Documentation | Augment AI Platform

Installation

Deploy AI CloudOptimizer to start intelligent resource optimization and predictive scaling across your cloud infrastructure.

System Requirements

Python 3.9 or higher
Terraform 1.0+ (for infrastructure modifications)
Kubernetes 1.20+ (for container optimization)
Minimum 8GB RAM (16GB recommended for large environments)
Network access to cloud provider APIs and monitoring systems

Install via Package Manager

Installationbash

# Install via pip
pip install augment-cloud-optimizer

# Install via Docker
docker pull augment/cloud-optimizer:latest

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

# Install cloud provider tools
pip install boto3 azure-mgmt google-cloud

# Verify installation
cloud-optimizer --version

Cloud Provider Authentication

Configure access to your cloud providers and monitoring systems:

Cloud Authenticationbash

# Set Augment API key
export AUGMENT_API_KEY=your_api_key_here

# Configure AWS credentials
export AWS_ACCESS_KEY_ID=your_aws_key
export AWS_SECRET_ACCESS_KEY=your_aws_secret

# Configure Azure credentials
az login

# Configure GCP credentials
gcloud auth application-default login

# Initialize cloud optimizer
cloud-optimizer init --providers aws,azure,gcp

# Verify cloud access
cloud-optimizer health-check --all-providers

Quick Start

Start optimizing your cloud resources and implementing predictive scaling in minutes.

1. Analyze Current Resources

Resource Analysisbash

# Discover and analyze current resources
cloud-optimizer analyze --providers aws,azure,gcp
cloud-optimizer analyze --regions us-east-1,us-west-2,eastus

# Generate baseline optimization report
cloud-optimizer baseline --timeframe 30d --include-costs

# Identify optimization opportunities
cloud-optimizer opportunities --min-savings 100 --risk-level low

2. Configure Optimization Policies

Optimization Setupbash

# Set optimization targets
cloud-optimizer policy --cost-reduction 30% --performance-maintain 95%

# Configure auto-scaling rules
cloud-optimizer autoscale --enable --prediction-window 7d
cloud-optimizer autoscale --metrics cpu,memory,network --thresholds auto

# Enable continuous optimization
cloud-optimizer continuous --enable --interval 1h --approve-low-risk

3. Start Optimization

Resource Optimizationbash

# Start optimization daemon
cloud-optimizer optimize --daemon --continuous

# Execute specific optimizations
cloud-optimizer optimize --resources ec2,rds,kubernetes --dry-run

# Monitor optimization progress
cloud-optimizer status --live --include-savings

# Generate optimization report
cloud-optimizer report --format html --output optimization-report.html

Configuration

Configure AI CloudOptimizer to align with your performance requirements and optimization goals.

Basic Configuration

.cloud-optimizer.yamlyaml

version: "1.0"
organization: "your-company"
environment: "production"

providers:
  aws:
    regions: ["us-east-1", "us-west-2", "eu-west-1"]
    accounts: ["123456789012", "987654321098"]
  azure:
    regions: ["eastus", "westus2", "northeurope"]
    subscriptions: ["sub-123", "sub-456"]
  gcp:
    regions: ["us-central1", "us-west1", "europe-west1"]
    projects: ["project-123", "project-456"]

optimization_targets:
  cost_reduction: 30
  performance_threshold: 95
  availability_requirement: 99.9
  risk_tolerance: "medium"

resource_policies:
  compute:
    right_sizing: true
    spot_instances: true
    reserved_instances: true
    auto_shutdown: true
  storage:
    lifecycle_management: true
    compression: true
    tiering: true
    cleanup_unused: true
  network:
    bandwidth_optimization: true
    cdn_usage: true
    load_balancer_optimization: true

predictive_scaling:
  enabled: true
  prediction_window: "7d"
  scaling_algorithms: ["linear", "seasonal", "ml"]
  confidence_threshold: 0.8
  metrics:
    - "cpu_utilization"
    - "memory_utilization"
    - "network_throughput"
    - "request_rate"

automation:
  continuous_optimization: true
  optimization_interval: "1h"
  auto_approve_thresholds:
    cost_impact: 1000
    risk_level: "low"
  notification_channels:
    - "slack"
    - "email"
  rollback_on_issues: true

Resource Optimization

AI CloudOptimizer provides comprehensive resource optimization across all major cloud resource types.

