AWS to GCP Migration Program Playbook
A phased program to migrate workloads, data, and identity from AWS to Google Cloud: service mapping, GCP foundation, replication-based data and workload migration, and optimize-and-decommission.
AWS to GCP Migration Program Playbook
Moving from one hyperscaler to another is a major program driven by economics, strategic alignment, or specific capabilities such as data and AI tooling. This program relocates workloads, data, and identity from AWS to Google Cloud, mapping services carefully because parity is rarely exact.
Phase-by-Phase
Service Mapping and Assessment. Map each AWS service to its GCP equivalent: S3 to Cloud Storage, RDS to Cloud SQL, EKS to GKE, Lambda to Cloud Functions. Flag gaps where parity is imperfect and decide how to bridge them. Assess data gravity, since large datasets dictate sequence and egress cost.
GCP Foundation. Build a GCP landing zone with resource hierarchy, org policies, and networking. Establish Interconnect or VPN between AWS and GCP for the coexistence period, and federate identity so users authenticate consistently during the transition.
Data and Workload Migration. Migrate data stores first or alongside their workloads, using replication to shrink the cutover window. Relocate workloads in dependency-grouped waves, using strangler-fig and expand-and-contract patterns to cut over incrementally with rollback at every step.
Optimize and Decommission. Optimize for GCP's pricing model rather than carrying AWS-shaped assumptions, validate compliance in the new estate, and decommission AWS to stop dual-running costs and realize the savings that justified the move.
Team and Roles
An architect owns service mapping. DevOps and SRE run the migration factory. DBAs and data engineers own data migration. Security owns identity cutover and compliance. Product owners accept each cutover.
Risks and Mitigations
Service parity gaps surprise teams; document them upfront and prototype the bridges. Egress cost can dominate; sequence to minimize cross-cloud transfer. The data migration window is mitigated with replication and incremental sync. Identity cutover is high-risk; federate and test before switching.
Success Criteria
All in-scope workloads run on GCP, run-rate cost drops, downtime stays within target, and AWS is fully decommissioned.
Tooling
Terraform provisions the GCP foundation, Kubernetes (GKE) runs workloads, database migration services and PostgreSQL handle data, Datadog provides cross-cloud observability, and Vault manages secrets through the transition.