/deploying-on-azure

name: deploying-on-azure
description: Design and implement Azure cloud architectures using best practices for compute, storage, databases, AI services, networking, and governance. Use when building applications on Microsoft Azure or migrating workloads to Azure cloud platform.

Azure Patterns

Design and implement Azure cloud architectures following Microsoft's Well-Architected Framework and best practices for service selection, cost optimization, and security.

When to Use

Use this skill when:

• Designing new applications for Azure cloud
• Selecting Azure compute services (Container Apps, AKS, Functions, App Service)
• Architecting storage solutions (Blob Storage, Files, Cosmos DB)
• Integrating Azure OpenAI or Cognitive Services
• Implementing messaging patterns (Service Bus, Event Grid, Event Hubs)
• Designing secure networks with Private Endpoints
• Applying Azure governance and compliance policies
• Optimizing Azure costs and performance

Core Concepts

Service Selection Philosophy

Azure offers 200+ services. Choose based on: 1. **Managed vs. IaaS** - Prefer fully managed services (lower operational burden) 2. **Cost Model** - Consumption vs. dedicated capacity 3. **Integration Requirements** - Microsoft 365, Active Directory, hybrid cloud 4. **Control vs. Simplicity** - More control = more operational overhead

Azure Well-Architected Framework (Five Pillars)

| Pillar | Focus | Key Practices | |--------|-------|---------------| | **Cost Optimization** | Maximize value within budget | Reserved Instances, auto-scaling, lifecycle management | | **Operational Excellence** | Run reliable systems | Azure Policy, automation, monitoring | | **Performance Efficiency** | Scale to meet demand | Autoscaling, caching, CDN | | **Reliability** | Recover from failures | Availability Zones, multi-region, backup | | **Security** | Protect data and assets | Managed Identity, Private Endpoints, Key Vault |

Reference `references/well-architected.md` for detailed pillar implementation patterns.

Compute Service Selection

Decision Framework

Container-based workload?
  YES → Need Kubernetes control plane?
          YES → Azure Kubernetes Service (AKS)
          NO → Azure Container Apps (recommended)
  NO → Event-driven function?
         YES → Azure Functions
         NO → Web application?
                YES → Azure App Service
                NO → Legacy/specialized → Virtual Machines

Service Comparison

| Service | Best For | Pricing Model | Operational Overhead | |---------|----------|---------------|---------------------| | **Container Apps** | Microservices, APIs, background jobs | Consumption or dedicated | Low | | **AKS** | Complex K8s workloads, service mesh | Node-based | High | | **Functions** | Event-driven, short tasks (<10 min) | Consumption or premium | Low | | **App Service** | Web apps, simple APIs | Dedicated plans | Low | | **Virtual Machines** | Legacy apps, specialized software | VM-based | High |

**Recommendation:** Start with Azure Container Apps for 80% of containerized workloads (simpler and cheaper than AKS).

Reference `references/compute-services.md` for detailed comparison with Bicep and Terraform examples.

Storage Architecture

Blob Storage Tier Selection

| Tier | Access Pattern | Cost/GB/Month | Minimum Storage Duration | |------|---------------|---------------|--------------------------| | **Hot** | Daily access | $0.018 | None | | **Cool** | <1/month access | $0.010 | 30 days | | **Cold** | <90 days access | $0.0045 | 90 days | | **Archive** | Rare access | $0.00099 | 180 days |

**Pattern:** Use lifecycle management policies to automatically move data to lower-cost tiers.

Storage Service Decision

File system interface required?
  YES → Protocol?
          SMB → Azure Files (or NetApp Files for high performance)
          NFS → Azure Files (NFS 4.1)
  NO → Object storage → Blob Storage
       Block storage → Managed Disks (Standard/Premium SSD/Ultra)
       Analytics → Data Lake Storage Gen2

Reference `references/storage-patterns.md` for lifecycle policies, redundancy options, and performance tuning.

Database Service Selection

Decision Framework

Relational data?
  YES → SQL Server compatible?
          YES → Need VM-level access?
                  YES → SQL Managed Instance
                  NO → Azure SQL Database
          NO → Open source?
                 PostgreSQL → PostgreSQL Flexible Server
                 MySQL → MySQL Flexible Server
  NO → Data model?
         Document/JSON → Cosmos DB (NoSQL API)
         Graph → Cosmos DB (Gremlin API)
         Wide-column → Cosmos DB (Cassandra API)
         Key-value cache → Azure Cache for Redis
         Time-series → Azure Data Explorer

Cosmos DB Consistency Levels

| Level | Use Case | Latency | Throughput | |-------|----------|---------|------------| | **Strong** | Financial transactions, inventory | Highest | Lowest | | **Bounded Staleness** | Real-time leaderboards with acceptable lag | High | Low | | **Session** | Shopping carts, user sessions (default) | Medium | Medium | | **Consistent Prefix** | Social feeds, IoT telemetry | Low | High | | **Eventual** | Analytics, ML training data | Lowest | Highest |

Reference `references/database-selection.md` for capacity planning, indexing strategies, and migration patterns.

AI and Machine Learning Integration

Azure OpenAI Service

**Use Cases:**

• Chatbots and conversational AI (GPT-4)
• Content generation and summarization
• Semantic search with embeddings (RAG pattern)
• Code generation and completion
• Function calling for structured outputs

**Key Advantages:**

• Enterprise data privacy (no model training on customer data)
• Regional deployment for data residency
• Microsoft enterprise SLAs
• Built-in content filtering

**Integration Pattern:**

from openai import AzureOpenAI
from azure.identity import DefaultAzureCredential

credential = DefaultAzureCrede