Monitoring › Cost Monitoring

Cost is a dimension of production health that is easy to overlook until a surprise bill arrives. Language models charge by the token, and token consumption can vary by orders of magnitude depending on user behavior. A simple cost monitoring strategy tracks three things: total token expenditure per day, the distribution of tokens per request, and the fraction of total cost contributed by each user segment or use case.

Total daily expenditure is the headline metric. Set a budget alert at 150% of your expected daily spend. If costs spike above that threshold, investigate immediately: it might indicate a loop in your application code that is calling the model in a tight loop, an abuse pattern where a user is submitting very large inputs, or legitimate usage growth that requires a capacity planning conversation.

The distribution of tokens per request reveals whether high costs are driven by many average-cost requests or by a few very expensive outliers. If a small fraction of requests (say the top 1%) accounts for 30-40% of total cost, you may want to add token limits or routing rules to protect against the most expensive inputs. This is a business decision as much as a technical one: very long-context requests might be your highest-value users, or they might be abusers circumventing rate limits.

Cost attribution by use case or user segment is important in multi-tenant or multi-feature applications. If your product has a document summarization feature and a chat feature, attributing costs separately lets you understand which feature is economically viable and which is a cost center. This is also essential for accurate product pricing: if you are charging users a flat fee, you need to know whether your cost per user is consistent across the user population or whether a small segment of heavy users is driving costs that cannot be covered by the flat fee.