The Prometheus instrumentation libraries offer four core metric types. With the exception of native histograms, these are currently only differentiated in the API of instrumentation libraries and in the exposition protocols. The Prometheus server does not yet make use of the type information and flattens all types except native histograms into untyped time series of floating point values.

They are used when the buckets of a metric are not known beforehand, but it is highly recommended to use histograms over summaries whenever possible. In this tutorial, we covered the types of metrics in detail and a few PromQL operations like rate, histogram_quantile, etc.

November 19, 2025 - Prometheus gives you a powerful foundation for understanding your system through metrics - counters for tracking events, gauges for real-time state, histograms for latency distributions, and summaries for client-side quantiles.

June 17, 2026 - Prometheus metric types explained: counters, gauges, histograms, and summaries, with PromQL query patterns, cardinality traps to avoid, and guidance on when to use each.

February 21, 2025 - Metrics come in different types: counters that only increase, gauges that fluctuate, histograms that show value distributions, and summaries that pre-calculate statistics.

September 25, 2020 - See also the best practices documentation about histogram and summary metrics to learn more about the tradeoffs of histograms and summaries, and how to choose histogram bucket boundaries correctly.

July 2, 2026 - The 4 types of Prometheus metrics explained: counter, gauge, histogram, and summary, when to use each, and how to pick the right one.

March 3, 2024 - There are four primary Prometheus metric types PromQL can query: counters, gauges, histograms, and summaries.

The Prometheus Java metrics library implements the metric types defined in the OpenMetrics standard: Counter Gauge Histogram Custom Bucket Boundaries Native Histograms with Custom Buckets (NHCB) Summary Info StateSet GaugeHistogram and Unknown Counter Counter is the most common and useful metric ...

December 10, 2025 - Our hope is that after reading ... collected by Prometheus as part of its exposition format include Counters, Gauges, Histograms, and Summaries....

October 14, 2024 - In exploring Prometheus metrics, we will examine the four primary types: gauges, counters, summaries, and histograms. Each metric serves a distinct purpose in monitoring applications, and understanding their functionalities is crucial for effective ...

July 23, 2025 - They indicate when and where problems have occurred or are occurring. Prometheus gathered four different kinds of metrics as part of its exposition format: counters, gauges, histograms, and summaries.

July 21, 2025 - Prometheus offers four metric types.

February 6, 2025 - Understanding Counter, Gauge, Histogram, and Summary metrics is essential for designing effective monitoring strategies with Prometheus. Each metric type serves a unique purpose:

April 2, 2025 - There are four primary Prometheus metric types PromQL can query: counters, gauges, histograms, and summary metrics. These metric types fulfill the needs or requirements for most use cases, and are found in Prometheus’ official client libraries: ...

March 20, 2025 - By choosing the right metric type, you ensure that your system monitoring delivers accurate and insightful data. Counters capture the totality of an event, continuously incrementing over time. Gauges reflect the instantaneous state, tracking values that fluctuate. Together, they offer a complete view of your application's performance, health, and system behaviour. Atatus supports Prometheus metrics ingestion through Prometheus Remote Write, allowing you to seamlessly forward metrics for advanced visualization and alerting.

November 1, 2022 - On 2022-11-01 12:09, Prashant Singh wrote: > Currently prometheus metrics types include counter, gauge, histogram, > and summary. They are all numeric types.

The table below summarizes the differences between the two metric types: ... You need to aggregate quantiles across multiple instances. You want the flexibility to calculate different quantiles or use different time windows later on. You are monitoring system-wide Service Level Objectives (SLOs). ... You are monitoring a single instance or service. You need high precision for specific quantiles with low server-side overhead. Aggregation is not required or practical. ... Prometheus is a powerful monitoring tool, but it can struggle to keep up as your systems grow.

December 2, 2019 - The histogram metric type measures the frequency of value observations that fall into specific predefined buckets. For example, you could measure request duration for a specific HTTP request call using histograms. Rather than storing every duration for every request, Prometheus will make an approximation by storing the frequency of requests that fall into particular buckets.

Information about the Prometheus metric types supported by CloudWatch.