A production deployment of Parseable handles over 100 million distinct time series generated by high-cardinality labels across resource metrics. Traditional time series databases struggle with this scale because they index and manage every series identity separately, requiring expensive operations to scan all matching series before aggregating results, even when only a small output is needed.