazon Q CLI with the AWS Value Evaluation MCP server.

While you present a immediate to Amazon Q CLI, the AWS Value Evaluation MCP server completes the next steps:

1. Interpret your necessities.
2. Retrieve pricing knowledge from AWS pricing sources.
3. Generate an in depth value evaluation report.
4. Present optimization suggestions.

This course of occurs seamlessly, so you’ll be able to give attention to describing what you need slightly than the best way to create it.

AWS Value Evaluation reviews sometimes embody the next data:

• Service prices – Breakdown of prices by AWS service
• Unit pricing – Detailed unit pricing data
• Utilization portions – Estimated utilization portions for every service
• Calculation particulars – Step-by-step calculations displaying how prices had been derived
• Assumptions – Clearly said assumptions used within the evaluation
• Exclusions – Prices that weren’t included within the evaluation
• Suggestions – Value optimization options

Instance 1: Analyze a serverless utility

Let’s create a price evaluation for a easy serverless utility. Use the next immediate:

Create a price evaluation for a serverless utility utilizing API Gateway, Lambda, and DynamoDB. Assume 1 million API calls per thirty days, common Lambda execution time of 200ms with 512MB reminiscence, and 10GB of DynamoDB storage with 5 million learn requests and 1 million write requests per thirty days. Convert estimation to a PDF format.

Upon getting into your immediate, Amazon Q CLI will retrieve pricing knowledge utilizing the get_pricing_from_web or get_pricing_from_api instruments, and can use generate_cost_report with awslabscost_analysis_mcp_server.

You need to obtain an output giving an in depth value breakdown based mostly on the immediate together with optimization suggestions.

The generated value evaluation reveals the next data:

• Amazon API Gateway prices for 1 million requests
• AWS Lambda prices for compute time and requests
• Amazon DynamoDB prices for storage, learn, and write capability
• Complete month-to-month value estimate
• Value optimization suggestions

Instance 2: Analyze multi-tier architectures

Multi-tier architectures separate purposes into practical layers (presentation, utility, and knowledge) to enhance scalability and safety. This instance analyzes prices for implementing such an structure on AWS with parts for every tier:

Create a price evaluation for a three-tier internet utility with a presentation tier (ALB and CloudFront), utility tier (ECS with Fargate), and knowledge tier (Aurora PostgreSQL). Embrace prices for two Fargate duties with 1 vCPU and 2GB reminiscence every, an Aurora db.r5.giant occasion with 100GB storage, an Utility Load Balancer with 10

This time, we’re formatting it into each PDF and DOCX.

The price evaluation reveals the next data:

Instance 3: Examine deployment choices

When deploying containers on AWS, selecting between Amazon ECS with Amazon Elastic Compute Cloud (Amazon EC2) or Fargate includes totally different value buildings and administration overhead. This instance compares these choices to find out essentially the most cost-effective answer for a selected workload:

Examine the prices between working a containerized utility on ECS with EC2 launch kind versus Fargate launch kind. Assume 4 containers every needing 1 vCPU and 2GB reminiscence, working 24/7 for a month. For EC2, use t3.medium situations. Present a suggestion on which choice is more cost effective for this workload. Convert estimation to a HTML webpage.

This time, we’re formatting it right into a HTML webpage.

The price comparability consists of the next data:

• Amazon ECS with Amazon EC2 launch kind prices
• Amazon ECS with Fargate launch kind prices
• Detailed breakdown of every choice’s pricing parts
• Facet-by-side comparability of complete prices
• Suggestions for essentially the most cost-effective choice
• Concerns for when every choice is perhaps most popular

Actual-world examples

Let’s discover some real-world structure patterns and the best way to analyze their prices utilizing Amazon Q CLI with the AWS Value Evaluation MCP server.

Ecommerce platform

Ecommerce platforms require scalable, resilient architectures with cautious value administration. These programs sometimes use microservices to deal with varied features independently whereas sustaining excessive availability. This instance analyzes prices for an entire ecommerce answer with a number of parts serving average site visitors ranges:

Create a price evaluation for an e-commerce platform with microservices structure. Embrace parts for product catalog, buying cart, checkout, fee processing, order administration, and consumer authentication. Assume average site visitors of 500,000 month-to-month energetic customers, 2 million web page views per day, and 50,000 orders per thirty days. Make sure the evaluation follows AWS finest practices for value optimization. Convert estimation to a PDF format.

