# Why AI Cost Monitoring Should Be Built Into Product Analytics AI features can look inexpensive during testing. A small team makes a few hundred requests. Production traffic can be very different. Thousands of users may generate long prompts, repeated retries and large outputs. For a Malaysia-focused platform such as **<a href="https://www.youtube.com/@winmyr_mi">WINMYR</a>**, AI cost should therefore be monitored alongside normal product metrics. Cost per request is useful. Cost per completed task is better. A cheap model may produce poor results that require several retries. A more expensive model might complete the task once. Caching can also change economics. Repeated system instructions or reference material may not need to be processed at full cost every time if the provider supports caching. Teams should also track output length. Long answers consume more tokens and may not improve user value. For **WINMYR**, the broader lesson is that AI efficiency is a product question, not only a finance question. Usage, quality and cost need to be viewed together. A successful AI feature is not simply the one with the lowest model price. It is the one that produces useful outcomes at a cost the product can sustain.