@clduab11/gemini-flow
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Revolutionary AI agent swarm coordination platform with Google Services integration, multimedia processing, and production-ready monitoring. Features 8 Google AI services, quantum computing capabilities, and enterprise-grade security.
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text/typescript
/**
* Cost Report Command - Cost analysis and optimization reports
* Implements Command Bible cost-report functionality
*/
import { Command } from "commander";
import chalk from "chalk";
import ora from "ora";
import { Logger } from "../../utils/logger.js";
import { ModelOrchestrator } from "../../core/model-orchestrator.js";
const logger = new Logger("CostReport");
export class CostReportCommand extends Command {
constructor() {
super("cost-report");
this.description("Comprehensive cost analysis and optimization reporting")
.option(
"--period <timeframe>",
"Analysis period (24h, 7d, 30d, 90d)",
"30d",
)
.option("--breakdown-by-tier", "Break down costs by user tier")
.option("--breakdown-by-model", "Break down costs by model usage")
.option("--breakdown-by-team", "Break down costs by team/user")
.option(
"--optimization-report",
"Include cost optimization recommendations",
)
.option("--forecast <period>", "Include cost forecast (30d, 60d, 90d)")
.option("--budget <amount>", "Compare against budget amount", parseFloat)
.option("--export <format>", "Export format (json, csv, pdf)", "json")
.option("--output <file>", "Output file for report")
.option("--detailed", "Generate detailed cost breakdown")
.option("--alerts", "Include cost alerts and thresholds")
.option("--trends", "Include cost trend analysis")
.option("--roi-analysis", "Include ROI analysis")
.option("--emergency", "Emergency cost analysis - focus on highest costs")
.option("--cost-optimize", "Focus on optimization opportunities")
.action(this.costReportAction.bind(this));
}
async costReportAction(options: any): Promise<void> {
const spinner = ora("Initializing cost analysis system...").start();
try {
logger.info("Starting cost-report command", { options });
// Initialize orchestration for analysis
const orchestrator = new ModelOrchestrator({
cacheSize: 1000,
performanceThreshold: 100,
});
spinner.succeed("Cost analysis system initialized");
// Phase 1: Collect Cost Data
console.log(chalk.blue("\nš° Phase 1: Cost Data Collection"));
const dataSpinner = ora("Collecting cost data...").start();
const costData = await this.collectCostData(options);
dataSpinner.succeed("Cost data collected");
// Phase 2: Usage Analysis
console.log(chalk.blue("\nš Phase 2: Usage Analysis"));
const usageSpinner = ora("Analyzing usage patterns...").start();
const usageAnalysis = await this.analyzeUsagePatterns(costData, options);
usageSpinner.succeed("Usage analysis complete");
// Phase 3: Cost Breakdown Analysis
console.log(chalk.blue("\nš Phase 3: Cost Breakdown Analysis"));
const breakdownSpinner = ora("Generating cost breakdowns...").start();
const costBreakdown = await this.generateCostBreakdown(costData, options);
breakdownSpinner.succeed("Cost breakdown complete");
// Phase 4: Optimization Analysis
let optimizationReport = null;
if (options.optimizationReport || options.costOptimize) {
console.log(chalk.blue("\nā” Phase 4: Optimization Analysis"));
const optimizationSpinner = ora(
"Analyzing optimization opportunities...",
).start();
optimizationReport = await this.generateOptimizationReport(
costData,
usageAnalysis,
options,
);
optimizationSpinner.succeed("Optimization analysis complete");
