Log Aggregation Cost Explosion from Unstructured Debug Logging in Production

Centralized logging costs (Datadog, Splunk, Elastic Cloud) spiral to $50K-$500K/year because microservices emit verbose unstructured debug logs in production, with no per-service log budgets or automatic sampling, and nobody owns the decision of what log level is appropriate for production. So what? When finance flags the logging bill, platform teams impose blunt log volume caps that force application teams to reduce logging indiscriminately, removing useful diagnostic logs alongside the noise. So what? Reduced logging means that when production incidents occur, engineers lack the log context needed to diagnose root causes, extending mean-time-to-resolution (MTTR) from minutes to hours. So what? Longer MTTR increases customer impact per incident, triggers SLA penalties, and creates pressure to add more logging 'just in case,' reigniting the cost spiral. So what? The oscillation between 'too much logging' and 'not enough logging' consumes platform engineering bandwidth in perpetual log infrastructure tuning instead of building features that improve developer productivity. So what? Platform teams become bottlenecks and gatekeepers rather than enablers, creating organizational friction between platform and product engineering that slows down the entire company. The structural root cause is that logging libraries default to 'log everything at debug level' in production because developers set log levels during local development and never adjust them for production, and there is no feedback mechanism that connects log volume to cost at the service-owner level.