Histograms versus summaries

Summaries calculate pre-configured quantiles over sliding windows inside the instrumented program: accurate and fast to query, but the quantiles cannot be recalculated later or aggregated across instances, and each observation is relatively expensive due to streaming quantile calculation. Histograms count observations in buckets — cheap to observe — and quantiles are computed later on the server with histogram_quantile(), so they aggregate freely. Both track count and sum of observations, giving averages as rate of sum over rate of count. Classic histograms and summaries expose separate _count, _sum, and per-quantile or per-bucket (_bucket with the le label) series, and every configured classic bucket creates a series even when unpopulated. Native histograms instead store count, sum, and a dynamic number of exponential buckets in a single composite series; you configure a resolution, not bucket boundaries. Native histogram support is still rare and requires protobuf exposition, though the server can ingest classic histograms as NHCBs (native histograms with custom bucket boundaries), which store more efficiently while keeping the same PromQL handling. Scope: Prometheus. Reviewed October 4, 2026.