Compute Optimization

• Right-sizing EC2, VM, and GCE instances
• Spot instance recommendations
• Reserved capacity optimization
• Auto-shutdown for idle resources

Storage Optimization

• Storage tier optimization
• Lifecycle policy automation
• Unused volume cleanup
• Compression and deduplication

Network Optimization

• Bandwidth usage optimization
• CDN configuration tuning
• Load balancer efficiency
• Data transfer cost reduction

Container Optimization

• Kubernetes resource limits
• Node pool optimization
• Container image optimization
• Workload placement

Environment Variables

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

Variable	Description	Default
AUGMENT_API_KEY	Your Augment API key	Required
CLOUD_OPTIMIZER_CONFIG	Path to configuration file	.cloud-optimizer.yaml
CLOUD_OPTIMIZER_DRY_RUN	Enable dry-run mode by default	false
CLOUD_OPTIMIZER_WORKERS	Number of optimization worker processes	4

Basic Usage

Learn the fundamental resource optimization patterns and cloud efficiency workflows.

Optimization Commands

Resource Optimizationbash

# Analyze resource utilization across providers
cloud-optimizer analyze --all-providers --detailed

# Optimize compute resources
cloud-optimizer optimize --resource-type compute --right-size --spot-instances

# Optimize storage resources
cloud-optimizer optimize --resource-type storage --lifecycle --cleanup

# Optimize network resources
cloud-optimizer optimize --resource-type network --bandwidth --cdn

CLI Commands Reference

Complete reference for all resource optimization and cloud efficiency commands.

optimize

Execute intelligent resource optimization with AI-powered recommendations

cloud-optimizer optimize [options]

Options:
  --resource-type <type>   Resource type (compute|storage|network|database|all)
  --provider <provider>    Cloud provider (aws|azure|gcp|all)
  --region <region>        Target region for optimization
  --cost-target <percent>  Target cost reduction percentage
  --performance <percent>  Minimum performance threshold
  --risk-level <level>     Risk tolerance (low|medium|high)
  --dry-run                Preview optimizations without applying
  --continuous             Enable continuous optimization
  --approve-threshold <amt> Auto-approve optimizations under threshold
  --output <file>          Output optimization report

predict

Generate predictive scaling recommendations

cloud-optimizer predict [options]

Options:
  --timeframe <period>     Prediction timeframe (1d|7d|30d|90d)
  --metrics <metrics>      Metrics to predict (cpu|memory|network|requests)
  --confidence <level>     Minimum prediction confidence
  --scaling-events         Include scaling event predictions
  --cost-forecast          Generate cost forecast
  --seasonality            Include seasonal patterns
  --growth-trends          Account for growth trends

Best Practices

Cloud optimization best practices to maximize efficiency while maintaining performance and reliability.

Optimization Strategy

Start with low-risk optimizations to build confidence
Establish performance baselines before optimization
Use gradual optimization rollouts in production
Monitor performance metrics continuously after changes
Implement automated rollback for performance degradation
Regular review and adjustment of optimization policies

Predictive Scaling

Advanced predictive scaling capabilities that anticipate resource needs and scale proactively.

Prediction Models

Linear Trends

Predict based on historical linear growth patterns

Seasonal Patterns

Account for daily, weekly, and monthly usage cycles

Machine Learning

Advanced ML models for complex usage patterns

Scaling Configuration

Predictive Scaling Setupbash

# Configure predictive scaling for auto-scaling groups
cloud-optimizer predict-scale --resource asg-web-servers \
  --prediction-window 7d \
  --confidence 0.8 \
  --scale-ahead 30m

# Set up Kubernetes predictive scaling
cloud-optimizer predict-scale --resource k8s-deployment \
  --namespace production \
  --metrics cpu,memory \
  --seasonal-patterns true

# Configure database predictive scaling
cloud-optimizer predict-scale --resource rds-cluster \
  --metrics connections,cpu \
  --scale-type read-replicas

Multi-Cloud Support

Comprehensive multi-cloud optimization capabilities for hybrid and distributed cloud architectures.

