Documentation | Augment AI Platform

Installation

Deploy AI CostOptimizer to start reducing cloud costs with intelligent analysis and automated optimization.

System Requirements

Python 3.9 or higher
Docker 20.0+ (for containerized deployment)
Node.js 16+ (for dashboard components)
Minimum 4GB RAM (8GB recommended for large accounts)
Access to cloud provider billing APIs

Install via Package Manager

Installationbash

# Install via pip
pip install augment-cost-optimizer

# Install via npm for Node.js projects
npm install -g @augment/cost-optimizer

# Install from source
git clone https://github.com/augment-ai/cost-optimizer
cd cost-optimizer
pip install -e .

# Verify installation
cost-optimizer --version

Billing API Access Setup

Configure access to your cloud provider billing APIs and Augment platform:

Billing API Setupbash

# Set Augment API key
export AUGMENT_API_KEY=your_api_key_here

# Configure AWS Cost Explorer access
aws configure
# Ensure billing access permissions are enabled

# Configure Azure Cost Management
az login
az account set --subscription your-subscription-id

# Configure GCP Billing API
gcloud auth application-default login
gcloud config set project your-project-id

# Verify billing access
cost-optimizer auth verify --all-providers

Quick Start

Get your first cost analysis and optimization recommendations in minutes.

1. Initialize Cost Profile

Profile Setupbash

# Create cost optimization profile
cost-optimizer init --organization "your-company"

# Configure billing scope
cost-optimizer config --providers aws,azure,gcp
cost-optimizer config --accounts account1,account2,account3

# Set optimization targets
cost-optimizer config --target-reduction 25%
cost-optimizer config --budget-threshold 10000

# This creates .cost-optimizer.yaml config file

2. Run Cost Analysis

Cost Analysisbash

# Analyze current month spending
cost-optimizer analyze --period current-month

# Analyze last 90 days for trends
cost-optimizer analyze --period 90-days --include-trends

# Quick cost assessment
cost-optimizer analyze --quick --high-impact-only

3. Generate Cost Reports

Cost Reportsbash

# Generate comprehensive cost report
cost-optimizer report --format html --output cost-report.html

# Export optimization recommendations
cost-optimizer report --format json --output recommendations.json

# Generate executive summary
cost-optimizer report --executive-summary --format pdf --output exec-summary.pdf

# Create budget forecast
cost-optimizer forecast --months 6 --output forecast.csv

Configuration

Configure AI CostOptimizer to align with your organization's financial policies and optimization goals.

Basic Configuration

.cost-optimizer.yamlyaml

version: "1.0"
organization: "your-company"
currency: "USD"

providers:
  aws:
    accounts: ["123456789012", "987654321098"]
    regions: ["us-east-1", "us-west-2", "eu-west-1"]
    linked_accounts: true
  azure:
    subscriptions: ["sub-123-456", "sub-789-012"]
    resource_groups: ["production", "staging"]
  gcp:
    projects: ["project-123", "project-456"]
    billing_accounts: ["billing-account-123"]

optimization_settings:
  target_reduction: 25
  min_savings_threshold: 100
  risk_tolerance: "medium"
  auto_implement: false
  approval_workflow: true

cost_policies:
  budget_alerts:
    - threshold: 80
      type: "percentage"
      notify: ["finance@company.com"]
  spending_limits:
    monthly_cap: 50000
    emergency_shutdown: true
  reserved_instances:
    recommendation_threshold: 0.7
    auto_purchase: false

Cost Models

AI CostOptimizer uses advanced machine learning models to analyze spending patterns and predict future costs.

Predictive Cost Modeling

• Time-series forecasting for resource usage
• Seasonal pattern recognition
• Growth trend analysis
• Budget variance prediction

Waste Detection Algorithms

• Idle resource identification
• Over-provisioned instance detection
• Unused storage analysis
• Zombie resource cleanup

Right-Sizing Intelligence

• Performance-based sizing recommendations
• Workload pattern analysis
• Cost-performance optimization
• Migration cost calculations

Commitment Analysis

• Reserved instance optimization
• Savings plan recommendations
• Spot instance opportunities
• Contract negotiation insights

Environment Variables

Configure AI CostOptimizer behavior using environment variables for different deployment scenarios.

