Cloud Behavioral Divergence Guide#

Running the “same” workload on AWS, Azure, and GCP does not produce the same behavior. The Kubernetes API is portable, application containers are portable, and SQL queries are portable. Everything else – identity, networking, storage, load balancing, DNS, and managed service behavior – diverges in ways that matter for production reliability.

This guide documents the specific divergence points with practical examples. Use it when translating infrastructure from one cloud to another, when debugging behavior that differs between environments, or when assessing migration risk.

Projects and Organization#

GCP organizes resources into Projects, which sit under Folders and an Organization. A project is the fundamental unit of resource organization, billing, and API enablement. Every GCP resource belongs to exactly one project.

# Set the active project
gcloud config set project my-prod-project

# List all projects
gcloud projects list

# Create a new project
gcloud projects create staging-project-2026 \
  --name="Staging" \
  --organization=ORG_ID

# Enable required APIs (must be done per-project)
gcloud services enable compute.googleapis.com
gcloud services enable container.googleapis.com
gcloud services enable sqladmin.googleapis.com

Check which project is currently active:

Terraform Cloud Architecture Patterns#

The three-tier architecture — networking, managed Kubernetes, managed database — is the most common pattern for production deployments on any major cloud. The concepts are identical across AWS, Azure, and GCP. The Terraform code is not. Resource names differ, required arguments differ, default behaviors differ, and the gotchas that catch agents and humans are cloud-specific.

This article shows the real Terraform for each layer on each cloud, side by side, so agents can write correct infrastructure code for whichever cloud the user deploys to.

Choosing a Database Strategy#

Every Kubernetes-based platform eventually faces two questions: should the database run inside the cluster or as a managed service, and which database engine fits the workload? These decisions are difficult to reverse. A database migration is one of the highest-risk operations in production. Getting the initial decision roughly right saves months of future pain.

Where to Run: Kubernetes vs Managed Service#

This is not a technology question. It is an organizational question about who owns database operations and what tradeoffs the team will accept.