ShelCron

DevOps & Cloud

Google Cloud

GCP project structure, networking, and managed services for product workloads.

Google Cloud work for teams building on GCP: project and folder hierarchy, VPC design, IAM, and managed services such as GKE, Cloud Run, or Cloud SQL. We keep architectures pragmatic and encode them so environments stay reproducible.

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Who it’s for

  • Product teams choosing GCP for data or container workloads
  • Orgs adopting Cloud Run or GKE for services
  • Companies needing cleaner GCP IAM and project hygiene

Problems we address

  • Projects proliferate without folder or IAM discipline
  • Networking and service identity are poorly understood
  • Environments diverge because changes were click-ops

Expected outcomes

  • Project hierarchy and IAM patterns that match ownership
  • VPC and service connectivity designed for your apps
  • Infrastructure as code for environments you will keep

Capabilities

Concrete engineering capabilities included in a typical engagement for this service.

GCP organization/folder/project guidance

VPC, Cloud NAT, and private service access patterns

Cloud Run, GKE, or Compute Engine as appropriate

Cloud SQL and storage baselines

Cloud Monitoring and Logging hooks

Technology

Representative technologies used for this service. Final stack depends on your estate.

  • Google Cloud
  • Terraform
  • GKE
  • Cloud Run
  • Cloud SQL
  • Cloud Monitoring

Architecture

Delivery pipeline

Source control through CI into containerized deploy and cloud runtime.

GitCIDockerKubernetesCloud

Deliverables

  • Scoped GCP foundations implemented
  • IaC for core resources
  • IAM and network documentation
  • Operational runbook snippets

Out of scope

  • GCP billing account ownership transfer

Timeline

Typical timeline

2–8 weeks

Timeline depends on scope, access, and dependencies—not a delivery guarantee.

Process

A clear delivery path from discovery through handover and optional support.

  1. 01

    Discovery

    Goals, constraints, success criteria, and current-state review.

  2. 02

    Architecture

    Target design, interfaces, risks, and delivery sequence.

  3. 03

    Implementation

    Incremental build with visible progress and documented decisions.

  4. 04

    Testing

    Functional checks, failure paths, and acceptance criteria validation.

  5. 05

    Deployment

    Controlled release to staging and production with rollback paths.

  6. 06

    Handover

    Runbooks, access notes, and operator/admin walkthrough.

  7. 07

    Support

    Optional hypercare window or retainer continuity after go-live.

Custom engagement

Pricing depends on architecture, traffic profile, and integration depth. Share your requirements for a scoped quote.

Related work

Example / concept projects shown for illustration unless otherwise verified.

FAQ

Cloud Run suits many request-driven services with less ops overhead. GKE fits when you need broader Kubernetes capabilities or existing k8s workloads.

We can design app and data plane boundaries that include BigQuery. Deep analytics modeling is better paired with data/analytics workstreams.

Ready to build?

Tell us about your environment, constraints, and target outcomes. We’ll recommend a package or a scoped quote.