Lambda pricing model in AWS - Mini Project: Build & Apply

Lambda Pricing Model Calculation

📖 Scenario: You are working as a cloud engineer for a startup. Your team wants to estimate the monthly cost of using AWS Lambda functions based on the number of requests and the duration of each request. AWS Lambda pricing depends on the number of requests and the compute time in GB-seconds.

🎯 Goal: Build a simple AWS Lambda pricing calculator that takes the number of requests and average duration per request, then calculates the total monthly cost.

📋 What You'll Learn

Create variables for the number of requests and average duration in milliseconds

Add configuration variables for free tier limits and pricing rates

Calculate the total compute time in GB-seconds

Calculate the total monthly cost including free tier deductions

💡 Why This Matters

🌍 Real World

Estimating AWS Lambda costs helps startups and developers manage cloud expenses effectively.

💼 Career

Cloud engineers and architects often calculate service costs to optimize budgets and design cost-efficient solutions.

1

Set up initial usage data

Create two variables: number_of_requests set to 1000000 and average_duration_ms set to 200 representing the number of Lambda requests and average duration in milliseconds.

AWS

# Your code here

Hint

Use simple assignment to create the two variables with the exact names and values.

2

Add pricing configuration variables

Add three variables: free_tier_requests set to 1000000, free_tier_gb_seconds set to 400000, and price_per_million_requests set to 0.20 representing the free tier limits and price per million requests.

AWS

number_of_requests = 1000000
average_duration_ms = 200
# Your code here

Hint

Define the three variables exactly as named with the given values.

3

Calculate total compute time in GB-seconds

Calculate the total compute time in GB-seconds and store it in total_gb_seconds. Assume each Lambda uses 1 GB of memory. Convert average_duration_ms to seconds and multiply by number_of_requests.

AWS

number_of_requests = 1000000
average_duration_ms = 200
free_tier_requests = 1000000
free_tier_gb_seconds = 400000
price_per_million_requests = 0.20
# Your code here

Hint

Remember to convert milliseconds to seconds by dividing by 1000.

4

Calculate total monthly cost

Calculate the total monthly cost and store it in total_cost. Subtract the free tier limits from number_of_requests and total_gb_seconds (use max with 0 to avoid negatives). Calculate request cost by dividing billable requests by 1,000,000 and multiplying by price_per_million_requests. Assume compute cost is $0.00001667 per GB-second. Sum request cost and compute cost for total_cost.

AWS

number_of_requests = 1000000
average_duration_ms = 200
free_tier_requests = 1000000
free_tier_gb_seconds = 400000
price_per_million_requests = 0.20
total_gb_seconds = (average_duration_ms / 1000) * number_of_requests
# Your code here

Hint

Use max to avoid negative billable amounts and multiply costs correctly.

Practice

1. What two main factors determine the cost of an AWS Lambda function?

easy

A. Number of requests and compute time

B. Storage size and network bandwidth

C. Number of users and API calls

D. Memory size and database queries

Solution

1. Step 1: Understand AWS Lambda pricing basics

   AWS Lambda charges based on how many times your function runs (requests) and how long it runs (compute time).

2. Step 2: Identify the pricing factors

   Memory size affects compute time cost but the main factors are requests and compute time.

3. Final Answer:

   Number of requests and compute time -> Option A

4. Quick Check:

   Requests + compute time = cost [OK]

Hint: Remember: pay for calls and duration only [OK]

Common Mistakes:

• Confusing storage or bandwidth as pricing factors
• Thinking memory size alone determines cost
• Mixing API calls with requests

2. Which of the following is the correct way to express AWS Lambda pricing units?

easy

A. Memory in MB and network packets

B. Requests per hour and CPU cycles

C. Number of requests and GB-seconds

D. Storage in GB and function calls

Solution

1. Step 1: Recall AWS Lambda pricing units

   Lambda pricing is based on number of requests and compute time measured in GB-seconds (memory size x execution time).

2. Step 2: Match correct units

   Requests count how many times function runs; GB-seconds measure memory used over time.

3. Final Answer:

   Number of requests and GB-seconds -> Option C

4. Quick Check:

   Requests + GB-seconds = pricing units [OK]

Hint: Think requests and memory-time units [OK]

Common Mistakes:

• Using CPU cycles or network packets incorrectly
• Confusing storage with compute time
• Mixing units like MB with packets

3. If a Lambda function runs 1,000,000 times with 512 MB memory for 1 second each, which factor mainly increases the cost?

medium

A. Execution time of 1 second per request

B. Network bandwidth used by the function

C. Memory size doubling from 256 MB to 512 MB

D. Number of requests being 1,000,000

Solution

1. Step 1: Understand cost components

   Cost depends on requests and GB-seconds (memory size x execution time x requests).

2. Step 2: Analyze impact of memory size

   Doubling memory from 256 MB to 512 MB doubles GB-seconds, increasing cost significantly, but the number of requests (1,000,000) is the main driver of total cost.

3. Final Answer:

   Number of requests being 1,000,000 -> Option D

4. Quick Check:

   Requests x GB-seconds = total cost [OK]

Hint: Number of requests greatly impacts total cost [OK]

Common Mistakes:

• Ignoring number of requests effect
• Thinking network bandwidth affects Lambda cost
• Assuming execution time or memory alone drives cost

4. A developer notices their Lambda cost is higher than expected. They set memory to 1024 MB but function runs only 100 ms. What is the likely issue?

medium

A. Memory size is too high for short execution time

B. Number of requests is too low

C. Function execution time is too long

D. Free tier is not applied to this function

Solution

1. Step 1: Review memory and execution time relation

   Cost depends on memory size multiplied by execution time. High memory with short time still costs more than lower memory.

2. Step 2: Identify cost cause

   Setting memory to 1024 MB increases GB-seconds cost even if function runs only 100 ms.

3. Final Answer:

   Memory size is too high for short execution time -> Option A

4. Quick Check:

   High memory + short time can increase cost [OK]

Hint: High memory with short run still costs more [OK]

Common Mistakes:

• Assuming short execution time always means low cost
• Ignoring memory size impact
• Thinking free tier always applies

5. A company uses Lambda functions with 256 MB memory running 200 ms per request. They want to reduce cost without changing code. Which approach helps most?

hard

A. Switch to EC2 instances for better pricing

B. Reduce memory size to 128 MB if function still runs correctly

C. Increase number of requests to use free tier fully

D. Increase memory size to reduce execution time

Solution

1. Step 1: Understand cost relation to memory and time

   Cost = requests x memory size x execution time. Reducing memory lowers GB-seconds cost if function still works.

2. Step 2: Evaluate options

   Reducing memory to 128 MB saves cost if performance is acceptable. Increasing memory or requests raises cost. Switching to EC2 is a different model and may not be cheaper.

3. Final Answer:

   Reduce memory size to 128 MB if function still runs correctly -> Option B

4. Quick Check:

   Lower memory reduces GB-seconds cost [OK]

Hint: Lower memory if function still works to save cost [OK]

Common Mistakes:

• Increasing memory without checking cost impact
• Thinking more requests reduce cost
• Assuming EC2 always cheaper than Lambda