The “Single Cloud” Consolidation Trend
The “Multi-Cloud” dream—avoiding vendor lock-in by spreading workloads across GCP, AWS, and Azure—has collided with the reality of fragmented data, tripled egress costs, and IAM complexity. In 2025, we are seeing a “Great Consolidation” where enterprises are standardizing on AWS to leverage its mature ecosystem and deep service integration.
Moving from GCP to AWS is not just a “lift and shift.” It is a fundamental translation of architecture: from Google’s global control plane to AWS’s region-centric model; from BigQuery’s serverless Dremel engine to Redshift’s provisioned clusters (or Athena’s serverless query engine); and from a hierarchical IAM model to an account-based one.
Go / No-Go Assessment
Before you commit to leaving GCP, assess your readiness.
| Criteria | Threshold for "Go" | Score (0-10) |
|---|---|---|
| **Data Gravity** | Primary data consumers are already on AWS or can move easily. | |
| **BigQuery Dependency** | Low usage of proprietary BigQuery features (e.g., BigQuery ML, deeply nested structs). | |
| **IAM Complexity** | Security team understands AWS IAM Roles vs. GCP Service Accounts. | |
| **Kubernetes Usage** | GKE workloads are standard K8s, not heavily reliant on GKE Autopilot specifics. | |
| **Egress Budget** | Budget allocated for one-time data transfer (unless qualifying for free exit). |
Top 3 Failure Modes
1. The "BigQuery is just SQL" Fallacy (45% of failures)
The Trap: Assuming BigQuery SQL will run on Redshift with minor tweaks.
The Reality: BigQuery uses Google Standard SQL and handles nested data (ARRAYs, STRUCTs) natively. Redshift is based on PostgreSQL and typically requires flattening nested data. Functions like ARRAY_AGG behave differently. Migrating 500+ complex queries often takes 3x longer than estimated due to manual rewriting.
2. IAM Hierarchy Mismatch (30% of failures)
The Trap: Trying to map GCP Projects 1:1 to AWS Accounts without rethinking permissions.
The Reality: GCP uses a strict hierarchy (Org -> Folder -> Project) with inheritance. AWS uses a flatter Account model. Simply copying roles leads to "permission explosion" or security gaps. You must redesign your landing zone using AWS Organizations and SCPs (Service Control Policies) to mimic GCP's governance.
3. The Egress Cost Surprise (25% of failures)
The Trap: Moving petabytes of data without leveraging "free exit" programs.
The Reality: Moving 1PB of data at standard rates can cost $50,000+. Both Google and AWS have programs to waive egress fees for full migrations, but they have strict eligibility windows (often 60 days) and approval processes. Missing this window destroys ROI.
5 Technical Traps: GCP to AWS
1. BigQuery vs. Redshift Architecture
BigQuery separates compute and storage completely. Redshift (provisioned) couples them more tightly, though Redshift Serverless is bridging the gap.
- Risk: Performance degradation on Redshift if distribution keys and sort keys aren’t optimized. BigQuery doesn’t need indexes; Redshift relies on them.
- Prevention: Perform a deep data profiling exercise. Identify join patterns to select the right Distribution Styles (KEY, EVEN, ALL) in Redshift.
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.
Migration Roadmap
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)
Use AWS DMS for databases. Use Storage Transfer Service for GCS to S3. Begin BigQuery to Redshift schema conversion and data load.
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).
Total Cost of Ownership (TCO)
Moving to AWS is rarely about “cheaper infrastructure” unit-for-unit. It’s about ecosystem value. However, you must manage the transition costs.
| Cost Category | GCP (Current) | AWS (Future) | Migration Bubble Cost |
|---|---|---|---|
| Compute | Sustained Use Discounts apply automatically. | Savings Plans require 1-3 year commit. | Parallel run cost (1-3 months). |
| Data Warehouse | BigQuery (pay-per-query or slots). | Redshift (provisioned nodes or serverless). | Schema conversion labor + dual run. |
| Egress | High inter-region costs. | Similar, but free within AZs (mostly). | High risk: One-time transfer fees. |
| Support | Google Cloud Support. | AWS Enterprise Support (tiered). | Overlap period. |
Break-even: Typically Month 18-24, driven by operational consolidation and AWS credits (MAP program).
Architecture: The Translation Layer
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.” (Lie. BigQuery to Redshift always requires manual optimization.)
- 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
We analyzed 40+ migration firms based on:
- 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:
- Analyze partner case studies for specific “GCP to AWS” examples.
- Verify AWS Competency status and partner tier.
- Evaluate their “Redshift vs. BigQuery” technical whitepapers or engineering blogs to assess depth.
- Exclude generalist IT shops that lack specific cloud-native migration tooling.
FAQ
How long does a GCP to AWS migration take?
For a mid-sized enterprise (50-200 VMs, 10TB data), expect 4-6 months. Large enterprises with petabytes of data and complex dependencies often take 12-24 months. The timeline is heavily dictated by the “refactor” vs. “rehost” strategy.
Is AWS cheaper than GCP?
Not necessarily on a unit basis. GCP’s sustained use discounts are automatic, while AWS requires committed use (Savings Plans) to match pricing. However, AWS often offers better price-performance for specific workloads like Graviton (ARM) compute and tiered storage.
Can I migrate BigQuery directly to Redshift?
No. There is no “direct” copy. You must export data to GCS, transfer to S3, and load into Redshift. Schema conversion is required because BigQuery uses nested data types that Redshift handles differently. AWS Schema Conversion Tool (SCT) helps, but manual work is required.
Does Google charge for data egress when migrating to AWS?
Usually, yes. However, Google Cloud has a program to waive egress fees if you are completely leaving the platform and terminate your contract. You must apply for this and complete the move within a specific window (typically 60 days).
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.