Cross-Cloud Optimization

Multi-Cloud Operationsbash

# Compare costs across cloud providers
cloud-optimizer compare --providers aws,azure,gcp --resource-types compute,storage

# Optimize workload placement
cloud-optimizer placement --workload web-tier \
  --optimize-for cost \
  --constraints latency<100ms,availability>99.9%

# Multi-cloud resource migration recommendations
cloud-optimizer migrate --from aws --to azure \
  --workload batch-processing \
  --cost-threshold 20%

API Integration

Integrate AI CloudOptimizer into your cloud management and automation workflows.

REST API

API Usagebash

# Trigger optimization analysis via API
curl -X POST https://api.augment.cfd/v1/optimization/analyze \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "providers": ["aws", "azure"],
    "resource_types": ["compute", "storage"],
    "optimization_targets": {
      "cost_reduction": 30,
      "performance_threshold": 95
    }
  }'

Python SDK

Python Integrationpython

from augment_cloud_optimizer import CloudOptimizer

# Initialize cloud optimizer
optimizer = CloudOptimizer(api_key=os.environ['AUGMENT_API_KEY'])

# Analyze current resource usage
analysis = await optimizer.analyze_resources(
    providers=['aws', 'azure', 'gcp'],
    timeframe='30d',
    include_costs=True
)

# Get optimization recommendations
recommendations = await optimizer.get_recommendations(
    cost_target=30,
    performance_threshold=95,
    risk_level='medium'
)

print(f"Found {len(recommendations)} optimization opportunities")

# Execute optimizations
for rec in recommendations:
    if rec.risk_level == 'low' and rec.estimated_savings > 1000:
        result = await optimizer.execute_optimization(rec.id)
        print(f"Applied optimization {rec.id}: {result.status}")

# Set up predictive scaling
scaling_config = await optimizer.configure_predictive_scaling(
    resources=['asg-web', 'k8s-api'],
    prediction_window='7d',
    confidence_threshold=0.8
)

API Reference

Complete API documentation for integrating cloud optimization into your applications.

Optimization Analysis Endpoint

POST /v1/optimization/analyze

Analyze cloud resources and generate optimization recommendations.

Request Body:

{
  "providers": ["aws", "azure", "gcp"],
  "scope": {
    "regions": ["us-east-1", "eastus", "us-central1"],
    "accounts": ["account1", "account2"],
    "resource_types": ["compute", "storage", "network"]
  },
  "optimization_targets": {
    "cost_reduction": 30,
    "performance_threshold": 95,
    "availability_requirement": 99.9
  },
  "analysis_options": {
    "timeframe": "30d",
    "include_predictions": true,
    "risk_tolerance": "medium",
    "include_multi_cloud": true
  }
}

Response:

{
  "analysis_id": "opt-analysis-123",
  "status": "completed",
  "summary": {
    "total_resources": 1247,
    "optimization_opportunities": 156,
    "potential_monthly_savings": 18450.67,
    "current_efficiency_score": 72
  },
  "recommendations": [
    {
      "id": "rec-001",
      "type": "right_sizing",
      "resource_type": "compute",
      "provider": "aws",
      "resource_id": "i-1234567890abcdef0",
      "current_config": {
        "instance_type": "m5.xlarge",
        "vcpu": 4,
        "memory": "16 GiB",
        "monthly_cost": 146.0
      },
      "recommended_config": {
        "instance_type": "m5.large",
        "vcpu": 2,
        "memory": "8 GiB",
        "monthly_cost": 73.0
      },
      "estimated_savings": 73.0,
      "confidence": 0.89,
      "risk_level": "low",
      "performance_impact": "minimal",
      "implementation_steps": [
        "Stop instance during maintenance window",
        "Change instance type to m5.large",
        "Start instance and verify performance"
      ]
    }
  ],
  "predictive_insights": {
    "scaling_predictions": [
      {
        "resource": "asg-web-servers",
        "prediction_window": "7d",
        "predicted_peak": "2025-09-25T14:00:00Z",
        "recommended_capacity": 8,
        "confidence": 0.92
      }
    ],
    "cost_forecast": {
      "next_month": 42300.45,
      "optimized_next_month": 29610.32,
      "savings_potential": 12690.13
    }
  }
}

Troubleshooting

Common issues and solutions when running cloud optimization and resource management.

