# Building a Custom Metrics Exporter for Kubernetes

DevFeed: [Building a Custom Metrics Exporter for Kubernetes](<https://devfeed.tech/articles/building-a-custom-metrics-exporter-for-kubernetes-4567.md>)

Original publisher: [Read original article](<https://kubernetes.io/blog/2026/07/14/custom-metrics-exporter-kubernetes/>)

Author: Victor David Effiok

Published: 2026-07-14T18:00:00Z

Content type: tutorial

Language: en

Sources: [Kubernetes Blog](<https://devfeed.tech/sources/kubernetes-blog.md>)

Topics: [Kubernetes](<https://devfeed.tech/topics/kubernetes.md>), [autoscaling](<https://devfeed.tech/topics/autoscaling.md>), [Grafana Cloud Metrics](<https://devfeed.tech/topics/grafana-cloud-metrics.md>), [Time Series](<https://devfeed.tech/topics/time-series.md>), [Go Language](<https://devfeed.tech/topics/go-language.md>), [WebSocket](<https://devfeed.tech/topics/websocket.md>)

Tags: [autoscaling](<https://devfeed.tech/tags/autoscaling.md>), [building](<https://devfeed.tech/tags/building.md>), [code](<https://devfeed.tech/tags/code.md>), [go](<https://devfeed.tech/tags/go.md>), [http](<https://devfeed.tech/tags/http.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [prometheus](<https://devfeed.tech/tags/prometheus.md>), [server](<https://devfeed.tech/tags/server.md>), [time-series](<https://devfeed.tech/tags/time-series.md>)

## AI overview

A tutorial on building a custom Kubernetes metrics exporter that exposes application or external state for Prometheus, enabling queries, alerts, and HorizontalPodAutoscaler scaling. It covers exporter architecture, direct instrumentation, metric formatting, and choosing counters, gauges, or histograms.

## Source excerpt

Kubernetes ships with built-in awareness of CPU and memory, but most real-world scaling decisions depend on signals that live entirely outside that narrow window: how many messages are waiting in a queue, how long the last batch job took, how many active WebSocket connections a pod is holding. When the built-in metrics are not enough, a metrics exporter bridges that gap. This post walks through writing one from scratch, packaging it as a container, and wiring it into a cluster so that Prometheus -- and ultimately the HorizontalPodAutoscaler -- can consume it. What a metrics exporter actually does An exporter is a small HTTP server with a single responsibility: expose application state as text on a /metrics endpoint. Prometheus scrapes that endpoint on a regular interval, stores the time-series data, and makes it available for queries, alerts, and autoscaling rules. In some cases you can instrument your application directly -- embedding the Prometheus client library and exposing /metrics from within the same process -- rather than running a separate exporter. A standalone exporter makes more sense when the data source is external to your application or when you do not control the application code. The format Prometheus expects is plain text -- one metric per line, with a name, optional labels, and a numeric value. Client libraries handle the serialization for you, so in practice you only need to decide what to measure and call the right function when that value changes. Choosing what to measure Before writing any code, it helps to decide what kind of signal you are dealing with. The Prometheus data model has three main types: Counters only ever increase. They are the right tool for totals: requests served, jobs processed, errors encountered. Never use a counter for a value that can go down. Gauges represent a current snapshot of a value that can rise and fall freely. Queue depth, active connections, and cache size are all gauges. Histograms record the distribution of obse