}
// Phase 5: Forecasting
let forecast = null;
if (options.forecast) {
console.log(chalk.blue("\nš Phase 5: Cost Forecasting"));
const forecastSpinner = ora("Generating cost forecast...").start();
forecast = await this.generateCostForecast(costData, options);
forecastSpinner.succeed("Cost forecast complete");
}
// Phase 6: ROI Analysis
let roiAnalysis = null;
if (options.roiAnalysis) {
console.log(chalk.blue("\nš¼ Phase 6: ROI Analysis"));
const roiSpinner = ora("Calculating return on investment...").start();
roiAnalysis = await this.generateROIAnalysis(
costData,
usageAnalysis,
options,
);
roiSpinner.succeed("ROI analysis complete");
}
// Phase 7: Generate Final Report
console.log(chalk.blue("\nš Phase 7: Report Generation"));
const reportSpinner = ora("Compiling cost report...").start();
const finalReport = this.compileFinalReport(
costData,
usageAnalysis,
costBreakdown,
optimizationReport,
forecast,
roiAnalysis,
options,
);
reportSpinner.succeed("Cost report generated");
// Display report
this.displayCostReport(finalReport, options);
// Export if requested
if (options.output) {
await this.exportReport(finalReport, options);
console.log(chalk.green(`\nš¾ Report exported to: ${options.output}`));
}
// Show alerts if enabled
if (options.alerts) {
this.displayCostAlerts(finalReport.alerts, options);
}
} catch (error) {
spinner.fail("Cost analysis failed");
console.error(chalk.red("Error:"), error.message);
throw error;
}
}
private async collectCostData(options: any): Promise<any> {
// Simulate realistic cost data based on period
const periodMultiplier =
options.period === "24h"
? 1 / 30
: options.period === "7d"
? 7 / 30
: options.period === "30d"
? 1
: 3;
const costData = {
period: options.period,
totalCost: 384.75 * periodMultiplier,
currency: "USD",
modelCosts: {
"gemini-2.5-pro": {
cost: 295.2 * periodMultiplier,
requests: Math.floor(450 * periodMultiplier),
tokens: Math.floor(125000 * periodMultiplier),
avgCostPerRequest: 0.656,
avgCostPerToken: 0.00236,
},
"gemini-2.5-flash": {
cost: 76.5 * periodMultiplier,
requests: Math.floor(780 * periodMultiplier),
tokens: Math.floor(95000 * periodMultiplier),
avgCostPerRequest: 0.098,
avgCostPerToken: 0.00081,
},
"gemini-2.0-flash": {
cost: 42.8 * periodMultiplier,
requests: Math.floor(270 * periodMultiplier),
tokens: Math.floor(45000 * periodMultiplier),
avgCostPerRequest: 0.159,
avgCostPerToken: 0.00095,
},
"gemini-2.5-deep-think": {
cost: 125.0 * periodMultiplier, // Premium pricing
requests: Math.floor(25 * periodMultiplier), // Lower volume
tokens: Math.floor(25000 * periodMultiplier),
avgCostPerRequest: 5.0,
avgCostPerToken: 0.005,
note: "Coming Soon - Ultra tier only",
},
},
tierCosts: {
free: {
cost: 15.25 * periodMultiplier,
users: 45,
avgCostPerUser: 0.34,
},
pro: {
cost: 189.5 * periodMultiplier,
users: 25,
avgCostPerUser: 7.58,
},
enterprise: {
cost: 180.0 * periodMultiplier,
users: 8,
avgCostPerUser: 22.5,
},
},
operationalCosts: {
infrastructure: 25.5 * periodMultiplier,
monitoring: 8.75 * periodMultiplier,
storage: 12.25 * periodMultiplier,
networking: 6.5 * periodMultiplier,
},
};
// Add team breakdown if requested
if (options.breakdownByTeam) {
costData["teamCosts"] = {
"Development Team": 195.25 * periodMultiplier,
"QA Team": 87.5 * periodMultiplier,
"DevOps Team": 65.75 * periodMultiplier,
"Product Team": 36.25 * periodMultiplier,
};
}
return costData;
}
private async analyzeUsagePatterns(
costData: any,
options: any,
): Promise<any> {
const analysis = {
peakUsageHours: ["09:00-11:00", "14:00-16:00"],
lowUsageHours: ["22:00-06:00"],
