Cloud Migration from Azure or GCP to AWS: A Realistic Timeline & Checklist

What this guide covers: Everything specific to moving from Azure or GCP to AWS - service equivalents, data transfer costs, networking differences, IAM translation, and the phase-by-phase migration timeline.

How it differs from on-prem migration: Cloud-to-cloud migrations often have cleaner dependency maps but face distinct challenges: egress costs, networking model differences, IAM translation, and service behavioral nuances that can break applications if unaddressed.

The bottom line: Azure → AWS and GCP → AWS migrations are well-understood patterns. With the right service mapping, cost planning, and phased execution, they deliver results in 8-20 weeks without a moment of customer-facing downtime.

TL;DR:

• Timeline & Savings: A cross-cloud migration typically takes 8–20 weeks and can slash ongoing infrastructure costs by 40–60%.
• Zero Downtime: With proper service mapping and a phased approach, the entire transition can be executed with zero customer-facing downtime.
• Hidden Egress Costs: Moving data out of Azure or GCP isn't free; you must budget for source egress fees (approx. $0.09/GB) or utilize an AWS Snowball Edge device for datasets larger than 10TB.
• Architectural Landmines: The biggest migration failures stem from trying to force GCP’s global network model into AWS’s regional structure, and misconfiguring application-layer IAM translations.
• The Safety Net: Always maintain a 30–45 day parallel validation window post-cutover where the source environment remains live in standby mode before final decommissioning.

1. Why Companies Move from Azure or GCP to AWS

The decision to migrate from one major cloud to another is rarely made lightly. It involves real cost, real disruption, and real risk. Companies make this move when the business case for doing so outweighs those costs - and several scenarios produce compelling cases.

2. Azure to AWS: Complete Service Equivalence Map

Service mapping is the foundation of every cross-cloud migration plan. Azure and AWS cover the same infrastructure domains but with different service names, APIs, configuration models, and behavioral nuances. This is the complete map for the services most commonly encountered in Azure → AWS migrations.

Compute

Storage

Databases

Networking and Identity

3. GCP to AWS: Complete Service Equivalence Map

GCP-to-AWS migrations share many characteristics with Azure-to-AWS migrations but have distinct nuances - particularly around networking (GCP's global VPC model vs. AWS's regional VPC model) and Kubernetes (GKE → EKS is the smoothest migration path in the cross-cloud space).

Compute and Containers

Storage and Databases

Networking - The Critical Difference

GCP's networking model is fundamentally different from AWS in one important way: GCP VPCs are global. A single GCP VPC spans all regions. Subnets are regional but resources across regions share the same VPC and can communicate without peering.

AWS VPCs are regional. Each region has its own VPC. Cross-region communication requires VPC Peering, Transit Gateway, or inter-region traffic over the public internet (with encryption). This is the biggest architectural redesign in a GCP → AWS migration - do not attempt to replicate GCP's flat network topology in AWS directly.

4. Data Egress: The Cost You Must Plan For

Data egress is the most commonly underestimated cost in cross-cloud migrations. Moving data out of Azure or GCP to AWS is not free - you pay the source cloud's egress rate on every gigabyte that leaves.

Egress cost calculator - five scenarios

Azure egress: ~$0.087/GB. GCP: ~$0.08-0.12/GB. We help you plan, optimize, and execute data transfer.

5. Realistic Migration Timelines: What to Expect

Cross-cloud migration timelines depend primarily on data volume, number of services, dependency complexity, and team availability. The ranges below reflect EaseCloud's experience across completed Azure → AWS and GCP → AWS migrations.

What extends cloud-to-cloud timelines vs. on-prem migrations

• Egress cost pressure: teams rush the parallel validation period to minimize ongoing egress costs - and cut it shorter than is safe. Budget for full validation time; the egress cost is worth it.
• Service behavioral differences: an Azure Cosmos DB → DynamoDB migration is not just a data move - it is a data model and application code change. These take longer than pure infrastructure migrations.
• IAM translation complexity: translating Azure AD / GCP IAM Service Accounts to AWS IAM Roles requires careful mapping and testing. Authentication failures post-cutover are the most common cross-cloud migration incident.
• Networking redesign: the GCP global VPC → AWS regional VPC redesign is the most time-consuming single item in GCP → AWS migrations when the application has multi-region routing dependencies.
• Source cloud contract timing: migration must align with Azure or GCP contract end dates to avoid paying for both clouds beyond the transition period.

6. The Cross-Cloud Migration Playbook: Phase by Phase

7. Pre-Migration Checklist: What Must Be True Before Cutover

Every item below must be confirmed before the first production workload is cut over to AWS. This checklist has prevented migration failures across every engagement EaseCloud has completed.

