Cloud Migration: AWS to Azure to Google Cloud Step-by-Step Guide

You've migrated once. Maybe you're about to migrate again. Each hop between AWS, Azure, and Google Cloud can eat up months, introduce new ways things can fail, and force your team to redo work you've already paid for. Sound familiar? This guide covers the full journey: whether you're moving from AWS to Azure, Azure to Google Cloud, or planning both in sequence.

Why Sequential Cloud Migration Creates Compounding Risk

Think about it.

Every cloud-to-cloud migration comes with the same baseline work: landing zones, identity mapping, network topology, database conversion, and team retraining. Do it twice and you've basically doubled the overhead.

The real question to answer upfront: is the intermediate platform a permanent part of your multicloud setup, or just a costly stopover?

Organizations planning AWS to Azure to Google Cloud should take a hard look at whether the Azure step is actually required. If Google Cloud is the end state, going directly from AWS removes a whole cycle of duplicated work. That's often 4-6 months saved, plus the cost of running parallel infrastructure.

That said, staged migrations can be the right call. Regulatory requirements may force specific data residency during the transition. Existing Azure contracts may need to run their course. Some workloads may stay on Azure long-term while others move to Google Cloud. The key insight: make this a deliberate decision, not the default path.

Prerequisites

Before starting any cross-cloud migration:

• Workload inventory with current costs, dependencies, and data volumes
• Landing zone built and validated in the target cloud (Azure or Google Cloud)
• Identity strategy mapped between source and target IAM systems
• Network connectivity established (VPN or dedicated interconnect)
• Rollback plan documented for each migration wave

Step 1: Classify Workloads Using the 7Rs Framework

Right.

The 7Rs framework from AWS Prescriptive Guidance still works even when you're moving off AWS. Before touching any infrastructure, classify every application. Every single one.

Large migrations typically lean heavily on rehost and replatform. Full refactoring usually waits until workloads are stable in the target environment. Trying to modernize and migrate at the same time adds too many moving parts when you're chasing down failures.

Step 2: Build the Target Landing Zone

Migration can't start until the target cloud has a secure, well-architected foundation. Both Azure and Google Cloud treat landing zone readiness as a prerequisite, not something to do on the side. Don't try to shortcut it.

Landing Zone Components

Identity and Access Management

This is where things usually get tricky.

AWS IAM concepts translate to Azure and Google Cloud, but the details are different in ways that matter. AWS IAM roles roughly map to Azure Managed Identities and Microsoft Entra ID (formerly Azure AD). In Google Cloud, you're looking at Cloud IAM with service accounts. The similarities are real, but the behavior and operational patterns aren't the same.

Network Topology

Amazon VPC maps to Azure Virtual Network or Google Cloud VPC. But availability zone designs vary across providers. AWS uses explicit AZ selection, while Azure uses availability sets and zones with different semantics. Google Cloud regions have zones that behave differently again. If your migration plan assumes a clean one-to-one mapping, it'll probably get messy fast.

Governance and Guardrails

Set policies, encryption standards, logging, and billing controls before workloads arrive. Trying to bolt governance on after migration is how security gaps and compliance surprises show up at the worst possible time.

Step 3: Plan Data Migration First

Data migration is usually the highest-risk workstream and the longest lead-time item. Start planning it before anything else moves. Seriously, before anything else.

Migration Patterns

Scheduled Maintenance Window

Got downtime tolerance?

If the workload can handle downtime, a clean cutover during a maintenance window is the simplest option. Stop writes on the source, copy data, validate, switch DNS, and bring up the target. Sometimes it's a weekend. Sometimes it's longer.

Continuous Replication

If you need lower downtime, set up ongoing replication from source to target. The final cutover is about stopping replication, validating consistency, and switching traffic. Netflix uses this pattern for cross-region migrations, keeping services running while data moves underneath.

Dual-Write Patterns

For complex active systems that need near-zero downtime, apps write to both source and target during the transition. This gets complicated fast because conflict resolution needs to be designed and tested carefully. Uber used this approach in their MySQL to Cassandra migration, and it took months to get right.

Data Transfer Considerations

Google Cloud's data transfer guidance calls out bandwidth planning for large datasets. A 10TB database over a 100Mbps connection takes roughly 9 days to transfer. A 100TB dataset usually pushes you toward dedicated interconnect or physical transfer appliances.

Schema compatibility needs real validation. MySQL on Amazon RDS doesn't map perfectly to Cloud SQL for MySQL. Character sets, collations, stored procedures, and extensions can require changes. Worth asking: have those stored procedures been reviewed recently?

Step 4: Migrate in Waves with Rollback Capability

Migrations work best in increments. Each wave should be small enough to roll back fully if something goes sideways. Plan in weeks, not months, per wave.

Wave Sequencing

Wave 1: Low-Risk Workloads

Start here.

Dev environments, internal tools, and non-critical apps go first. They prove out the landing zone and your migration process without putting the business at risk. If something breaks, it's far less likely someone's pager goes off at 3 AM.

Wave 2: Databases and Data Stores

Move databases after replication is in place. Keep source databases running and replicating until dependent apps are safely migrated. Shopify maintains 30-day rollback windows for database migrations, and that level of caution is often justified.

Wave 3: Integration Layers

APIs, message queues, and middleware move after their data dependencies. Update connection strings and credentials in the target environment. Test every integration point. Then test them again.

Wave 4: Mission-Critical Applications

Workloads with SLAs move last, after the process has been proven on less critical systems. By this point, the runbooks should be tested and the team should know the failure patterns to watch for.

Wave 5: Shared Services

Monitoring, logging, CI/CD pipelines, and other shared infrastructure move after dependent workloads. These are foundational services. Pulling them out too early tends to create chaos.