Variable	Description	Default
AUGMENT_API_KEY	Your Augment API key	Required
COST_OPTIMIZER_CONFIG	Path to configuration file	.cost-optimizer.yaml
COST_OPTIMIZER_CURRENCY	Default currency for reports	USD
COST_OPTIMIZER_CACHE_TTL	Cache time-to-live in seconds	3600

Basic Usage

Learn the fundamental cost analysis patterns and optimization workflows.

Analysis Commands

Cost Analysisbash

# Full cost analysis across all providers
cost-optimizer analyze --all-providers

# Focus on specific cost categories
cost-optimizer analyze --categories compute,storage,network

# Analyze specific time periods
cost-optimizer analyze --start-date 2025-01-01 --end-date 2025-01-31

# High-impact optimization opportunities only
cost-optimizer analyze --min-savings 1000 --high-impact-only

CLI Commands Reference

Complete reference for all cost analysis and optimization commands.

analyze

Run comprehensive cost analysis and generate optimization recommendations

cost-optimizer analyze [options]

Options:
  --provider <provider>     Cloud provider (aws|azure|gcp|all)
  --account <account>       Specific account to analyze
  --period <period>         Analysis period (current-month|last-month|90-days|custom)
  --start-date <date>       Start date for custom period (YYYY-MM-DD)
  --end-date <date>         End date for custom period (YYYY-MM-DD)
  --categories <list>       Cost categories to analyze (compute,storage,network)
  --min-savings <amount>    Minimum savings threshold
  --output <file>           Output file path
  --format <format>         Output format (json|html|csv|pdf)
  --quick                   Fast analysis mode
  --detailed                Comprehensive analysis with trends

optimize

Implement cost optimization recommendations

cost-optimizer optimize [options]

Options:
  --recommendation-id <id>  Specific recommendation to implement
  --category <category>     Optimize specific category
  --auto-approve           Auto-approve low-risk changes
  --dry-run                Preview changes without implementing
  --schedule <time>         Schedule optimization for later
  --rollback-plan          Generate rollback plan

Best Practices

Cost optimization best practices to maximize savings while maintaining performance and reliability.

Cost Optimization Strategy

Run cost analysis weekly to identify trends early
Start with low-risk, high-impact optimizations
Implement budget alerts and spending thresholds
Monitor performance after cost optimizations
Use staging environments to test optimization changes
Review and adjust reserved instance commitments quarterly

Automation

Automate cost optimization with intelligent workflows and approval processes.

Automated Optimization Rules

Automation Rulesyaml

automation_rules:
  - name: "Auto-shutdown idle instances"
    trigger:
      condition: "idle_time > 24h AND cpu_utilization < 5%"
      cost_impact: "> 50"
    action: "shutdown"
    approval_required: false
    
  - name: "Right-size over-provisioned instances"
    trigger:
      condition: "cpu_utilization < 20% AND memory_utilization < 30%"
      duration: "7_days"
    action: "resize"
    approval_required: true
    
  - name: "Delete unused storage volumes"
    trigger:
      condition: "unattached_days > 30"
      cost_impact: "> 10"
    action: "delete"
    approval_required: true
    backup_required: true

Scheduled Optimization

Scheduled Analysisbash

# Schedule daily cost analysis
cost-optimizer schedule --analysis daily --time "02:00"

# Schedule weekly optimization review
cost-optimizer schedule --optimization weekly --day monday --time "09:00"

# Schedule monthly budget forecast
cost-optimizer schedule --forecast monthly --day 1 --time "08:00"

Reporting

Generate comprehensive cost reports for stakeholders and financial planning.

Report Types

Executive Dashboard

High-level cost trends and optimization ROI

Detailed Analysis

Resource-level cost breakdown and recommendations

Budget Forecast

Predictive cost modeling and budget planning

API Integration

Integrate AI CostOptimizer into your financial systems and FinOps workflows.