Common Issues

Optimization Rollbacks

Optimizations causing performance issues requiring rollback

Solutions:

Use more conservative optimization thresholds
Implement gradual rollout with monitoring
Extend baseline analysis period
Test optimizations in staging environments first

Predictive Scaling Accuracy

Predictive scaling making inaccurate resource predictions

Solutions:

Increase historical data collection period
Account for business events and seasonality
Adjust confidence thresholds for predictions
Use multiple prediction models for consensus

Multi-Cloud Complexity

Difficulties managing optimization across multiple cloud providers

Solutions:

Start optimization with single provider first
Use provider-specific optimization policies
Implement centralized monitoring and alerting
Standardize resource tagging across providers

Cloud Optimization Documentation Complete!

You now have comprehensive knowledge to implement AI CloudOptimizer in your cloud infrastructure. From intelligent resource optimization to predictive scaling, you're equipped to maximize efficiency and reduce costs with AI-powered cloud management.

Ready to optimize your cloud resources? Start your free optimization assessment today and discover how AI can reduce costs while improving performance across your multi-cloud infrastructure.

Installation

Deploy AI CloudOptimizer to start intelligent resource optimization and predictive scaling across your cloud infrastructure.

System Requirements

Python 3.9 or higher
Terraform 1.0+ (for infrastructure modifications)
Kubernetes 1.20+ (for container optimization)
Minimum 8GB RAM (16GB recommended for large environments)
Network access to cloud provider APIs and monitoring systems

Install via Package Manager

Installationbash

# Install via pip
pip install augment-cloud-optimizer

# Install via Docker
docker pull augment/cloud-optimizer:latest

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

# Install cloud provider tools
pip install boto3 azure-mgmt google-cloud

# Verify installation
cloud-optimizer --version

Cloud Provider Authentication

Configure access to your cloud providers and monitoring systems:

Cloud Authenticationbash

# Set Augment API key
export AUGMENT_API_KEY=your_api_key_here

# Configure AWS credentials
export AWS_ACCESS_KEY_ID=your_aws_key
export AWS_SECRET_ACCESS_KEY=your_aws_secret

# Configure Azure credentials
az login

# Configure GCP credentials
gcloud auth application-default login

# Initialize cloud optimizer
cloud-optimizer init --providers aws,azure,gcp

# Verify cloud access
cloud-optimizer health-check --all-providers

Quick Start

Start optimizing your cloud resources and implementing predictive scaling in minutes.

1. Analyze Current Resources

Resource Analysisbash

# Discover and analyze current resources
cloud-optimizer analyze --providers aws,azure,gcp
cloud-optimizer analyze --regions us-east-1,us-west-2,eastus

# Generate baseline optimization report
cloud-optimizer baseline --timeframe 30d --include-costs

# Identify optimization opportunities
cloud-optimizer opportunities --min-savings 100 --risk-level low

2. Configure Optimization Policies

Optimization Setupbash

# Set optimization targets
cloud-optimizer policy --cost-reduction 30% --performance-maintain 95%

# Configure auto-scaling rules
cloud-optimizer autoscale --enable --prediction-window 7d
cloud-optimizer autoscale --metrics cpu,memory,network --thresholds auto

# Enable continuous optimization
cloud-optimizer continuous --enable --interval 1h --approve-low-risk

3. Start Optimization

Resource Optimizationbash

# Start optimization daemon
cloud-optimizer optimize --daemon --continuous

# Execute specific optimizations
cloud-optimizer optimize --resources ec2,rds,kubernetes --dry-run

# Monitor optimization progress
cloud-optimizer status --live --include-savings

# Generate optimization report
cloud-optimizer report --format html --output optimization-report.html

Configuration

Configure AI CloudOptimizer to align with your performance requirements and optimization goals.

