azure-cloud-architect

name	azure-cloud-architect
description	Design Azure architectures for startups and enterprises. Use when asked to design Azure infrastructure, create Bicep/ARM templates, optimize Azure costs, set up Azure DevOps pipelines, or migrate to Azure. Covers AKS, App Service, Azure Functions, Cosmos DB, and cost optimization.

Azure Cloud Architect

Design scalable, cost-effective Azure architectures for startups and enterprises with Bicep infrastructure-as-code templates.

---

Workflow

Step 1: Gather Requirements

Collect application specifications:

- Application type (web app, mobile backend, data pipeline, SaaS, microservices)
- Expected users and requests per second
- Budget constraints (monthly spend limit)
- Team size and Azure experience level
- Compliance requirements (GDPR, HIPAA, SOC 2, ISO 27001)
- Availability requirements (SLA, RPO/RTO)
- Region preferences (data residency, latency)

Step 2: Design Architecture

Run the architecture designer to get pattern recommendations:

python scripts/architecture_designer.py \
  --app-type web_app \
  --users 10000 \
  --requirements '{"budget_monthly_usd": 500, "compliance": ["SOC2"]}'

Example output:

{
  "recommended_pattern": "app_service_web",
  "service_stack": ["App Service", "Azure SQL", "Front Door", "Key Vault", "Entra ID"],
  "estimated_monthly_cost_usd": 280,
  "pros": ["Managed platform", "Built-in autoscale", "Deployment slots"],
  "cons": ["Less control than VMs", "Platform constraints", "Cold start on consumption plans"]
}

Select from recommended patterns:

• App Service Web: Front Door + App Service + Azure SQL + Redis Cache
• Microservices on AKS: AKS + Service Bus + Cosmos DB + API Management
• Serverless Event-Driven: Functions + Event Grid + Service Bus + Cosmos DB
• Data Pipeline: Data Factory + Synapse Analytics + Data Lake Storage + Event Hubs

See references/architecture_patterns.md for detailed pattern specifications.

Validation checkpoint: Confirm the recommended pattern matches the team's operational maturity and compliance requirements before proceeding to Step 3.

Step 3: Generate IaC Templates

Create infrastructure-as-code for the selected pattern:

# Web app stack (Bicep)
python scripts/bicep_generator.py --arch-type web-app --output main.bicep

Example Bicep output (core web app resources):

@description('The environment name')
param environment string = 'dev'

@description('The Azure region for resources')
param location string = resourceGroup().location

@description('The application name')
param appName string = 'myapp'

// App Service Plan
resource appServicePlan 'Microsoft.Web/serverfarms@2023-01-01' = {
  name: '${environment}-${appName}-plan'
  location: location
  sku: {
    name: 'P1v3'
    tier: 'PremiumV3'
    capacity: 1
  }
  properties: {
    reserved: true // Linux
  }
}

// App Service
resource appService 'Microsoft.Web/sites@2023-01-01' = {
  name: '${environment}-${appName}-web'
  location: location
  properties: {
    serverFarmId: appServicePlan.id
    httpsOnly: true
    siteConfig: {
      linuxFxVersion: 'NODE|20-lts'
      minTlsVersion: '1.2'
      ftpsState: 'Disabled'
      alwaysOn: true
    }
  }
  identity: {
    type: 'SystemAssigned'
  }
}

// Azure SQL Database
resource sqlServer 'Microsoft.Sql/servers@2023-05-01-preview' = {
  name: '${environment}-${appName}-sql'
  location: location
  properties: {
    administrators: {
      azureADOnlyAuthentication: true
    }
    minimalTlsVersion: '1.2'
  }
}

resource sqlDatabase 'Microsoft.Sql/servers/databases@2023-05-01-preview' = {
  parent: sqlServer
  name: '${appName}-db'
  location: location
  sku: {
    name: 'GP_S_Gen5_2'
    tier: 'GeneralPurpose'
  }
  properties: {
    autoPauseDelay: 60
    minCapacity: json('0.5')
  }
}

Full templates including Front Door, Key Vault, Managed Identity, and monitoring are generated by bicep_generator.py and also available in references/architecture_patterns.md.

Bicep is the recommended IaC language for Azure. Prefer Bicep over ARM JSON templates: Bicep compiles to ARM JSON, has cleaner syntax, supports modules, and is first-party supported by Microsoft.

