An AI model provides token usage data with each response. A developer must capture this data to track actual costs. Ignoring this data leads to inaccurate cost estimates.
By Pawan Nayar · from the LLOS AI Learning record
An AI model provides token usage data with each response.
A developer must capture this data to track actual costs. Ignoring this data leads to inaccurate cost estimates.
An AI model's response includes a 'usageMetadata' block. This block shows the number of tokens used for the prompt and the response. A developer should read this data from every AI model call. This ensures accurate cost tracking. Most server code often ignores this important data. This means cost figures are often guesses. The real cost data is freely available in the response.
A developer builds an application that uses an AI model. The developer estimates the cost based on average token usage. After 16,000 calls, the actual cost is much higher than the estimate. The developer did not capture the 'usageMetadata' from each response. This led to a large difference between the estimated and actual spending.
Resolved from this lesson's own record — real definitions, not invented for this page.
More from the same record, pulled by shared topic — not written new for this page.
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An AI model provides token usage data with each response. A developer must capture this data to track actual costs. Ignoring this data leads to inaccurate cost estimates.