REST API

API Usagebash

# Trigger cost analysis via API
curl -X POST https://api.augment.cfd/v1/cost/analyze \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "providers": ["aws", "azure"],
    "period": "current-month",
    "min_savings": 500,
    "categories": ["compute", "storage"]
  }'

Python SDK

Python Integrationpython

from augment_cost_optimizer import CostOptimizer

# Initialize cost optimizer
optimizer = CostOptimizer(api_key=os.environ['AUGMENT_API_KEY'])

# Run cost analysis
analysis_result = await optimizer.analyze_costs(
    providers=['aws', 'azure', 'gcp'],
    period='last-30-days',
    min_savings=1000
)

# Get high-impact recommendations
recommendations = analysis_result.get_recommendations(impact='high')
print(f"Found {len(recommendations)} high-impact cost optimization opportunities")

# Generate cost forecast
forecast = await optimizer.generate_forecast(
    months=6,
    include_trends=True,
    scenario='current_growth'
)

# Implement approved recommendations
for rec in recommendations:
    if rec.approved and rec.risk_level == 'low':
        await optimizer.implement_recommendation(rec.id)

API Reference

Complete API documentation for integrating cost optimization into your applications.

Cost Analysis Endpoint

POST /v1/cost/analyze

Analyze cloud costs and provide optimization recommendations.

Request Body:

{
  "providers": ["aws", "azure", "gcp"],
  "accounts": ["account1", "account2"],
  "period": "current-month|last-month|90-days|custom",
  "start_date": "2025-01-01",
  "end_date": "2025-01-31",
  "categories": ["compute", "storage", "network", "database"],
  "min_savings": 500,
  "optimization_goals": {
    "target_reduction": 25,
    "risk_tolerance": "medium"
  }
}

Response:

{
  "analysis_id": "cost-analysis-123",
  "status": "completed",
  "summary": {
    "total_cost": 89450.67,
    "potential_savings": 23120.45,
    "savings_percentage": 25.85,
    "optimization_score": 82
  },
  "breakdown": {
    "aws": {
      "current_cost": 45230.12,
      "potential_savings": 12450.30
    },
    "azure": {
      "current_cost": 28940.33,
      "potential_savings": 7820.15
    },
    "gcp": {
      "current_cost": 15280.22,
      "potential_savings": 2850.00
    }
  },
  "recommendations": [
    {
      "id": "rec-001",
      "category": "compute",
      "priority": "high",
      "title": "Right-size EC2 instances",
      "description": "25 EC2 instances are over-provisioned by 40% on average",
      "estimated_savings": 4200.00,
      "implementation_effort": "low",
      "risk_level": "low",
      "affected_resources": 25
    }
  ],
  "forecast": {
    "next_month": 87230.45,
    "next_quarter": 261450.20,
    "confidence_level": 0.89
  }
}

Troubleshooting

Common issues and solutions when running cost analysis and optimization.

Common Issues

Billing API Access Denied

Error: Unable to access billing data from cloud provider

Solutions:

Verify billing API permissions are enabled
Check if MFA is required for billing access
Ensure service account has Cost Explorer/Billing permissions
Validate API credentials are current

Incomplete Cost Data

Missing or delayed cost data affecting analysis accuracy

Solutions:

Wait 24-48 hours for complete billing data
Use longer analysis periods for more accurate trends
Check if linked accounts are properly configured
Verify timezone settings match billing periods

Optimization Conflicts

Conflicting recommendations or failed optimizations

Solutions:

Review resource dependencies before optimization
Use staging environments to test changes
Implement changes gradually with rollback plans
Check for resource locks or policies blocking changes

Cost Optimization Documentation Complete!

You now have comprehensive knowledge to implement AI CostOptimizer in your FinOps workflow. From basic cost analysis to advanced predictive modeling, you're equipped to reduce cloud spending with AI-powered insights and automation.

Ready to optimize your cloud costs? Start your free cost analysis today and discover how AI can reduce your cloud spending by 25% or more.

Installation

Deploy AI CostOptimizer to start reducing cloud costs with intelligent analysis and automated optimization.

System Requirements

Python 3.9 or higher
Docker 20.0+ (for containerized deployment)
Node.js 16+ (for dashboard components)
Minimum 4GB RAM (8GB recommended for large accounts)
Access to cloud provider billing APIs

Install via Package Manager

Installationbash

# Install via pip
pip install augment-cost-optimizer

# Install via npm for Node.js projects
npm install -g @augment/cost-optimizer

# Install from source
git clone https://github.com/augment-ai/cost-optimizer
cd cost-optimizer
pip install -e .

# Verify installation
cost-optimizer --version

Billing API Access Setup

Configure access to your cloud provider billing APIs and Augment platform:

Billing API Setupbash

# Set Augment API key
export AUGMENT_API_KEY=your_api_key_here

# Configure AWS Cost Explorer access
aws configure
# Ensure billing access permissions are enabled

# Configure Azure Cost Management
az login
az account set --subscription your-subscription-id

# Configure GCP Billing API
gcloud auth application-default login
gcloud config set project your-project-id

# Verify billing access
cost-optimizer auth verify --all-providers

Quick Start

Get your first cost analysis and optimization recommendations in minutes.

