Every AWS to GCP migration conversation starts the same way: someone on the leadership team asks why the AWS bill keeps climbing, or why a security review flagged sprawling IAM policies, or why a multi-region rollout is taking three sprints longer than it should. By the time the question reaches engineering, the business case is already half-written. What is usually missing is an honest answer to a simpler question: what actually changes when you move from AWS to GCP, and what stays exactly the same?

This is not a marketing comparison. This is an architectural breakdown for CTOs, VPs of Engineering, and senior developers who need to plan a real cloud migration strategy, not read another vendor scorecard. AWS to GCP migration is not a lift and shift exercise. Some parts of your architecture translate almost directly. Others require you to unlearn assumptions you have carried since your first EC2 instance.

The Core Architectural Question: Are You Migrating the Workload or the AWS Operating Model?

This is the first decision CTOs and architects need to make.

Many AWS environments have accumulated years of cloud-specific operating assumptions:

• AWS account structures
• IAM roles and trust policies
• Availability Zone placement
• VPC peering and Transit Gateway dependencies
• Elastic Load Balancing configurations
• CloudWatch dashboards and alerts
• Amazon Machine Images
• Auto Scaling Groups
• Lambda event sources
• EKS add-ons
• S3 event notifications
• RDS operational procedures
• CloudFormation or AWS-specific Terraform modules

If the migration team attempts to reproduce every AWS construct in Google Cloud, it usually creates a compromised architecture.

The resulting platform technically runs on GCP but still thinks like AWS.

This produces unnecessary complexity because Google Cloud has different primitives, resource hierarchies, networking behavior, managed-service capabilities, and operational conventions.

A better AWS to GCP migration strategy separates the architecture into three categories.

Category 1: Cloud-Independent Architecture

These components should remain mostly unchanged:

• Domain boundaries
• API contracts
• Business workflows
• Data ownership
• Service responsibilities
• Resilience requirements
• Recovery objectives
• Security objectives
• Performance requirements
• Compliance requirements
• Observability requirements

Category 2: Portable Implementation

These components may require configuration changes but should not require major redesign:

• Containers
• Kubernetes Deployments
• OpenTelemetry instrumentation
• Standard relational database schemas
• Linux-based application services
• Terraform modules with limited provider coupling
• CI/CD systems that already deploy across environments
• Kafka-compatible event producers and consumers
• Application code using open protocols

Category 3: AWS-Coupled Implementation

These components require the most analysis and often the most refactoring:

• Lambda functions tightly coupled to AWS event sources
• Applications calling AWS SDKs directly
• IAM policies embedded in application deployment
• EKS workloads depending on AWS load-balancer annotations
• S3-specific object operations
• DynamoDB-specific data models
• Step Functions state machines
• CloudWatch-specific monitoring logic
• Aurora-specific performance or scaling assumptions
• Systems depending on AWS PrivateLink, Transit Gateway, KMS, Secrets Manager, or Route 53 behavior

The migration scope should be based on this coupling analysis, not merely on the number of virtual machines or databases.

Why AWS to GCP Migration Is on the Table for So Many Engineering Teams

Search interest around AWS cloud migration, AWS cost optimization, and multi cloud strategy has been climbing steadily, and the reasons are consistent across industries. Rising AWS bills without a matching increase in output. Vendor lock-in concerns after a security or compliance audit. A push from the board to avoid single-vendor dependency. Kubernetes-native teams who want tighter integration with GKE. Data and AI workloads that increasingly favor BigQuery and Vertex AI.

None of these reasons make the migration easy. They make it necessary. The teams that get burned are the ones who treat AWS to GCP migration as a rename exercise, mapping EC2 to Compute Engine and calling it done. The teams that succeed are the ones who understand exactly where the architectural ground shifts under their feet.

What Changes Architecturally: Networking Is the First Shock

If there is one thing every engineer moving from AWS to GCP needs to internalize before touching a single workload, it is this: AWS VPCs are regional. GCP VPCs are global.

In AWS, a VPC lives inside one region. If you need resources in multiple regions to talk to each other, you are setting up VPC Peering or a Transit Gateway, and you are doing it per region, per connection. Subnets in AWS are zonal, tied to a specific Availability Zone within that regional VPC. This gives you strong regional isolation and granular control, but it also means multi-region architecture on AWS requires deliberate planning and ongoing management overhead.

GCP flips this model. A single VPC in GCP is a global resource by default. You create one VPC, then add regional subnets to it as needed. A VM in us-central1 and a VM in asia-east1 can sit inside the same VPC and communicate over Google’s internal backbone using internal IPs, with no peering, no transit gateway, and no VPN required. Firewall rules in GCP are also global in scope, even though they are commonly applied using network tags or service accounts rather than the AWS model of instance-level security groups.