Basic Configuration

.cloud-optimizer.yamlyaml

version: "1.0"
organization: "your-company"
environment: "production"

providers:
  aws:
    regions: ["us-east-1", "us-west-2", "eu-west-1"]
    accounts: ["123456789012", "987654321098"]
  azure:
    regions: ["eastus", "westus2", "northeurope"]
    subscriptions: ["sub-123", "sub-456"]
  gcp:
    regions: ["us-central1", "us-west1", "europe-west1"]
    projects: ["project-123", "project-456"]

optimization_targets:
  cost_reduction: 30
  performance_threshold: 95
  availability_requirement: 99.9
  risk_tolerance: "medium"

resource_policies:
  compute:
    right_sizing: true
    spot_instances: true
    reserved_instances: true
    auto_shutdown: true
  storage:
    lifecycle_management: true
    compression: true
    tiering: true
    cleanup_unused: true
  network:
    bandwidth_optimization: true
    cdn_usage: true
    load_balancer_optimization: true

predictive_scaling:
  enabled: true
  prediction_window: "7d"
  scaling_algorithms: ["linear", "seasonal", "ml"]
  confidence_threshold: 0.8
  metrics:
    - "cpu_utilization"
    - "memory_utilization"
    - "network_throughput"
    - "request_rate"

automation:
  continuous_optimization: true
  optimization_interval: "1h"
  auto_approve_thresholds:
    cost_impact: 1000
    risk_level: "low"
  notification_channels:
    - "slack"
    - "email"
  rollback_on_issues: true

Resource Optimization

AI CloudOptimizer provides comprehensive resource optimization across all major cloud resource types.

Compute Optimization

• Right-sizing EC2, VM, and GCE instances
• Spot instance recommendations
• Reserved capacity optimization
• Auto-shutdown for idle resources

Storage Optimization

• Storage tier optimization
• Lifecycle policy automation
• Unused volume cleanup
• Compression and deduplication

Network Optimization

• Bandwidth usage optimization
• CDN configuration tuning
• Load balancer efficiency
• Data transfer cost reduction

Container Optimization

• Kubernetes resource limits
• Node pool optimization
• Container image optimization
• Workload placement

Environment Variables

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

Variable	Description	Default
AUGMENT_API_KEY	Your Augment API key	Required
CLOUD_OPTIMIZER_CONFIG	Path to configuration file	.cloud-optimizer.yaml
CLOUD_OPTIMIZER_DRY_RUN	Enable dry-run mode by default	false
CLOUD_OPTIMIZER_WORKERS	Number of optimization worker processes	4

Basic Usage

Learn the fundamental resource optimization patterns and cloud efficiency workflows.

Optimization Commands

Resource Optimizationbash

# Analyze resource utilization across providers
cloud-optimizer analyze --all-providers --detailed

# Optimize compute resources
cloud-optimizer optimize --resource-type compute --right-size --spot-instances

# Optimize storage resources
cloud-optimizer optimize --resource-type storage --lifecycle --cleanup

# Optimize network resources
cloud-optimizer optimize --resource-type network --bandwidth --cdn

CLI Commands Reference

Complete reference for all resource optimization and cloud efficiency commands.

optimize

Execute intelligent resource optimization with AI-powered recommendations

cloud-optimizer optimize [options]

Options:
  --resource-type <type>   Resource type (compute|storage|network|database|all)
  --provider <provider>    Cloud provider (aws|azure|gcp|all)
  --region <region>        Target region for optimization
  --cost-target <percent>  Target cost reduction percentage
  --performance <percent>  Minimum performance threshold
  --risk-level <level>     Risk tolerance (low|medium|high)
  --dry-run                Preview optimizations without applying
  --continuous             Enable continuous optimization
  --approve-threshold <amt> Auto-approve optimizations under threshold
  --output <file>          Output optimization report

predict

Generate predictive scaling recommendations

cloud-optimizer predict [options]

Options:
  --timeframe <period>     Prediction timeframe (1d|7d|30d|90d)
  --metrics <metrics>      Metrics to predict (cpu|memory|network|requests)
  --confidence <level>     Minimum prediction confidence
  --scaling-events         Include scaling event predictions
  --cost-forecast          Generate cost forecast
  --seasonality            Include seasonal patterns
  --growth-trends          Account for growth trends

Best Practices

Cloud optimization best practices to maximize efficiency while maintaining performance and reliability.

