If a metric has multiple dimensions, how does it affect DDU consumption?

When a metric has multiple dimensions, it increases Dynatrace Data Usage (DDU) consumption. This is because each unique combination of dimensions represents a distinct data point that Dynatrace needs to monitor and store. For example, if you have a metric measuring response times and it is categorized by both application and user region, each combination of application and region results in a separate entry in Dynatrace's data model.

As the number of dimensions increases, the number of unique data combinations grows exponentially. This leads to a higher volume of metrics being captured and reported, which in turn results in increased DDU consumption. Monitoring all these variations requires more data storage and processing power, leading to potential additional costs or limits on data usage depending on the pricing model being used. Therefore, understanding the impact of multidimensional metrics on DDU is crucial for efficient management and optimization of Dynatrace resources.