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AIF-C01 Practice Question 3: Token Based Inference Cost

AWS Certified AI Practitioner (AIF-C01) · Test 1 · question 3 of 10

Fundamentals of GenAIeasy

A team runs a customer-support assistant on Amazon Bedrock using on-demand inference with a text model. After they added a 3,000-word policy document to the prompt of every request, their monthly bill rose sharply even though the number of requests stayed the same. What explains the increase?

Select one answer
Show answer and explanation

Answer:

  • A. On-demand text generation is billed by the number of input and output tokens, so a longer prompt costs more on every request

Models process text as tokens, which are words or pieces of words. With on-demand inference, text models are priced by the input tokens they read plus the output tokens they generate. A 3,000-word document adds thousands of input tokens to *every* call, so cost grows even though the request count doesn't change (option A). Longer prompts can also add latency. That's why teams trim context, or retrieve only the relevant passages instead of the whole document.

- B is wrong because on-demand text pricing doesn't split long prompts into multiple billable requests. Tokens drive the charge. - C is wrong because hourly, reserved capacity is a different purchasing option (Provisioned Throughput). On-demand has no instances for the customer to scale. - D is wrong because Bedrock doesn't charge per user for model inference.

Reference: Amazon Bedrock pricing

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