Optimization Strategy

Start with low-risk optimizations to build confidence
Establish performance baselines before optimization
Use gradual optimization rollouts in production
Monitor performance metrics continuously after changes
Implement automated rollback for performance degradation
Regular review and adjustment of optimization policies

Predictive Scaling

Advanced predictive scaling capabilities that anticipate resource needs and scale proactively.

Prediction Models

Linear Trends

Predict based on historical linear growth patterns

Seasonal Patterns

Account for daily, weekly, and monthly usage cycles

Machine Learning

Advanced ML models for complex usage patterns

Scaling Configuration

Predictive Scaling Setupbash

# Configure predictive scaling for auto-scaling groups
cloud-optimizer predict-scale --resource asg-web-servers \
  --prediction-window 7d \
  --confidence 0.8 \
  --scale-ahead 30m

# Set up Kubernetes predictive scaling
cloud-optimizer predict-scale --resource k8s-deployment \
  --namespace production \
  --metrics cpu,memory \
  --seasonal-patterns true

# Configure database predictive scaling
cloud-optimizer predict-scale --resource rds-cluster \
  --metrics connections,cpu \
  --scale-type read-replicas

Multi-Cloud Support

Comprehensive multi-cloud optimization capabilities for hybrid and distributed cloud architectures.

Cross-Cloud Optimization

Multi-Cloud Operationsbash

# Compare costs across cloud providers
cloud-optimizer compare --providers aws,azure,gcp --resource-types compute,storage

# Optimize workload placement
cloud-optimizer placement --workload web-tier \
  --optimize-for cost \
  --constraints latency<100ms,availability>99.9%

# Multi-cloud resource migration recommendations
cloud-optimizer migrate --from aws --to azure \
  --workload batch-processing \
  --cost-threshold 20%

API Integration

Integrate AI CloudOptimizer into your cloud management and automation workflows.

REST API

API Usagebash

# Trigger optimization analysis via API
curl -X POST https://api.augment.cfd/v1/optimization/analyze \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "providers": ["aws", "azure"],
    "resource_types": ["compute", "storage"],
    "optimization_targets": {
      "cost_reduction": 30,
      "performance_threshold": 95
    }
  }'

Python SDK

Python Integrationpython

from augment_cloud_optimizer import CloudOptimizer

# Initialize cloud optimizer
optimizer = CloudOptimizer(api_key=os.environ['AUGMENT_API_KEY'])

# Analyze current resource usage
analysis = await optimizer.analyze_resources(
    providers=['aws', 'azure', 'gcp'],
    timeframe='30d',
    include_costs=True
)

# Get optimization recommendations
recommendations = await optimizer.get_recommendations(
    cost_target=30,
    performance_threshold=95,
    risk_level='medium'
)

print(f"Found {len(recommendations)} optimization opportunities")

# Execute optimizations
for rec in recommendations:
    if rec.risk_level == 'low' and rec.estimated_savings > 1000:
        result = await optimizer.execute_optimization(rec.id)
        print(f"Applied optimization {rec.id}: {result.status}")

# Set up predictive scaling
scaling_config = await optimizer.configure_predictive_scaling(
    resources=['asg-web', 'k8s-api'],
    prediction_window='7d',
    confidence_threshold=0.8
)

API Reference

Complete API documentation for integrating cloud optimization into your applications.

Optimization Analysis Endpoint

POST /v1/optimization/analyze

Analyze cloud resources and generate optimization recommendations.

