Tokens are Cash

In the world of Generative AI, tokens are the currency. Every word you send (Input) and every word the AI sends back (Output) has a price tag. If you are building a production-scale application with millions of users, a 10% reduction in token usage can save your company thousands of dollars per month.

In the AWS Certified Generative AI Developer – Professional exam, you must demonstrate "Operational Excellence" by proving you can build cost-effective AI systems.

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1. Understanding the Billing Model

AWS models (Bedrock) are billed on:

• Input Tokens: The prompt and context you send.
• Output Tokens: The text the model generates. (Usually 2x-5x more expensive than input tokens).

The Strategy: Keep the AI's output concise. Long-winded models are expensive models.

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2. Techniques for Token Pruning

Your prompt often contains "Fluff" that the model doesn't need to understand the intent.

• Pruning the Context: If you are doing RAG, don't send the entire 10-page document. Only send the top 3 relevant chunks.
• Stop Sequences: Tell the model to stop writing once it has finished the specific task (e.g., "Stop generation when you reach the closing JSON brace").
• Summarized History: In a long chat, dont send the whole conversation history. Send a summarized version of the previous turns to "Reset" the token counter.

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3. The "Haiku-First" Routing Strategy

As we touched on in Module 7, Model Routing is your most powerful cost-control tool.

The Pro Pattern:

1. Use Claude 3.5 Haiku (Cheapest) for intent classification and simple screening.
2. Only "Escalate" the request to Claude 3.5 Sonnet or Opus if the task requires deep reasoning.
3. The Result: 80% of your requests are handled by the model that costs 10x less.

graph TD
    U[User Request] --> R[Small Model: Haiku]
    R -->|Can I answer this?| Yes[Haiku Generates Answer]
    R -->|Too complex| No[Route to Large Model: Sonnet]
    
    style R fill:#e8f5e9,stroke:#2e7d32
    style No fill:#fff3e0,stroke:#ef6c00

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4. Prompt Engineering for Economy

• Concise Instructions: "Give a 1-sentence summary" is cheaper than "Summarize the following text."
• N-Shot Reduction: If the model can perform a task with 1-shot (one example), don't provide 5-shot prompting.
• Few-shot Pruning: Only include examples that are most similar to the current user query.

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5. Cost Observability and Guardrails

You cannot optimize what you do not measure.

• Billing Alarms: Set an alarm in AWS Budgets to alert you when AI spend exceeds $100.
• User Quotas: In your application code, track how many tokens a specific user_id has consumed using DynamoDB. If they exceed their daily limit, block their requests or switch them to a "Lite" model.

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6. Pro-Tip: The "Semantic Cache" ROI

Recall Semantic Caching from Module 7. Every time you serve a response from your Redis cache instead of calling Bedrock:

• Cost: $0.00
• Latency: < 10ms
• Result: You have achieved infinite ROI for that specific request.

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Knowledge Check: Test Your Cost Optimization Knowledge

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Summary

Cost optimization is an active engineering task. By pruning tokens, routing carefully, and caching repeatedly, you turn a "Costly" project into a "Profitable" one. In the next lesson, we move to the other side of the coin: Improving Latency and Throughput.

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Next Lesson: The Need for Speed: Improving Latency and Throughput