Documentation | Augment AI Platform

Installation

Deploy AI CloudArchitect to start optimizing your multi-cloud infrastructure with intelligent recommendations.

System Requirements

Python 3.9 or higher
Terraform 1.0+ (for infrastructure analysis)
Docker 20.0+ (for containerized deployments)
Minimum 8GB RAM (16GB recommended for large infrastructures)
Network access to cloud provider APIs

Install via Package Manager

Installationbash

# Install via pip
pip install augment-cloud-architect

# Install via conda
conda install -c augment cloud-architect

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

# Verify installation
cloud-architect --version

Multi-Cloud Authentication

Configure access to your cloud providers and Augment API:

Authentication Setupbash

# Set Augment API key
export AUGMENT_API_KEY=your_api_key_here

# Configure AWS credentials
aws configure
# or
export AWS_ACCESS_KEY_ID=your_aws_key
export AWS_SECRET_ACCESS_KEY=your_aws_secret

# Configure Azure CLI
az login

# Configure GCP credentials
gcloud auth application-default login

# Verify multi-cloud access
cloud-architect auth verify --all-providers

Quick Start

Get intelligent architecture recommendations for your infrastructure in minutes.

1. Initialize Architecture Profile

Profile Setupbash

# Create architecture profile
cloud-architect init --environment production

# Configure analysis scope
cloud-architect config --providers aws,azure,gcp
cloud-architect config --regions us-east-1,us-west-2,eu-west-1

# This creates .cloud-architect.yaml config file

2. Run Infrastructure Analysis

Architecture Analysisbash

# Analyze entire infrastructure
cloud-architect analyze

# Analyze specific cloud provider
cloud-architect analyze --provider aws

# Focus on cost optimization
cloud-architect analyze --focus cost-optimization

# Quick architecture assessment
cloud-architect analyze --quick

3. Generate Architecture Report

Architecture Reportsbash

# Generate comprehensive architecture report
cloud-architect report --format html --output architecture-report.html

# Export recommendations as JSON
cloud-architect report --format json --output recommendations.json

# Generate executive summary
cloud-architect report --summary --format pdf --output executive-summary.pdf

Configuration

Configure AI CloudArchitect to align with your organization's architecture standards and optimization goals.

Basic Configuration

.cloud-architect.yamlyaml

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

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

optimization_goals:
  cost_reduction:
    target_percentage: 20
    priority: high
  performance:
    latency_target: "100ms"
    throughput_increase: "50%"
  reliability:
    uptime_target: "99.99%"
    disaster_recovery: true

architecture_patterns:
  - microservices
  - serverless
  - event_driven
  - multi_region

Architecture Patterns

AI CloudArchitect recognizes and optimizes various architecture patterns for maximum efficiency and performance.

Microservices Architecture

• Service mesh optimization
• Container right-sizing
• Load balancer configuration
• Inter-service communication optimization

Serverless Architecture

• Function memory optimization
• Cold start reduction
• Event source configuration
• Cost-effective trigger patterns

Event-Driven Architecture

• Event streaming optimization
• Message queue tuning
• Event sourcing patterns
• Pub/sub configuration

Multi-Region Architecture

• Global load balancing
• Data replication strategies
• Disaster recovery planning
• Cross-region networking

Environment Variables

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

Variable	Description	Default
AUGMENT_API_KEY	Your Augment API key	Required
CLOUD_ARCHITECT_CONFIG	Path to configuration file	.cloud-architect.yaml
CLOUD_ARCHITECT_PARALLEL	Number of parallel analysis threads	4

Basic Usage

Learn the fundamental analysis patterns and optimization workflows for cloud architecture.