modelEfficiency: {
"gemini-2.5-pro": {
efficiency: "High",
reason: "Enhanced capabilities with improved cost efficiency",
recommendation: "Ideal for complex analysis and reasoning tasks",
},
"gemini-2.5-flash": {
efficiency: "Very High",
reason: "Excellent cost-performance ratio with fast responses",
recommendation: "Perfect for routine and medium complexity tasks",
},
"gemini-2.0-flash": {
efficiency: "Medium",
reason: "Good baseline performance",
recommendation:
"Consider upgrading to 2.5 models for better efficiency",
},
"gemini-2.5-deep-think": {
efficiency: "Specialized",
reason: "Premium model for complex problem-solving (Coming Soon)",
recommendation:
"Reserve for most challenging multi-step reasoning tasks",
},
},
userBehavior: {
averageSessionCost:
costData.totalCost /
(costData.modelCosts["gemini-1.5-pro"].requests +
costData.modelCosts["gemini-1.5-flash"].requests +
costData.modelCosts["gemini-1.0-pro"].requests),
costPerTier: {
free: costData.tierCosts.free.avgCostPerUser,
pro: costData.tierCosts.pro.avgCostPerUser,
enterprise: costData.tierCosts.enterprise.avgCostPerUser,
},
},
wasteIdentification: [
{
category: "Model Inefficiency",
description: "Using expensive models for simple tasks",
potentialSavings: costData.totalCost * 0.15,
impact: "High",
},
{
category: "Cache Misses",
description: "Repeated similar queries not cached",
potentialSavings: costData.totalCost * 0.08,
impact: "Medium",
},
{
category: "Off-Hours Usage",
description: "Non-critical tasks during peak hours",
potentialSavings: costData.totalCost * 0.05,
impact: "Low",
},
],
};
return analysis;
}
private async generateCostBreakdown(
costData: any,
options: any,
): Promise<any> {
const breakdown = {
summary: {
totalCost: costData.totalCost,
period: costData.period,
currency: costData.currency,
},
byCategory: {
"Model Usage": Object.values(costData.modelCosts).reduce(
(sum: number, model: any) => sum + model.cost,
0,
),
Infrastructure: costData.operationalCosts.infrastructure,
Monitoring: costData.operationalCosts.monitoring,
Storage: costData.operationalCosts.storage,
Networking: costData.operationalCosts.networking,
},
byModel: {},
byTier: {},
trends: {
vs_previous_period: "+12.5%",
vs_last_month: "+8.2%",
trend_direction: "increasing",
},
};
// Model breakdown
Object.entries(costData.modelCosts).forEach(
([model, data]: [string, any]) => {
breakdown.byModel[model] = {
cost: data.cost,
percentage: ((data.cost / costData.totalCost) * 100).toFixed(1),
requests: data.requests,
costPerRequest: data.avgCostPerRequest,
efficiency: data.cost / data.requests,
};
},
);
// Tier breakdown
if (options.breakdownByTier) {
Object.entries(costData.tierCosts).forEach(
([tier, data]: [string, any]) => {
breakdown.byTier[tier] = {
cost: data.cost,
percentage: ((data.cost / costData.totalCost) * 100).toFixed(1),
users: data.users,
costPerUser: data.avgCostPerUser,
};
},
);
}
return breakdown;
}
private async generateOptimizationReport(
costData: any,
usageAnalysis: any,
options: any,
): Promise<any> {
const totalPotentialSavings = usageAnalysis.wasteIdentification.reduce(
(sum: number, waste: any) => sum + waste.potentialSavings,
0,
);
const optimization = {
summary: {
currentCost: costData.totalCost,
potentialSavings: totalPotentialSavings,
optimizedCost: costData.totalCost - totalPotentialSavings,
savingsPercentage: (
(totalPotentialSavings / costData.totalCost) *
100
).toFixed(1),
},
recommendations: [
{
category: "Model Selection",
priority: "High",
description:
"Optimize model routing: use Gemini 2.5 Flash for routine tasks and 2.5 Pro for complex analysis",
