GCP cloud transformation and modernization.
A sound Google Cloud foundation, workloads migrated in planned waves, and services modernized onto GKE and Cloud Run where they fit. Landing zone, IaC, observability and cost control designed in from the start, so you get a platform your team owns rather than a pile of projects nobody can reason about.
Built for scale-ups and established engineering teams.
A move to Google Cloud pays off when the foundation is the thing holding you back: sprawling projects, unclear IAM, workloads that will not scale, or a bill nobody can explain. These are the situations where GCP transformation earns its keep, whether you are a fast-growing product company or an established business modernising off older infrastructure.
Standing up GCP properly
You are new to Google Cloud, or your projects grew organically without an organization structure, and you want a real landing zone before more workloads land on sand.
Migrating from AWS or on-prem
Existing estate on AWS, on-prem virtual machines or legacy PaaS needs to move to GCP, and you want waves and rollback rather than a risky lift-and-shift weekend.
Modernising onto GKE
Services are outgrowing single VMs or bespoke orchestration and you want managed Kubernetes on GKE, with the operational load kept sane.
Serverless where it fits
You want Cloud Run for the request-driven edges so simple services scale to zero, without forcing everything through a cluster it does not need.
Data and analytics on GCP
You are consolidating operational data on Cloud SQL and analytics on BigQuery, with Pub/Sub tying events together, and want it wired cleanly.
Cost and security under pressure
The Google Cloud bill and the risk surface are both climbing. You need attributable spend, committed-use decisions made deliberately, and least-privilege IAM.
Foundation first, then waves.
Most GCP trouble traces back to a weak foundation: projects created ad hoc, IAM granted individually, networking duplicated per project, and no guardrails. We set the landing zone right first, then migrate and modernize in reversible waves. The new platform runs alongside what you have, workloads move a few at a time, and every wave stays reversible until you are confident.
Principles we hold to on every GCP engagement.
- The landing zone comes first: organization, folders, projects, Shared VPC, org policies and IAM designed as code before workloads move.
- Everything is code in your repositories: Terraform or OpenTofu, GKE and Cloud Run definitions, pipelines, reviewed like any other change.
- Stateless workloads move first to prove the platform; data stores are planned separately with replication, backups and restore drills.
- Least-privilege from the start: Workload Identity over static keys, groups over individual grants, and Security Command Center watching the estate.
- Vendor-neutral advice with no reseller lock-in. Recommendations follow your workloads, and you own the code either way.
What a GCP transformation covers.
Landing zone
Organization, folder and project hierarchy, Shared VPC, org policies and least-privilege IAM, aligned to Cloud Foundation Fabric and well-architected patterns.
Migration waves
Workload inventory, rehost, replatform or modernize decisions per service, and a ranked, scheduled plan to move off AWS or on-prem.
GKE & Cloud Run
GKE Autopilot or Standard for the platform core, Cloud Run for stateless containers that scale to zero, wired with networking and ingress as code.
Data & messaging
Cloud SQL for operational data, BigQuery for analytics and Pub/Sub for messaging, migrated with replication and a tested cutover.
Observability & cost
Cloud Operations and Managed Service for Prometheus, Grafana and OpenTelemetry, plus budgets, labels and right-sizing so spend stays visible.
Handover or managed run
Runbooks, IaC and team pairing for a clean handover, or an agreed managed operating model. You own the code either way.
GCP transformation is one path within our broader cloud transformation practice. When the focus is Kubernetes specifically, teams pair it with GKE consulting, and estates moving off another platform often start with a cloud migration or a targeted Kubernetes migration.
Our GCP transformation process.
Four phases, planned backwards from a safe running platform. The architecture review confirms exact scope and timeline before anything moves.
Discovery
Workload inventory, dependencies, data stores and compliance constraints, turned into a landing-zone design and a ranked migration wave plan.
Foundation
Organization, folders, projects, Shared VPC, org policies and IAM stood up as Terraform, with observability and guardrails in place.
