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Real-Time AWS Cost Estimation Using the Pricing MCP Server and Amazon Q CLI

#aws #ai #mcp #cloud

👋 Hey there, tech enthusiasts!

I'm Sarvar, a Cloud Architect with a passion for transforming complex technological challenges into elegant solutions. With extensive experience spanning Cloud Operations (AWS & Azure), Data Operations, Analytics, DevOps, and Generative AI, I've had the privilege of architecting solutions for global enterprises that drive real business impact. Through this article series, I'm excited to share practical insights, best practices, and hands-on experiences from my journey in the tech world. Whether you're a seasoned professional or just starting out, I aim to break down complex concepts into digestible pieces that you can apply in your projects.

Let's dive in and explore the fascinating world of cloud technology together! 🚀

Calculating cloud resource costs has always been one of the biggest challenges for organizations. As businesses adopt AWS at scale, the complexity of pricing models, dynamic usage, and multi-account structures makes it difficult to predict and optimize costs effectively. Finance and engineering teams often spend hours digging through cost explorer, exporting CSVs, and analyzing spreadsheets, only to get rough estimates that may already be outdated.

Imagine this: you’re planning to launch a global app. Before you even deploy, you need to know will the AWS cost be $10K or $100K per month? Traditionally, this meant endless spreadsheets, manual calculations, and guesswork. But now, with the AWS Pricing MCP Server, you can simply ask in plain English and instantly get a reliable, real-time answer.

This is where the AWS Pricing MCP Server completely changes the game. It allows developers, architects, and FinOps teams to interact with AWS pricing data in a conversational, AI-powered way. When combined with Amazon Q CLI and the broader Model Context Protocol (MCP) ecosystem, it enables cost exploration, estimation, and analyze directly from your terminal or development workflow.

In this article, I will walk you through what the AWS Pricing MCP Server is, how it works, and how it can be applied in real-world enterprise scenarios.

What is the AWS Pricing MCP Server?

The AWS Pricing MCP Server is part of the MCP ecosystem developed by AWS Labs. MCP (Model Context Protocol) is an open standard that allows different tools, services, and AI assistants to communicate in a structured way.

The Pricing MCP Server connects directly to the AWS Pricing API and exposes endpoints that make it possible for AI-powered tools like Amazon Q to fetch real-time cost and pricing information for AWS services. Instead of manually searching through the AWS Pricing Calculator or console, you can now ask natural language questions such as:

  • “How much does it cost to run an m5.large EC2 instance in Mumbai for 1 month?”
  • “What is the price difference between S3 Standard and S3 Intelligent-Tiering in Singapore?”
  • “Estimate the monthly cost of running 3 EKS clusters with 10 worker nodes each in us-east-1.”

The MCP Server retrieves pricing directly from AWS and delivers structured results that AI assistants can process, analyze, and explain in human-friendly language.

Why is it Important?

Currently, I am working as a Cloud Architect for one of the largest clients in the oil and gas sector. This organization operates with more than 50 AWS accounts distributed across multiple business units, each supporting unique workloads. Traditionally, whenever a business unit planned to provision new infrastructure, the process required preparing a detailed cost estimation document. This exercise often took an entire day or sometimes longer since it involved manually calculating resources, applying pricing models, and validating assumptions.

With the introduction of the AWS Pricing MCP Server, this process has been transformed. What previously consumed hours of manual effort can now be completed in less than an hour, with far greater accuracy. All we need to do is provide the infrastructure requirements in plain English, and the system instantly generates precise cost calculations. This eliminates the need for repetitive manual work, accelerates planning, and ensures cost transparency from the very beginning. Later in this article, we will demonstrate exactly how Amazon Q and the AWS Pricing MCP Server work together through sample prompts and responses.

Cloud pricing is inherently complex. For AWS alone, the challenges include:

  • Over 200 services, each with its own billing model
  • Regional pricing variations that affect cost forecasts
  • Multiple commitment options such as Savings Plans and Reserved Instances
  • Pricing flexibility with On-Demand, Spot, and Reserved options

For most organizations, this complexity makes cost visibility and forecasting a significant challenge. The AWS Pricing MCP Server directly addresses these pain points by:

  1. Simplifying access to pricing – Natural language queries mean you don’t need to be a cost expert to get accurate results.
  2. Providing real-time accuracy – All calculations are powered by live AWS APIs, ensuring the latest pricing data is used.
  3. Seamlessly integrating into workflows – Developers and architects can evaluate costs directly within their CLI or IDE, during design or deployment phases.

This combination of Amazon Q and the AWS Pricing MCP Server goes beyond cost estimation it enables organizations to practice proactive cost optimization at scale, ensuring every architectural decision is informed by accurate financial insights.

Configure Amazon Q CLI

This section covers the step-by-step configuration of Amazon Q CLI on an Ubuntu 22.04 LTS instance to ensure seamless integration and optimal performance.