This is not a minor implementation detail. It is a different philosophy. AWS gives you granular, regionally-scoped building blocks that you compose into a global footprint. GCP gives you global building blocks by default, which simplifies multi-region deployment but increases the blast radius of a misconfiguration. Teams that do not redesign their network segmentation strategy before migrating tend to either over-restrict GCP unnecessarily, reproducing AWS’s regional walls out of habit, or under-restrict it, assuming GCP’s defaults behave like AWS defaults when they do not.

For any engineering team building a cloud migration strategy around AWS to GCP migration, network architecture redesign has to happen before the first workload moves, not after.

IAM and Identity: From Flat Policies to a Resource Hierarchy

The second major architectural shift is identity and access management, and it catches almost every AWS-fluent engineer off guard at least once.

AWS IAM is flat and identity-centric. You attach policies directly to users, groups, or roles. There is no built-in hierarchy for permission inheritance, so organizations that need centralized governance layer AWS Organizations and Service Control Policies on top. It works, but it requires discipline. Permission sprawl across dozens of accounts is one of the most common AWS migration challenges cited by security teams, and it is exactly the kind of technical debt that makes leadership start asking about alternatives.

GCP IAM is resource-centric and hierarchical by design. Every resource lives inside a Project, Projects live inside Folders, and Folders live inside an Organization node. Permissions granted at a higher level are automatically inherited by everything beneath it. Grant Storage Admin at the Folder level, and every project and every bucket inside that folder inherits it, with no explicit trust configuration required.

This is a real tradeoff, not a straightforward upgrade. GCP’s inheritance model is easier to reason about at scale and simplifies audits, but it also means a single misconfigured binding at the Organization or Folder level can cascade access far wider than intended. AWS’s flat model is more tedious to manage across many accounts, but a mistake in one account stays contained to that account by default.

Service identity also works differently. AWS uses IAM Roles combined with STS to issue short-lived credentials, with explicit trust relationships between the role and whatever is assuming it. GCP uses Service Accounts, which function as password-less identities tied directly to a resource like a Compute Engine instance or a Cloud Run service, often paired with Workload Identity to avoid long-lived keys entirely.

Neither of these two models can be mapped one-to-one. Any AWS to GCP migration plan needs a dedicated IAM redesign phase, not a policy-by-policy translation. This is one of the most underestimated line items in every migration timeline, and it is also one of the biggest security risks if it gets rushed.

Compute and Kubernetes: Familiar Concepts, Different Defaults

Compute is where AWS to GCP migration feels the most reassuring, and that reassurance is partly deserved. EC2 maps to Compute Engine. Both offer virtual machines, custom machine types, autoscaling groups, and spot or preemptible pricing tiers for fault-tolerant workloads. If your team already thinks in terms of instance types, autoscaling policies, and load balancer tiers, the mental model transfers.

Kubernetes migration is similarly close in concept but different in defaults. EKS and GKE both run standard Kubernetes, and most of your Helm charts, manifests, and container images will move with minimal changes. The differences show up in operations. GKE Autopilot removes node management entirely, billing per pod resource request rather than per node, which changes how your platform team thinks about capacity planning and cost allocation. EKS requires more explicit node group management unless you are using Fargate profiles, and IAM-to-Kubernetes RBAC mapping works differently between IRSA on AWS and Workload Identity on GKE.

Serverless functions are the closest thing to a direct swap. Lambda functions map to Cloud Functions or Cloud Run, and for stateless, event-driven workloads the migration is usually mechanical rather than architectural. Where it gets harder is any function relying on deep integration with AWS-specific services like Step Functions or EventBridge, since the GCP equivalents (Workflows, Eventarc) have different triggering models and quotas.

Storage and Database: Where the Real Data Migration Service Work Happens

Object storage looks like a straightforward S3 to Cloud Storage swap on the surface, and for basic use cases it mostly is. Both offer tiered storage classes for cost optimization, both support lifecycle policies, and both integrate with their respective compute and analytics stacks. The friction shows up in access control models, signed URL generation, and any workload relying on S3-specific features like Object Lambda or S3 Select, which do not have a direct GCS equivalent and need to be re-architected rather than translated.

Databases are the highest-risk, highest-effort part of any AWS to GCP migration, and this is exactly why a solid database migration service strategy matters more here than anywhere else in the stack. RDS maps conceptually to Cloud SQL for standard relational workloads, but Cloud SQL’s high availability model, backup mechanics, and read replica behavior are configured differently enough that a straight configuration copy will not work. Teams running Aurora need to evaluate whether Cloud SQL or Spanner is the right target, since Spanner solves a different problem (globally distributed, horizontally scalable relational data) than Aurora does, and treating them as equivalents leads to over-engineering or under-provisioning.

