# PRSM Cost Optimization & ROI Calculator Tools

A comprehensive suite of tools for analyzing costs, calculating ROI, and optimizing infrastructure spending for PRSM deployments.

## 🎯 Overview

This toolset provides enterprise-grade financial analysis capabilities for PRSM platforms, enabling organizations to:

- **Optimize Infrastructure Costs** - Right-size resources and reduce waste
- **Calculate ROI** - Quantify business value and return on investment
- **Monitor Budgets** - Track spending and prevent overruns
- **Predict Costs** - Forecast future expenses and plan accordingly
- **Compare Scenarios** - Evaluate different deployment strategies

## 📦 Tools Included

### 1. ROI Calculator (`roi_calculator.py`)
Comprehensive ROI analysis and scenario modeling tool.

**Features:**
- Multi-scenario ROI calculations
- Infrastructure vs AI model cost analysis
- Business value quantification
- Deployment tier comparisons
- Interactive configuration and analysis

**Usage:**
```bash
# Interactive mode
python roi_calculator.py --interactive

# Quick analysis
python roi_calculator.py --daily-requests 5000 --revenue-per-request 0.50

# Export analysis
python roi_calculator.py --tier production --export analysis_report.json
```

### 2. Cost Optimizer (`cost_optimizer.py`)
Intelligent cost optimization with real-time monitoring and recommendations.

**Features:**
- Real-time cost monitoring
- Automated optimization recommendations
- Resource utilization analysis
- Budget alerts and controls
- Predictive cost modeling

**Usage:**
```bash
# Demo with sample data
python cost_optimizer.py --demo --strategy minimize_cost

# Real-time monitoring
python cost_optimizer.py --monitor --budget 5000

# Budget monitoring with alerts
python cost_optimizer.py --demo --budget 3000 --strategy balanced
```

## 🚀 Quick Start

### Installation

```bash
# Install required dependencies
pip install pandas numpy

# Make scripts executable
chmod +x roi_calculator.py cost_optimizer.py
```

### Basic ROI Analysis

```bash
# Run interactive ROI calculator
python roi_calculator.py --interactive

# Follow the prompts to configure:
# 1. Usage parameters (requests, tokens, resources)
# 2. Business metrics (revenue, costs, savings)
# 3. Analyze scenarios and get recommendations
```

### Cost Optimization Demo

```bash
# Run cost optimizer demo
python cost_optimizer.py --demo --strategy balanced

# View real-time dashboard with:
# - System efficiency score
# - Resource utilization
# - Cost trends
# - Optimization recommendations
# - Budget alerts
```

## 📊 Key Metrics & Analysis

### ROI Scenarios

The ROI calculator evaluates multiple deployment scenarios:

1. **All OpenAI** - 100% OpenAI GPT models
2. **All Anthropic** - 100% Anthropic Claude models  
3. **Mixed Commercial** - 50% OpenAI, 30% Anthropic, 20% Hugging Face
4. **Hybrid Self-Hosted** - 30% OpenAI, 50% self-hosted, 20% PRSM Network
5. **PRSM Optimized** - 60% PRSM Network, 30% self-hosted, 10% OpenAI

### Cost Categories

- **Infrastructure Costs**
  - CPU, Memory, GPU compute
  - Storage (SSD, standard)
  - Database and caching
  - Network transfer
  
- **AI Model Costs**
  - Token-based pricing (input/output)
  - Inference service fees
  - Self-hosted operational costs
  - PRSM Network token costs

- **Business Value**
  - Direct revenue generation
  - Automation cost savings
  - Error reduction benefits
  - Time-to-market improvements

### Optimization Recommendations

The cost optimizer provides actionable recommendations across categories:

- **Infrastructure Optimization**
  - Resource right-sizing
  - Auto-scaling implementation
  - Provider optimization
  
- **AI Model Optimization** 
  - Provider mix optimization
  - Model routing intelligence
  - Caching strategies
  
- **Usage Pattern Optimization**
  - Peak load management
  - Predictive scaling
  - Cost-aware scheduling

## 📈 Sample Analysis Results

### ROI Comparison Example

| Scenario | Monthly Cost | Monthly Value | ROI | Payback |
|----------|-------------|---------------|-----|---------|
| PRSM Optimized | $2,847 | $8,450 | 196.8% | 0.5 months |
| Hybrid Self-Hosted | $3,234 | $8,450 | 161.3% | 0.6 months |
| Mixed Commercial | $4,125 | $8,450 | 104.8% | 1.0 months |
| All Anthropic | $4,567 | $8,450 | 85.0% | 1.2 months |
| All OpenAI | $5,234 | $8,450 | 61.5% | 1.6 months |

### Cost Optimization Impact

**Before Optimization:**
- Monthly Cost: $5,234
- System Efficiency: 45%
- Resource Waste: ~40%

**After Optimization:**
- Monthly Cost: $2,847 (-45.6%)
- System Efficiency: 87%
- Resource Waste: ~8%

**Key Improvements:**
- GPU utilization optimization: $800/month savings
- AI model provider optimization: $500/month savings
- Infrastructure right-sizing: $350/month savings
- Automated scaling: $300/month savings

