GCP to AWS Migration: Cost, Timeline & Decision Guide

Should you migrate from GCP to AWS?

GCP-to-AWS migration fits organizations standardizing compute and storage on AWS with limited BigQuery or Vertex AI dependency. Keep or optimize GCP when proprietary services or committed-use penalties outweigh consolidation benefits.

Evidence note: Project records were not available for review, so we have omitted cost, timeline, and outcome benchmarks. The schedule below is a planning example.

The work extends beyond moving virtual machines. Teams must translate Google's global control plane into AWS's regional model, decide whether BigQuery workloads belong on Redshift or Athena, and redesign project-based IAM around AWS accounts, roles, and service control policies.

How do you decide whether to leave GCP?

A GCP exit is viable when data consumers, engineering skills, contract economics, and cloud-native dependencies all favor AWS. Score each criterion before approving the migration; a weak result in BigQuery dependency or egress planning can invalidate the business case.

What causes GCP-to-AWS migrations to fail?

BigQuery-to-Redshift incompatibility, IAM-model mismatch, and GCP contract or exit costs need separate owners and acceptance tests before workload waves begin. The engineering risks below are assessment prompts.

Which technical differences require redesign?

Five GCP-to-AWS differences usually require design work rather than service substitution: analytics architecture, regional networking, Kubernetes operations, messaging semantics, and workload identity. Treat each mapping as an architecture decision with performance, security, and operating-model acceptance criteria.

1. BigQuery vs. Redshift Architecture

Redshift's managed-storage provisioned clusters can scale compute and storage independently. Choose the deployment and compute capacity using your query workload.

• Risk: Query performance can change with data distribution, sort order, and the target compute configuration. Redshift omits PostgreSQL-style secondary indexes.
• Prevention: Profile join and filter patterns, test representative workloads, and evaluate automatic table optimization or explicit distribution and sort keys.

2. Networking & VPCs

GCP VPCs are global; subnets span regions. AWS VPCs are regional.

• Risk: Broken connectivity for multi-region applications.
• Prevention: Re-architect network topology. You will need VPC Peering or AWS Transit Gateway to connect VPCs across regions, which adds complexity compared to GCP's global VPC.

3. Kubernetes (GKE vs. EKS)

GKE is widely considered the "gold standard" of managed Kubernetes. EKS is powerful but requires more "assembly."

• Risk: Operational overhead increase. GKE automates master upgrades and node repairs more aggressively than standard EKS.
• Prevention: Use EKS Managed Node Groups and consider tools like Karpenter for autoscaling to match GKE's efficiency.

4. Pub/Sub vs. Kinesis/SQS/SNS

GCP Pub/Sub is a global, unified messaging service. AWS splits this into Kinesis (streams), SQS (queues), and SNS (topics).

• Risk: Architectural complexity. You can't just "replace" Pub/Sub with one AWS service.
• Prevention: Map use cases carefully. Use Kinesis for high-throughput ingestion, SQS for decoupling microservices, and SNS for fan-out notifications.

5. Identity Federation

GCP Service Accounts are first-class identities. AWS uses IAM Roles assumed by resources.

• Risk: Hardcoded keys. Developers might try to generate long-lived access keys for AWS to mimic Service Account keys.
• Prevention: Enforce the use of IAM Roles for EC2 (Instance Profiles) and IRSA (IAM Roles for Service Accounts) in EKS. Ban long-lived access keys.

What is the GCP-to-AWS migration roadmap?

A GCP-to-AWS program typically moves through discovery and TCO, AWS landing-zone design, data migration, and application cutover. The sequence keeps identity and network controls ahead of workload replication and reserves parallel running for evidence-based acceptance tests.

Phase 1: Discovery & TCO (Weeks 1-4)

Inventory all GCP resources. Map dependencies. Calculate AWS TCO including support and data transfer. Apply for egress waivers.

Phase 2: Landing Zone & Security (Weeks 5-8)

Build AWS Control Tower. Configure AWS Organizations. Map GCP IAM roles to AWS IAM policies. Set up Direct Connect or VPN.

Phase 3: Data Migration (Weeks 9-16)

Check AWS DMS support for each source database. Use AWS DataSync for Google Cloud Storage to Amazon S3; Google's Storage Transfer Service supports S3 as a source, not an S3 destination. Begin BigQuery-to-Redshift data-model conversion and test data loads.

Phase 4: App Migration & Cutover (Weeks 17-24)

Deploy apps to EKS/EC2. Point to new data sources. Run parallel environments. Perform DNS cutover (Route53).

What does a GCP-to-AWS migration cost?

Request an estimate for the workloads in scope. Include committed-use penalties, data egress, dual-running costs, warehouse conversion, and AWS support. Compare those costs with the savings you can substantiate from workload consolidation.

Break-even: Calculate when net savings repay the migration cost. Account for how quickly savings begin and the cost of running both platforms. Count credits only after confirming eligibility and terms.

How does the target architecture change?

