Cloud-Cost-Intelligence

A cloud cost monitoring and anomaly detection dashboard built using AWS Cost Explorer API, FastAPI, and React.

This project provides real-time AWS billing visibility, service-level breakdown, and rule-based anomaly detection to help monitor abnormal spending patterns.

Features

• AWS Cost Explorer API integration
• Daily cost tracking
• Service-wise cost breakdown
• Rule-based anomaly detection
• Cost spike severity classification
• Interactive dashboard with charts
• Currency formatting abstraction
• Auto refresh support
• CSV export support
• JWT-based User Authentication
• Clean modular backend architecture

Architecture Overview

Frontend (React)
⬇
FastAPI Backend
⬇
AWS Cost Explorer API
⬇
Anomaly Detection Service

The backend fetches billing data from AWS Cost Explorer, processes it, detects anomalies, and serves structured data to the frontend dashboard.

Core Functionalities

1. Cost Monitoring

• Fetches daily unblended cost data
• Groups cost by AWS service
• Displays total cost trend over time
• Shows service-level cost contribution

2. Anomaly Detection

Implements rule-based anomaly detection:

• Compares latest cost against baseline average
• Flags abnormal percentage increases
• Classifies severity levels
• Highlights cost spikes in UI

3. Anomaly Detection

• Drill-down modal for service-wise daily breakdown
• Displays individual AWS service contribution
• Useful for identifying cost-heavy services

Why This Project?

Cloud cost optimization is a real-world problem in cloud engineering and DevOps environments.

This project demonstrates:

• Practical use of AWS APIs
• Backend data processing logic
• Cost analytics fundamentals
• Basic anomaly detection logic
• API design with FastAPI
• Data visualization using React

Tech Stack

Backend

• Python
• FastAPI
• PostgreSQL
• pyjwt & passlib (Authentication)
• Boto3 (AWS SDK)
• AWS Cost Explorer API

Frontend

• React
• React Router
• Recharts
• Axios

Project Structure

backend/
  app/
    main.py
    routers/
    services/
      aws_service.py
      anomaly_service.py
      explanation_service.py
    models/
    schemas/

frontend/
  src/
    App.js

Setup Instructions

1. Clone Repository

git clone <your-repo-url>
cd cloud-cost-intelligence

2. Backend Setup

Create virtual environment:

python -m venv venv
venv\Scripts\activate

Configure Environment Variables:

Create a .env file in the backend/ directory:

DATABASE_URL=postgresql://user:password@localhost/dbname
SECRET_KEY=your_super_secret_key

Install dependencies:

pip install -r requirements.txt

Configure AWS credentials:

aws configure

Run backend:

uvicorn app.main:app --reload

Backend runs at:

http://127.0.0.1:8000

3. Frontend Setup

cd frontend
npm install
npm start

Frontend runs at:

http://localhost:3000

Example Use Case

If daily AWS spending suddenly increases by 300% compared to baseline average:

• System flags anomaly
• Displays severity indicator
• Shows service responsible
• Allows drill-down analysis

Design Decisions

• Used UnblendedCost for raw cost visibility
• Grouped by SERVICE for service-level transparency
• Separated anomaly logic into dedicated service
• Centralized currency formatting for maintainability
• Modular backend structure for scalability

Limitations

• Single AWS account integration
• Rule-based anomaly detection (not statistical)
• No caching layer
• Not production deployed

Future Improvements

• Multi-account support using IAM role assumption
• Database-backed caching layer
• Statistical anomaly detection
• Role-Based Access Control (RBAC)
• Dockerized deployment
• CI/CD pipeline

Target Audience

• Cloud Engineers
• DevOps Engineers
• Platform Engineers
• Students learning AWS cost optimization

License

For educational and demonstration purposes.