Cloud Functions pricing in GCP - Mini Project: Build & Apply

Understanding Cloud Functions Pricing on GCP

📖 Scenario: You are working as a cloud engineer for a small startup. Your team wants to use Google Cloud Functions to run small pieces of code in response to events. To plan the budget, you need to understand how Cloud Functions pricing works.

🎯 Goal: Build a simple data structure that models Cloud Functions pricing components, then calculate the estimated monthly cost for a given usage scenario.

📋 What You'll Learn

Create a dictionary with exact pricing values for Cloud Functions usage.

Add a variable for the number of function invocations per month.

Calculate the total cost based on invocations and compute time.

Add the final cost calculation including free tier deductions.

💡 Why This Matters

🌍 Real World

Cloud engineers and developers use pricing models to estimate costs before deploying serverless functions.

💼 Career

Understanding pricing helps in budgeting, cost optimization, and communicating costs to stakeholders.

1

Create the Cloud Functions pricing dictionary

Create a dictionary called pricing with these exact entries: 'invocation_cost_per_million': 0.40, 'compute_cost_per_gb_second': 0.0000025, 'free_invocations_per_month': 2000000, and 'free_gb_seconds_per_month': 400000.

GCP

# Your code here

Hint

Use a dictionary with the exact keys and values given.

2

Set the monthly usage variables

Create two variables: monthly_invocations set to 5000000 and monthly_gb_seconds set to 600000 representing the usage for the month.

GCP

pricing = {
    'invocation_cost_per_million': 0.40,
    'compute_cost_per_gb_second': 0.0000025,
    'free_invocations_per_month': 2000000,
    'free_gb_seconds_per_month': 400000
}
# Your code here

Hint

Use the exact variable names and values given.

3

Calculate billable usage after free tier

Create two variables: billable_invocations and billable_gb_seconds. Calculate them by subtracting the free tier values from monthly_invocations and monthly_gb_seconds respectively. If the result is negative, set the value to zero.

GCP

pricing = {
    'invocation_cost_per_million': 0.40,
    'compute_cost_per_gb_second': 0.0000025,
    'free_invocations_per_month': 2000000,
    'free_gb_seconds_per_month': 400000
}

monthly_invocations = 5000000
monthly_gb_seconds = 600000

# Your code here

Hint

Use subtraction and if statements to ensure no negative values.

4

Calculate the total monthly cost

Create a variable called total_cost. Calculate it by multiplying billable_invocations divided by 1,000,000 by pricing['invocation_cost_per_million'], then adding the product of billable_gb_seconds and pricing['compute_cost_per_gb_second'].

GCP

pricing = {
    'invocation_cost_per_million': 0.40,
    'compute_cost_per_gb_second': 0.0000025,
    'free_invocations_per_month': 2000000,
    'free_gb_seconds_per_month': 400000
}

monthly_invocations = 5000000
monthly_gb_seconds = 600000

billable_invocations = monthly_invocations - pricing['free_invocations_per_month']
if billable_invocations < 0:
    billable_invocations = 0

billable_gb_seconds = monthly_gb_seconds - pricing['free_gb_seconds_per_month']
if billable_gb_seconds < 0:
    billable_gb_seconds = 0

# Your code here

Hint

Use arithmetic operations to calculate the total cost.

Practice

1. What are the main factors that determine the cost of Google Cloud Functions?

easy

A. Number of API calls, database queries, and cache hits

B. Number of invocations, compute time, and outbound data

C. CPU type, memory size, and disk I/O

D. Number of users, storage size, and network speed

Solution

1. Step 1: Identify pricing components

   Google Cloud Functions pricing is based on how many times functions run (invocations), how long they run (compute time), and how much data they send out (outbound data).

2. Step 2: Match factors to options

   Number of invocations, compute time, and outbound data lists these exact factors, while others mention unrelated metrics like users or storage.

