AWS to Azure Migration: Complete Guide for Enterprise Workloads

Migrating from AWS to Azure is more common than you might think. Organizations switch cloud providers for various reasons: Microsoft enterprise agreements, Azure’s hybrid cloud capabilities, specific service offerings, or simply negotiating better pricing.

After executing dozens of cross-cloud migrations, we have learned that success depends on understanding the differences between platforms, not just mapping services one-to-one. This guide covers what you need to know.

Why Organizations Migrate from AWS to Azure

Before diving into the how, it helps to understand the why. Common drivers we see:

Enterprise Microsoft agreements - Organizations with existing Microsoft 365, Dynamics, or Windows Server licenses often get significant Azure discounts through Enterprise Agreements.

Hybrid cloud requirements - Azure Arc and Azure Stack provide tighter integration for organizations maintaining on-premise infrastructure alongside cloud workloads.

Specific service needs - Azure’s strengths in areas like Active Directory integration, Power Platform, and certain AI/ML services drive some migrations.

Cost optimization - Depending on workload profiles, Azure reserved instances and hybrid benefit pricing can reduce costs significantly.

Vendor diversification - Some organizations migrate portions of their infrastructure to avoid single-vendor dependency.

AWS to Azure Service Mapping

Understanding equivalent services is the foundation of any cross-cloud migration.

Compute Services

AWS Service	Azure Equivalent	Notes
EC2	Virtual Machines	Similar instance families, different naming
Lambda	Azure Functions	Comparable serverless compute
ECS/Fargate	Azure Container Instances	Container orchestration differs
EKS	Azure Kubernetes Service (AKS)	Both managed Kubernetes
Elastic Beanstalk	Azure App Service	PaaS deployment models

For Kubernetes workloads, migrating from EKS to AKS requires attention to ingress controllers, storage classes, and networking models. We cover AKS-specific considerations in our AKS consulting services.

Storage Services

AWS Service	Azure Equivalent	Notes
S3	Blob Storage	Object storage, different APIs
EBS	Managed Disks	Block storage for VMs
EFS	Azure Files	Managed file shares
Glacier	Archive Storage	Cold storage tiers

S3 to Blob Storage migration requires careful attention to:

• Access patterns (Azure uses different authentication)
• Lifecycle policies (similar but different syntax)
• Cross-region replication configuration
• Event notifications (S3 events vs Event Grid)

Database Services

AWS Service	Azure Equivalent	Notes
RDS	Azure SQL Database	Managed relational databases
Aurora	Azure SQL Hyperscale	High-performance SQL
DynamoDB	Cosmos DB	NoSQL with different consistency models
ElastiCache	Azure Cache for Redis	In-memory caching
Redshift	Azure Synapse Analytics	Data warehousing

Database migrations are often the most complex part. DynamoDB to Cosmos DB requires particular attention because the data models and consistency guarantees differ significantly.

Networking Services

AWS Service	Azure Equivalent	Notes
VPC	Virtual Network (VNet)	Core networking differs
Route 53	Azure DNS	DNS management
CloudFront	Azure CDN / Front Door	Content delivery
Direct Connect	ExpressRoute	Dedicated connectivity
ELB/ALB	Azure Load Balancer / App Gateway	Load balancing tiers

Networking architecture changes are often underestimated. AWS VPCs and Azure VNets have different default behaviors, subnet configurations, and security group implementations.

Monitoring and Operations

AWS Service	Azure Equivalent	Notes
CloudWatch	Azure Monitor	Metrics and logs
CloudTrail	Azure Activity Log	Audit logging
X-Ray	Application Insights	Distributed tracing
Systems Manager	Azure Automation	Operations management

For production monitoring, we typically implement Prometheus-based observability that works consistently across cloud providers, reducing vendor lock-in in the monitoring layer.

Migration Strategies

Lift-and-Shift (Rehost)

The fastest approach moves workloads with minimal changes.

When to use:

• Legacy applications with limited cloud-native features
• Time-constrained migrations
• Applications scheduled for retirement

Tools:

• Azure Migrate for VM discovery and migration
• Azure Site Recovery for replication
• Database Migration Service for database moves

# Example: Using Azure Migrate for VM assessment
az migrate project create \
  --name "aws-migration-project" \
  --resource-group "migration-rg" \
  --location "eastus"

Replatform

Optimize during migration by adopting Azure-managed services.

When to use:

• Applications that can benefit from managed services
• Workloads with moderate refactoring budget
• Teams ready to learn Azure-native services

Example transformations:

• EC2 + self-managed MySQL → Azure App Service + Azure SQL
• EC2 + self-managed Redis → Azure Functions + Azure Cache for Redis
• ECS containers → Azure Container Apps

Refactor (Re-architect)

Redesign applications to be cloud-native on Azure.

