GCP to AWS Migration: A Practical Guide to Planning and Executing a Successful Move
Moving from Google Cloud to AWS can provide access to services and operating models that better support your business and technical requirements.
This GCP to AWS migration guide explains how to evaluate the move, map services, select a strategy, and execute it in manageable phases.
Quick summary
- A GCP to AWS migration should support a clear business or technical outcome.
- Google Cloud and AWS services can differ in architecture, identity, networking, pricing, and operations.
- A phased process reduces risk: assess, prepare AWS, migrate, and optimize.
- Include data egress, parallel environments, engineering, testing, training, and optimization in the budget.
Why companies migrate from GCP to AWS
Expanding AWS service capabilities
AWS provides a broad portfolio across compute, databases, analytics, security, networking, and AI. A company may move to use specific AWS capabilities or standardize technology across business units. Stormit's AWS vs GCP comparison breaks down how the two platforms differ across these areas.
Confirm that required services are available in the target AWS Region and meet performance, resilience, and compliance requirements.
Security and compliance alignment
Some organizations already operate security controls and audits around AWS. AWS Organizations, AWS Control Tower, AWS Identity and Access Management (IAM), AWS CloudTrail, AWS Config, Amazon GuardDuty, and AWS Security Hub can support centralized governance.
Migration does not improve security by default. Identity, encryption, logging, network controls, and incident response still require careful design and testing.
Global infrastructure and scalability
Region availability can influence application placement, data residency, and disaster recovery. Verify service availability in the target AWS Region and test latency from the locations where your users actually connect.
Long-term cost optimization opportunities
Moving from GCP to AWS can create savings through right-sizing, Spot Instances, Savings Plans, managed services, and storage lifecycle policies. Lower costs are not guaranteed and depend on how workloads are designed, configured, and operated.
For example, a workload running continuously on oversized virtual machines may reduce costs after right-sizing and moving to Savings Plans, while highly variable workloads may benefit more from autoscaling and consumption-based services.
A realistic total cost of ownership calculation should include Google Cloud egress, duplicate environments, migration tools, engineering, testing, support, licenses, backups, and training. Use Stormit's AWS cost optimization guide to plan savings before the move and identify further opportunities once workloads are running in AWS. For practical ways to control transfer-related spend, explore our AWS data transfer pricing guide.
When is a GCP to AWS migration worth it?
Before you migrate from GCP to AWS, assess each workload separately. Some applications may have a strong reason to move, while others are better retained, replaced, or retired.
| Decision factor | Migration is worth considering when | Reasons to pause |
| Business | AWS is strategic for customers, partners, or the organization | The decision is mainly provider preference |
| Architecture | AWS provides a measurable technical or operational advantage | The workload relies heavily on GCP-specific behavior |
| Security | AWS supports the required governance and compliance controls | Controls have not been validated in the target design |
| Geography | AWS better supports target markets, latency, or data residency | Critical services are unavailable in the target Region |
| Cost | A total cost of ownership (TCO) analysis shows sustainable savings or greater value | The estimate excludes egress, engineering, or parallel operation |
| Operations | The team has the required AWS skills and support model | The move creates a significant capability gap |
| Timing | A renewal, acquisition, or modernization creates a natural transition | The deadline forces a risky big-bang migration |
Pause if the problem can be solved within Google Cloud, no suitable target exists, or the value does not justify the disruption.
