Case Study · 01
Cloud Transformation
From on-premises infrastructure to GCP — containerized, observable, cloud-native.
The problem was the data center.
The platform ran on roughly 80 virtual machines across 6 databases in an on-premises data center. Every request for capacity — a new environment, a scale-out, a product launch — eventually collided with hardware. Headroom was expensive, failure risk was structural, and recovery depended on manual procedure.
Impact · Cloud Transformation
From on-premises infrastructure to Google Cloud.
A full platform migration: ~80 VMs and 6 databases out of a hardware-constrained data center, into a containerized, observable, cloud-native operating model.
On-premises
- ~80 VMs
- 6 databases
- Hardware constraints
- Failure risk
Google Cloud
- Containerized workloads
- GKE
- Autoscaling
- Centralized observability
- Cloud-native operations
A migration designed as a platform change, not a lift-and-shift.
Moving VMs to the cloud preserves their constraints. The work was containerizing the workloads themselves — so the destination platform, not the source estate, defined the operating model.
Approach
- Containerized applications and supporting services; standard Helm packaging for lifecycle management.
- Landed workloads on GKE with autoscaling, replacing capacity planning with demand-driven scaling.
- Segmented GCP projects by product, environment, and workload domain; enforced separation with NetworkPolicy.
- Built centralized observability before the first big cutover, so operations moved with working eyes.
- Ran staged cutovers by workload domain, with rollback paths defined per wave.
The platform that replaced the data center.
Today the workloads run containerized on GKE: autoscaling instead of hardware headroom, centralized observability instead of per-server logins, and cloud-native operations instead of manual runbooks. The same estate that needed ~80 VMs to breathe now runs as workloads that scale with demand.
Outcome
- ~80 VMs and 6 databases migrated out of the on-premises data center.
- Containerized workloads on GKE with demand-driven autoscaling.
- Centralized metrics, logs, and alerting from day one of the new platform.
- A cloud operating model — segmentation, policy, and automation — rather than a virtual data center.