## 🛠️ Configuration Options

### ROI Calculator Configuration

```python
# Usage Metrics
usage = UsageMetrics(
    daily_requests=1000,
    avg_input_tokens=500,
    avg_output_tokens=200,
    cpu_cores=8,
    memory_gb=32,
    gpu_count=1
)

# Business Metrics  
business = BusinessMetrics(
    revenue_per_request=0.25,
    cost_per_engineer_hour=150.0,
    hours_saved_per_day=2.0,
    error_reduction_percentage=15.0
)
```

### Cost Optimizer Configuration

```python
# Optimization Strategy
strategy = OptimizationStrategy.BALANCED  # or MINIMIZE_COST, MAXIMIZE_PERFORMANCE

# Alert Thresholds
thresholds = {
    "cpu_low_utilization": 20.0,      # % - trigger right-sizing
    "memory_low_utilization": 30.0,   # % - trigger memory optimization
    "gpu_low_utilization": 40.0,      # % - trigger GPU optimization
    "budget_warning": 0.8,            # 80% of budget - warning alert
    "budget_critical": 0.95           # 95% of budget - critical alert
}
```

## 📊 Dashboard & Monitoring

### Real-Time Cost Dashboard

```
🎯 PRSM Cost Optimization Dashboard
==================================================

💡 System Efficiency: 87.3% [█████████████████░░░]

📊 Current Resource Utilization:
   🟢 CPU   :  67.3% [█████████████░░░░░░░]
   🟡 Memory:  78.9% [███████████████░░░░░]
   🟢 GPU   :  54.2% [██████████░░░░░░░░░░]

💰 Cost Analysis:
   Current hourly rate: $3.42
   24h projection: $82.08
   Monthly projection: $2,462.40
   Trend: 📉 -$0.008/hour

🎯 Top Optimization Opportunities:
   1. GPU Resource Optimization
      💰 Potential savings: $800.00/month
      ⚡ Impact score: 90/100

   2. AI Model Cost Optimization  
      💰 Potential savings: $500.00/month
      ⚡ Impact score: 85/100
```

### Budget Monitoring

```
🚨 Budget Alerts:
   🟡 Budget warning: 83.2% of monthly budget used
      💡 Review and implement cost optimization recommendations
   
   🟡 Projected to exceed budget by 12.4%
      💡 Implement cost reduction measures to stay within budget
```

## 🔧 Integration Examples

### API Integration

```python
from cost_optimization.roi_calculator import ROICalculator
from cost_optimization.cost_optimizer import CostOptimizer

# Initialize tools
roi_calc = ROICalculator()
optimizer = CostOptimizer()

# Calculate ROI for current setup
scenarios = roi_calc.calculate_roi_scenarios(usage_metrics, business_metrics, tier)
best_scenario = max(scenarios.items(), key=lambda x: x[1]["roi_percentage"])

# Get optimization recommendations
recommendations = optimizer.generate_recommendations(OptimizationStrategy.BALANCED)

# Monitor budget
alerts = optimizer.monitor_budget(monthly_budget=5000)
```

### Automated Reporting

```python
# Generate monthly optimization report
report_file = optimizer.export_optimization_report(
    strategy=OptimizationStrategy.MINIMIZE_COST,
    filename="monthly_optimization_report.json"
)

# Export ROI analysis
analysis_file = roi_calc.export_analysis(scenarios, "roi_analysis.json")
```

## 📚 Advanced Features

### Predictive Cost Modeling

- **Trend Analysis** - Linear and polynomial trend fitting
- **Anomaly Detection** - Statistical outlier identification  
- **Seasonality Modeling** - Daily/weekly usage pattern recognition
- **Capacity Planning** - Growth-based resource projection

### Multi-Cloud Cost Optimization

- **Provider Comparison** - AWS, GCP, Azure cost analysis
- **Spot Instance Optimization** - Dynamic pricing strategies
- **Reserved Instance Planning** - Long-term commitment analysis
- **Cross-Cloud Load Balancing** - Cost-aware workload distribution

### Enterprise Features

- **Role-Based Access** - Team-specific cost visibility
- **Department Allocation** - Cost center assignment and tracking
- **Compliance Reporting** - Financial audit trail maintenance
- **Integration APIs** - ERP and accounting system connectivity

## 🎯 Best Practices

### 1. Regular Analysis
- Run ROI analysis monthly
- Monitor cost optimization weekly
- Review recommendations quarterly

### 2. Gradual Implementation
- Start with low-risk recommendations
- Implement changes incrementally
- Monitor impact continuously

### 3. Budget Management
- Set realistic budget targets
- Configure appropriate alert thresholds
- Review budget allocation quarterly

### 4. Performance Monitoring
- Track efficiency scores
- Monitor system performance impact
- Validate optimization results

## 🤝 Contributing

We welcome contributions to improve the cost optimization tools:

1. **Bug Reports** - Submit issues via GitHub
2. **Feature Requests** - Propose new capabilities
3. **Code Contributions** - Submit pull requests
4. **Documentation** - Improve guides and examples

## 📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

## 📞 Support

For support and questions:

- **Documentation**: [PRSM Docs](../../docs/)
- **GitHub Issues**: [Report Issues](https://github.com/Ryno2390/PRSM/issues)
- **Community**: [Join Discord](https://discord.gg/prsm)

---

*Built with ❤️ by the PRSM Platform Team*