GCP to AWS service mapping reference
Service mapping is only a starting point. Similar services can differ in APIs, scaling, identity, networking, quotas, and pricing. So migrations should focus on workload requirements rather than direct replacements. The mappings below show common architectural targets, not one-to-one equivalents.
| Google Cloud service | Common AWS target | Key considerations |
| Compute Engine | Amazon EC2 | Images, instance families, storage, Auto Scaling |
| Google Kubernetes Engine | Amazon EKS | Add-ons, ingress, storage, nodes, workload identity |
| Cloud Run | AWS App Runner, Amazon ECS with AWS Fargate, or AWS Lambda | Runtime control, traffic model, limits, networking |
| Cloud Run functions | AWS Lambda | Runtimes, triggers, concurrency, VPC access |
| Cloud Storage | Amazon S3 | Storage classes, lifecycle, metadata, permissions |
| Cloud SQL | Amazon RDS or Amazon Aurora | Compatibility, extensions, HA, replication, cutover |
| BigQuery | Amazon Redshift, Amazon Athena, or a data lake on Amazon S3 | Queries, ingestion, governance, performance, pricing |
| Pub/Sub | Amazon SNS with Amazon SQS, Amazon EventBridge, Amazon Kinesis Data Streams, or Amazon Managed Streaming for Apache Kafka (Amazon MSK) | Ordering, retries, retention, replay, delivery |
| Dataflow | AWS Glue or Amazon Managed Service for Apache Flink | Batch, streaming, orchestration, code portability |
| Gemini Enterprise Agent Platform (formerly Vertex AI) | Amazon SageMaker AI, Amazon Bedrock, and Amazon Bedrock AgentCore | Model access, training, hosting, pipelines, governance |
| Cloud Logging and Cloud Monitoring | Amazon CloudWatch | Logs, metrics, alarms, dashboards, retention |
| Identity and Access Management (IAM) | AWS IAM and AWS IAM Identity Center | Accounts, federation, roles, policies, workload access |
Before finalizing the architecture, use a proof of concept to test any GCP-specific behavior that must be preserved.
Choosing the right migration strategy for a GCP to AWS migration
A virtual machine may be rehosted, while a managed database or serverless application may need replatforming or refactoring. Other workloads may be retained, retired, or replaced.
Choose the approach for each workload based on business value, portability, technical debt, timeline, and risk. Not sure which approach fits each workload? Compare the options in Stormit's guide to the 6 Rs of AWS migration.
A practical GCP to AWS migration roadmap
Break the work into four phases: assess, prepare, migrate, and optimize. Each one creates a clear checkpoint before the next step.
AWS groups large migrations into three phases: assess, mobilize, and migrate and modernize. The roadmap below adapts this model to a cloud-to-cloud move.
Phase 1: Assess the migration
1. Define the business case and success criteria
Set the outcomes the migration should deliver, such as lower costs, improved resilience, simpler governance, or access to specific AWS services.
Create baselines for cost, latency, availability, recovery objectives, security findings, and operational effort. Use them to evaluate the target.
2. Inventory workloads and dependencies
Document the current environment, including virtual machines, containers, databases, storage, identities, DNS, CI/CD pipelines, monitoring, backups, and integrations. Record owners, criticality, downtime tolerance, and costs.
Map workload dependencies before grouping applications into migration waves. Moving tightly connected components at different times can increase latency, data transfer costs, and operational risk.
Phase 2: Prepare the AWS environment
3. Build the AWS foundation
Set up the AWS account structure, identity federation, networking, encryption, logging, security controls, budgets, tagging, and backups before moving production workloads.
Use infrastructure as code and policy automation to reduce manual effort and configuration drift. For a broader planning framework, explore Stormit's cloud migration strategy guide.
4. Select target architectures and migration waves
Assign a target and migration strategy to each workload. Record dependencies, risks, owners, downtime, rollback needs, and estimated effort.
Choose a representative but non-critical pilot. A pilot that is too simple may miss issues that could affect later migration waves.
Phase 3: Migrate and validate
5. Plan connectivity and data transfer
GCP and AWS will typically operate in parallel during part of the migration. Before transferring data, plan for bandwidth, latency, routing, DNS, firewall rules, encryption, and cross-cloud costs.
AWS DataSync can transfer object data from Google Cloud Storage to Amazon S3. AWS Database Migration Service supports full loads and ongoing replication for supported engines. Server workloads may be candidates for rehosting with AWS Transform MGN.
