GCP to AWS Migration: Service Mapping, Cost Comparison & Complete Timeline Guide (2026)

Migrating from Google Cloud Platform to Amazon Web Services isn't a simple re-hosting exercise. GCP and AWS have fundamentally different networking models, IAM architectures, and service paradigms. Companies that treat this as a lift-and-shift end up with AWS infrastructure that's more expensive to run and harder to manage than what they had on GCP.

This guide covers everything you need to execute a GCP-to-AWS migration properly — from the strategic reasons companies make this move, to the service-by-service mapping and cost reality, to the exact phases and timeline you should plan for. We're drawing on migrations we've completed across fintech, SaaS, and healthcare platforms in 2025–2026, including a recent GCP-to-AWS migration for a B2B SaaS company running 40+ microservices on GKE.

Why Do Companies Migrate from GCP to AWS?

Let's be direct: if cost reduction is your only goal, GCP-to-AWS migration is almost certainly the wrong move. AWS is more expensive for most compute workloads. Companies migrate to AWS for strategic, technical, and ecosystem reasons that outweigh the cost premium.

GCP vs AWS Cost Comparison: The Honest Picture

We're not going to pretend AWS is cheaper — in most cases, it's not. But understanding where AWS costs more, where it's competitive, and where its pricing model actually wins is critical for budgeting your migration accurately. Here's a service-by-service breakdown based on 2026 pricing data.

Compute: Compute Engine vs EC2

Key insight: GCP's sustained-use discounts are automatic — they kick in when instances run more than 25% of the month, no commitment required. AWS has no equivalent. This means for always-on workloads without upfront commitments, AWS costs 30–57% more. However, AWS Graviton instances (ARM-based) deliver 40% better price-performance than x86, and if your application supports ARM, this single change can make AWS competitive or even cheaper than GCP on-demand pricing.

Managed Kubernetes: GKE vs EKS

Key insight: EKS is operationally heavier than GKE. The control plane cost is unavoidable, node pricing lacks auto-discounts, and the upgrade process requires more manual intervention. According to the CNCF 2024 Annual Survey, GKE consistently ranks as the most popular managed Kubernetes service, and teams moving to EKS should plan for increased operational overhead — especially around cluster upgrades, ingress management, and service mesh setup.

Database: Cloud SQL vs RDS

However, AWS offers Amazon Aurora — a MySQL/PostgreSQL-compatible database with 5x the throughput of standard MySQL and 3x the throughput of standard PostgreSQL, with storage that auto-scales up to 128 TB. Aurora has no direct GCP equivalent. If you're running write-heavy or high-throughput databases, Aurora's performance advantage can justify the cost premium over Cloud SQL. Aurora Serverless v2 also eliminates capacity planning entirely — something Cloud SQL doesn't offer.

Serverless: Cloud Functions/Cloud Run vs Lambda/Fargate

Key insight: AWS Lambda's ecosystem is more mature — better cold start performance (especially with SnapStart for Java), more event source integrations (EventBridge, SQS, DynamoDB Streams, Kinesis), and a larger library of Lambda Layers. If your GCP workloads use Cloud Run extensively, the closest AWS equivalent is ECS on Fargate or App Runner, not Lambda.

Storage & Data Transfer

Important: AWS recently introduced free data transfer for migrations to AWS using AWS DataSync or AWS Transfer Family. If you're migrating large datasets from GCP, this can save thousands in one-time transfer costs. Post-migration, AWS egress is cheaper for smaller volumes (<10 TB/month), but GCP wins at scale and for inter-region traffic.

How AWS Closes the Cost Gap

AWS has more aggressive cost optimisation tools than GCP. Companies that use them effectively can bring AWS costs to within 5–10% of GCP — or even lower for specific workloads:

• Savings Plans — 1-year or 3-year commitments that cover EC2, Lambda, and Fargate with up to 72% savings. More flexible than Reserved Instances — they apply across instance families, regions, and even operating systems.
• Graviton Instances — ARM-based processors offering 40% better price-performance than x86. If your application runs on Linux and doesn't depend on x86-specific binaries, Graviton alone can make AWS cheaper than GCP on-demand.
• Spot Instances — Up to 90% off on-demand pricing for fault-tolerant workloads (batch processing, CI/CD runners, data pipelines, dev/test environments). AWS's Spot market is more mature and liquid than GCP's Preemptible/Spot VMs.
• Enterprise Discount Program (EDP) — For companies spending $1M+/year on AWS, EDPs provide custom volume discounts of 10–25% across all services. This is the single biggest cost lever for large migrations.
• SpendZero — Our cloud cost optimisation tool with 37+ automated waste detection checks across 25+ AWS services. Identifies idle resources, oversized instances, and unused reservations with one-click remediation. Typical savings: 20–35% on existing AWS spend.