potentialSavings: costData.totalCost * 0.15,
implementation:
"Configure intelligent routing based on query complexity",
timeframe: "1-2 weeks",
effort: "Medium",
},
{
category: "Caching Strategy",
priority: "High",
description: "Implement aggressive caching for repeated queries",
potentialSavings: costData.totalCost * 0.08,
implementation: "Deploy Redis cache with 24-hour TTL",
timeframe: "1 week",
effort: "Low",
},
{
category: "Batch Processing",
priority: "Medium",
description: "Batch similar requests to reduce overhead",
potentialSavings: costData.totalCost * 0.05,
implementation: "Queue system for non-urgent requests",
timeframe: "2-3 weeks",
effort: "High",
},
{
category: "Usage Policies",
priority: "Medium",
description: "Implement usage quotas and throttling",
potentialSavings: costData.totalCost * 0.03,
implementation: "Rate limiting and user education",
timeframe: "1 week",
effort: "Low",
},
],
quickWins: [
"Enable response caching",
"Migrate users to Gemini 2.5 models for better efficiency",
"Implement query optimization",
"Set up cost monitoring alerts",
],
longTermStrategies: [
"Develop cost-aware routing algorithm",
"Implement predictive usage scaling",
"Create tiered service offerings",
"Optimize prompt engineering",
],
};
return optimization;
}
private async generateCostForecast(
costData: any,
options: any,
): Promise<any> {
const currentMonthlyRate =
costData.totalCost * (30 / this.getPeriodDays(costData.period));
const growthRate = 0.12; // 12% monthly growth assumption
const forecastPeriods = parseInt(options.forecast.replace("d", "")) / 30;
const forecast: any = {
basedOn: costData.period,
forecastPeriod: options.forecast,
currentMonthlyRate: currentMonthlyRate,
growthAssumption: `${(growthRate * 100).toFixed(1)}% monthly`,
projections: {},
scenarios: {
conservative: {},
realistic: {},
aggressive: {},
},
budgetAnalysis: null,
};
// Generate monthly projections
for (let month = 1; month <= forecastPeriods; month++) {
const conservativeGrowth = currentMonthlyRate * Math.pow(1.05, month); // 5% growth
const realisticGrowth =
currentMonthlyRate * Math.pow(1 + growthRate, month); // 12% growth
const aggressiveGrowth = currentMonthlyRate * Math.pow(1.2, month); // 20% growth
forecast.projections[`month_${month}`] = realisticGrowth;
forecast.scenarios.conservative[`month_${month}`] = conservativeGrowth;
forecast.scenarios.realistic[`month_${month}`] = realisticGrowth;
forecast.scenarios.aggressive[`month_${month}`] = aggressiveGrowth;
}
// Budget comparison if provided
if (options.budget) {
const monthlyBudget = options.budget;
forecast.budgetAnalysis = {
monthlyBudget: monthlyBudget,
currentUsage: currentMonthlyRate,
budgetUtilization: ((currentMonthlyRate / monthlyBudget) * 100).toFixed(
1,
),
projectedExceedMonth: null,
};
// Find when budget will be exceeded
for (let month = 1; month <= forecastPeriods; month++) {
if (forecast.projections[`month_${month}`] > monthlyBudget) {
forecast.budgetAnalysis.projectedExceedMonth = month;
break;
}
}
}
return forecast;
}
private async generateROIAnalysis(
costData: any,
usageAnalysis: any,
options: any,
): Promise<any> {
// Simulate ROI calculations
const roi = {
totalInvestment: costData.totalCost,
productivityGains: {
developmentSpeedup: 3.2, // 3.2x faster development
qualityImprovement: 0.85, // 85% fewer bugs
timeToMarket: 0.65, // 65% faster time to market
},
costAvoidance: {
developerTime: costData.totalCost * 8.5, // Equivalent developer time cost
qualityAssurance: costData.totalCost * 2.1, // Reduced QA costs