When to use:

• Strategic applications with long lifespans
• Workloads needing significant performance improvements
• Applications where Azure-specific services provide clear advantages

This approach requires more investment but delivers the most value. For containerized workloads, our DevOps consulting team helps redesign CI/CD pipelines for Azure DevOps or GitHub Actions.

Step-by-Step Migration Process

Phase 1: Discovery and Assessment

Inventory existing AWS resources:

# List all EC2 instances
aws ec2 describe-instances \
  --query 'Reservations[].Instances[].[InstanceId,InstanceType,State.Name,Tags[?Key==`Name`].Value|[0]]' \
  --output table

# List all RDS instances
aws rds describe-db-instances \
  --query 'DBInstances[].[DBInstanceIdentifier,Engine,DBInstanceClass]' \
  --output table

# List S3 buckets with sizes
aws s3 ls --summarize --human-readable --recursive s3://bucket-name

Document dependencies:

• Application-to-database connections
• Inter-service communication patterns
• External API integrations
• Data flows between services

Assess migration complexity:

• Stateless vs stateful applications
• Data volume and transfer requirements
• Compliance and data residency needs
• Acceptable downtime windows

Phase 2: Architecture Design

Design the target Azure architecture before migrating.

Networking foundation:

# Terraform example for Azure networking
resource "azurerm_virtual_network" "main" {
  name                = "production-vnet"
  address_space       = ["10.0.0.0/16"]
  location            = azurerm_resource_group.main.location
  resource_group_name = azurerm_resource_group.main.name
}

resource "azurerm_subnet" "app" {
  name                 = "app-subnet"
  resource_group_name  = azurerm_resource_group.main.name
  virtual_network_name = azurerm_virtual_network.main.name
  address_prefixes     = ["10.0.1.0/24"]
}

resource "azurerm_subnet" "data" {
  name                 = "data-subnet"
  resource_group_name  = azurerm_resource_group.main.name
  virtual_network_name = azurerm_virtual_network.main.name
  address_prefixes     = ["10.0.2.0/24"]
}

For infrastructure automation, our Terraform consulting services help establish consistent infrastructure-as-code practices across cloud providers.

Identity and access:

• Map AWS IAM roles to Azure RBAC
• Configure Azure Active Directory integration
• Plan service principal and managed identity usage

Phase 3: Data Migration

Data migration typically drives the overall timeline.

S3 to Blob Storage:

# Using AzCopy for S3 to Blob migration
azcopy copy \
  "https://s3.amazonaws.com/source-bucket" \
  "https://destaccount.blob.core.windows.net/container" \
  --recursive

Database migration approaches:

For RDS to Azure SQL:

1. Use Azure Database Migration Service for minimal downtime
2. Configure replication from source to target
3. Perform cutover when replication catches up

-- Verify data consistency after migration
SELECT COUNT(*) as row_count FROM important_table;
-- Compare with source database count

Large dataset considerations:

• AWS Snowball → Azure Data Box for offline transfer
• Plan for network bandwidth limitations
• Consider compression and incremental sync

Phase 4: Application Migration

Migrate applications in waves, starting with lower-risk workloads.

Wave planning criteria:

• Business criticality (start with non-critical)
• Technical complexity
• Dependencies (migrate dependencies first)
• Team familiarity

Containerized application migration:

For EKS to AKS migrations, export Kubernetes manifests and adapt for AKS:

# Adapted for AKS - key differences
apiVersion: apps/v1
kind: Deployment
metadata:
  name: web-app
spec:
  replicas: 3
  selector:
    matchLabels:
      app: web-app
  template:
    spec:
      containers:
      - name: web
        image: myacr.azurecr.io/web-app:latest  # ACR instead of ECR
        resources:
          requests:
            memory: "256Mi"
            cpu: "250m"
---
apiVersion: v1
kind: Service
metadata:
  name: web-app
  annotations:
    service.beta.kubernetes.io/azure-load-balancer-internal: "true"  # Azure annotation
spec:
  type: LoadBalancer
  ports:
  - port: 80

Phase 5: Cutover and Validation

Pre-cutover checklist:

• All data synchronized
• Applications tested in Azure
• DNS TTLs reduced (24-48 hours before)
• Rollback plan documented
• Monitoring configured
• Team on standby

Cutover steps:

1. Stop writes to AWS (if zero-downtime not possible)
2. Final data sync
3. Update DNS records
4. Validate application functionality
5. Monitor for issues

Post-cutover validation:

# Verify Azure resources are healthy
az vm list --query "[].{Name:name,Status:powerState}" --output table

# Check application endpoints
curl -I https://app.yourdomain.com

# Verify database connectivity
az sql db show --name mydb --server myserver --resource-group myrg

Cost Considerations

Azure Pricing Differences

Azure pricing structures differ from AWS:

Reserved Instances:

• Azure offers 1-year and 3-year reservations
• Hybrid Benefit provides additional discounts for Windows/SQL licenses
• Reserved capacity applies across subscriptions in the same enrollment

Compute pricing:

• Compare specific instance sizes (Azure Dv5 vs AWS m6i)
• Factor in hybrid benefit if you have existing licenses
• Consider Azure Spot VMs for fault-tolerant workloads

Storage pricing:

• Blob Storage tiers (Hot, Cool, Archive) differ from S3 tiers
• Egress pricing varies by region
• Reserved capacity available for predictable storage needs

For ongoing cost optimization after migration, we help organizations implement cloud cost management practices that apply across providers.