Overall Cost Impact When Migrating GCP to AWS

Bottom line: With disciplined use of Graviton, Savings Plans, and Spot instances, most companies can bring the AWS cost premium to under 10% — and for some workloads, AWS is actually cheaper. The cost premium is the price of a broader service catalog, better compliance, and a deeper talent pool. Whether that trade-off is worth it depends on your specific situation.

Complete GCP to AWS Service Mapping

One of the most time-consuming parts of any cloud migration is understanding which AWS service replaces which GCP service — and what changes in the migration. Here's the complete mapping for the services most companies use, with migration complexity ratings.

What Does a GCP to AWS Migration Timeline Look Like?

Migration timelines depend on infrastructure complexity, data volume, team availability, and how many services need rearchitecting (not just rehosting). Here are realistic timelines based on migrations we've executed — not vendor marketing estimates.

Phase 1: Assessment & Planning (2–4 Weeks)

• Infrastructure inventory — Catalogue all GCP resources using gcloud asset search-all-resources. Document every Compute Engine instance, GKE cluster, Cloud SQL database, Cloud Storage bucket, Pub/Sub topic, and IAM binding.
• Dependency mapping — Identify service-to-service dependencies, external API integrations, VPN/Interconnect connections, and data flows between services. This is where 80% of migration surprises hide.
• Cost modelling — Build a detailed AWS cost projection using actual GCP usage data (not list prices). Include Graviton savings, Savings Plan projections, and EDP eligibility. Use the AWS Pricing Calculator with your real workload profiles.
• Migration strategy per service — Decide rehost, re-platform, or re-architect for each component. Not everything needs the same approach.
• Risk assessment — Identify data sovereignty requirements, compliance constraints, integration dependencies, and services with no direct AWS equivalent (BigQuery, Cloud Spanner).
• Timeline and cutover plan — Define migration waves, rollback criteria, success metrics, and the communication plan for internal teams and external stakeholders.

This phase is non-negotiable. Companies that skip assessment and jump straight to migration spend 2–3x longer on the overall project. Every hour spent on assessment saves 3–5 hours during execution.

Phase 2: AWS Foundation Setup (2–3 Weeks)

• AWS account structure — AWS Organizations with SCPs (Service Control Policies), dedicated accounts for production/staging/dev, centralised billing. This replaces GCP's project/folder hierarchy.
• IAM architecture — Role definitions, IAM policies, cross-account access patterns, SSO configuration (AWS IAM Identity Center). Replace GCP Workload Identity with IRSA (IAM Roles for Service Accounts) for Kubernetes. This is the most labour-intensive setup task.
• Networking — VPC design (AWS VPCs are regional, not global like GCP), subnets across availability zones, security groups, NACLs, VPN or Direct Connect for hybrid connectivity during migration. See the networking section below for the critical GCP → AWS differences.
• Infrastructure as Code — Terraform modules for all AWS resources. If you're using Terraform on GCP already, you'll rewrite the provider blocks and resource definitions but keep the same module structure and state management approach.
• Monitoring and logging — CloudWatch dashboards, CloudTrail for audit logging, alerting policies, SNS notification channels. Or (recommended) deploy a cloud-agnostic stack: Prometheus + Grafana + Loki on EKS.
• CI/CD pipelines — Set up CodePipeline + CodeBuild, or (recommended) migrate to GitHub Actions for cloud-agnostic CI/CD that works identically across GCP and AWS.

Phase 3: Data Migration (2–6 Weeks)

• Database migration — AWS DMS for live replication from Cloud SQL to RDS/Aurora. Set up continuous replication, validate schema compatibility, run data integrity checks. For PostgreSQL, consider pglogical for logical replication if DMS doesn't support specific extensions.
• Object storage migration — AWS DataSync for Cloud Storage to S3 bulk transfer. For large datasets (50+ TB), consider using GCP's Storage Transfer Service to push to S3 directly, or AWS Snowball for physical transfer.
• Stateful workloads — Cache warming strategies for ElastiCache, session migration, queue draining (Pub/Sub → SQS), and state file migration for any stateful services.
• Data validation — Automated consistency checks: row counts, checksum verification, query result comparison between GCP and AWS databases. Never trust a migration without validation.

Data migration is almost always the longest phase. A 5 TB PostgreSQL database with continuous replication typically takes 1–3 weeks depending on the change rate and network bandwidth between GCP and AWS.