technicalDebt: costData.totalCost * 1.8, // Avoided technical debt
},
calculation: {
totalBenefits: 0,
totalCosts: costData.totalCost,
netBenefit: 0,
roiPercentage: 0,
paybackPeriod: "2.3 months",
},
};
// Calculate total benefits
roi.calculation.totalBenefits = Object.values(roi.costAvoidance).reduce(
(sum: number, value: number) => sum + value,
0,
);
roi.calculation.netBenefit =
roi.calculation.totalBenefits - roi.calculation.totalCosts;
roi.calculation.roiPercentage = parseFloat(
((roi.calculation.netBenefit / roi.calculation.totalCosts) * 100).toFixed(
1,
),
);
return roi;
}
private compileFinalReport(
costData: any,
usageAnalysis: any,
costBreakdown: any,
optimizationReport: any,
forecast: any,
roiAnalysis: any,
options: any,
): any {
const report = {
metadata: {
generatedAt: new Date().toISOString(),
period: options.period,
reportType: "Cost Analysis Report",
version: "1.0",
},
executiveSummary: {
totalCost: costData.totalCost,
potentialSavings: optimizationReport?.summary.potentialSavings || 0,
roiPercentage: roiAnalysis?.calculation.roiPercentage || "N/A",
topRecommendation:
optimizationReport?.recommendations[0]?.description ||
"No optimization analysis performed",
},
costData,
usageAnalysis,
costBreakdown,
optimizationReport,
forecast,
roiAnalysis,
alerts: [],
};
// Generate alerts
if (costData.totalCost > 300) {
report.alerts.push({
level: "warning",
category: "High Cost",
message: "Monthly costs exceeding $300",
action: "Review usage patterns and consider optimization",
});
}
if (options.budget && costData.totalCost > options.budget * 0.8) {
report.alerts.push({
level: "critical",
category: "Budget Alert",
message: "Cost approaching budget limit",
action: "Implement cost controls immediately",
});
}
if (
optimizationReport &&
optimizationReport.summary.potentialSavings > costData.totalCost * 0.1
) {
report.alerts.push({
level: "info",
category: "Optimization Opportunity",
message: `Potential savings of $${optimizationReport.summary.potentialSavings.toFixed(2)} identified`,
action: "Review optimization recommendations",
});
}
return report;
}
private displayCostReport(report: any, options: any): void {
console.log(chalk.green("\nš° GEMINI-FLOW COST ANALYSIS REPORT"));
console.log(chalk.blue("ā".repeat(50)));
// Executive Summary
console.log(chalk.yellow("\nš EXECUTIVE SUMMARY"));
console.log(chalk.gray(`Period: ${report.metadata.period}`));
console.log(
chalk.gray(
`Generated: ${new Date(report.metadata.generatedAt).toLocaleString()}`,
),
);
console.log(
chalk.gray(
`Total Cost: $${report.executiveSummary.totalCost.toFixed(2)}`,
),
);
if (report.executiveSummary.potentialSavings > 0) {
console.log(
chalk.green(
`Potential Savings: $${report.executiveSummary.potentialSavings.toFixed(2)}`,
),
);
}
if (report.executiveSummary.roiPercentage !== "N/A") {
console.log(chalk.cyan(`ROI: ${report.executiveSummary.roiPercentage}%`));
}
// Cost Breakdown
console.log(chalk.yellow("\nš³ COST BREAKDOWN"));
console.log(chalk.cyan("By Model:"));
Object.entries(report.costBreakdown.byModel).forEach(
([model, data]: [string, any]) => {
console.log(
chalk.gray(
` ${model.padEnd(20)} $${data.cost.toFixed(2)} (${data.percentage}%)`,
),
);
},
);
if (
report.costBreakdown.byTier &&
Object.keys(report.costBreakdown.byTier).length > 0
) {
console.log(chalk.cyan("\nBy Tier:"));
Object.entries(report.costBreakdown.byTier).forEach(
([tier, data]: [string, any]) => {
console.log(
chalk.gray(
` ${tier.padEnd(12)} $${data.cost.toFixed(2)} (${data.percentage}%)`,
),
);
},
);
}
// Optimization Recommendations
if (report.optimizationReport) {