Analysis Commands

Architecture Analysisbash

# Full multi-cloud analysis
cloud-architect analyze --all-providers

# Cost optimization focus
cloud-architect analyze --focus cost --threshold 15%

# Performance optimization
cloud-architect analyze --focus performance --target-latency 50ms

# Security architecture review
cloud-architect analyze --focus security --compliance soc2

CLI Commands Reference

Complete reference for all architecture analysis and optimization commands.

analyze

Run comprehensive architecture analysis and optimization

cloud-architect analyze [options]

Options:
  --provider <provider>   Cloud provider (aws|azure|gcp|all)
  --region <region>       Target region for analysis
  --focus <area>          Focus area (cost|performance|security|reliability)
  --threshold <percent>   Minimum improvement threshold
  --output <file>         Output file path
  --format <format>       Output format (json|html|pdf|yaml)
  --recommendations       Include detailed recommendations
  --quick                 Fast analysis mode
  --detailed              Comprehensive analysis mode

Best Practices

Architecture optimization best practices to maximize the value of AI-driven recommendations.

Architecture Optimization Strategy

Run comprehensive analysis monthly for production environments
Implement high-impact, low-risk recommendations first
Monitor performance after implementing changes
Use staging environments to validate recommendations
Establish baseline metrics before optimization

Multi-Cloud Support

AI CloudArchitect provides comprehensive support for multi-cloud and hybrid cloud architectures.

Cross-Cloud Optimization

Multi-Cloud Analysisbash

# Compare costs across cloud providers
cloud-architect compare --metric cost --providers aws,azure,gcp

# Analyze workload placement optimization
cloud-architect optimize --workload web-tier --target-cost 1000

# Generate migration recommendations
cloud-architect migrate --from aws --to azure --workload database

# Multi-cloud disaster recovery planning
cloud-architect dr-plan --primary aws --secondary azure

Cost Optimization

Advanced cost optimization features to reduce cloud spending while maintaining performance.

Cost Analysis Features

Right-Sizing

Optimize instance sizes based on actual usage patterns

Reserved Instances

Identify opportunities for reserved capacity savings

Spot Instances

Recommend spot instance usage for fault-tolerant workloads

API Integration

Integrate AI CloudArchitect into your infrastructure automation and DevOps pipelines.

REST API

API Usagebash

# Trigger architecture analysis via API
curl -X POST https://api.augment.cfd/v1/architecture/analyze \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "providers": ["aws", "azure"],
    "regions": ["us-east-1", "eastus"],
    "focus": "cost_optimization",
    "threshold": 10
  }'

Python SDK

Python Integrationpython

from augment_cloud_architect import CloudArchitect

# Initialize cloud architect
architect = CloudArchitect(api_key=os.environ['AUGMENT_API_KEY'])

# Run multi-cloud analysis
analysis_result = await architect.analyze_infrastructure(
    providers=['aws', 'azure', 'gcp'],
    focus='cost_optimization',
    threshold=15
)

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

# Generate cost savings report
savings_report = await architect.generate_savings_report(
    format='pdf',
    include_timeline=True
)

API Reference

Complete API documentation for integrating architecture analysis into your applications.

Architecture Analysis Endpoint

POST /v1/architecture/analyze

Analyze cloud infrastructure and provide optimization recommendations.

Request Body:

{
  "providers": ["aws", "azure", "gcp"],
  "regions": ["us-east-1", "eastus", "us-central1"],
  "focus": "cost_optimization|performance|security|reliability",
  "threshold": 10,
  "architecture_patterns": ["microservices", "serverless"],
  "optimization_goals": {
    "cost_reduction": 20,
    "performance_improvement": 30
  }
}

Response:

{
  "analysis_id": "arch-analysis-123",
  "status": "completed",
  "summary": {
    "total_resources": 234,
    "optimization_score": 87,
    "potential_monthly_savings": 12400,
    "performance_improvement": 45
  },
  "recommendations": [
    {
      "id": "rec-001",
      "category": "cost_optimization",
      "priority": "high",
      "title": "Right-size EC2 instances",
      "description": "15 EC2 instances are over-provisioned",
      "estimated_savings": 3200,
      "implementation_effort": "low",
      "impact": "high",
      "resources": ["i-123456", "i-789012"]
    }
  ],
  "cost_analysis": {
    "current_monthly_cost": 45600,
    "optimized_monthly_cost": 33200,
    "savings_percentage": 27.2
  }
}

Troubleshooting

Common issues and solutions when running architecture analysis and optimization.

