Lambda pricing model in AWS - Step-by-Step Execution

Process Flow - Lambda pricing model

Request triggers Lambda

↓

Count number of requests

↓

Measure execution duration

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Calculate GB-seconds used

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Apply free tier if eligible

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Calculate cost based on requests + duration

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Generate billing amount

This flow shows how AWS Lambda pricing is calculated step-by-step from requests and execution time to final billing.

Execution Sample

AWS

Requests = 3,000,000
Duration = 200 ms
Memory = 512 MB

Calculate GB-seconds and cost

Calculates cost for 3 million Lambda requests with 200 ms duration and 512 MB memory.

Process Table

Step	Metric	Value	Calculation	Result
1	Requests	3,000,000	Count total requests	3,000,000
2	Duration per request	200 ms	Given	0.2 seconds
3	Memory allocated	512 MB	Given	0.5 GB
4	Total compute (GB-seconds)	3,000,000 * 0.2 * 0.5	3,000,000 * 0.1	300,000 GB-seconds
5	Free tier requests	1,000,000	Subtract free tier	2,000,000 billable requests
6	Free tier compute	400,000 GB-seconds	Subtract free tier	300,000 - 400,000 = 0 (free tier covers all)
7	Billable compute	0	No charge for compute	0 GB-seconds
8	Request cost	2,000,000	2,000,000 / 1,000,000 * $0.20	$0.40
9	Compute cost	0 GB-seconds	0 * $0.0000166667	$0.00
10	Total cost	-	$0.40 + $0.00	$0.40

💡 Free tier covers all compute usage; only requests beyond free tier are charged.

Status Tracker

Variable	Start	After Step 4	After Step 6	Final
Requests	3,000,000	3,000,000	2,000,000	2,000,000
Duration (seconds)	0.2	0.2	0.2	0.2
Memory (GB)	0.5	0.5	0.5	0.5
Compute (GB-seconds)	N/A	300,000	0	0
Request Cost ($)	N/A	N/A	N/A	0.40
Compute Cost ($)	N/A	N/A	N/A	0.00
Total Cost ($)	N/A	N/A	N/A	0.40

Key Moments - 3 Insights

Why is the compute cost zero even though there is compute usage?

Because the free tier includes 400,000 GB-seconds, which covers the 300,000 GB-seconds used (see step 6 in execution_table). So no compute charges apply.

Why do we subtract 1,000,000 requests in step 5?

AWS Lambda offers 1 million free requests per month. We subtract this free tier from total requests to find billable requests (step 5).

How is the total compute (GB-seconds) calculated?

Multiply number of requests by duration in seconds and memory in GB (step 4). Here: 3,000,000 * 0.2 * 0.5 = 300,000 GB-seconds.

Visual Quiz - 3 Questions

Look at the execution_table at step 4. What is the total compute in GB-seconds?

A 600,000

B 300,000

C 150,000

D 3,000,000

💡 Hint

Check the 'Result' column in step 4 of execution_table.

At which step does the free tier reduce the billable compute to zero?

A Step 6

B Step 5

C Step 7

D Step 8

💡 Hint

Look for where compute usage is adjusted by free tier in execution_table.

If the number of requests was 500,000 instead of 3,000,000, what would be the billable requests?

A 500,000

B 1,000,000

C 0

D 2,000,000

💡 Hint

Refer to step 5 where free tier requests are subtracted from total requests.

Concept Snapshot

AWS Lambda pricing is based on two parts:
- Number of requests (first 1M free, then $0.20 per million)
- Compute time in GB-seconds (memory GB * execution seconds)
Free tier covers 1M requests and 400,000 GB-seconds monthly.
Charges apply only beyond free tier.
Calculate total cost = request cost + compute cost.

Full Transcript

AWS Lambda pricing works by counting how many times your function runs and how long it runs with how much memory. Each request counts, and each second of execution with allocated memory counts as GB-seconds. AWS gives you a free tier of 1 million requests and 400,000 GB-seconds per month. You pay only for what you use beyond that. For example, if you run 3 million requests with 512 MB memory and 200 ms duration, you calculate total GB-seconds by multiplying requests, duration in seconds, and memory in GB. Then subtract free tier amounts. The remaining billable requests and compute are multiplied by their rates to get the total cost. This step-by-step calculation helps you understand your Lambda bill clearly.

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