1. Initialize Cost Profile

Profile Setupbash

# Create cost optimization profile
cost-optimizer init --organization "your-company"

# Configure billing scope
cost-optimizer config --providers aws,azure,gcp
cost-optimizer config --accounts account1,account2,account3

# Set optimization targets
cost-optimizer config --target-reduction 25%
cost-optimizer config --budget-threshold 10000

# This creates .cost-optimizer.yaml config file

2. Run Cost Analysis

Cost Analysisbash

# Analyze current month spending
cost-optimizer analyze --period current-month

# Analyze last 90 days for trends
cost-optimizer analyze --period 90-days --include-trends

# Quick cost assessment
cost-optimizer analyze --quick --high-impact-only

3. Generate Cost Reports

Cost Reportsbash

# Generate comprehensive cost report
cost-optimizer report --format html --output cost-report.html

# Export optimization recommendations
cost-optimizer report --format json --output recommendations.json

# Generate executive summary
cost-optimizer report --executive-summary --format pdf --output exec-summary.pdf

# Create budget forecast
cost-optimizer forecast --months 6 --output forecast.csv

Configuration

Configure AI CostOptimizer to align with your organization's financial policies and optimization goals.

Basic Configuration

.cost-optimizer.yamlyaml

version: "1.0"
organization: "your-company"
currency: "USD"

providers:
  aws:
    accounts: ["123456789012", "987654321098"]
    regions: ["us-east-1", "us-west-2", "eu-west-1"]
    linked_accounts: true
  azure:
    subscriptions: ["sub-123-456", "sub-789-012"]
    resource_groups: ["production", "staging"]
  gcp:
    projects: ["project-123", "project-456"]
    billing_accounts: ["billing-account-123"]

optimization_settings:
  target_reduction: 25
  min_savings_threshold: 100
  risk_tolerance: "medium"
  auto_implement: false
  approval_workflow: true

cost_policies:
  budget_alerts:
    - threshold: 80
      type: "percentage"
      notify: ["finance@company.com"]
  spending_limits:
    monthly_cap: 50000
    emergency_shutdown: true
  reserved_instances:
    recommendation_threshold: 0.7
    auto_purchase: false

Cost Models

AI CostOptimizer uses advanced machine learning models to analyze spending patterns and predict future costs.

Predictive Cost Modeling

• Time-series forecasting for resource usage
• Seasonal pattern recognition
• Growth trend analysis
• Budget variance prediction

Waste Detection Algorithms

• Idle resource identification
• Over-provisioned instance detection
• Unused storage analysis
• Zombie resource cleanup

Right-Sizing Intelligence

• Performance-based sizing recommendations
• Workload pattern analysis
• Cost-performance optimization
• Migration cost calculations

Commitment Analysis

• Reserved instance optimization
• Savings plan recommendations
• Spot instance opportunities
• Contract negotiation insights

Environment Variables

Configure AI CostOptimizer behavior using environment variables for different deployment scenarios.

Variable	Description	Default
AUGMENT_API_KEY	Your Augment API key	Required
COST_OPTIMIZER_CONFIG	Path to configuration file	.cost-optimizer.yaml
COST_OPTIMIZER_CURRENCY	Default currency for reports	USD
COST_OPTIMIZER_CACHE_TTL	Cache time-to-live in seconds	3600

Basic Usage

Learn the fundamental cost analysis patterns and optimization workflows.