Request Body:

{
  "providers": ["aws", "azure", "gcp"],
  "scope": {
    "regions": ["us-east-1", "eastus", "us-central1"],
    "accounts": ["account1", "account2"],
    "resource_types": ["compute", "storage", "network"]
  },
  "optimization_targets": {
    "cost_reduction": 30,
    "performance_threshold": 95,
    "availability_requirement": 99.9
  },
  "analysis_options": {
    "timeframe": "30d",
    "include_predictions": true,
    "risk_tolerance": "medium",
    "include_multi_cloud": true
  }
}

Response:

{
  "analysis_id": "opt-analysis-123",
  "status": "completed",
  "summary": {
    "total_resources": 1247,
    "optimization_opportunities": 156,
    "potential_monthly_savings": 18450.67,
    "current_efficiency_score": 72
  },
  "recommendations": [
    {
      "id": "rec-001",
      "type": "right_sizing",
      "resource_type": "compute",
      "provider": "aws",
      "resource_id": "i-1234567890abcdef0",
      "current_config": {
        "instance_type": "m5.xlarge",
        "vcpu": 4,
        "memory": "16 GiB",
        "monthly_cost": 146.0
      },
      "recommended_config": {
        "instance_type": "m5.large",
        "vcpu": 2,
        "memory": "8 GiB",
        "monthly_cost": 73.0
      },
      "estimated_savings": 73.0,
      "confidence": 0.89,
      "risk_level": "low",
      "performance_impact": "minimal",
      "implementation_steps": [
        "Stop instance during maintenance window",
        "Change instance type to m5.large",
        "Start instance and verify performance"
      ]
    }
  ],
  "predictive_insights": {
    "scaling_predictions": [
      {
        "resource": "asg-web-servers",
        "prediction_window": "7d",
        "predicted_peak": "2025-09-25T14:00:00Z",
        "recommended_capacity": 8,
        "confidence": 0.92
      }
    ],
    "cost_forecast": {
      "next_month": 42300.45,
      "optimized_next_month": 29610.32,
      "savings_potential": 12690.13
    }
  }
}

Troubleshooting

Common issues and solutions when running cloud optimization and resource management.

Common Issues

Optimization Rollbacks

Optimizations causing performance issues requiring rollback

Solutions:

Use more conservative optimization thresholds
Implement gradual rollout with monitoring
Extend baseline analysis period
Test optimizations in staging environments first

Predictive Scaling Accuracy

Predictive scaling making inaccurate resource predictions

Solutions:

Increase historical data collection period
Account for business events and seasonality
Adjust confidence thresholds for predictions
Use multiple prediction models for consensus

Multi-Cloud Complexity

Difficulties managing optimization across multiple cloud providers

Solutions:

Start optimization with single provider first
Use provider-specific optimization policies
Implement centralized monitoring and alerting
Standardize resource tagging across providers

Cloud Optimization Documentation Complete!

Ready to optimize your cloud resources? Start your free optimization assessment today and discover how AI can reduce costs while improving performance across your multi-cloud infrastructure.

AI CloudOptimizer

Intelligent Resource Optimization

Predictive Scaling

Multi-Cloud Efficiency

Cost Savings

Installation

System Requirements

Install via Package Manager

Cloud Provider Authentication

Quick Start

1. Analyze Current Resources

2. Configure Optimization Policies

3. Start Optimization

Configuration

Basic Configuration

Resource Optimization

Compute Optimization

Storage Optimization

Network Optimization

Container Optimization

Environment Variables

Basic Usage

Optimization Commands

CLI Commands Reference

optimize

predict

Best Practices

Optimization Strategy

Predictive Scaling

Prediction Models

Linear Trends

Seasonal Patterns

Machine Learning

Scaling Configuration

Multi-Cloud Support

Cross-Cloud Optimization

API Integration

REST API

Python SDK

API Reference

Optimization Analysis Endpoint

POST /v1/optimization/analyze

Request Body:

Response:

Troubleshooting

Common Issues

Optimization Rollbacks

Predictive Scaling Accuracy

Multi-Cloud Complexity

Cloud Optimization Documentation Complete!

AI CloudOptimizer

Intelligent Resource Optimization

Predictive Scaling

Multi-Cloud Efficiency

Cost Savings

Installation

System Requirements

Install via Package Manager

Cloud Provider Authentication

Quick Start

1. Analyze Current Resources

2. Configure Optimization Policies

3. Start Optimization

Configuration

Basic Configuration

Resource Optimization

Compute Optimization

Storage Optimization

Network Optimization

Container Optimization

Environment Variables

Basic Usage

Optimization Commands

CLI Commands Reference

optimize

predict

Best Practices

Optimization Strategy

Predictive Scaling

Prediction Models