Common Issues

API Rate Limits

Error: Cloud provider API rate limits exceeded

Solutions:

Reduce analysis parallelism with --parallel flag
Implement exponential backoff in automation
Use provider-specific rate limit configurations
Schedule analysis during off-peak hours

Large Infrastructure Analysis

Analysis taking too long on large infrastructures

Solutions:

Use regional analysis to break down scope
Filter by resource types or tags
Use quick analysis mode for initial assessment
Increase memory allocation for analysis process

Inconsistent Recommendations

Recommendations vary between analysis runs

Solutions:

Use consistent time windows for usage analysis
Account for workload seasonality patterns
Set stable baseline metrics
Use deterministic analysis modes

Architecture Documentation Complete!

You now have comprehensive knowledge to implement AI CloudArchitect in your multi-cloud environment. From basic infrastructure analysis to advanced cost optimization, you're equipped to transform your cloud architecture with AI-powered insights.

Ready to optimize your cloud architecture? Start your free analysis today and discover how AI can reduce costs and improve performance across your multi-cloud infrastructure.

Installation

Deploy AI CloudArchitect to start optimizing your multi-cloud infrastructure with intelligent recommendations.

System Requirements

Python 3.9 or higher
Terraform 1.0+ (for infrastructure analysis)
Docker 20.0+ (for containerized deployments)
Minimum 8GB RAM (16GB recommended for large infrastructures)
Network access to cloud provider APIs

Install via Package Manager

Installationbash

# Install via pip
pip install augment-cloud-architect

# Install via conda
conda install -c augment cloud-architect

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

# Verify installation
cloud-architect --version

Multi-Cloud Authentication

Configure access to your cloud providers and Augment API:

Authentication Setupbash

# Set Augment API key
export AUGMENT_API_KEY=your_api_key_here

# Configure AWS credentials
aws configure
# or
export AWS_ACCESS_KEY_ID=your_aws_key
export AWS_SECRET_ACCESS_KEY=your_aws_secret

# Configure Azure CLI
az login

# Configure GCP credentials
gcloud auth application-default login

# Verify multi-cloud access
cloud-architect auth verify --all-providers

Quick Start

Get intelligent architecture recommendations for your infrastructure in minutes.

1. Initialize Architecture Profile

Profile Setupbash

# Create architecture profile
cloud-architect init --environment production

# Configure analysis scope
cloud-architect config --providers aws,azure,gcp
cloud-architect config --regions us-east-1,us-west-2,eu-west-1

# This creates .cloud-architect.yaml config file

2. Run Infrastructure Analysis

Architecture Analysisbash

# Analyze entire infrastructure
cloud-architect analyze

# Analyze specific cloud provider
cloud-architect analyze --provider aws

# Focus on cost optimization
cloud-architect analyze --focus cost-optimization

# Quick architecture assessment
cloud-architect analyze --quick

3. Generate Architecture Report

Architecture Reportsbash

# Generate comprehensive architecture report
cloud-architect report --format html --output architecture-report.html

# Export recommendations as JSON
cloud-architect report --format json --output recommendations.json

# Generate executive summary
cloud-architect report --summary --format pdf --output executive-summary.pdf

Configuration

Configure AI CloudArchitect to align with your organization's architecture standards and optimization goals.

Basic Configuration

.cloud-architect.yamlyaml

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

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

optimization_goals:
  cost_reduction:
    target_percentage: 20
    priority: high
  performance:
    latency_target: "100ms"
    throughput_increase: "50%"
  reliability:
    uptime_target: "99.99%"
    disaster_recovery: true

architecture_patterns:
  - microservices
  - serverless
  - event_driven
  - multi_region

Architecture Patterns

AI CloudArchitect recognizes and optimizes various architecture patterns for maximum efficiency and performance.