Analysis Commands

Cost Analysisbash

# Full cost analysis across all providers
cost-optimizer analyze --all-providers

# Focus on specific cost categories
cost-optimizer analyze --categories compute,storage,network

# Analyze specific time periods
cost-optimizer analyze --start-date 2025-01-01 --end-date 2025-01-31

# High-impact optimization opportunities only
cost-optimizer analyze --min-savings 1000 --high-impact-only

CLI Commands Reference

Complete reference for all cost analysis and optimization commands.

analyze

Run comprehensive cost analysis and generate optimization recommendations

cost-optimizer analyze [options]

Options:
  --provider <provider>     Cloud provider (aws|azure|gcp|all)
  --account <account>       Specific account to analyze
  --period <period>         Analysis period (current-month|last-month|90-days|custom)
  --start-date <date>       Start date for custom period (YYYY-MM-DD)
  --end-date <date>         End date for custom period (YYYY-MM-DD)
  --categories <list>       Cost categories to analyze (compute,storage,network)
  --min-savings <amount>    Minimum savings threshold
  --output <file>           Output file path
  --format <format>         Output format (json|html|csv|pdf)
  --quick                   Fast analysis mode
  --detailed                Comprehensive analysis with trends

optimize

Implement cost optimization recommendations

cost-optimizer optimize [options]

Options:
  --recommendation-id <id>  Specific recommendation to implement
  --category <category>     Optimize specific category
  --auto-approve           Auto-approve low-risk changes
  --dry-run                Preview changes without implementing
  --schedule <time>         Schedule optimization for later
  --rollback-plan          Generate rollback plan

Best Practices

Cost optimization best practices to maximize savings while maintaining performance and reliability.

Cost Optimization Strategy

Run cost analysis weekly to identify trends early
Start with low-risk, high-impact optimizations
Implement budget alerts and spending thresholds
Monitor performance after cost optimizations
Use staging environments to test optimization changes
Review and adjust reserved instance commitments quarterly

Automation

Automate cost optimization with intelligent workflows and approval processes.

Automated Optimization Rules

Automation Rulesyaml

automation_rules:
  - name: "Auto-shutdown idle instances"
    trigger:
      condition: "idle_time > 24h AND cpu_utilization < 5%"
      cost_impact: "> 50"
    action: "shutdown"
    approval_required: false
    
  - name: "Right-size over-provisioned instances"
    trigger:
      condition: "cpu_utilization < 20% AND memory_utilization < 30%"
      duration: "7_days"
    action: "resize"
    approval_required: true
    
  - name: "Delete unused storage volumes"
    trigger:
      condition: "unattached_days > 30"
      cost_impact: "> 10"
    action: "delete"
    approval_required: true
    backup_required: true

Scheduled Optimization

Scheduled Analysisbash

# Schedule daily cost analysis
cost-optimizer schedule --analysis daily --time "02:00"

# Schedule weekly optimization review
cost-optimizer schedule --optimization weekly --day monday --time "09:00"

# Schedule monthly budget forecast
cost-optimizer schedule --forecast monthly --day 1 --time "08:00"

Reporting

Generate comprehensive cost reports for stakeholders and financial planning.

Report Types

Executive Dashboard

High-level cost trends and optimization ROI

Detailed Analysis

Resource-level cost breakdown and recommendations

Budget Forecast

Predictive cost modeling and budget planning

API Integration

Integrate AI CostOptimizer into your financial systems and FinOps workflows.

REST API

API Usagebash

# Trigger cost analysis via API
curl -X POST https://api.augment.cfd/v1/cost/analyze \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "providers": ["aws", "azure"],
    "period": "current-month",
    "min_savings": 500,
    "categories": ["compute", "storage"]
  }'

Python SDK

Python Integrationpython

from augment_cost_optimizer import CostOptimizer

# Initialize cost optimizer
optimizer = CostOptimizer(api_key=os.environ['AUGMENT_API_KEY'])

# Run cost analysis
analysis_result = await optimizer.analyze_costs(
    providers=['aws', 'azure', 'gcp'],
    period='last-30-days',
    min_savings=1000
)

# Get high-impact recommendations
recommendations = analysis_result.get_recommendations(impact='high')
print(f"Found {len(recommendations)} high-impact cost optimization opportunities")

# Generate cost forecast
forecast = await optimizer.generate_forecast(
    months=6,
    include_trends=True,
    scenario='current_growth'
)

# Implement approved recommendations
for rec in recommendations:
    if rec.approved and rec.risk_level == 'low':
        await optimizer.implement_recommendation(rec.id)

API Reference

Complete API documentation for integrating cost optimization into your applications.

