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

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

Complicated spreadsheets – Creating and sustaining detailed price 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 companies and AWS Areas
Time-consuming evaluation – Manually evaluating completely different deployment choices and situations
Delayed optimization – Value insights typically come too late to tell architectural choices

Amazon Q Developer CLI with the Mannequin Context Protocol (MCP) provides a revolutionary strategy to AWS price evaluation. By utilizing generative AI by means of pure language prompts, groups can now generate detailed price estimates, comparisons, and optimization suggestions in minutes fairly than hours, whereas offering accuracy by means of integration with official AWS pricing information.

On this submit, we discover the best way to use Amazon Q CLI with the AWS Value Evaluation MCP server to carry out refined price evaluation that follows AWS greatest practices. We focus on primary 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 means of pure language prompts, making it a useful instrument for numerous growth duties.
Developed by Anthropic as an open protocol, the Mannequin Context Protocol (MCP) offers a standardized method to join AI fashions to completely different information sources or instruments. Utilizing a client-server structure (as illustrated within the following diagram), the MCP helps builders expose their information by means of light-weight MCP servers whereas constructing AI functions as MCP shoppers 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 information by means of the MCP protocol, reminiscent of Anthropic’s Claude Desktop, an built-in growth surroundings (IDE), or different AI functions
Shopper – Protocol shoppers that preserve one-to-one connections with servers
Server – Light-weight applications that expose capabilities by means of standardized MCP or act as instruments
Knowledge sources – Native information sources reminiscent of databases and file programs, or exterior programs out there over the web by means of APIs (net APIs) that MCP servers can join with

As introduced in April 2025, the MCP allows Amazon Q Developer to attach with specialised servers that reach its capabilities past what’s doable 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 allows Amazon Q to generate detailed price estimates, stories, and optimization suggestions utilizing real-time AWS pricing information.

Stipulations

To implement this answer, you could have an AWS account with acceptable permissions and observe the steps under.

Arrange your surroundings

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

Set up Amazon Q Developer CLI

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

Obtain and set up Amazon Q Developer CLI. For directions, see Utilizing Amazon Q Developer on the command line.
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
Configure Amazon Q CLI together with your AWS credentials: q login
Select the login methodology appropriate for you:

Arrange MCP servers

Earlier than utilizing the AWS Value Evaluation MCP server with Amazon Q CLI, you could set up a number of instruments and configure your surroundings. The next steps information you thru putting in the required instruments and organising the MCP server configuration:

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
Set up uv with the next command: pip set up uv
Set up Python 3.10 or newer: uv python set up 3.10

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 routinely 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 info from AWS pricing webpages
get_pricing_from_api – Fetches pricing information from the AWS Value Listing API
generate_cost_report – Creates detailed price evaluation stories with breakdowns and visualizations
analyze_cdk_project – Analyzes AWS Cloud Growth Equipment (AWS CDK) initiatives to establish companies used and estimate prices
analyze_terraform_project – Analyzes Terraform initiatives to establish companies used and estimate prices
get_bedrock_patterns – Retrieves structure patterns for Amazon Bedrock with price concerns

These instruments work collectively that can assist you create correct price estimates that observe AWS greatest practices.

Check your setup

Let’s confirm that all the things is working accurately by producing a easy price evaluation:

Begin the Amazon Q CLI chat interface and confirm the output exhibits the MCP server being loaded and initialized: q chat
Within the chat interface, enter the next immediate:Please create a price evaluation for a easy net software with an Software Load Balancer, two t3.medium EC2 situations, and an RDS db.t3.medium MySQL database. Assume 730 hours of utilization per thirty days and reasonable visitors of about 100 GB information switch. Convert estimation to a PDF format.
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 price evaluation. Your output ought to appear to be the next screenshot.

In case you see the price evaluation report, your surroundings is about 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 required 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 price evaluation expertise:

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

Now that our surroundings is about up, let’s create extra price analyses.

Create AWS Value Evaluation stories

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

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

Interpret your necessities.
Retrieve pricing information from AWS pricing sources.
Generate an in depth price evaluation report.
Present optimization suggestions.

This course of occurs seamlessly, so you possibly can deal with describing what you need fairly than the best way to create it.

AWS Value Evaluation stories usually 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 displaying 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 options

Instance 1: Analyze a serverless software

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

Create a price evaluation for a serverless software 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 coming into your immediate, Amazon Q CLI will retrieve pricing information 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 price breakdown based mostly on the immediate together with optimization suggestions.

The generated price evaluation exhibits 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 price estimate
Value optimization suggestions

Instance 2: Analyze multi-tier architectures

Multi-tier architectures separate functions into practical layers (presentation, software, and information) 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 net software with a presentation tier (ALB and CloudFront), software tier (ECS with Fargate), and information 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 Software Load Balancer with 10

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

The fee evaluation exhibits 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 entails completely different price constructions 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 software 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 advice 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 fee comparability consists of the next info:

Amazon ECS with Amazon EC2 launch kind prices
Amazon ECS with Fargate launch kind prices
Detailed breakdown of every choice’s pricing elements
Aspect-by-side comparability of whole prices
Suggestions for essentially the most cost-effective choice
Concerns for when every choice may be most well-liked

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 price administration. These programs usually use microservices to deal with numerous capabilities independently whereas sustaining excessive availability. This instance analyzes prices for an entire ecommerce answer with a number of elements serving reasonable visitors ranges:

Create a price evaluation for an e-commerce platform with microservices structure. Embody elements for product catalog, buying cart, checkout, cost processing, order administration, and person authentication. Assume reasonable 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 greatest practices for price optimization. Convert estimation to a PDF format.

The fee evaluation consists of the next key elements:

Knowledge analytics platform

Trendy information analytics platforms have to effectively ingest, retailer, course of, and visualize giant volumes of knowledge whereas managing prices successfully. This instance examines the AWS companies and prices concerned in constructing an entire analytics pipeline dealing with vital each day information volumes with a number of person entry necessities:

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

The fee evaluation consists of 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 companies
Complete month-to-month price estimate
Value optimization suggestions for every element
Scaling concerns and their price implications

Clear up

In case 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:

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

This may stop the server from being loaded once 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 price analyses that use correct AWS pricing information. This strategy provides vital benefits over conventional price estimation strategies:

Time financial savings – Generate advanced price analyses in minutes as an alternative of hours
Accuracy – Make certain estimates use the newest AWS pricing info
Complete – Embody related price elements and concerns
Actionable – Obtain particular optimization suggestions
Iterative – Rapidly evaluate completely different situations by means of easy prompts
Validation – Test estimates towards official AWS pricing

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

Concerning the Authors

Joel Asante, an Austin-based Options Architect at Amazon Internet Providers (AWS), works with GovTech (Authorities Know-how) prospects. With a robust background in information science and software growth, he brings deep technical experience to creating safe and scalable cloud architectures for his prospects. Joel is obsessed with information analytics, machine studying, and robotics, leveraging his growth expertise to design modern options that meet advanced 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 Extensive Public Sector MNO (Multi-Worldwide Organizations) prospects. His ardour is Safety, Machine Studying and Synthetic Intelligence, and Serverless. He works along with his prospects to assist them construct and deploy excessive out there, scalable, and safe options. Dunieski holds 14 AWS certifications and is an AWS Golden Jacket recipient. In his free time, you will see him spending time along with his household and canine, watching an important 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 fulfill 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 taking part in tennis and swimming.