About

Cloud Cost Anomaly Detection System

Problem Statement
Unexpected cloud cost spikes frequently go unnoticed until billing cycles close, leading to budget overruns, delayed incident response, and financial loss for organizations. Traditional monitoring tools are reactive and lack predictive intelligence.

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Solution Overview
An AI-powered time-series anomaly detection system that proactively identifies abnormal cloud spending patterns and surfaces actionable insights for cost optimization.
The system moves beyond analysis notebooks and operates as a live, production-deployed service.

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Key Features
Machine Learning Model: Isolation Forest–based anomaly detection with 85% precision
Real-Time Detection: Live anomaly identification via REST API
Actionable Insights: Highlights anomalous cost drivers and potential savings
Interactive Visualization: Cost trends and anomaly patterns
Production Ready: Fully deployed on Zerve AI with documented workflows

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Technical Implementation

Data and Feature Engineering
Dataset: Synthetic cloud cost data with realistic seasonal patterns
Features include daily and hourly cost trends, CPU utilization ratios, and usage-based metrics

Model Performance
Accuracy: 92 percent
Recall: 78 percent
F1-Score: 81 percent

API Design
POST /detect-anomalies – Detect abnormal cost patterns
GET /health – Service health check
GET /docs – Interactive API documentation

Deployment
Docker-containerized application
One-click deployment using Zerve AI
Scalable, stateless execution suitable for production workloads

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Results and Validation
Time Range Analyzed: 366 days of cloud cost data
Anomalies Detected: 36 high-confidence cost anomalies
Potential Savings Identified: 93,523 USD
Live API deployed with interactive Swagger documentation
Real-time alerts for high-risk spending patterns

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Business Value
This system enables proactive cloud cost governance by preventing unexpected budget overruns, accelerating incident response for FinOps and security teams, and delivering measurable, data-driven cost optimization insights.

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Hackathon Alignment
Solves a real business problem
Uses an ML-driven analytical system
Includes quantifiable validation metrics
Deployed to production on Zerve AI