DynamoDB workloads moving to GCP typically land on Firestore or Bigtable depending on access patterns, and this is rarely a mechanical migration. Query patterns, indexing strategy, and consistency guarantees differ enough that data modeling needs to be revisited, not just re-pointed at a new endpoint.

The practical takeaway: budget real engineering time for schema and access-pattern review before you touch a database migration service tool. The tooling will move the bytes. It will not redesign your data model for you.

What Doesn’t Change: The Part Nobody Talks About Enough

Every AWS to GCP migration conversation focuses on what’s different, which makes sense given the risk involved. But an accurate cloud migration strategy also needs to name what stays the same, because overestimating the disruption leads to unnecessary rewrites and wasted budget.

Your application code, in most cases, does not need to change. If your services are built on standard frameworks, containerized, and not tightly coupled to AWS SDKs in your business logic, the application layer moves largely intact. This is exactly why teams that already invested in clean separation between infrastructure and application logic have dramatically shorter migration timelines than teams with AWS-specific calls scattered through their codebase.

Your container strategy does not change. Docker images are Docker images. If you are running Kubernetes, the orchestration layer, your deployment manifests, and your CI/CD pipeline concepts transfer with configuration changes, not full rewrites.

Your infrastructure-as-code discipline does not change, even if the specific templates do. Terraform supports both AWS and GCP as providers, and teams already using Terraform for a multi cloud strategy have a meaningfully easier migration than teams relying on AWS CloudFormation, which has no GCP equivalent and requires a genuine rewrite of provisioning logic.

Your observability philosophy does not change. The specific tools differ (CloudWatch versus Cloud Monitoring and Cloud Logging), but the discipline of setting SLOs, tracking error budgets, and alerting on business-relevant signals rather than raw infrastructure metrics is portable knowledge, not a platform-specific skill.

And your team’s engineering judgment does not change. The architects who understood why your AWS setup was configured the way it was are the same people who will make the best decisions about how to configure GCP. This is the strongest argument against outsourcing the entire migration to a black-box vendor tool and the strongest argument for pairing internal engineering knowledge with migration-specific execution support.

AWS Cost Optimization vs GCP Pricing: Where the Math Actually Differs

Cost is usually the reason the migration conversation started, so it deserves a direct answer instead of a hand-wave.

AWS offers On-Demand, Reserved Instances, Savings Plans, and Spot Instances, with Reserved Instances and Savings Plans reaching up to roughly 55 to 66 percent off on-demand pricing at three-year terms, and Spot Instances offering up to 90 percent off for fault-tolerant, interruptible workloads. None of these discounts apply automatically. You have to actively enroll and manage them, and unmanaged AWS accounts routinely leave significant savings on the table simply because nobody assigned ownership of the Savings Plan strategy.

GCP’s pricing model includes a mechanism AWS does not have at all: Sustained Use Discounts, which apply automatically once a Compute Engine instance runs more than roughly 25 percent of a billing month, with no commitment, no upfront payment, and no action required. On top of that, GCP offers Committed Use Discounts, similar in concept to AWS Reserved Instances, reaching up to 57 percent off for one-year terms and 70 percent for three-year terms on resource-based commitments, plus more flexible spend-based CUDs that behave more like AWS Savings Plans.

The practical result: for steady-state, always-on workloads, GCP’s effective on-demand cost tends to run lower than AWS’s before any commitment is made, purely because of automatic sustained use discounting. For teams that are disciplined about actively managing Reserved Instances and Spot capacity, AWS can still come out ahead. The honest answer to “is GCP cheaper” is that it depends on how well your organization currently manages its AWS cost optimization practice. Teams with weak FinOps discipline see the biggest cost improvement from switching, not because GCP is inherently cheaper, but because its automatic discounting compensates for the discipline they were missing.

Data egress is the other line item that catches teams off guard in either direction. Compare your actual egress patterns, not list pricing, before finalizing the business case, since egress costs can quietly erase a discount advantage on the compute side.

