https://github.com/impesud/ai-finops-platform
AI FinOps is an AI-powered platform for cloud cost optimization and forecasting. Built with FastAPI, Python, and modern MLOps tools, it allows teams to track multi-cloud usage, detect anomalies, and predict future expenses using real-time data and machine learning.
https://github.com/impesud/ai-finops-platform
aws docker fastapi jupyter mlflow python react scikit-learn statsmodels tailwindcss terraform xgboost
Last synced: 2 months ago
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AI FinOps is an AI-powered platform for cloud cost optimization and forecasting. Built with FastAPI, Python, and modern MLOps tools, it allows teams to track multi-cloud usage, detect anomalies, and predict future expenses using real-time data and machine learning.
- Host: GitHub
- URL: https://github.com/impesud/ai-finops-platform
- Owner: Impesud
- Created: 2025-06-11T18:43:01.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-15T18:05:16.000Z (about 1 year ago)
- Last Synced: 2025-06-15T19:02:25.158Z (about 1 year ago)
- Topics: aws, docker, fastapi, jupyter, mlflow, python, react, scikit-learn, statsmodels, tailwindcss, terraform, xgboost
- Language: Jupyter Notebook
- Homepage:
- Size: 16.6 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# AI FinOps Platform
## 🚀 Tech Stack Badges
---
## 📖 Overview
**AI FinOps Platform** is an AI-powered platform for cloud cost optimization
and forecasting. Built with FastAPI, Python, and modern MLOps tools, it allows
teams to track multi-cloud usage (AWS, Azure, GPC), detect anomalies, and predict
future expenses using real-time data and machine learning.
Key features:
- Unified ingestion pipelines for each provider
- Powerful REST API with FastAPI & OpenAPI docs
- Interactive React dashboard (Next.js + TailwindCSS)
- ML-driven forecasting & anomaly detection
- Full IaC deployment via Terraform & Helm
### Recent UI Updates
---
## 🧱 Tech Stack
### Infrastructure & DevOps
- Terraform (infrastructure provisioning)
- Kubernetes + Helm (orchestration)
- Docker (containerization)
- GitHub Actions (CI/CD)
- Prometheus + Grafana (monitoring)
- Cloud Billing APIs (AWS, Azure, GCP)
### Backend
- Python (FastAPI)
- PostgreSQL (relational DB)
- InfluxDB (time series data)
### AI/ML
- Forecasting: XGBoost, statsmodels
- Clustering: KMeans
- Anomaly Detection: Isolation Forest, Autoencoders
- Recommendation: Reinforcement Learning models
### Frontend
- React + TailwindCSS (Next.js)
- Data visualization with Recharts / Chart.js
---
## 🎯 Objectives
- Optimize cloud resource usage and cost efficiency
- Predict monthly spending using machine learning
- Detect anomalous cost spikes and resource misusage
- Provide actionable AI-based cost-saving recommendations
- Offer a user-friendly dashboard for Finance & Tech teams
---
## 🆕 Changelog
### July 2025 Updates
- **Enhanced API docs** with detailed parameter descriptions & examples
- **Ingestion**: Added per-provider scripts, unified CSV loader and header-only fallback
- **Makefile**: New targets for `ingest-api`, `fetch-aws`, `fetch-azure`, `fetch-gcp`
- **Infrastructure**: Updated Terraform modules partially.
- **Security**: Enforced CORS policies and OAuth2/JWT authentication on backend
- **User Manual**: Expanded with Docker Compose, Jupyter, and CLI workflows
### June 2025 Updates
- Added `provider` field to all cost endpoints and UI filters
- Switched to stacked bar & multi-series line charts for richer insights
- Introduced infinite scroll and deduplication in cost tables
- Proxying `/docs` & `/redoc` through Next.js for consolidated UX
- Updated complete Helm modules for deployment in AWS.
---
## 🚀 Quick Start
```bash
# 1. Clone repo
git clone https://github.com/Impesud/ai-finops-platform.git
cd ai-finops-platform
# 2. Setup venv & install
make init
# 3. Ingest sample data
make fetch-aws
make fetch-azure
make fetch-gcp
# or via API
make ingest-api
# 4. Run app
make dev
# 5. Explore
- Dashboard: http://localhost:3000
- Swagger: http://localhost:3000/docs
- ReDoc: http://localhost:3000/redoc
# 6. Deploy Pipeline on AWS (Helm + EKS)
# To provision the full environment
# (EKS cluster, IAM, ALB controller, Helm setup, etc.)
make full-setup
# To deploy or update the platform on an existing cluster
# (after building & pushing images)
make deploy
```
---
## 📂 Repository Structure
```bash
app/ # FastAPI backend
frontend/ # Next.js dashboard
services/ # ETL & ingestion modules
scripts/ # Ingestion & deployment scripts
notebooks/ # ML notebooks
infra/ # Terraform & Helm
app/data/ # Generated CSVs
docs/ # Documentation & images
tests/ # Pytest suites
Makefile # Task automation
README.md # Project overview
```
---
**Maintainer:** Erick Jara — CTO & AI/Data Engineer\
📧 [erick.jara@hotmail.it](mailto\:erick.jara@hotmail.it) | 🌐 GitHub: [Impesud](https://github.com/Impesud)