Hidden Migration Costs

Budget for these often-overlooked expenses:

• Data egress from AWS - Transfer out charges can be significant for large datasets
• Parallel running costs - Both environments running during transition
• Training and ramp-up - Team learning curve for Azure
• Application refactoring - Code changes for Azure-specific services
• Testing infrastructure - Non-production environments for validation

Common Pitfalls and How to Avoid Them

Networking Assumptions

Problem: Assuming Azure networking works like AWS.

AWS and Azure handle networking differently:

• Azure VNets don’t have the same NAT gateway model
• Security groups vs Network Security Groups have different rule processing
• Subnet sizing and IP addressing patterns differ

Solution: Document your AWS network architecture and redesign for Azure rather than copying directly.

IAM Mapping Complexity

Problem: Underestimating identity and access migration complexity.

AWS IAM and Azure RBAC have different models:

• AWS uses policies attached to roles/users
• Azure uses role assignments with scope inheritance
• Service principals work differently than IAM roles

Solution: Create a detailed IAM mapping document before migration. Test access patterns in a sandbox environment.

Data Transfer Time

Problem: Underestimating time for large data migrations.

Network transfer for terabytes of data takes longer than expected:

• 10TB at 1Gbps theoretical max = 22+ hours
• Real-world speeds are typically 50-70% of theoretical
• Replication adds ongoing bandwidth needs

Solution: Start data migration early. Use offline transfer (Data Box) for very large datasets. Plan for incremental sync.

Application Dependencies

Problem: Discovering hidden dependencies during migration.

Applications often have undocumented dependencies:

• Hardcoded AWS service endpoints
• Region-specific configurations
• AWS SDK version requirements

Solution: Thorough application analysis before migration. Run applications in Azure test environments to surface issues.

Monitoring Gaps

Problem: Losing visibility during and after migration.

AWS CloudWatch dashboards and alerts don’t transfer to Azure Monitor.

Solution: Implement cloud-agnostic monitoring early. We recommend Prometheus and Grafana for metrics that work across cloud providers, providing continuity during migration.

Post-Migration Optimization

After migration completes, focus on optimization:

Right-sizing:

# Azure Advisor recommendations for VM sizing
az advisor recommendation list \
  --category cost \
  --query "[?shortDescription.solution=='Right-size or shutdown underutilized virtual machines']"

Cost management:

• Set up Azure Cost Management budgets and alerts
• Review and apply Reserved Instance recommendations
• Implement auto-shutdown for non-production resources

Performance tuning:

• Analyze Application Insights data for bottlenecks
• Optimize database query performance
• Configure appropriate caching layers

Security hardening:

• Enable Azure Security Center recommendations
• Configure Azure Sentinel for security monitoring
• Review and tighten network security groups

Hybrid and Multi-Cloud Considerations

Some organizations maintain presence in both clouds.

When hybrid makes sense:

• Gradual migration over extended timeline
• Specific workloads optimized for each cloud
• Disaster recovery across providers
• Regulatory requirements for geographic distribution

Managing multi-cloud complexity:

• Use Terraform or Pulumi for infrastructure-as-code across providers
• Implement unified monitoring with cloud-agnostic tools
• Standardize on Kubernetes for application portability
• Establish consistent security policies

Our cloud migration services include multi-cloud architecture design for organizations requiring presence across AWS and Azure.

Summary

AWS to Azure migration requires careful planning but is achievable with the right approach:

1. Understand your why - Clear objectives guide architectural decisions
2. Map services carefully - Don’t assume one-to-one equivalence
3. Plan for data migration - Often the longest timeline item
4. Migrate in waves - Start with lower-risk workloads
5. Budget realistically - Include hidden costs and parallel running
6. Monitor throughout - Maintain visibility during transition

Success comes from treating migration as an opportunity to optimize, not just a lift-and-shift exercise.

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Need Help with Cloud Migration?

We execute cross-cloud migrations for organizations of all sizes. Our cloud migration services include assessment, planning, execution, and post-migration optimization.

Book a free 30-minute consultation to discuss your AWS to Azure migration project.