Microservices Architecture

• Service mesh optimization
• Container right-sizing
• Load balancer configuration
• Inter-service communication optimization

Serverless Architecture

• Function memory optimization
• Cold start reduction
• Event source configuration
• Cost-effective trigger patterns

Event-Driven Architecture

• Event streaming optimization
• Message queue tuning
• Event sourcing patterns
• Pub/sub configuration

Multi-Region Architecture

• Global load balancing
• Data replication strategies
• Disaster recovery planning
• Cross-region networking

Environment Variables

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

Variable	Description	Default
AUGMENT_API_KEY	Your Augment API key	Required
CLOUD_ARCHITECT_CONFIG	Path to configuration file	.cloud-architect.yaml
CLOUD_ARCHITECT_PARALLEL	Number of parallel analysis threads	4

Basic Usage

Learn the fundamental analysis patterns and optimization workflows for cloud architecture.

Analysis Commands

Architecture Analysisbash

# Full multi-cloud analysis
cloud-architect analyze --all-providers

# Cost optimization focus
cloud-architect analyze --focus cost --threshold 15%

# Performance optimization
cloud-architect analyze --focus performance --target-latency 50ms

# Security architecture review
cloud-architect analyze --focus security --compliance soc2

CLI Commands Reference

Complete reference for all architecture analysis and optimization commands.

analyze

Run comprehensive architecture analysis and optimization

cloud-architect analyze [options]

Options:
  --provider <provider>   Cloud provider (aws|azure|gcp|all)
  --region <region>       Target region for analysis
  --focus <area>          Focus area (cost|performance|security|reliability)
  --threshold <percent>   Minimum improvement threshold
  --output <file>         Output file path
  --format <format>       Output format (json|html|pdf|yaml)
  --recommendations       Include detailed recommendations
  --quick                 Fast analysis mode
  --detailed              Comprehensive analysis mode

Best Practices

Architecture optimization best practices to maximize the value of AI-driven recommendations.

Architecture Optimization Strategy

Run comprehensive analysis monthly for production environments
Implement high-impact, low-risk recommendations first
Monitor performance after implementing changes
Use staging environments to validate recommendations
Establish baseline metrics before optimization

Multi-Cloud Support

AI CloudArchitect provides comprehensive support for multi-cloud and hybrid cloud architectures.

Cross-Cloud Optimization

Multi-Cloud Analysisbash

# Compare costs across cloud providers
cloud-architect compare --metric cost --providers aws,azure,gcp

# Analyze workload placement optimization
cloud-architect optimize --workload web-tier --target-cost 1000

# Generate migration recommendations
cloud-architect migrate --from aws --to azure --workload database

# Multi-cloud disaster recovery planning
cloud-architect dr-plan --primary aws --secondary azure

Cost Optimization

Advanced cost optimization features to reduce cloud spending while maintaining performance.

Cost Analysis Features

Right-Sizing

Optimize instance sizes based on actual usage patterns

Reserved Instances

Identify opportunities for reserved capacity savings

Spot Instances

Recommend spot instance usage for fault-tolerant workloads

API Integration

Integrate AI CloudArchitect into your infrastructure automation and DevOps pipelines.

REST API

API Usagebash

# Trigger architecture analysis via API
curl -X POST https://api.augment.cfd/v1/architecture/analyze \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "providers": ["aws", "azure"],
    "regions": ["us-east-1", "eastus"],
    "focus": "cost_optimization",
    "threshold": 10
  }'

Python SDK

Python Integrationpython

from augment_cloud_architect import CloudArchitect

# Initialize cloud architect
architect = CloudArchitect(api_key=os.environ['AUGMENT_API_KEY'])

# Run multi-cloud analysis
analysis_result = await architect.analyze_infrastructure(
    providers=['aws', 'azure', 'gcp'],
    focus='cost_optimization',
    threshold=15
)

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

# Generate cost savings report
savings_report = await architect.generate_savings_report(
    format='pdf',
    include_timeline=True
)

API Reference

Complete API documentation for integrating architecture analysis into your applications.