The Correct AWS to GCP Migration Sequence

Phase 1: Assess the Existing AWS Architecture

Create inventories for:

• AWS accounts
• Regions
• Availability Zones
• VPCs and subnets
• Compute instances
• Containers and clusters
• Databases
• Storage
• Queues and topics
• Serverless functions
• IAM roles
• Encryption keys
• Secrets
• Load balancers
• DNS zones
• CI/CD pipelines
• Monitoring
• Security controls
• SaaS and third-party dependencies

Then classify each workload by:

• Business criticality
• Cloud coupling
• Data sensitivity
• Dependency complexity
• Downtime tolerance
• Recovery requirements
• Migration pattern
• Refactoring effort

Phase 2: Build the Google Cloud Foundation

Before moving production applications, establish:

• Organization and folder hierarchy
• Project strategy
• Billing accounts
• IAM model
• Centralized logging
• Shared VPC
• DNS
• Hybrid connectivity
• Security policies
• Encryption strategy
• Secret management
• CI/CD identity
• Monitoring
• Policy enforcement
• Cost allocation

Google’s EC2, S3, and EKS migration guidance consistently places resource hierarchy, IAM, billing, networking, security, and observability in the foundation stage.

Phase 3: Migrate a Representative Pilot

Do not choose the easiest workload.

Choose a workload that exercises enough of the target platform to expose architectural weaknesses:

• Compute
• Network
• Identity
• Database
• Storage
• Deployment
• Monitoring
• Security
• Traffic management

The pilot should be important enough to be realistic but not so critical that migration risk becomes unacceptable.

Phase 4: Create Migration Waves

Group workloads based on dependency relationships.

A wave may contain:

• Front-end service
• API service
• Database
• Queue
• Object store
• Scheduled jobs
• Monitoring
• DNS
• Security controls

Migrating isolated infrastructure components without their dependencies prolongs the multi-cloud transition and increases data-transfer charges, latency, and operational complexity.

Phase 5: Shift Traffic Gradually

Preferred traffic-shifting techniques include:

• Weighted DNS
• Load-balancer traffic splitting
• Canary deployment
• Blue-green deployment
• Tenant-based routing
• Region-based routing
• Header-based routing

For EKS to GKE migration, Google recommends gradual traffic shifting and describes options including DNS-based distribution and load-balancer configurations spanning source and target environments.

Phase 6: Validate Before Decommissioning AWS

Validation must include:

• Functional testing
• Integration testing
• Load testing
• Failure testing
• Recovery testing
• Security testing
• Compliance validation
• Cost validation
• Operational readiness
• Rollback testing

Do not decommission the AWS environment immediately after traffic reaches GCP.

Wait until:

• Data reconciliation is complete
• Backup and restore are tested
• Performance is stable
• Incident response is validated
• Billing is understood
• Dependency monitoring shows no unexpected AWS traffic

How ISHIR Can Help With AWS to GCP Migration

ISHIR helps technology organizations assess AWS dependencies, design the target Google Cloud foundation, migrate applications and data, refactor AWS-coupled services, and execute controlled production cutovers.

Our cloud migration approach covers architecture discovery, workload classification, GCP landing-zone design, IAM and network modernization, database migration, Kubernetes migration, serverless refactoring, observability, DevOps automation, testing, rollback planning, and post-migration cost optimization.

FAQs

Q. Is AWS to GCP migration harder than AWS to Azure migration?

Not inherently harder, but the friction shows up in different places. GCP’s IAM hierarchy and global VPC model require more conceptual relearning than Azure’s resource group model, which maps more closely to how AWS Organizations are structured. The tradeoff is that GCP’s model simplifies multi-region architecture once your team adapts to it.

Q. Can Kubernetes workloads move from EKS to GKE without major changes?

Most standard Kubernetes manifests, Helm charts, and container images move with minimal changes. The differences are operational: GKE Autopilot’s pod-based billing model, Workload Identity configuration, and networking defaults require configuration changes even though the underlying orchestration layer is unchanged.

Q. What is the biggest hidden cost in AWS to GCP migration?

Database migration and IAM redesign are consistently underestimated in both time and engineering effort. Teams that budget primarily for compute and storage migration and treat identity and data as afterthoughts are the ones most likely to see timelines slip by a quarter or more.

Q. Does GCP’s Sustained Use Discount make it automatically cheaper than AWS?

Only for consistently running, steady-state workloads, and only relative to AWS On-Demand pricing. Organizations that actively manage AWS Reserved Instances, Savings Plans, and Spot Instances can match or beat GCP’s automatic discounting. The advantage GCP offers is that its savings apply without requiring that level of active management.

Q. Should a multi cloud strategy be the goal instead of a full migration?

For many organizations, yes. A full AWS to GCP migration makes sense when there is a clear driver, such as Kubernetes-native tooling, AI and data workload alignment with BigQuery and Vertex AI, or a specific cost or compliance issue. When the driver is more about avoiding vendor lock-in generally, a deliberate multi cloud strategy for new workloads, rather than migrating existing stable systems, is often the lower-risk path.