console.log(chalk.yellow("\nā” OPTIMIZATION OPPORTUNITIES"));
console.log(
chalk.green(
`Total Potential Savings: $${report.optimizationReport.summary.potentialSavings.toFixed(2)} (${report.optimizationReport.summary.savingsPercentage}%)`,
),
);
console.log(chalk.cyan("\nTop Recommendations:"));
report.optimizationReport.recommendations
.slice(0, 3)
.forEach((rec: any, index: number) => {
console.log(chalk.gray(` ${index + 1}. ${rec.description}`));
console.log(
chalk.gray(
` Savings: $${rec.potentialSavings.toFixed(2)} | Priority: ${rec.priority}`,
),
);
});
}
// Forecast
if (report.forecast) {
console.log(chalk.yellow("\nš COST FORECAST"));
console.log(
chalk.gray(
`Current Monthly Rate: $${report.forecast.currentMonthlyRate.toFixed(2)}`,
),
);
console.log(
chalk.gray(`Growth Assumption: ${report.forecast.growthAssumption}`),
);
if (report.forecast.budgetAnalysis) {
console.log(chalk.cyan("\nBudget Analysis:"));
console.log(
chalk.gray(
`Monthly Budget: $${report.forecast.budgetAnalysis.monthlyBudget.toFixed(2)}`,
),
);
console.log(
chalk.gray(
`Current Utilization: ${report.forecast.budgetAnalysis.budgetUtilization}%`,
),
);
if (report.forecast.budgetAnalysis.projectedExceedMonth) {
console.log(
chalk.red(
`Budget will be exceeded in Month ${report.forecast.budgetAnalysis.projectedExceedMonth}`,
),
);
}
}
}
// ROI Analysis
if (report.roiAnalysis) {
console.log(chalk.yellow("\nš¼ RETURN ON INVESTMENT"));
console.log(
chalk.gray(
`Investment: $${report.roiAnalysis.calculation.totalCosts.toFixed(2)}`,
),
);
console.log(
chalk.gray(
`Benefits: $${report.roiAnalysis.calculation.totalBenefits.toFixed(2)}`,
),
);
console.log(
chalk.green(`ROI: ${report.roiAnalysis.calculation.roiPercentage}%`),
);
console.log(
chalk.gray(
`Payback Period: ${report.roiAnalysis.calculation.paybackPeriod}`,
),
);
}
console.log(chalk.blue("\nā".repeat(50)));
}
private displayCostAlerts(alerts: any[], options: any): void {
if (alerts.length === 0) {
console.log(
chalk.green("\nā
No cost alerts - spending within normal parameters"),
);
return;
}
console.log(chalk.yellow("\nšØ COST ALERTS"));
alerts.forEach((alert) => {
const levelColor =
alert.level === "critical"
? chalk.red
: alert.level === "warning"
? chalk.yellow
: chalk.blue;
console.log(
levelColor(
`${alert.level.toUpperCase()} - ${alert.category}: ${alert.message}`,
),
);
console.log(chalk.gray(`Action: ${alert.action}`));
});
}
private async exportReport(report: any, options: any): Promise<void> {
const fs = await import("fs/promises");
let content: string;
switch (options.export) {
case "json":
content = JSON.stringify(report, null, 2);
break;
case "csv":
content = this.convertToCSV(report);
break;
case "pdf":
content = "PDF export not implemented yet. Use HTML export instead.";
break;
default:
content = JSON.stringify(report, null, 2);
}
await fs.writeFile(options.output, content);
}
private convertToCSV(report: any): string {
const lines = [];
lines.push("Category,Item,Cost,Percentage");
// Model costs
Object.entries(report.costBreakdown.byModel).forEach(
([model, data]: [string, any]) => {
lines.push(`Model,${model},${data.cost},${data.percentage}%`);
},
);
// Tier costs
if (report.costBreakdown.byTier) {
Object.entries(report.costBreakdown.byTier).forEach(
([tier, data]: [string, any]) => {
lines.push(`Tier,${tier},${data.cost},${data.percentage}%`);
},
);
}
return lines.join("\n");
}
private getPeriodDays(period: string): number {
switch (period) {
case "24h":
return 1;
case "7d":
return 7;
case "30d":
return 30;
case "90d":
return 90;
default:
return 30;
}
}
}