Architecture Analysis Endpoint

POST /v1/architecture/analyze

Analyze cloud infrastructure and provide optimization recommendations.

Request Body:

{
  "providers": ["aws", "azure", "gcp"],
  "regions": ["us-east-1", "eastus", "us-central1"],
  "focus": "cost_optimization|performance|security|reliability",
  "threshold": 10,
  "architecture_patterns": ["microservices", "serverless"],
  "optimization_goals": {
    "cost_reduction": 20,
    "performance_improvement": 30
  }
}

Response:

{
  "analysis_id": "arch-analysis-123",
  "status": "completed",
  "summary": {
    "total_resources": 234,
    "optimization_score": 87,
    "potential_monthly_savings": 12400,
    "performance_improvement": 45
  },
  "recommendations": [
    {
      "id": "rec-001",
      "category": "cost_optimization",
      "priority": "high",
      "title": "Right-size EC2 instances",
      "description": "15 EC2 instances are over-provisioned",
      "estimated_savings": 3200,
      "implementation_effort": "low",
      "impact": "high",
      "resources": ["i-123456", "i-789012"]
    }
  ],
  "cost_analysis": {
    "current_monthly_cost": 45600,
    "optimized_monthly_cost": 33200,
    "savings_percentage": 27.2
  }
}

Troubleshooting

Common issues and solutions when running architecture analysis and optimization.

Common Issues

API Rate Limits

Error: Cloud provider API rate limits exceeded

Solutions:

Reduce analysis parallelism with --parallel flag
Implement exponential backoff in automation
Use provider-specific rate limit configurations
Schedule analysis during off-peak hours

Large Infrastructure Analysis

Analysis taking too long on large infrastructures

Solutions:

Use regional analysis to break down scope
Filter by resource types or tags
Use quick analysis mode for initial assessment
Increase memory allocation for analysis process

Inconsistent Recommendations

Recommendations vary between analysis runs

Solutions:

Use consistent time windows for usage analysis
Account for workload seasonality patterns
Set stable baseline metrics
Use deterministic analysis modes

Architecture Documentation Complete!

Ready to optimize your cloud architecture? Start your free analysis today and discover how AI can reduce costs and improve performance across your multi-cloud infrastructure.

AI CloudArchitect

AI-Powered Architecture

Multi-Cloud Optimization

Cost Reduction

Performance Tuning

Installation

System Requirements

Install via Package Manager

Multi-Cloud Authentication

Quick Start

1. Initialize Architecture Profile

2. Run Infrastructure Analysis

3. Generate Architecture Report

Configuration

Basic Configuration

Architecture Patterns

Microservices Architecture

Serverless Architecture

Event-Driven Architecture

Multi-Region Architecture

Environment Variables

Basic Usage

Analysis Commands

CLI Commands Reference

analyze

Best Practices

Architecture Optimization Strategy

Multi-Cloud Support

Cross-Cloud Optimization

Cost Optimization

Cost Analysis Features

Right-Sizing

Reserved Instances

Spot Instances

API Integration

REST API

Python SDK

API Reference

Architecture Analysis Endpoint

POST /v1/architecture/analyze

Request Body:

Response:

Troubleshooting

Common Issues

API Rate Limits

Large Infrastructure Analysis

Inconsistent Recommendations

Architecture Documentation Complete!

AI CloudArchitect

AI-Powered Architecture

Multi-Cloud Optimization

Cost Reduction

Performance Tuning

Installation

System Requirements

Install via Package Manager

Multi-Cloud Authentication

Quick Start

1. Initialize Architecture Profile

2. Run Infrastructure Analysis

3. Generate Architecture Report

Configuration

Basic Configuration

Architecture Patterns

Microservices Architecture

Serverless Architecture

Event-Driven Architecture

Multi-Region Architecture

Environment Variables

Basic Usage

Analysis Commands

CLI Commands Reference

analyze

Best Practices

Architecture Optimization Strategy

Multi-Cloud Support

Cross-Cloud Optimization

Cost Optimization

Cost Analysis Features

Right-Sizing