Migrate & modernize
Workloads move in waves onto GKE and Cloud Run, data stores migrate with replication and cutover, and each wave has a tested rollback.
Operate & own
Handover with runbooks and IaC, or an agreed managed operating model. Everything lives in your repositories.
The Google Cloud services we actually use.
No reseller agreements and no partner quota. Recommendations follow your workloads. Typical building blocks on a GCP transformation:
Foundation
Organization, folders and projects, Shared VPC, org policies and IAM, structured with Cloud Foundation Fabric and well-architected patterns.
Compute
GKE Autopilot or Standard for Kubernetes, Cloud Run for serverless containers, and Compute Engine with Spot VMs where batch or elastic capacity fits.
Infrastructure as code
Terraform or OpenTofu for organization, network and cluster provisioning, with modular, reviewed, version-controlled definitions.
Data & messaging
Cloud SQL for relational data, BigQuery for analytics at scale, and Pub/Sub for event-driven and asynchronous messaging.
Observability
Cloud Operations and Google Cloud Managed Service for Prometheus, with Grafana and OpenTelemetry for metrics, logs and traces, plus SLOs.
Security & cost
Security Command Center, Workload Identity and least-privilege IAM, with budgets, labels, right-sizing and committed-use awareness for cost.
GCP cloud transformation FAQ.
What does GCP cloud transformation involve?
GCP cloud transformation covers the full path from foundation to running platform: a Google Cloud landing zone with a sound organization, folder and project structure, migration of existing workloads from AWS or on-prem, and modernization onto GKE and Cloud Run where they fit. It also includes managed data services, infrastructure-as-code with Terraform or OpenTofu, observability through Cloud Operations, cost controls and security hardening with Security Command Center. We scope each of these to your estate rather than delivering a generic reference build.
How do you structure a Google Cloud landing zone?
We start at the organization node and design a folder hierarchy that mirrors how your teams and environments are separated, with projects as the unit of isolation for billing, IAM and blast radius. Networking is usually a Shared VPC so host and service projects share connectivity without duplicating it, org policies enforce guardrails centrally, and IAM follows least-privilege with groups rather than individual grants. The whole landing zone is expressed as Terraform, often aligned to Cloud Foundation Fabric and well-architected patterns, so it is reviewable and repeatable.
When should we use Cloud Run vs GKE?
Cloud Run suits stateless, request-driven containers that scale to zero and where you want minimal cluster operations: APIs, event handlers and background jobs. GKE earns its place when you need fine-grained control over networking and scheduling, run stateful or long-lived workloads, want a rich ecosystem of operators and controllers, or are standardising many teams onto one platform. GKE Autopilot narrows the operational gap by managing nodes for you. In practice many estates run both, with Cloud Run for the simple edges and GKE for the core platform, and we help you draw that line per workload.
Can you migrate us to Google Cloud from AWS or on-prem?
Yes. Common starting points are AWS, on-prem virtual machines and legacy PaaS. We inventory the workloads, decide per service whether to rehost, replatform or modernize, and move them in planned waves onto GCP. Stateless services move first to prove the platform, stateful data stores are migrated with replication and a tested cutover onto Cloud SQL or the right managed service, and every wave has a rollback path. Data can be kept in specific regions through region selection where residency matters.
How do you control Google Cloud costs?
Cost control starts in the landing zone with per-project billing, budgets and labels so spend is attributable from day one. On the workload side we right-size requests and limits, use Spot VMs and GKE Autopilot for elastic capacity, and make committed use discounts a deliberate decision once usage patterns are stable rather than a guess. Observability feeds this loop: we track spend against utilisation so waste is visible and addressed, not discovered at invoice time. We do not quote prices here because they depend on your workloads and region.
Planning a move to Google Cloud?
Start with the readiness scorecard, or book a free 30-minute architecture call. A senior engineer reviews your workloads and returns a landing-zone design and a ranked, wave-by-wave migration plan.