Step 4: Review Costs

Analyze estimated costs and optimization opportunities:

python scripts/cost_optimizer.py \
  --config current_resources.json \
  --json

Example output:

{
  "current_monthly_usd": 2000,
  "recommendations": [
    { "action": "Right-size SQL Database GP_S_Gen5_8 to GP_S_Gen5_2", "savings_usd": 380, "priority": "high" },
    { "action": "Purchase 1-year Reserved Instances for AKS node pools", "savings_usd": 290, "priority": "high" },
    { "action": "Move Blob Storage to Cool tier for objects >30 days old", "savings_usd": 65, "priority": "medium" }
  ],
  "total_potential_savings_usd": 735
}

Output includes:

• Monthly cost breakdown by service
• Right-sizing recommendations
• Reserved Instance and Savings Plan opportunities
• Potential monthly savings

Step 5: Configure CI/CD

Set up Azure DevOps Pipelines or GitHub Actions with Azure:

# GitHub Actions — deploy Bicep to Azure
name: Deploy Infrastructure
on:
  push:
    branches: [main]

permissions:
  id-token: write
  contents: read

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - uses: azure/login@v2
        with:
          client-id: ${{ secrets.AZURE_CLIENT_ID }}
          tenant-id: ${{ secrets.AZURE_TENANT_ID }}
          subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}

      - uses: azure/arm-deploy@v2
        with:
          resourceGroupName: rg-myapp-dev
          template: ./infra/main.bicep
          parameters: environment=dev

# Azure DevOps Pipeline
trigger:
  branches:
    include:
      - main

pool:
  vmImage: 'ubuntu-latest'

steps:
  - task: AzureCLI@2
    inputs:
      azureSubscription: 'MyServiceConnection'
      scriptType: 'bash'
      scriptLocation: 'inlineScript'
      inlineScript: |
        az deployment group create \
          --resource-group rg-myapp-dev \
          --template-file infra/main.bicep \
          --parameters environment=dev

Step 6: Security Review

Validate security posture before production:

• Identity: Entra ID (Azure AD) with RBAC, Managed Identity for service-to-service auth — never store credentials in code
• Secrets: Key Vault for all secrets, certificates, and connection strings
• Network: NSGs on all subnets, Private Endpoints for PaaS services, Application Gateway with WAF
• Encryption: TLS 1.2+ in transit, Azure-managed or customer-managed keys at rest
• Monitoring: Microsoft Defender for Cloud enabled, Azure Policy for guardrails
• Compliance: Azure Policy assignments for SOC 2 / HIPAA / ISO 27001 initiatives

If deployment fails:

1. Check the deployment status:

   az deployment group show \
     --resource-group rg-myapp-dev \
     --name main \
     --query 'properties.error'
   

2. Review Activity Log for RBAC or policy errors.
3. Validate the Bicep template before deploying:

   az bicep build --file main.bicep
   az deployment group validate \
     --resource-group rg-myapp-dev \
     --template-file main.bicep
   

Common failure causes:

• RBAC permission errors — verify the deploying principal has Contributor on the resource group
• Resource provider not registered — run az provider register --namespace Microsoft.Web
• Naming conflicts — Azure resource names are often globally unique (storage accounts, web apps)
• Quota exceeded — request quota increase via Azure Portal > Subscriptions > Usage + quotas

---

Tools

architecture_designer.py

Generates architecture pattern recommendations based on requirements.

python scripts/architecture_designer.py \
  --app-type web_app \
  --users 50000 \
  --requirements '{"budget_monthly_usd": 1000, "compliance": ["HIPAA"]}' \
  --json

Input: Application type, expected users, JSON requirements Output: Recommended pattern, service stack, cost estimate, pros/cons

cost_optimizer.py

Analyzes Azure resource configurations for cost savings.

python scripts/cost_optimizer.py --config resources.json --json

Input: JSON file with current Azure resource inventory Output: Recommendations for:

• Idle resource removal
• VM and database right-sizing
• Reserved Instance purchases
• Storage tier transitions
• Unused public IPs and load balancers

bicep_generator.py

Generates Bicep template scaffolds from architecture type.

python scripts/bicep_generator.py --arch-type microservices --output main.bicep

Output: Production-ready Bicep templates with:

• Managed Identity (no passwords)
• Key Vault integration
• Diagnostic settings for Azure Monitor
• Network security groups
• Tags for cost allocation

