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 usually 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 information – Understanding the nuances of AWS pricing throughout providers and AWS Areas
• Time-consuming evaluation – Manually evaluating completely different deployment choices and eventualities
• Delayed optimization – Value insights usually come too late to tell architectural selections

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

On this submit, we discover methods to use Amazon Q CLI with the AWS Value Evaluation MCP server to carry out refined value evaluation that follows AWS finest practices. We talk about fundamental setup and superior methods, 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 pure language prompts, making it a useful instrument for varied improvement duties.
Developed by Anthropic as an open protocol, the Mannequin Context Protocol (MCP) gives a standardized solution to join AI fashions to completely different knowledge sources or instruments. Utilizing a client-server structure (as illustrated within the following diagram), the MCP helps builders expose their knowledge by 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 elements:

• Host – A program or AI instrument that requires entry to knowledge by the MCP protocol, similar 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 packages that expose capabilities by standardized MCP or act as instruments
• Knowledge sources – Native knowledge sources similar to databases and file methods, or exterior methods obtainable over the web by 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 information 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.

Conditions

To implement this resolution, it’s essential to have an AWS account with acceptable permissions and observe the steps beneath.

Arrange your atmosphere

Earlier than you can begin analyzing prices, you have to arrange your atmosphere with Amazon Q CLI and the AWS Value Evaluation MCP server. This part gives detailed directions for set up and configuration.

Set up Amazon Q Developer CLI

Amazon Q Developer CLI is accessible 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
   It is best to see output much like the next: Amazon Q Developer CLI model 1.x.x
3. Configure Amazon Q CLI along 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 organising the MCP server configuration:

1. Set up Panoc utilizing the next command (you possibly can set up with brew as effectively), 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 gives a number of highly effective instruments:

• get_pricing_from_web – Retrieves pricing info from AWS pricing webpages
• get_pricing_from_api – Fetches pricing knowledge from the AWS Worth 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 establish providers used and estimate prices
• analyze_terraform_project – Analyzes Terraform tasks to establish providers used and estimate prices
• get_bedrock_patterns – Retrieves structure patterns for Amazon Bedrock with value concerns

These instruments work collectively that will help you create correct value estimates that observe AWS finest practices.

Take a look at your setup

Let’s confirm that every part 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 cases, and an RDS db.t3.medium MySQL database. Assume 730 hours of utilization per thirty days and average 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 appear like the next screenshot.
   
   In the event you see the fee evaluation report, your atmosphere is about up accurately. In the event 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 package deal for) for value reviews
• Pricing model – Specify on-demand, reserved cases, 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 about up, let’s create extra value analyses.

Create AWS Value Evaluation reviews

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

Once 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 possibly can concentrate on describing what you need moderately than methods to create it.

AWS Value Evaluation reviews sometimes embody the next info:

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

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.

It is best 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 info:

• 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 useful layers (presentation, utility, and knowledge) to enhance scalability and safety. This instance analyzes prices for implementing such an structure on AWS with elements 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). Embody 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 associated fee evaluation reveals the next info:

Instance 3: Examine deployment choices

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

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

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

The associated fee comparability contains the next info:

• Amazon ECS with Amazon EC2 launch sort prices
• Amazon ECS with Fargate launch sort prices
• Detailed breakdown of every choice’s pricing elements
• Facet-by-side comparability of whole prices
• Suggestions for essentially the most cost-effective choice
• Issues for when every choice could be most well-liked

Actual-world examples

Let’s discover some real-world structure patterns and methods 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 methods sometimes use microservices to deal with varied features independently whereas sustaining excessive availability. This instance analyzes prices for a whole ecommerce resolution with a number of elements serving average visitors ranges:

Create a price evaluation for an e-commerce platform with microservices structure. Embody elements for product catalog, purchasing cart, checkout, cost processing, order administration, and consumer authentication. Assume average visitors of 500,000 month-to-month lively 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 associated fee evaluation contains the next key elements:

Knowledge analytics platform

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

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

The associated fee evaluation contains the next key elements:

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

Clear up

In the event you now not want to make use of the AWS Value Evaluation MCP server with Amazon Q CLI, you possibly can 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.

It will forestall the server from being loaded whenever you begin Amazon Q CLI sooner or later.

Conclusion

On this submit, we explored methods 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 presents vital benefits over conventional value estimation strategies:

• Time financial savings – Generate complicated value analyses in minutes as a substitute of hours
• Accuracy – Make sure that estimates use the newest AWS pricing info
• Complete – Embody related value elements and concerns
• Actionable – Obtain particular optimization suggestions
• Iterative – Shortly evaluate completely different eventualities by easy prompts
• Validation – Verify estimates in opposition to official AWS pricing

As you proceed exploring AWS value evaluation, we encourage you to deepen your information by studying extra concerning the Mannequin Context Protocol (MCP) to grasp the way it enhances the capabilities of Amazon Q. For hands-on value estimation, the AWS Pricing Calculator presents an interactive expertise to mannequin and evaluate completely different deployment eventualities. To verify your architectures observe monetary finest practices, the AWS Properly-Architected Framework Value Optimization Pillar gives complete steering on constructing cost-efficient methods. And to remain on the innovative of those instruments, keep watch over updates to the official AWS MCP servers—they’re continuously evolving with new options to make your value evaluation expertise much more highly effective and correct.

In regards to the Authors

Joel Asante, an Austin-based Options Architect at Amazon Internet Providers (AWS), works with GovTech (Authorities Expertise) prospects. With a powerful background in knowledge science and utility improvement, he brings deep technical experience to creating safe and scalable cloud architectures for his prospects. Joel is obsessed with knowledge analytics, machine studying, and robotics, leveraging his improvement expertise to design modern 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 Internet Providers 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 see that him spending time together with his household and canine, watching an amazing film, coding, or flying his drone.

Varun Jasti is a Options Architect at Amazon Internet Providers, working with AWS Companions to design and scale synthetic intelligence options for public sector use circumstances to satisfy compliance requirements. With a background in Pc 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.