Service Mapping & Architecture

• Every source cloud service has an identified AWS equivalent or a documented application change plan
• Services with no direct equivalent (Cosmos DB, Cloud Spanner, Durable Functions) have tested workarounds
• AWS VPC architecture designed from scratch - not copied from source cloud topology
• All VPC CIDR ranges documented and confirmed non-overlapping with source cloud if VPN needed during transition
• AWS account structure configured (Landing Zone, IAM Identity Center, multi-account if appropriate)

Data Migration

• DMS replication running with CDC enabled; replication lag confirmed < 5 seconds sustained
• Full data sync completed for all object storage (S3 sync from Blob/GCS)
• Database row counts and checksums validated in AWS target vs. source
• Snowball Edge shipment received and data loaded (if applicable)
• All backup procedures configured and tested end-to-end in AWS

Application & IAM

• All SDK references updated from Azure/GCP SDKs to AWS SDK
• All Managed Identity / Service Account references replaced with IAM role assumption
• No secrets hardcoded in application - all using AWS Secrets Manager
• Application functional test suite passing 100% against AWS environment
• Performance benchmark completed - no regressions vs. source cloud baseline

DNS, Networking & Cutover Readiness

• DNS TTLs reduced to 60 seconds (done 48 hours before cutover window)
• Rollback procedure documented and rehearsed with the team
• Cutover window confirmed: lowest-traffic period, all stakeholders notified
• AWS CloudWatch dashboards configured; alarms active for error rate and latency
• Source cloud environment confirmed stable and not in the process of any changes

8. The Most Common Cross-Cloud Migration Failures

• Replicating the source cloud's network topology in AWS. Azure VNets and GCP global VPCs have different models from AWS regional VPCs. Attempting to replicate the source topology directly leads to routing problems, incorrect CIDR ranges, and missing AZ redundancy. Always design the AWS VPC from scratch based on your actual requirements.
• Treating service equivalents as drop-in replacements. Cosmos DB and DynamoDB are not interchangeable at the application layer. Azure Blob SDK and AWS S3 SDK have different authentication, error handling, and multipart upload patterns. Application code changes are required for every service with a behavioral difference - and most have at least some.
• Underestimating IAM translation complexity. Azure Managed Identities and GCP Service Accounts to AWS IAM Roles is not a configuration change - it is an authentication model change. Every application that authenticates to a cloud service must be updated. Missing one results in a post-cutover authentication failure in production.
• Decommissioning the source cloud too quickly. Egress costs during the parallel operation period are real but small compared to the cost of a failed cutover with no working source environment to fall back to. Keep the source cloud running for 30 days post-cutover. This is non-negotiable.
• Not testing database query performance in AWS before cutover. Database query performance in AWS may differ from Azure/GCP due to instance type differences, parameter group defaults, and connection pooling behavior. Run your actual query workload against the AWS database for at least 1 week before cutover. Surprises here cause the most painful post-cutover incidents.
• Ignoring egress costs in migration budget. A $500,000 AWS Snowball Edge vs. $4,350 network egress decision for a 50TB migration sounds obvious in isolation - but teams routinely choose network transfer without modeling the cost because 'we'll do it quickly.' Plan egress costs before starting, not after the bills arrive.

Conclusion

A cross-cloud migration is a strategic business pivot driven by cost arbitrage, scaling advantages, or enterprise customer demands. While cloud-to-cloud migrations offer cleaner dependency mapping than legacy on-premise transitions, they introduce unique challenges around networking architecture, proprietary SDK translations, and strict contract alignments.

By systematically mapping services, accounting for hidden egress costs early, and utilizing a phased migration playbook, engineering teams can transition smoothly to AWS without interrupting the end-user experience.

FAQ: Azure / GCP to AWS Migration

Can we run a hybrid environment (some services in Azure/GCP, some in AWS) permanently?

Technically yes - VPN or Direct Connect can bridge source cloud and AWS networks during migration, and permanently if needed. But permanent multi-cloud architectures add significant operational complexity: two IAM systems to manage, two monitoring stacks to maintain, data transfer costs between clouds, and skill requirements across multiple platforms. EaseCloud recommends minimizing the hybrid window and targeting full consolidation within 6-12 months of beginning migration.

How do we handle Azure Active Directory users and groups in AWS?

Two paths: (1) Federate Azure AD with AWS IAM Identity Center - users continue to authenticate via Azure AD SSO, and IAM Identity Center manages their AWS permissions. This is the preferred approach when Azure AD will remain in use long-term. (2) Migrate users to a new identity provider (Okta, Google Workspace) that federates with AWS IAM Identity Center. Choose based on whether Azure AD is used for other organizational purposes beyond cloud access.

What happens to our GKE or AKS clusters - can we migrate containers directly?

Kubernetes workloads are the most portable assets in a cloud-to-cloud migration. Container images built for GKE or AKS run on EKS without modification. Kubernetes manifests (Deployments, Services, ConfigMaps) work with minimal changes - primarily around storage class names, load balancer annotations, and IAM references. Helm charts are largely portable. Expect 1-2 weeks of testing and adjustment for a medium-complexity Kubernetes workload.

We're on Azure with an EA contract. How do we handle the contract transition?

Azure Enterprise Agreement contracts have specific termination terms. The migration timeline should be designed to complete before the EA renewal date - or at minimum before the next major commitment period. EaseCloud always requests contract end dates at the start of discovery and designs the migration timeline around them. Running both Azure EA and AWS simultaneously beyond the EA period means paying for both - a significant cost that should be avoided.

How long should we plan to run both environments in parallel?

30-45 days post-cutover for most migrations. During this window: all production traffic is on AWS, the source environment is in read-only or standby mode, ongoing database replication continues for rollback capability, and your team validates AWS stability. After 30-45 days of clean operation, decommission the source environment. Do not reduce this window under cost pressure - the rollback option is worth the egress cost.

Ready to Move from Azure or GCP to AWS?

EaseCloud has completed cross-cloud migrations from Azure, GCP, and legacy hosting providers - all with zero customer-facing downtime. We start with a free discovery assessment that produces an accurate timeline, egress cost model, and service mapping before any paid scope begins.