The price evaluation consists of the next key parts:

Information analytics platform

Fashionable knowledge analytics platforms must effectively ingest, retailer, course of, and visualize giant volumes of information whereas managing prices successfully. This instance examines the AWS companies and prices concerned in constructing a whole analytics pipeline dealing with vital day by day knowledge volumes with a number of consumer entry necessities:

Create a price evaluation for a knowledge analytics platform processing 500GB of latest knowledge day by day. Embrace parts for knowledge ingestion (Kinesis), storage (S3), processing (EMR), and visualization (QuickSight). Assume 50 customers accessing dashboards day by day and knowledge retention of 90 days. Make sure the evaluation follows AWS finest practices for value optimization and consists of suggestions for cost-effective scaling. Convert estimation to a HTML webpage.

The price evaluation consists of the next key parts:

• Information ingestion prices (Amazon Kinesis Information Streams and Amazon Information Firehose)
• Storage prices (Amazon S3 with lifecycle insurance policies)
• Processing prices (Amazon EMR cluster)
• Visualization prices (Amazon QuickSight)
• Information switch prices between companies
• Complete month-to-month value estimate
• Value optimization suggestions for every element
• Scaling issues and their value implications

Clear up

In case you not want to make use of the AWS Value Evaluation MCP server with Amazon Q CLI, you’ll be able to take away it out of your configuration:

1. Open your ~/.aws/amazonq/mcp.json file.
2. Take away or remark out the “awslabs.cost-analysis-mcp-server” entry.
3. Save the file.

This may stop the server from being loaded if you begin Amazon Q CLI sooner or later.

Conclusion

On this submit, we explored the best way to use Amazon Q CLI with the AWS Value Evaluation MCP server to create detailed value analyses that use correct AWS pricing knowledge. This method gives vital benefits over conventional value estimation strategies:

• Time financial savings – Generate complicated value analyses in minutes as a substitute of hours
• Accuracy – Be certain that estimates use the newest AWS pricing data
• Complete – Embrace related value parts and issues
• Actionable – Obtain particular optimization suggestions
• Iterative – Shortly examine totally different eventualities by way of easy prompts
• Validation – Verify estimates in opposition to official AWS pricing

As you proceed exploring AWS value evaluation, we encourage you to deepen your data by studying extra concerning the Model Context Protocol (MCP) to grasp the way it enhances the capabilities of Amazon Q. For hands-on value estimation, the AWS Pricing Calculator gives an interactive expertise to mannequin and examine totally different deployment eventualities. To verify your architectures observe monetary finest practices, the AWS Effectively-Architected Framework Value Optimization Pillar offers complete steering on constructing cost-efficient programs. And to remain on the leading edge of those instruments, regulate updates to the official AWS MCP servers—they’re consistently evolving with new options to make your value evaluation expertise much more highly effective and correct.

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Concerning the Authors

Joel Asante, an Austin-based Options Architect at Amazon Net Companies (AWS), works with GovTech (Authorities Know-how) prospects. With a robust background in knowledge science and utility improvement, he brings deep technical experience to creating safe and scalable cloud architectures for his prospects. Joel is enthusiastic about knowledge analytics, machine studying, and robotics, leveraging his improvement expertise to design revolutionary options that meet complicated authorities necessities. He holds 13 AWS certifications and enjoys household time, health, and cheering for the Kansas Metropolis Chiefs and Los Angeles Lakers in his spare time.

Dunieski Otano is a Options Architect at Amazon Net Companies based mostly out of Miami, Florida. He works with World Vast Public Sector MNO (Multi-Worldwide Organizations) prospects. His ardour is Safety, Machine Studying and Synthetic Intelligence, and Serverless. He works together with his prospects to assist them construct and deploy excessive obtainable, scalable, and safe options. Dunieski holds 14 AWS certifications and is an AWS Golden Jacket recipient. In his free time, you will discover him spending time together with his household and canine, watching an awesome film, coding, or flying his drone.

Varun Jasti is a Options Architect at Amazon Net Companies, working with AWS Companions to design and scale synthetic intelligence options for public sector use circumstances to fulfill compliance requirements. With a background in Laptop Science, his work covers broad vary of ML use circumstances primarily specializing in LLM coaching/inferencing and pc imaginative and prescient. In his spare time, he loves enjoying tennis and swimming.