Phase 4: Application Migration & Testing (3–6 Weeks)

• Wave 1 — Dev/staging environments — Deploy non-production workloads on AWS first. This validates your Terraform modules, CI/CD pipelines, and networking configuration without risking production.
• Wave 2 — Stateless services — Migrate stateless microservices (APIs, web frontends, workers) to EKS or ECS. These are lowest risk since they can be rolled back quickly.
• Wave 3 — Stateful services — Migrate services with databases, caches, and persistent storage. Coordinate with data migration timelines.
• Integration testing — Verify all service connections, API endpoints, external integrations, webhook URLs, and third-party SaaS connections.
• Performance testing — Load testing on AWS to validate latency, throughput, and autoscaling behaviour. Compare against GCP baselines.
• Security validation — Penetration testing, compliance checks, IAM policy review, and Security Hub findings remediation.

Phase 5: Cutover & Optimisation (1–2 Weeks)

• DNS cutover — Gradual traffic shift from GCP to AWS using weighted DNS routing in Route 53. Start with 10% → 25% → 50% → 100% over 48–72 hours.
• Monitoring — 24/7 observation during the first 72 hours post-cutover. Have rollback procedures documented and tested.
• GCP decommission — Scale down GCP resources immediately after cutover, but keep them running (scaled to minimum) for 30 days as a rollback safety net. Terminate after 30 days of stable AWS operation.
• Cost optimisation — Right-size instances based on actual AWS usage data (not GCP estimates), apply Savings Plans, enable cost anomaly detection, and clean up unused resources.

Total Timeline Summary

How Should You Rearchitect Networking for AWS?

This is one of the most critical — and most underestimated — parts of a GCP-to-AWS migration. GCP and AWS have fundamentally different networking models, and copying your GCP network design to AWS will cause problems.

Recommendation: Design your AWS network from scratch based on the AWS VPC best practices, not by copying your GCP topology. Use Transit Gateway for multi-region connectivity, create VPC endpoints for all AWS services you'll use, and implement a consistent CIDR allocation strategy that allows for future expansion.

What Changes in IAM When Moving from GCP to AWS?

IAM is the single most underestimated task in any GCP-to-AWS migration. The two platforms use fundamentally different permission models, and there's no automated conversion tool.

Migration approach: Audit all GCP IAM bindings using gcloud projects get-iam-policy. Map each GCP role to the equivalent AWS managed policy or create custom policies. Implement least-privilege from day one on AWS — don't migrate overly broad GCP roles into equally broad AWS policies. Budget 2–4 weeks for IAM migration on any non-trivial infrastructure.

What Are the Biggest Challenges in GCP to AWS Migration?

1. BigQuery Has No Direct AWS Equivalent

BigQuery's serverless, pay-per-query model with automatic scaling is fundamentally different from anything AWS offers. Redshift requires cluster management, capacity planning, and has a completely different pricing model. Athena is serverless but limited to S3-based queries and lacks BigQuery's real-time streaming inserts and ML capabilities.

Recommendation: If BigQuery is a core part of your data stack, consider keeping it on GCP and using BigQuery Omni to query data stored in S3 — or use cross-cloud connectivity to access BigQuery from AWS-hosted applications. Not every service needs to move.

2. GCP's Global VPC → AWS's Regional VPC Model

This isn't just a configuration change — it's an architecture redesign. GCP's single global VPC becomes multiple regional VPCs connected via Transit Gateway on AWS. This affects firewall rules, routing tables, cross-region service discovery, and load balancing topology. See the networking section above for the full comparison.

3. Kubernetes Migration Complexity (GKE → EKS)

While K8s manifests are portable, the platform integrations are not. GKE-specific features that need rework on EKS:

• Ingress — GKE Ingress → AWS ALB Ingress Controller (different annotations, different health check models)
• Autoscaling — GKE Autopilot or Cluster Autoscaler → Karpenter (AWS's next-gen autoscaler, significantly more capable)
• Workload Identity → IRSA (IAM Roles for Service Accounts — requires OIDC provider setup)
• Config Connector — GKE's Config Connector for managing GCP resources from K8s has no direct EKS equivalent. Use Terraform or AWS Controllers for Kubernetes (ACK) instead.
• Anthos Service Mesh → Self-managed Istio or AWS App Mesh (less mature)

4. Data Transfer Costs and Time

Moving large datasets between clouds is expensive and slow. GCP charges egress fees on data leaving their network. For a 10 TB database with continuous replication running for 2 weeks, expect $1,200–$1,500 in egress costs alone. For datasets over 50 TB, consider physical transfer options (though neither AWS Snowball nor GCP Transfer Appliance supports direct cloud-to-cloud physical transfer — you'd need an intermediary).

