Migrate from AWS
to Google Cloud Platform
Migrating from AWS to Google Cloud Platform is appropriate when AWS's service pricing, networking model, or data analytics capabilities conflict with GCP's sustained-use pricing, global network architecture, and BigQuery-native analytics advantages. The primary risks are service mapping gaps, networking reconfiguration, and IAM policy translation, which can be eliminated with a structured migration process that maps AWS services to GCP equivalents, migrates workloads incrementally by tier, and maintains multi-cloud connectivity until full cutover.
When AWS stops working
AWS stops being the right choice when data analytics costs on Redshift/Athena exceed BigQuery economics, reserved instance management creates operational overhead that sustained-use discounts eliminate, the organization needs Kubernetes-native infrastructure and GKE outperforms EKS for the use case, data residency requirements favor GCP's regional availability, or the organization's AI/ML strategy aligns more closely with Vertex AI than SageMaker.
What Google Cloud Platform unlocks
GCP unlocks BigQuery's serverless analytics (no cluster management, per-query pricing), sustained-use and committed-use discounts without reserved instance complexity, GKE's Kubernetes management (autopilot mode), Vertex AI for ML model training and serving, Cloud Run for serverless containers, and a global private network with lower inter-region latency.
Who should not migrate
Organizations deeply invested in AWS-specific services with no GCP equivalent (certain IoT services, Outposts for on-prem, specific compliance frameworks). Teams where AWS expertise is the core competency and retraining cost exceeds migration benefit. Workloads with hard dependencies on AWS marketplace AMIs or third-party integrations that only support AWS.
What usually goes wrong
IAM policy translation is more complex than expected — AWS IAM and GCP IAM have fundamentally different models (resource-based policies vs. organization-level bindings). Networking configuration (VPCs, subnets, security groups vs. firewall rules) requires redesign, not translation. Data transfer costs from AWS egress are significant for large datasets and often underestimated. Service-specific configurations (Lambda → Cloud Functions, RDS → Cloud SQL) have behavioral differences that surface in production.
Risk Matrix: AWS to Google Cloud Platform
AWS IAM uses resource-based policies, service-linked roles, and permission boundaries. GCP uses organization/folder/project hierarchy with IAM bindings. The models are structurally different.
Map all AWS IAM policies to GCP IAM equivalents at the organization level. Audit for privilege escalation paths in the translated policies. Run access verification tests for every role before migrating workloads.
AWS VPCs, subnets, security groups, NACLs, and route tables do not map 1:1 to GCP VPCs, subnets, firewall rules, and routes. Fundamental differences in how default networking works.
Redesign the network architecture for GCP rather than translating AWS configs. Use GCP's Shared VPC for multi-project networking. Establish VPN or Interconnect between AWS and GCP during parallel operation.
AWS Lambda and Cloud Functions have different cold start behavior, timeout limits, and concurrency models. RDS and Cloud SQL have different parameter defaults, backup schedules, and failover behavior.
For each migrated service, document the behavioral differences that affect your workload. Run integration tests against GCP services before migrating production traffic. Maintain AWS as fallback during validation.
DNS propagation delays when switching from AWS-hosted endpoints to GCP-hosted endpoints cause intermittent failures during cutover.
Use low TTL DNS records before migration. Implement health-check-based routing (Cloud DNS or external DNS with failover). Run both endpoints simultaneously and shift traffic gradually using weighted routing.
AWS charges egress fees. Migrating petabytes of data from S3 to GCS costs significantly in bandwidth fees and takes days or weeks depending on volume.
Use Transfer Service for large datasets. Negotiate with AWS on egress fees for migration (possible for large accounts). Migrate data in tiers — hot data first via direct transfer, cold data via Storage Transfer Service. Budget egress costs explicitly.
Skills for AWS → Google Cloud Platform
Agent skills that accelerate this migration
What Must Not Change During This Migration
All workloads must maintain their current SLAs for availability and latency during and after migration
Data integrity must be verified — byte-level checksums for object storage, record-level reconciliation for databases
Security posture must not degrade — IAM policies must enforce equivalent or stricter access controls
Cost must be modeled and verified — GCP total cost must not exceed AWS total cost for equivalent workloads
Rollback to AWS must be possible per workload for 30 days after migration
Migration Process: AWS to Google Cloud Platform
System inventory
Catalog all AWS services in use: compute instances, databases, storage buckets, Lambda functions, API Gateways, IAM policies, VPC configurations, and CloudFormation/Terraform templates. Document monthly cost per service.
Dependency mapping
Map each AWS service to its GCP equivalent. Identify services with no direct equivalent that require architectural changes. Map network dependencies (cross-VPC, cross-region, on-prem connectivity). Classify workloads by migration complexity tier.
Data and configuration translation
Migrate infrastructure-as-code from CloudFormation to Terraform or Deployment Manager. Translate IAM policies. Migrate S3 buckets to GCS. Migrate databases using Database Migration Service. Validate data integrity after each transfer.
Parallel deployment
Deploy GCP infrastructure alongside AWS. Establish network connectivity between clouds (VPN or Interconnect). Run workloads in both environments. Route traffic to GCP for migrated workloads, AWS for remaining.
Incremental traffic shift
Migrate workloads in tiers: stateless services first (easiest to roll back), then stateful services, then databases last. Validate performance, cost, and correctness after each tier before proceeding.
Verification and cleanup
Run workloads on GCP for 30 days while maintaining AWS fallback. Compare performance metrics, error rates, and costs. Decommission AWS resources only after verification period with no critical issues. Archive AWS data per retention policy.
How This Migration Changes at Scale
Petabyte-scale data (S3 storage > 1PB)
Data transfer becomes the critical path. Use Google Transfer Appliance for bulk data movement. Budget 4-8 weeks for data migration alone. Implement incremental sync for data that changes during migration.
Multi-region deployment
Network architecture must be redesigned per region. GCP's global VPC model differs fundamentally from AWS's regional VPC model. Cross-region latency testing must verify application behavior.
Kubernetes-heavy workloads (EKS to GKE)
Container images and Kubernetes manifests transfer cleanly. EKS-specific features (IAM roles for service accounts, ALB ingress controller) need GKE equivalents (Workload Identity, GKE Ingress). Test all cluster autoscaling behavior on GKE.
Compliance requirements (HIPAA, PCI, FedRAMP)
Verify GCP's compliance coverage matches AWS for your specific use case. Some GCP compliance certifications cover fewer services than AWS equivalents. Compliance team must approve the new architecture before migration.
Related Analysis
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