Token Tags: Granular Cost Attribution

When you are move a large AI platform, knowing "Total Cost" is useless for business logic. You need to know:

• "How much did we spend on the Client A project?"
• "Is the Legal Team exceeding their AI budget?"
• "Which Feature Flag is the most expensive to run?"

Token Tags are metadata attributes attached to your LLM requests. They allow you to perform "Financial Drill-downs" into your usage data.

In this lesson, we learn how to implement Tagging Architecture and how to use those tags to build Customer Billing Reports.

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1. The Tag Hierarchy

A robust tagging system uses a 3-tier hierarchy:

1. Owner (Who): The specific User ID or Tenant ID.
2. Project (What): The specific workflow (e.g., "Resume Parser," "Chatbot").
3. Context (Why): The specific trigger (e.g., "Manual Refresh," "Automated Sync").

Token Saving Insight: In a production audit, you might find that "Automated Sync" (Context) accounts for 90% of your costs but produces only 1% of your user value. You just found a target for major pruning.

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2. Universal Metadata Injection

Most LLM providers (especially AWS Bedrock and Azure) allow you to pass "ClientRequestToken" or "Metadata" fields. Even if they don't, your API Gateway should wrap the request.

graph LR
    A[Client App] -->|Request + Tags| B[AI Gateway]
    B -->|Strip Tags| C[LLM API]
    C -->|Response| B
    B -->|Log Usage + Tags| D[(Analytics SQL)]
    B -->|Return Response| A

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3. Implementation: The Tagged Request (Python)

Python Code: Logging with Context

def call_tagged_llm(prompt, tags: dict):
    start_time = time.time()
    
    # 1. External API Call
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}]
    )
    
    # 2. ENRICHED LOGGING
    usage_data = {
        "tokens": response.usage.total_tokens,
        "model": response.model,
        "duration": time.time() - start_time,
        **tags # {dept: 'marketing', feature: 'fb-ads-gen'}
    }
    
    db.insert_token_log(usage_data)
    return response

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4. Feature-Based ROI Analysis

By using Token Tags, you can calculate the Gross Margin of your AI features.

• If "Feature A" costs $10.00 in tokens but is part of a $5.00/month subscription, you are losing money on every user.
• The Solution: Move Feature A to a Tier 1 model (Module 14) or add a Limit (Module 16.1).

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5. Summary and Key Takeaways

1. Tag Everything: Never send a token without a "Why" and an "Owner."
2. Gateway Pattern: Manage your tags at the API Gateway level to keep individual agents clean.
3. Align with Revenue: Use tags to ensure your AI-powered features are actually profitable.
4. Identify Waste: Use departmental tags to find internal teams that are over-using resources on low-value tasks.

In the next lesson, Predictive Token Accounting: Forecasting the Bill, we lead Module 17 by looking at چگونه to see into the future.

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Exercise: The Billing Drill-down

1. You have a table of 1 million token logs with tags for feature_id: ['chat', 'summary', 'code'].
2. Write a SQL Query to find the most expensive feature.
3. Write a SQL Query to find the "Most Expensive User" for the 'summary' feature.
4. Reflect: If you had to cut costs by 20% today, which feature would you target based on your query results?

• (Logic: Usually, you target the highest cost / lowest frequency feature).

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Congratulations on completing Module 17 Lesson 4! You are now a financial AI architect.