Project Echo: GCP Build and Running

Cloud architecture, delivery flow, and measurable improvements.

Project Echo Cloud Build & Running

From manual local runs to a managed, observable cloud delivery model.

Google Cloud Terraform IaC GKE Cloud Build Monitoring

Problem

• Local setup was not stable enough for production rollouts.
• Environment drift caused inconsistent behavior between machines.
• Infrastructure cost and scalability decisions were not yet systematically controlled.
• The team needed a versioned, repeatable cloud workflow.

Success criteria

• Predictable release path
• Repeatable infra provisioning
• Better operational visibility
• Evidence-backed outcomes

How I changed the stack

1) Infrastructure as Code

• Built and organized cloud foundation under infra/terraform/.
• Core modules:
  • network: VPC, subnets, firewall, NAT
  • gke: managed cluster, autoscaling, workload identity
  • nodepools: right-sized compute setup
  • registry: Artifact Registry + IAM permissions

2) Containerized and deployable services

• Standardized image delivery for API and HMI services.
• Provisioned managed stateful data layer through Kubernetes:
  • MongoDB StatefulSet
  • supporting services and ingress routing

3) Automated release pipeline

• Added Cloud Build release path: GitHub push → Cloud Build → Artifact Registry → kubectl apply/set image → GKE
• Kept secrets and configs external to source logic.

Why this structure

• GKE: production-ready orchestration for long-running services.
• Terraform: consistent and reviewable infra across environments.
• Cloud Build: same path for every change reduces release mistakes.
• Externalized config: same container works across environments with safe overrides.
• Autoscaling + right-sizing: fewer manual tuning steps; better cost control.

Deployment path

Git-driven

Push-based rollout with Cloud Build and GKE updates.

Infrastructure control

IaC modules

network/gke/nodepools/registry all codified.

Cost result

~75% improvement

Reported reduction after optimization in T3 records.

Delivery quality

Repeatable

Reduced manual setup and drift risk across runs.

Architecture and release flow (quick view)

Figure 1: End-to-end delivery flow used for stable releases.

Figure 2: Cloud architecture layout in the stabilized Project Echo stack.

Observed cost

$24.5/day → A$6.19/day

December 2025 to January 2026 (as reported in T3).

Savings

~A$550/month

In the reported monthly projection improvement.

Improvements and outcomes

• Deployment changed from a manual path to a consistent Git-driven workflow.
• Infrastructure changed from ad-hoc setup to coded, reusable modules.
• Operational checks became trackable through logs and dashboarded behavior.
• Cost management became measurable with documented optimization gains.

What I delivered

• Stable GKE-based delivery topology.
• CI/CD pipeline for predictable image build/publish/deploy.
• Terraformized network and registry foundation for future scaling.
• Monitoring checkpoints added for reliability and troubleshooting.

Proof (hard evidence)

Source	Evidence
PROJECT_ECHO_TEAM_REPORT_T3_2025.md	Cloud/DevOps section includes GCP deployment, CI/CD pipeline, IaC modules, cost optimization.
Project_Echo_T3_2025_Report.csv	My contribution note: stabilized GCP deployment and infrastructure readiness.
_Tennie – Backend & Cloud Lead.md	Leadership and rollout planning notes, branch ownership, deployment blockers.
T3 deliverables list	“GCP Production Deployment”, “CI/CD Pipeline”, and “Docker Containerization” marked complete.

Read the full T3 report

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