☁️ CloudSpend — Cloud Cost Optimizer (AWS/Azure + AI + RAG + Docker)

Cloud Cost Monitoring | AI Recommendations | Anomaly Detection | RAG Assistant

CloudSpend is an AI-powered FinOps + Cloud Cost Optimization Platform that helps organizations track, analyze, and reduce expenses across AWS/Azure cloud infrastructure.

The system detects anomalies in billing, recommends cost savings (rightsizing, idle instance shutdowns), and responds to queries using an integrated RAG-based assistant.

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🚀 Features Feature Description 📊 Cost Dashboard Visualizes AWS/Azure spend history 🔥 AI Anomaly Detection Detects unusual spikes in billing 💰 Smart Recommendations Idle resource & rightsizing suggestions 🤖 RAG Chat Assistant Ask queries like "Why did EC2 cost rise?" 🧠 ML Microservice FastAPI-based inference & anomaly logic 🐳 Dockerized Deployment Run everything using docker-compose up --build 🧩 Modular Backend Node.js + Express micro-service architecture

Project Architecture

CloudSpend/ │── backend/ # Node.js + Express API Gateway │ ├── src/ │ │ ├── routes/ # REST API endpoints │ │ ├── controllers/ # Request handlers │ │ ├── services/ # Cost + Anomaly + RAG logic │ │ ├── models/ # MongoDB Schemas │ │ ├── config/ # DB connections + cloud setup │ │ └── server.js # Core server entry │ ├── package.json │ ├── Dockerfile │ │── frontend/ # React-based cost dashboard UI │ ├── src/ │ │ ├── components/ # Tables, Charts, Cards, UI blocks │ │ ├── pages/ # Dashboard, Recommendations, RAG Chat │ │ ├── services/ # Axios API clients │ │ └── App.jsx # UI entry │ ├── vite.config.js │ ├── package.json │ ├── Dockerfile │ │── ml-engine/ # AI + ML Microservice │ ├── api.py # FastAPI routes │ ├── scripts/ # anomaly + forecasting logic (future) │ ├── requirements.txt │ ├── Dockerfile │ │── docker-compose.yml # Run entire system → 1 command │── README.md # Project documentation

🔄 Workflow

Frontend UI (React) ↓ Backend API (Node/Express) ↓ MongoDB (Cost + Logs + Recommendations) ↓ ML Engine (FastAPI - Anomaly + Forecasting) ↓ AI RAG Assistant (Embeddings + Docs)

✔ Frontend calls REST API
✔ Backend returns cost anomalies & savings suggestions
✔ ML engine generates cost anomalies
✔ RAG answers cloud questions

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Deployment (One Command)

Make sure Docker Desktop is running

docker-compose up --build

Service URL UI Dashboard http://localhost:3000

Backend API http://localhost:5000

ML Engine http://localhost:8000

MongoDB internal container

How to Run (Step-By-Step)

git clone https://github.com/<your-username>/CloudSpend.git
cd CloudSpend

Start Everything

docker-compose up --build

Open Browser

http://localhost:3000

API Endpoints Endpoint Description GET /api/cost/summary Get daily billing usage GET /api/anomaly Fetch anomaly logs POST /api/anomaly/run Trigger anomaly detection GET /api/recommendations Cost-saving recommendations POST /api/rag/ask Ask AI cost related query