3. Final Answer:

   Number of invocations, compute time, and outbound data -> Option B

4. Quick Check:

   Pricing factors = invocations, compute time, outbound data [OK]

Hint: Remember: cost depends on usage, not users or storage [OK]

Common Mistakes:

• Confusing storage or users with function pricing
• Thinking CPU type alone affects cost
• Mixing up inbound and outbound data charges

2. Which of the following is the correct way to describe the free tier for Google Cloud Functions?

easy

A. It offers unlimited invocations and compute time for all users

B. It charges a small fee for every invocation regardless of usage

C. It provides a fixed number of free invocations and compute time each month

D. It only applies to functions running in specific regions

Solution

1. Step 1: Understand free tier basics

   The free tier gives a set amount of free invocations and compute time monthly, not unlimited usage.

2. Step 2: Compare options

   It provides a fixed number of free invocations and compute time each month correctly states the free tier provides fixed free usage. It offers unlimited invocations and compute time for all users is wrong because it says unlimited. It charges a small fee for every invocation regardless of usage is wrong because free tier means no charge up to limits. It only applies to functions running in specific regions is incorrect as free tier applies globally.

3. Final Answer:

   It provides a fixed number of free invocations and compute time each month -> Option C

4. Quick Check:

   Free tier = fixed free usage monthly [OK]

Hint: Free tier means limited free usage, not unlimited [OK]

Common Mistakes:

• Assuming free tier is unlimited
• Thinking free tier applies only in some regions
• Believing every invocation is charged

3. A Cloud Function runs 1,000,000 times in a month, each execution lasting 200 milliseconds with 256 MB memory allocated. Which factor primarily affects the compute time cost?

medium

A. Number of invocations only

B. Outbound data volume

C. Function trigger type

D. Execution duration and memory size

Solution

1. Step 1: Identify compute time cost factors

   Compute time cost depends on how long the function runs and how much memory it uses during execution.

2. Step 2: Analyze options

   Execution duration and memory size correctly identifies execution duration and memory size as key for compute cost. Invocations count affects invocation cost, not compute time alone. Outbound data and trigger type do not affect compute time cost.

3. Final Answer:

   Execution duration and memory size -> Option D

4. Quick Check:

   Compute cost = duration x memory [OK]

Hint: Compute cost = run time x memory size [OK]

Common Mistakes:

• Confusing invocation count with compute time cost
• Ignoring memory size in cost calculation
• Thinking trigger type affects compute cost

4. You notice your Cloud Function costs are unexpectedly high. Which of the following is the most likely cause?

medium

A. Function runs longer than expected or uses more memory

B. You exceeded the free tier for outbound data only

C. You deployed the function in multiple regions accidentally

D. You used a deprecated runtime version

Solution

1. Step 1: Identify common cost drivers

   High costs usually come from functions running longer or using more memory, increasing compute time charges.

2. Step 2: Evaluate other options

   You exceeded the free tier for outbound data only is less likely since outbound data is often smaller cost. You deployed the function in multiple regions accidentally might increase cost but is less common. You used a deprecated runtime version affects functionality, not pricing directly.

3. Final Answer:

   Function runs longer than expected or uses more memory -> Option A

4. Quick Check:

   Longer run or more memory = higher cost [OK]

Hint: Check function duration and memory to find cost spikes [OK]

Common Mistakes:

• Blaming deprecated runtime for cost increase
• Ignoring function execution time impact
• Assuming outbound data is main cost driver

5. You want to optimize costs for a Cloud Function that processes large files and sends results over the network. Which combination of strategies will most effectively reduce your bill?

hard

A. Reduce function memory allocation and minimize outbound data size

B. Increase function timeout and add more retries

C. Deploy multiple identical functions in different regions

D. Use a higher memory allocation to speed up execution regardless of data size

Solution

1. Step 1: Identify cost factors for large file processing

   Costs come from compute time (affected by memory and duration) and outbound data size.

2. Step 2: Evaluate optimization strategies

   Reducing memory allocation lowers compute cost if performance is sufficient. Minimizing outbound data reduces network charges. Increasing timeout or retries raises cost. Deploying multiple functions increases cost. Higher memory may speed execution but can increase cost if not balanced.

3. Final Answer:

   Reduce function memory allocation and minimize outbound data size -> Option A

4. Quick Check:

   Lower memory + less data = cost savings [OK]

Hint: Cut memory and data size to save costs effectively [OK]

Common Mistakes:

• Thinking more memory always reduces cost
• Adding retries without cost consideration
• Deploying duplicates increases cost unnecessarily