Select tools based on source technology, data volume, change rate, downtime tolerance, and validation needs. Test permissions, throughput, integrity, and cost before cutover. To compare the options, explore Stormit's guide to the best AWS migration tools.
6. Migrate and test the pilot
Use the automation, tools, and procedures planned for production. Test functionality, performance, security, backup and restore, failure handling, monitoring, and cost.
Compare the pilot results with the original baseline to confirm that the migration delivers the expected operational and financial benefits, not just technical functionality.
7. Move workloads in controlled waves
Use the pilot findings to improve runbooks, estimates, and automation. Group workloads by dependency, criticality, and migration pattern.
Each wave needs an owner, test plan, rollback criteria, and change window. Before cutover, complete synchronization, verify monitoring and backups, control source changes, and confirm how the rollback will handle new data.
Phase 4: Stabilize and optimize
8. Optimize AWS and decommission GCP
After stabilization, right-size resources, review storage classes, tune monitoring, and remove temporary components. Consider Savings Plans only when the usage is predictable.
Keep GCP until rollback, retention, audit, billing, backup, and contract requirements are resolved. Then remove unused resources and confirm that GCP charges have stopped.
Common GCP to AWS migration challenges
Treating similar services as direct replacements
Similar categories do not guarantee the same behavior. Test APIs, consistency, delivery, quotas, scaling, failure modes, and pricing before finalizing the target.
Copying identity and permissions directly
Google Cloud projects, service accounts, and Workload Identity do not translate directly into AWS accounts, roles, policies, and federation. Redesign the access model around AWS account boundaries and the principle of least privilege.
Overlooking cross-cloud dependencies
Traffic between GCP and AWS can increase latency, transfer cost, and complexity. Identify these connections early and move tightly coupled components together where practical.
Underestimating data and cutover risk
Frequently updated datasets may require incremental synchronization or change data capture. Validate counts and checksums, control writes, and test rollback.
Missing costs from the migration budget
Commonly missed items include GCP egress, API requests, parallel environments, logging, backups, NAT gateways, cross-Availability Zone traffic, support, and engineering. Track migration spend separately from the AWS run rate.
Modernizing too much during migration
Modernization can improve the target, but too much scope makes delivery harder to control. Separate the migration essentials from improvements that can wait until the workload is stable.
Preparing the platform but not the team
Update CI/CD pipelines, monitoring, access procedures, incident response, runbooks, ownership, on-call responsibilities, and training before cutover.
FAQs
An isolated workload may move in weeks, while a complex portfolio can take months. Dependencies, data volume, testing, team capacity, refactoring, and downtime determine the timeline.
Near-zero downtime may be possible through replication, change data capture, blue-green deployment, and controlled traffic switching. It depends on the architecture, consistency requirements, and rollback design.
Common options include AWS DataSync for supported storage transfers, AWS Database Migration Service for supported databases, and AWS Transform MGN for rehosting server workloads. Native Google Cloud tools and third-party products may also be required.
It can be, depending on workload architecture, usage patterns, Region, data transfer, licensing, support, and pricing commitments. Compare projected AWS costs with your current GCP spending, then use the migration pilot to verify the assumptions.
Conclusion
A successful GCP to AWS migration starts with a clear reason to move and a realistic plan for applications, data, identity, networking, security, operations, and cost.
Assess each workload, prepare the AWS foundation, validate the design with a pilot, and migrate in controlled waves. Applications can then be rehosted, replatformed, refactored, retained, replaced, or retired.
Considering a migration to AWS? A migration assessment can help turn the initial idea into a clear plan covering costs, risks, dependencies, and the right approach for each workload.
An AWS Solutions Architect with over 5 years of experience in designing, assessing, and optimizing AWS cloud architectures. At Stormit, he supports customers across the full cloud lifecycle — from pre-sales consulting and solution design to AWS funding programs such as AWS Activate, Proof of Concept (PoC), and the Migration Acceleration Program (MAP).
