Model Pipeline - Token usage and cost tracking
This pipeline tracks how many tokens are used during AI model interactions and calculates the cost based on token usage. It helps manage expenses and optimize usage.
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Token usage and cost tracking in Agentic Ai - Model Pipeline Trace
Data Flow - 7 Stages
1Input Text
1 text string→User provides input text to the AI model→1 text string
"Hello, how are you today?"
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2Tokenization
1 text string→Text is split into tokens (small pieces like words or subwords)→1 list of tokens
["Hello", ",", "how", "are", "you", "today", "?"]
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3Token Counting
1 list of tokens→Count the number of tokens in the input→1 integer (token count)
7
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4Model Processing
1 list of tokens→Model processes tokens to generate output tokens→1 list of output tokens
["I", "am", "fine", "."]
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5Output Token Counting
1 list of output tokens→Count the number of tokens in the output→1 integer (output token count)
4
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6Total Token Usage Calculation
input token count + output token count→Sum input and output tokens to get total tokens used→1 integer (total tokens)
11
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7Cost Calculation
total tokens→Calculate cost by multiplying total tokens by cost per token→1 float (cost in dollars)
0.00022 (assuming $0.00002 per token)
Training Trace - Epoch by Epoch
N/A
| Epoch | Loss ↓ | Accuracy ↑ | Observation |
|---|---|---|---|
| 1 | N/A | N/A | This pipeline does not involve model training but tracks token usage and cost. |
Prediction Trace - 6 Layers
Layer 1: Tokenization
Layer 2: Token Counting (Input)
Layer 3: Model Processing
Layer 4: Token Counting (Output)
Layer 5: Total Token Usage Calculation
Layer 6: Cost Calculation
Model Quiz - 3 Questions
Test your understanding
What does the tokenization stage do in this pipeline?
Key Insight
Tracking token usage and cost helps users understand and control how much they spend when interacting with AI models. It encourages efficient use of tokens and better budgeting.