---

Quick Start

Web App Architecture (< $100/month)

Ask: "Design an Azure web app for a startup with 5000 users"

Result:
- App Service (B1 Linux) for the application
- Azure SQL Serverless for relational data
- Azure Blob Storage for static assets
- Front Door (free tier) for CDN and routing
- Key Vault for secrets
- Estimated: $40-80/month

Microservices on AKS ($500-2000/month)

Ask: "Design a microservices architecture on Azure for a SaaS platform with 50k users"

Result:
- AKS cluster with 3 node pools (system, app, jobs)
- API Management for gateway and rate limiting
- Cosmos DB for multi-model data
- Service Bus for async messaging
- Azure Monitor + Application Insights for observability
- Multi-zone deployment

Serverless Event-Driven (< $200/month)

Ask: "Design an event-driven backend for processing orders"

Result:
- Azure Functions (Consumption plan) for compute
- Event Grid for event routing
- Service Bus for reliable messaging
- Cosmos DB for order data
- Application Insights for monitoring
- Estimated: $30-150/month depending on volume

Data Pipeline ($300-1500/month)

Ask: "Design a data pipeline for ingesting 10M events/day"

Result:
- Event Hubs for ingestion
- Stream Analytics or Functions for processing
- Data Lake Storage Gen2 for raw data
- Synapse Analytics for warehouse
- Power BI for dashboards

---

Input Requirements

Provide these details for architecture design:

Requirement	Description	Example
Application type	What you're building	SaaS platform, mobile backend
Expected scale	Users, requests/sec	10k users, 100 RPS
Budget	Monthly Azure limit	$500/month max
Team context	Size, Azure experience	3 devs, intermediate
Compliance	Regulatory needs	HIPAA, GDPR, SOC 2
Availability	Uptime requirements	99.9% SLA, 1hr RPO

JSON Format:

{
  "application_type": "saas_platform",
  "expected_users": 10000,
  "requests_per_second": 100,
  "budget_monthly_usd": 500,
  "team_size": 3,
  "azure_experience": "intermediate",
  "compliance": ["SOC2"],
  "availability_sla": "99.9%"
}

---

Anti-Patterns

Anti-Pattern	Why It Fails	Do This Instead
ARM JSON templates for new projects	Verbose, hard to read, no modules	Use Bicep — compiles to ARM, cleaner syntax
Storing secrets in App Settings	Secrets visible in portal, no rotation	Use Key Vault references in App Settings
Single large AKS node pool	Cannot optimize for different workloads	Use multiple node pools: system, app, jobs
Public endpoints on PaaS services	Exposed attack surface	Use Private Endpoints + VNet integration
Over-provisioning "just in case"	Wastes budget month one	Start small, use autoscale, right-size monthly
Shared resource groups for everything	Blast radius, RBAC nightmares	One resource group per environment per workload
No tagging strategy	Cannot track costs or ownership	Tag: environment, owner, cost-center, app-name
Using classic resources	Deprecated, limited features	Use ARM/Bicep resources exclusively

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Output Formats

Architecture Design

• Pattern recommendation with rationale
• Service stack diagram (ASCII)
• Monthly cost estimate and trade-offs

IaC Templates

• Bicep: Recommended — first-party, module support, clean syntax
• ARM JSON: Generated from Bicep when needed
• Terraform HCL: Multi-cloud compatible using azurerm provider

Cost Analysis

• Current spend breakdown with optimization recommendations
• Priority action list (high/medium/low) and implementation checklist

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Cross-References

Skill	Relationship
engineering-team/aws-solution-architect	AWS equivalent — same 6-step workflow, different services
engineering-team/gcp-cloud-architect	GCP equivalent — completes the cloud trifecta
engineering-team/senior-devops	Broader DevOps scope — pipelines, monitoring, containerization
engineering/terraform-patterns	IaC implementation — use for Terraform modules targeting Azure
engineering/ci-cd-pipeline-builder	Pipeline construction — automates Azure DevOps and GitHub Actions

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Reference Documentation

Document	Contents
references/architecture_patterns.md	5 patterns: web app, microservices/AKS, serverless, data pipeline, multi-region
references/service_selection.md	Decision matrices for compute, database, storage, messaging, networking
references/best_practices.md	Naming conventions, tagging, RBAC, network security, monitoring, DR