People rarely leave GCP because it is bad. They leave to consolidate onto one cloud, to get better enterprise commercial terms, or because the rest of the org and the third-party ecosystem already live on AWS. Be clear-eyed up front: base compute pricing between the two is close enough that the migration itself will not save you money. Savings come later, from commitments and rightsizing. The move is about leverage and ecosystem, not the sticker price of a VM.
Service mapping
Most of GCP has a clean AWS counterpart. The translation is rarely one-to-one in features, but the shape holds:
| GCP | AWS | Notes |
|---|---|---|
| Compute Engine | EC2 | Rebuild instance types; ARM Graviton can cut cost |
| Cloud Storage | S3 | Closest mapping; storage classes differ |
| Cloud SQL | RDS | Same engines (Postgres, MySQL) ease the move |
| BigQuery | Redshift or Athena | Athena over S3 if you want serverless and decoupled storage |
| GKE | EKS | Kubernetes is portable; the control plane and IAM glue is not |
| Cloud Run | Fargate or App Runner | Container serverless, different cold-start and scaling behavior |
| Pub/Sub | SNS plus SQS | AWS splits fan-out and queueing into two services |
BigQuery is the one to think hardest about. There is no drop-in equivalent. Redshift is a provisioned warehouse with a different cost and tuning model; Athena over S3 (Parquet) is closer to BigQuery’s serverless feel but needs you to manage partitioning and file layout yourself.
Moving the data
Pick the tool by data type and volume.
- Object data (GCS to S3): AWS DataSync or S3 Storage Transfer-style jobs. For very large or one-time loads, consider Snowball to ship disks rather than fight bandwidth.
- Databases: AWS Database Migration Service (DMS) does continuous replication with minimal downtime, including heterogeneous moves via the Schema Conversion Tool.
- Data warehouse: export BigQuery to GCS as Parquet, copy to S3, then load into Redshift or query in place with Athena.
# One-time bulk copy from a GCS bucket to S3, run from a box with both creds
gsutil -m cp -r gs://my-gcp-bucket/data /local/stage
aws s3 cp /local/stage s3://my-aws-bucket/data --recursive
# For ongoing sync, prefer DataSync tasks over scripted gsutilThe egress cost gotcha
This is the line item that ambushes people. GCP charges egress to pull your data out, metered per GB across the whole migration. A multi-hundred-terabyte store can run a serious one-time bill just to leave. Estimate egress before you start, prune dead data first, and weigh Snowball-style physical transfer for the largest datasets. It does not show up in the steady-state comparison, but it is real cash on the way out.
IAM and networking: rebuild, do not port
None of this migrates. You recreate it.
- GCP IAM bindings and service accounts become AWS IAM roles, policies, and instance profiles. The model differs: AWS leans on roles assumed by resources, so re-architect rather than translate line by line.
- VPCs, subnets, routes, firewall rules, and load balancers are rebuilt in AWS networking primitives. Treat it as a fresh network design and capture it in Terraform so it is reviewable and repeatable.
- Secrets move from Secret Manager to AWS Secrets Manager or Parameter Store.
Doing this as code, not console clicks, is what keeps the migration auditable and lets you stand up a staging copy before you cut production over.
Cutover via DNS
Once a workload’s servers, data, and networking are verified on AWS, the switch itself should be boring. Run a final delta sync so the AWS side is current with GCP, freeze writes on the source, and move DNS to the target using Route 53. Validate the application, its integrations, and its cost on AWS before releasing all traffic, then hold a hypercare window with GCP still running as a fallback. Keep the DMS and DataSync tasks alive until cutover is signed off, so a rollback is a DNS change rather than a re-migration. Rehearse the cutover on a low-risk service first, so the sequence and the rollback path are proven before you point production at it.
Where GCP still wins: when not to migrate
Honesty check before you commit the quarter.
- BigQuery-heavy analytics. If your business runs on BigQuery’s serverless scale and SQL ergonomics, the AWS side is a real downgrade in convenience. This alone has killed many migrations.
- Deep GKE and Anthos investment. If you have leaned hard into Google’s Kubernetes and service-mesh tooling, the rebuild cost is high.
- Data gravity and egress. If most of your data and traffic already sit in GCP, the egress bill plus rebuild effort can outweigh any commercial gain.
- Vertex AI and BigQuery ML pipelines do not have clean lift-and-shift targets.
If GCP is serving you well and the only driver is a modest discount, the migration math often does not close.
How to actually do it
Go workload by workload, not big-bang. Stand up the AWS account structure and networking first, migrate a low-risk stateless service to prove the pipeline, then move stateful systems once the pattern is solid. Keep the GCP resources until the AWS side is verified in production.
What to do next
GCP to AWS is mostly a rebuild, not a lift-and-shift, and the base compute prices are too similar for the move itself to pay for it. Your real savings come afterward from Savings Plans, Reserved Instances, Graviton, and rightsizing, while your real one-time cost is egress plus the engineering months to recreate IAM, networking, and any BigQuery workload. Map every service, estimate the egress, and model the commitments and migration labor in the calculator above before you commit.