Step 1: Update System Packages

It’s always good practice to update your package list before installing new software.

sudo apt update -y
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Step 2: Download the Amazon Q CLI Package

Use wget to download the latest .deb package from the official Amazon Q release server:

wget https://desktop-release.q.us-east-1.amazonaws.com/latest/amazon-q.deb
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Step 3: Install Dependencies (Optional)

Before installing the package, make sure all required dependencies are present and if you have already perform 1st update command then this step is option for you you can skip it for now.

sudo apt-get install -f
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Step 4: Install the Amazon Q CLI Package

Now install the .deb package using dpkg:

sudo dpkg -i amazon-q.deb
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Step 5: Verify Amazon Q 

q --version
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If you face any dependency issues, re-run sudo apt-get install -f to auto-fix them.

Amazon Q CLI Login with Builder ID

After successfully installing Amazon Q CLI, the next step is to authenticate. Here's how to log in using your Builder ID:

Step 1: Run the Login Command

In your terminal, enter:

q login
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You’ll see a prompt with two options. Choose:

Use for Free with Builder ID
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If you don’t have a Builder ID yet, you can create one using your email during this step.

Step 2: Confirm Authorization in Browser

Amazon Q will generate a unique confirmation link and code. You must:

  • Manually open the provided link in a browser and login with your mail id.

  • Enter the verification code when prompted.

Step 3: Allow Access

Once the code is verified, Amazon Q will ask for permission to access your Builder ID account. Click Allow.

Launch Amazon Q CLI

Start Amazon Q using the following command:

q
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Configure MCP Server for AWS Pricing MCP

This section covers how to set up an MCP (Model Context Protocol) server that allows Amazon Q to get AWS pricing access.

Step 1: Install Python 3.10

To run the MCP server locally, Amazon Q requires Python 3.10. Here's a breakdown of each command to install it properly on Ubuntu 22.04 LTS.

1. Update the package list

sudo apt update -y
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What it does:
Fetches the latest list of available packages and versions from the Ubuntu repositories. Always a good first step before installing anything new.

2. Install software-properties-common

sudo apt install -y software-properties-common
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What it does:
Installs a package that allows you to manage additional repositories (like PPAs). Required to add the Deadsnakes PPA for Python 3.10.

3. Add the Deadsnakes PPA

sudo add-apt-repository ppa:deadsnakes/ppa -y
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What it does:
Adds the Deadsnakes Personal Package Archive (PPA) to your system. This PPA maintains up-to-date versions of Python not available in the default Ubuntu repos.

4. Install Python 3.10 and related tools

sudo apt install -y python3.10 python3.10-venv python3.10-dev
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What it does:

  • python3.10: Installs the Python 3.10 interpreter
  • python3.10-venv: Enables creating virtual environments with python3.10 -m venv
  • python3.10-dev: Provides headers and development tools needed to build Python packages with native extensions

Once these steps are complete, Python 3.10 will be available on your EC2 instance.

You can verify the version using:

python3.10 --version
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Step 2: Set Up a Virtual Environment

Create a virtual environment to isolate the MCP server:

python3.10 -m venv ~/aws-diagram-mcp-env
source ~/aws-diagram-mcp-env/bin/activate
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Step 3: Install MCP Server and Dependencies

Use pip to install the required libraries:

pip install --upgrade pip
pip install uv uvenv awslabs.aws-diagram-mcp-server trio
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Step 4: Configure Amazon Q to Use the MCP Server

First, change directory to the Amazon Q configuration directory:

mkdir -p ~/.aws/amazonq
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Then create the config file at ~/.aws/amazonq/mcp.json:

{
  "mcpServers": {
      "awslabs.aws-pricing-mcp-server": {
      "command": "uvx",
      "args": [
        "awslabs.aws-pricing-mcp-server@latest"
      ],
      "env": {
        "FASTMCP_LOG_LEVEL": "ERROR",
        "AWS_PROFILE": "default",
        "AWS_REGION": "us-east-1"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}
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You can create the file using nano or vim:

nano ~/aws/amazonq/mcp.json
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Paste the above configuration and save the file.

Note: If you face any issue in MCP configuration code please follow this link.

Optional: Use Amazon Q CLI to Set Up MCP

Alternatively, Amazon Q CLI itself can help you set up the MCP server if you provide the right prompts. You can ask:

Set up a local MCP server for AWS Pricing MCP
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This approach may simplify the process by handling package installation and configuration automatically.

Next Step

After successfully completing the setup, initiate Amazon Q CLI by executing the q command. This will trigger the initialization of the MCP server, as illustrated in the image below. Here I've other MCP server.

Real-World Examples: How It Works

Let’s look at a few real-world examples:

Example 1: Lambda Function with High Invocations

A developer is designing a serverless workflow expected to handle 100 million invocations per month, each with 256 MB memory and 1 second execution time.

Query:

Estimate monthly cost of running 100M Lambda invocations with 256MB memory and 1 second duration in us-east-1
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Response:The Pricing MCP Server not only calculates Lambda execution costs, and free tier deductions, but also provides cost optimization suggestions. This ensures the developer receives both an accurate monthly estimate and actionable insights to optimize expenses before scaling the function.

