Serverless vs GKE decision in GCP - Hands-On Comparison

Serverless vs GKE Decision

📖 Scenario: You are a cloud architect helping a small company decide how to deploy their new web application on Google Cloud Platform (GCP). They want to understand the difference between using a serverless approach with Cloud Run and using Google Kubernetes Engine (GKE) for container orchestration.Your task is to create a simple decision helper that models the choice between Serverless and GKE based on application needs.

🎯 Goal: Build a Python script that stores application requirements, sets a threshold for complexity, and uses a decision logic to recommend either Serverless (Cloud Run) or GKE deployment.

📋 What You'll Learn

Create a dictionary with exact application requirements and their values

Add a complexity threshold variable

Write a decision logic using an if-else statement to choose between Serverless and GKE

Output the final deployment recommendation as a variable

💡 Why This Matters

🌍 Real World

Cloud architects often need to decide the best deployment method for applications based on their requirements and complexity.

💼 Career

Understanding how to model application needs and make deployment decisions is key for roles in cloud architecture and infrastructure engineering.

1

Create application requirements dictionary

Create a dictionary called app_requirements with these exact entries: 'concurrent_users': 1000, 'cpu_intensive': false, 'requires_scaling': true, 'deployment_complexity': 3

GCP

# Create the app_requirements dictionary with the specified entries
# Your code here

Need a hint?

Use curly braces to create a dictionary and separate key-value pairs with commas.

2

Set complexity threshold variable

Create a variable called complexity_threshold and set it to 5

GCP

app_requirements = {
    'concurrent_users': 1000,
    'cpu_intensive': False,
    'requires_scaling': True,
    'deployment_complexity': 3
}
# Create complexity_threshold variable and set it to 5
# Your code here

Need a hint?

Assign the number 5 to the variable named complexity_threshold.

3

Write decision logic for deployment choice

Write an if-else statement that checks if app_requirements['deployment_complexity'] is less than complexity_threshold. If true, assign 'Serverless' to a variable called deployment_choice. Otherwise, assign 'GKE' to deployment_choice.

GCP

app_requirements = {
    'concurrent_users': 1000,
    'cpu_intensive': False,
    'requires_scaling': True,
    'deployment_complexity': 3
}

complexity_threshold = 5
# Add if-else to set deployment_choice based on deployment_complexity
# Your code here

Need a hint?

Use the if keyword to compare values and assign the correct string to deployment_choice.

4

Add final deployment recommendation variable

Create a variable called final_recommendation and set it equal to deployment_choice

GCP

app_requirements = {
    'concurrent_users': 1000,
    'cpu_intensive': False,
    'requires_scaling': True,
    'deployment_complexity': 3
}

complexity_threshold = 5

if app_requirements['deployment_complexity'] < complexity_threshold:
    deployment_choice = 'Serverless'
else:
    deployment_choice = 'GKE'
# Set final_recommendation equal to deployment_choice
# Your code here

Need a hint?

Assign the value of deployment_choice to final_recommendation.