Token usage and cost tracking in Agentic AI

Introduction

Tracking tokens and costs helps you understand how much you use and spend when working with AI models.

When you want to keep your AI usage within a budget.

When you need to know how many tokens your input and output use.

When you want to optimize your prompts to save money.

When you want to report usage for billing or analysis.

When you want to compare costs between different AI models.

Syntax

Agentic AI

tokens_used = count_tokens(prompt_text)
cost = tokens_used * cost_per_token

Tokens are pieces of words that AI models use to understand text.

Cost per token depends on the AI model and provider pricing.

Examples

Count tokens in a simple greeting and calculate cost with a token price of 0.0001.

Agentic AI

prompt = "Hello, how are you?"
tokens_used = count_tokens(prompt)
cost = tokens_used * 0.0001

Calculate tokens and cost for a question prompt with a different token price.

Agentic AI

input_text = "Explain machine learning in simple words."
tokens = count_tokens(input_text)
cost = tokens * 0.0002

Sample Model

This program counts tokens by splitting the prompt on spaces and calculates the cost using a fixed price per token.

Agentic AI

def count_tokens(text):
    # Simple token count by splitting on spaces
    return len(text.split())

prompt = "What is AI and how does it work?"
cost_per_token = 0.00015

tokens_used = count_tokens(prompt)
cost = tokens_used * cost_per_token

print(f"Tokens used: {tokens_used}")
print(f"Cost: ${cost:.5f}")

Important Notes

Token counting can be more complex than splitting by spaces because AI models use special tokenizers.

Always check your AI provider's documentation for exact token counting methods and pricing.

Tracking tokens helps avoid unexpected high costs.

Summary

Tokens are small pieces of text used by AI models to process language.

Counting tokens helps estimate how much you will pay for AI usage.

Tracking usage and cost keeps your AI projects affordable and efficient.

Practice

1. What is a token in the context of AI language models?

easy

A. A programming language used for AI

B. A type of AI model architecture

C. A small piece of text like a word or part of a word

D. A hardware component for AI processing

Solution

1. Step 1: Understand the definition of a token

   A token is a small piece of text that AI models use to understand language. It can be a word or part of a word.

2. Step 2: Differentiate tokens from other AI terms

   Tokens are not models, programming languages, or hardware. They are units of text input.

3. Final Answer:

   A small piece of text like a word or part of a word -> Option C

4. Quick Check:

   Token = small text piece [OK]

Hint: Tokens are text pieces, not models or hardware [OK]

Common Mistakes:

• Confusing tokens with AI models
• Thinking tokens are programming languages
• Assuming tokens are hardware parts

2. Which of the following is the correct way to track token usage in a Python script using an AI API?

easy

A. tokens_used = response['total_tokens']

B. tokens_used = response['usage']['total_tokens']

C. tokens_used = response['usage']['tokens']

D. tokens_used = response['token_count']

Solution

1. Step 1: Identify the correct key for token usage in API response

   Most AI APIs return token usage under response['usage']['total_tokens'].

2. Step 2: Compare options with common API response structure

   Only tokens_used = response['usage']['total_tokens'] matches the standard nested key for total tokens used.

3. Final Answer:

   tokens_used = response['usage']['total_tokens'] -> Option B

4. Quick Check:

   Correct key path = response['usage']['total_tokens'] [OK]

Hint: Look for nested 'usage' then 'total_tokens' key [OK]

Common Mistakes:

• Using wrong key names like 'token_count'
• Missing nested 'usage' dictionary
• Assuming flat keys for token counts

3. Given the following code snippet, what will be printed?

response = {'usage': {'prompt_tokens': 50, 'completion_tokens': 30, 'total_tokens': 80}}
print(response['usage']['total_tokens'])

medium

A. 80

B. 30

C. 50

D. Error

Solution

1. Step 1: Access the 'total_tokens' key in the nested dictionary

   The code accesses response['usage']['total_tokens'], which is 80.

2. Step 2: Confirm the print output

   Printing this value outputs 80 without error.

3. Final Answer:

   80 -> Option A

4. Quick Check:

   response['usage']['total_tokens'] = 80 [OK]

Hint: Check nested dictionary keys carefully [OK]

Common Mistakes:

• Confusing prompt_tokens with total_tokens
• Mixing completion_tokens with total_tokens
• Assuming code causes error

4. You wrote this code to track token usage but get a KeyError:

tokens = response['usage']['token_total']
print(tokens)

What is the likely cause?

medium

A. The 'usage' key is missing in the response

B. The response variable is not defined

C. The print statement syntax is incorrect

D. The key 'token_total' does not exist in the response dictionary

Solution

1. Step 1: Check the key names in the response dictionary

   The correct key is 'total_tokens', not 'token_total'.

2. Step 2: Understand KeyError cause

   Using a wrong key name causes KeyError because that key does not exist.

3. Final Answer:

   The key 'token_total' does not exist in the response dictionary -> Option D

4. Quick Check:

   Wrong key name causes KeyError [OK]

Hint: Verify exact key names in API response [OK]

Common Mistakes:

• Assuming similar key names exist
• Ignoring case sensitivity in keys
• Thinking print syntax causes error

5. You want to estimate the cost of an AI request. The model charges $0.002 per 1000 tokens. If your request uses 2500 tokens, what is the total cost?

hard

A. $0.005

B. $0.0025

C. $0.05

D. $0.0005

Solution

1. Step 1: Calculate cost per token

   Cost per token = $0.002 / 1000 = $0.000002 per token.

2. Step 2: Multiply by number of tokens used

   Total cost = 2500 tokens * $0.000002 = $0.005.

3. Final Answer:

   $0.005 -> Option A

4. Quick Check:

   2500 tokens x $0.002/1000 = $0.005 [OK]

Hint: Multiply tokens by cost per token (divide by 1000 first) [OK]

Common Mistakes:

• Multiplying by 0.002 directly without dividing by 1000
• Using wrong token count
• Confusing decimals in cost calculation