Example 2: Machine Learning Training with GPU Instances

A data scientist needs to train a deep learning model using 4 p3.8xlarge instances for 72 hours.

Query:

Estimate total cost of running 4 p3.8xlarge instances for 72 hours in us-west-2
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Response: Detailed breakdown of instance pricing, and total training cost and cost optimization. The scientist compares it against alternatives like Spot pricing for savings.

Example 3: Multi-Region Disaster Recovery Setup

An enterprise wants to replicate a production workload across us-east-1 and ap-south-1, with:

  • 10 EC2 m5.large instances in each region
  • 2 RDS MySQL db.m5.large instances (Multi-AZ)
  • 5 TB S3 storage replicated cross-region

Query:

Calculate monthly cost for 10 EC2 m5.large + 2 RDS db.m5.large Multi-AZ + 5TB cross-region replicated S3 in us-east-1 and ap-south-1
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Response: The Pricing MCP Server provides region-wise and total costs, showing replication charges, storage, compute, and database costs for clear financial visibility.

Example 4: High-Availability Web Application

A startup wants to deploy a highly available web app with:

  • 4 EC2 t3.medium instances behind ALB
  • RDS PostgreSQL db.t3.medium Multi-AZ
  • 1 TB/month data transfer out to internet

Query:

Calculate monthly cost of 4 EC2 t3.medium + ALB + RDS db.t3.medium Multi-AZ + 1TB data transfer in us-east-1
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Response: Pricing MCP Server breaks down EC2, RDS, ALB, and data transfer costs. The CTO uses this for pre-launch financial planning.

Example 5: Data Lake Storage Strategy

A data architect wants to evaluate storage costs for:

  • 200 TB in S3 Standard
  • 100 TB in S3 Glacier Deep Archive
  • 10 TB retrieval per month from Glacier

Query:

Estimate cost for 200TB S3 Standard + 100TB Glacier Deep Archive + 10TB monthly retrieval in us-east-1
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Response: Shows breakdown by storage tier, retrieval costs, and monitoring charges, helping in lifecycle policy design.

Example 6: Elastic Kubernetes Service (EKS) with Spot Instances

A DevOps team runs 3 EKS clusters with:

  • Each cluster having 6 m5.large Spot nodes
  • 100 GB EBS gp3 per node

Query:

Estimate monthly cost for 3 EKS clusters with 6 m5.large Spot nodes each and 100GB gp3 storage per node in us-east-1
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Response: Pricing MCP Server shows EKS control plane charges, EC2 Spot cost, EBS storage, and compares it with On-Demand savings.

Integration with Amazon Q CLI

The real power of the AWS Pricing MCP Server comes when it is used with Amazon Q CLI.

  • Developers can ask cost-related questions directly while writing infrastructure code.
  • FinOps teams can run automated scripts to pull cost estimates into reports.
  • Enterprises can integrate cost validation into CI/CD pipelines to prevent deploying expensive resources without approvals.

For example, before deploying a CloudFormation template, Amazon Q CLI can validate the estimated monthly cost. If it exceeds a budget threshold, the pipeline can alert or stop deployment.

AWS Pricing MCP vs. Billing & Cost Management MCP

It’s important to note that there are two related MCP servers:

  • AWS Pricing MCP Server – Focuses on service pricing, regional differences, and cost estimates.
  • Billing & Cost Management MCP Server – Works with actual account billing data, usage patterns, and budgets.

Together, they provide a complete FinOps solution:

  • Pricing MCP helps plan and estimate future costs.
  • Billing MCP helps track and optimize actual costs.

The Future of Cloud Cost Management

With AI assistants like Amazon Q and MCP servers, the future of cloud cost management will move from reactive analysis to proactive optimization. Instead of analyzing bills after the month ends, enterprises will get real-time guidance on cost impact before deployment.

Imagine an AI assistant that says:

“Switching from m5.large to t3.large in your staging environment will save $200/month.”

Or:

“Your multi-region deployment will exceed your budget. Consider using CloudFront caching to reduce EC2 spend.”

This is the future the AWS Pricing MCP Server is enabling today.

Conclusion: The AWS Pricing MCP Server goes beyond price lookups it embeds cost awareness directly into development and operations. With real-time insights powered by Amazon Q CLI, it enables developers to make smarter choices, finance teams to forecast accurately, and enterprises to optimize at scale. Instead of reacting to bills later, organizations can design cost-efficient systems from the start, making the AWS Pricing MCP Server a key driver of proactive cloud cost management.

📌 Wrapping Up
Thank you for investing your time in reading this article! I hope these insights have provided you with practical value and a clearer understanding of the topic. Your engagement and learning journey matter to me.

💡 What's Next?
Stay tuned for more in-depth articles where we'll explore other exciting aspects of cloud operations, GenAI, DevOps, and data operations. Follow me for weekly content that aims to demystify complex tech concepts and provide actionable insights.

🤝 Let's Connect!
I'd love to hear your thoughts and experiences! Drop your comments below or connect with me on LinkedIn. Your feedback helps me create more valuable content for our tech community.

Happy Learning! 🚀

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