azure-cloud-architect

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.

9,958 stars

Best use case

azure-cloud-architect is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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.

Teams using azure-cloud-architect should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/azure-cloud-architect/SKILL.md --create-dirs "https://raw.githubusercontent.com/alirezarezvani/claude-skills/main/.gemini/skills/azure-cloud-architect/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/azure-cloud-architect/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How azure-cloud-architect Compares

Feature / Agentazure-cloud-architectStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# 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:

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

**Example output:**

```json
{
  "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:

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

**Example Bicep output (core web app resources):**

```bicep
@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:

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

**Example output:**

```json
{
  "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:

```yaml
# 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
```

```yaml
# 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:
   ```bash
   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:
   ```bash
   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.

```bash
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.

```bash
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.

```bash
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:**

```json
{
  "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 |

---

## 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

---

## 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 |

---

## 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 |

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