Rollback Planning

Every wave needs a written rollback procedure. For rehosted VMs, that might mean keeping source instances stopped but ready. For databases with continuous replication, it usually means maintaining reverse replication. For refactored apps, rollback may require redeploying the previous version back in the source cloud.

Yes, rollback capability costs money: parallel infrastructure, replication bandwidth, and longer timelines. But the alternative is worse: getting stuck mid-migration with no clean path back when something breaks.

Step 5: Manage Costs During Parallel Operations

Flexera's 2026 State of the Cloud report reports estimated wasted cloud spend has risen to 29%, the first increase in five years. Migrations tend to amplify that risk because they require parallel infrastructure by design.

Cost Control Practices

Consistent Tagging from Day One

Tag everything.

Every resource in the target cloud should have project, environment, owner, and migration-wave identifiers. That's how cost attribution and cleanup tracking stays sane. Without tags, you're guessing when the CFO asks why the bill doubled.

Budget Alerts

Set budget alerts at 50%, 75%, and 90% thresholds for each migration wave. Cost spikes often point to misconfigurations or runaway resources. You want to catch those early, not at month-end.

Delay Committed-Use Purchases

Avoid reserved instances or committed-use discounts in the target cloud until workloads settle. The flexibility of on-demand pricing during migration often beats the 30-40% discount. Optimization can come after stability.

Rightsize After Migration

Workloads often get oversized during migration to keep headroom. Schedule rightsizing reviews 30-60 days after each wave stabilizes. That m5.4xlarge might only need to be an m5.large.

Decommission Source Resources Promptly

The most common cost leak in migrations is simple: source infrastructure doesn't get shut down after cutover. Track source resources explicitly and decommission within defined windows. Put calendar holds in place if needed.

Step 6: Validate and Optimize Post-Migration

Finishing the migration isn't the same as finishing the project. Post-migration optimization is where the value shows up. Otherwise, your team may have just moved the mess to a new provider.

Validation Checklist

• Performance benchmarks match or exceed source environment
• Security controls validated through penetration testing
• Disaster recovery procedures tested in target cloud
• Monitoring and alerting operational with appropriate thresholds
• Runbooks updated for target cloud operations

Optimization Opportunities

Managed Service Adoption

Now's the time to modernize.

Rehosted workloads on VMs can often shift to managed services. A self-managed PostgreSQL instance on Compute Engine might become Cloud SQL. A container workload on VMs might move to GKE. These moves typically reduce ops overhead and simplify patching.

Cloud-Native Features

The target cloud may offer capabilities you didn't have (or didn't use) before. Auto-scaling, spot/preemptible instances, and serverless options can cut costs by 40-60% and improve reliability. Pinterest saved millions after migration by leaning into spot instances.

AI/ML Workload Optimization

A lot of organizations migrate specifically to access certain AI capabilities. Google Cloud's TPUs can deliver 10x performance for some ML workloads. Azure's OpenAI integration can give GPT-4 access without API limits. If AI is part of the business case, make sure those workloads are tuned for the target platform because defaults usually won't be.

Common Pitfalls and How to Avoid Them

Underestimating Identity Complexity

AWS IAM, Azure Entra ID, and Google Cloud IAM use fundamentally different models. Service accounts, roles, and permissions don't map cleanly. Treat identity as its own workstream, not something to squeeze in at the end. In many migrations, allocating around 20% of the timeline to identity isn't overkill.

Assuming Network Equivalence

Latency matters.

Apps that used to communicate inside a single AWS region may now span providers during the transition. That 1ms latency can turn into 50ms. Test performance with realistic traffic patterns before cutover. Load test everything.

Skipping Disaster Recovery Testing

Backup and recovery procedures don't transfer automatically. DR runbooks usually need a full rewrite for the target environment. Test recovery before calling the migration done. Actually restore from backup. In a different region. Under pressure.

Neglecting Team Training

Ops teams need to be comfortable in the target cloud before they own production workloads. Training should run alongside early migration waves, not after. Official training and certifications tend to pay back quickly by preventing avoidable outages.

Hands-On Steps

Start here (your first step)

Export your complete workload inventory from AWS or Azure, including compute instances, databases, storage volumes, and their current monthly costs.

Quick wins (immediate impact)

• Classify each workload using the 7Rs framework and identify candidates for retirement
• Establish network connectivity (VPN or interconnect) between source and target clouds

Deep dive (for those who want more)

• Build and validate a complete landing zone in the target cloud following provider guidance
• Run a proof-of-concept migration with one low-risk workload to validate the end-to-end process

Useful Resources

• AWS Prescriptive Guidance: Migration Strategies - Detailed 7Rs framework and large-scale migration patterns
• Microsoft Learn: Migrate Workloads to Azure - Azure landing zone guidance and migration tooling
• Microsoft Learn: Migrate from AWS to Azure - AWS-specific migration guidance and service mappings
• Google Cloud: Migrate from Azure - Azure-to-Google Cloud migration planning and execution
• Google Cloud: Large Dataset Transfer - Data migration patterns and bandwidth planning

Wrapping Up

Cloud migration between major providers is a portfolio modernization program, not a simple infrastructure move. Teams that succeed tend to treat each phase intentionally: classify workloads before touching infrastructure, build landing zones before migrating anything, move data before applications, and keep optimizing after cutover.

The most important decision often happens before the first workload moves: whether each platform in the migration path is truly necessary.

With Gartner projecting $723 billion in cloud spending for 2025 and 90% of organizations adopting hybrid cloud approaches, the ability to migrate confidently between providers is becoming a core competency, not a one-time project. Teams that build that muscle will be better positioned as cloud platforms keep evolving and pulling further apart.