Cost Analysis Endpoint

POST /v1/cost/analyze

Analyze cloud costs and provide optimization recommendations.

Request Body:

{
  "providers": ["aws", "azure", "gcp"],
  "accounts": ["account1", "account2"],
  "period": "current-month|last-month|90-days|custom",
  "start_date": "2025-01-01",
  "end_date": "2025-01-31",
  "categories": ["compute", "storage", "network", "database"],
  "min_savings": 500,
  "optimization_goals": {
    "target_reduction": 25,
    "risk_tolerance": "medium"
  }
}

Response:

{
  "analysis_id": "cost-analysis-123",
  "status": "completed",
  "summary": {
    "total_cost": 89450.67,
    "potential_savings": 23120.45,
    "savings_percentage": 25.85,
    "optimization_score": 82
  },
  "breakdown": {
    "aws": {
      "current_cost": 45230.12,
      "potential_savings": 12450.30
    },
    "azure": {
      "current_cost": 28940.33,
      "potential_savings": 7820.15
    },
    "gcp": {
      "current_cost": 15280.22,
      "potential_savings": 2850.00
    }
  },
  "recommendations": [
    {
      "id": "rec-001",
      "category": "compute",
      "priority": "high",
      "title": "Right-size EC2 instances",
      "description": "25 EC2 instances are over-provisioned by 40% on average",
      "estimated_savings": 4200.00,
      "implementation_effort": "low",
      "risk_level": "low",
      "affected_resources": 25
    }
  ],
  "forecast": {
    "next_month": 87230.45,
    "next_quarter": 261450.20,
    "confidence_level": 0.89
  }
}

Troubleshooting

Common issues and solutions when running cost analysis and optimization.

Common Issues

Billing API Access Denied

Error: Unable to access billing data from cloud provider

Solutions:

Verify billing API permissions are enabled
Check if MFA is required for billing access
Ensure service account has Cost Explorer/Billing permissions
Validate API credentials are current

Incomplete Cost Data

Missing or delayed cost data affecting analysis accuracy

Solutions:

Wait 24-48 hours for complete billing data
Use longer analysis periods for more accurate trends
Check if linked accounts are properly configured
Verify timezone settings match billing periods

Optimization Conflicts

Conflicting recommendations or failed optimizations

Solutions:

Review resource dependencies before optimization
Use staging environments to test changes
Implement changes gradually with rollback plans
Check for resource locks or policies blocking changes

Cost Optimization Documentation Complete!

Ready to optimize your cloud costs? Start your free cost analysis today and discover how AI can reduce your cloud spending by 25% or more.

AI CostOptimizer

AI-Driven Cost Analysis

Predictive Cost Modeling

Automated Optimization

Multi-Cloud FinOps

Installation

System Requirements

Install via Package Manager

Billing API Access Setup

Quick Start

1. Initialize Cost Profile

2. Run Cost Analysis

3. Generate Cost Reports

Configuration

Basic Configuration

Cost Models

Predictive Cost Modeling

Waste Detection Algorithms

Right-Sizing Intelligence

Commitment Analysis

Environment Variables

Basic Usage

Analysis Commands

CLI Commands Reference

analyze

optimize

Best Practices

Cost Optimization Strategy

Automation

Automated Optimization Rules

Scheduled Optimization

Reporting

Report Types

Executive Dashboard

Detailed Analysis

Budget Forecast

API Integration

REST API

Python SDK

API Reference

Cost Analysis Endpoint

POST /v1/cost/analyze

Request Body:

Response:

Troubleshooting

Common Issues

Billing API Access Denied

Incomplete Cost Data

Optimization Conflicts

Cost Optimization Documentation Complete!

AI CostOptimizer

AI-Driven Cost Analysis

Predictive Cost Modeling

Automated Optimization

Multi-Cloud FinOps

Installation

System Requirements

Install via Package Manager

Billing API Access Setup

Quick Start

1. Initialize Cost Profile

2. Run Cost Analysis

3. Generate Cost Reports

Configuration

Basic Configuration

Cost Models

Predictive Cost Modeling

Waste Detection Algorithms

Right-Sizing Intelligence

Commitment Analysis

Environment Variables

Basic Usage

Analysis Commands

CLI Commands Reference

analyze

optimize

Best Practices

Cost Optimization Strategy

Automation

Automated Optimization Rules