Why AI Teams Must Set Time Budgets, Not Just Token Limits

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Engineering teams relying solely on token-usage dashboards to manage AI workloads risk missing a critical second constraint: wall-clock time. Unlike token counts, deadlines tied to human waiting, deploys, or customer tickets cannot be deferred without real cost. The article argues that free model capacity should be treated as opportunistic, suitable only for jobs that can be safely aborted if time runs out. A proposed control loop recommends stamping jobs at intended start time, separating queue-wait from generation time, and aborting at either threshold. Logging both token and calendar clocks together is presented as the baseline for making data-driven decisions rather than relying on assumptions.

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Framework Helps Product Teams Decide Whether and How to Build MCP Apps by 2026

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ProgrammingDEV Community ·

How One Educator Uses Generative AI for Lesson Prep Without Outsourcing Judgment

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ProgrammingDEV Community ·

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ProgrammingDEV Community ·

How to Import Excel Data into SQL Server Using the SSMS Import Wizard

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