The target architecture replaces GCP service boundaries with AWS equivalents, but the mapping is rarely one-to-one. Compute, analytics, networking, messaging, and identity need separate decisions so a nominal service replacement does not import the wrong operating model.

Before (GCP)

• Compute: GKE Autopilot, Cloud Run
• Database: Cloud SQL (Postgres), BigQuery
• Storage: Google Cloud Storage (Multi-region)
• Identity: Cloud Identity, Service Accounts

After (AWS)

• Compute: EKS (Managed Node Groups), AWS Fargate
• Database: RDS for PostgreSQL (Aurora), Redshift / Athena
• Storage: Amazon S3 (Intelligent Tiering)
• Identity: IAM Identity Center, IAM Roles

How to Choose a Migration Partner

If you need a complete "lift and modernize": Mission Cloud or Caylent. These firms are AWS-exclusive or highly specialized, bringing deep engineering talent that can refactor applications, not just move VMs.

If you are a Global 2000 enterprise: Accenture or Deloitte. You need a partner who can handle the organizational change management, compliance, and massive scale of thousands of applications, often bundling business transformation with the technical move.

If you need ongoing management: Rackspace Technology. If your goal is to shut down your data center or GCP footprint and have someone else run the ops on AWS, their managed service model is ideal.

Red flags when evaluating vendors:

• "We have a tool that does 100% of the conversion." Require a demonstration on your schemas and queries, with explicit unsupported features and acceptance tests.
• No mention of Egress Waivers. (They should be guiding you through the AWS MAP program and Google's exit policies to save you money.)
• Weak IAM answers. (If they can't explain the difference between a GCP Project and an AWS Account governance model, run.)

How We Select Vendors

Evaluate migration firms using:

• AWS Competency: specifically the "Migration Competency" and "Data & Analytics Competency".
• Case Studies: Proven track record of moving complex data workloads (PB-scale) from GCP.
• Engineering Depth: Ability to refactor code (Python, Java, Go) to run natively on AWS services, not just rehosting.

Vetting Process:

1. Analyze partner case studies for specific "GCP to AWS" examples.
2. Verify AWS Competency status and partner tier.
3. Evaluate their "Redshift vs. BigQuery" technical whitepapers or engineering blogs to assess depth.
4. Exclude generalist IT shops that lack specific cloud-native migration tooling.

What else should buyers know about GCP-to-AWS migration?

GCP-to-AWS buyers need clarity on schedule, platform economics, BigQuery conversion, data egress, IAM, and migration tooling. The answers below identify workload-specific decisions that require assessment.

How long does a GCP to AWS migration take?

We have no verified median for this guide. Estimate the schedule from workload count, data volume, dependency mapping, warehouse conversion, security approvals, and cutover rehearsals. Rehosting and refactoring require different work; the schedule above is a planning example.

Is AWS cheaper than GCP?

There is no universal price advantage. Compare current quotes for the required region, service configuration, utilization, and eligible discounts. Include migration, transfer, support, and dual-running costs, and benchmark representative workloads before comparing price-performance.

Can I migrate BigQuery directly to Redshift?

Treat it as a data-model and SQL conversion, not a database-compatible copy. One staged approach exports BigQuery data to GCS, transfers it to S3, and loads Redshift. Nested data can use Redshift SUPER or a relational model; test query semantics, row counts, and performance. Tool support and manual conversion needs depend on the features in use.

Does Google charge for data egress when migrating to AWS?

Budget for the applicable source-service transfer charges unless a waiver is approved. Google offers a data-transfer fee waiver process; confirm current eligibility, timing, and contractual conditions for your move.

What is the biggest technical risk?

Identity management (IAM). GCP's project-based hierarchy is fundamentally different from AWS's account-based model. Failing to map this correctly results in a security nightmare where users have too much access or applications break.

What tools help with GCP to AWS migration?

Key tools include AWS Application Migration Service (MGN) for lift-and-shift, AWS Database Migration Service (DMS) for SQL databases, AWS Schema Conversion Tool (SCT) for data warehouses, and AWS DataSync for object storage transfer.

Risk Factors

BigQuery to Redshift SQL Incompatibility

BigQuery's data models and SQL functions do not map 1:1 to Redshift. Test compatibility; manual refactoring depends on the features and queries in use.

IAM Model Mismatch

GCP's project hierarchy doesn't exist in AWS. Mapping permissions to AWS Accounts/Roles requires a complete security redesign.

Egress Cost Traps

Moving petabytes of data out of GCP triggers massive egress fees unless you qualify for and strictly follow the 'free exit' program.

Feasibility Analysis

Ideal candidates

• GCP usage is primarily compute and storage with minimal BigQuery or Vertex AI dependency
• AWS enterprise agreement offers meaningful cost reduction vs GCP pricing
• Team already has AWS certification or AWS-heavy infrastructure elsewhere Talent risk: Medium. GCP-specific skills (Spanner, BigQuery) need retraining to AWS equivalents.