5. Team Skill Gap

Your engineering team knows GCP. They think in GCP concepts (projects, global VPCs, Cloud Build). AWS uses different terminology, different console layouts, different CLI patterns, and different architectural patterns. Budget for training: AWS Cloud Practitioner certification for the broader team and Solutions Architect certification for the infrastructure team. This typically takes 4–8 weeks of part-time study.

6. Firestore/Spanner → DynamoDB Data Model Redesign

If you're using Firestore's document/collection model or Spanner's globally distributed SQL, migrating to DynamoDB requires a complete data model redesign. DynamoDB's single-table design patterns, partition key selection, and GSI (Global Secondary Index) strategies are fundamentally different. This is not a migration — it's a rebuild. Budget 4–6 weeks for data modelling and application code changes.

When Should You NOT Migrate from GCP to AWS?

Honest advice: not every GCP-to-AWS migration makes sense. Don't migrate if:

• BigQuery is central to your data stack — No AWS service matches BigQuery's serverless analytics model. If your company runs complex analytics workloads, data science pipelines, or real-time dashboards on BigQuery, moving to Redshift will cost more and require more operational effort.
• You're running GKE Autopilot successfully — GKE is the best managed Kubernetes service. If your entire platform runs on GKE Autopilot and your team is productive, the operational overhead increase of moving to EKS may not justify the migration.
• Cost is the primary driver — AWS is more expensive for most workloads. If your GCP bill is your main pain point, migrating to AWS will make it worse.
• Your team lacks AWS experience and you can't hire — A migration to a platform your team doesn't know, without budget for training or hiring, will result in poorly architected infrastructure that's expensive and hard to maintain.
• You're in the middle of a product launch — Cloud migrations are disruptive. Don't start one during a critical business period. Wait for a relative calm in your product roadmap.

How to Choose a Cloud Migration Partner for GCP to AWS

The migration partner you choose determines whether you complete the migration on time and under budget — or end up in a 6-month firefight with unexpected downtime, data loss, and cost overruns.

1. Dual-Cloud Expertise (Both GCP AND AWS)

A migration partner must have deep expertise in both platforms. An AWS-only partner won't understand your GCP architecture deeply enough to migrate it safely. A GCP-only partner won't design optimal AWS architectures. Look for dual partner status — companies certified by both Google Cloud and AWS.

2. Infrastructure as Code from Day One

If your migration partner is clicking through the AWS Console instead of writing Terraform, walk away. Every AWS resource should be defined in code from day one. This ensures reproducibility, enables disaster recovery, and makes rollback possible if something goes wrong.

3. Zero-Downtime Migration Capability

For production workloads, downtime during migration is not acceptable. Your partner should have proven experience with live database replication (DMS), blue/green DNS cutover, and gradual traffic shifting — not "we'll schedule a maintenance window."

4. Cost Optimisation Built In

A good migration partner doesn't just move your infrastructure — they optimise it for AWS from day one. Graviton instances, Savings Plans, Spot instances for non-production, and right-sizing should all be part of the migration plan, not an afterthought. Look for partners with FinOps expertise.

5. Post-Migration Support

Migration day is not the finish line. You need 24/7 support during the stabilisation period, ongoing optimisation, and a partner who will help your team build AWS expertise over time.

Partner Evaluation Scorecard

Why SquareOps for GCP to AWS Migration

SquareOps is both an AWS Advanced Consulting Partner and a GCP Partner — one of the few cloud migration companies with deep certification and hands-on experience on both platforms. Here's what makes us different:

• 100+ Cloud Migrations Completed — across startups, mid-market, and enterprise clients in US, India, UK, Germany, UAE, Singapore, Japan, and Australia
• Terraform-First, Always — every AWS resource is defined in IaC from day one. You own the code in your Git repos.
• Zero-Downtime Guarantee — live database replication via DMS, blue/green DNS cutover, and gradual traffic shifting for production workloads
• Built-In FinOps — we use SpendZero (37+ automated checks across 25+ AWS services) to identify and eliminate waste before, during, and after migration
• ISO 27001 Certified — security and compliance baked into every migration, not bolted on afterward
• Kubernetes Specialists — deep GKE and EKS expertise with 50+ K8s clusters managed. We handle the GKE → EKS migration including ingress, autoscaling (Karpenter), IRSA, and service mesh setup
• 24/7 Post-Migration Support — dedicated on-call coverage during stabilisation, with optional ongoing managed AWS operations

Get a free GCP-to-AWS migration assessment — we'll give you a detailed cost comparison, architecture review, timeline, and migration plan within 48 hours.