Prometheus vs Grafana: Metrics Collection vs Visualization

Mon, 03 Aug 2026 05:30:34 +0900

Overview

Prometheus is a time-series database that scrapes and stores metrics with its own query language and alerting engine, while Grafana is a visualization layer that queries Prometheus (and many other backends) to render dashboards. They’re often deployed together, not as alternatives, because each solves a different half of the observability pipeline.

Comparison Diagram

Comparison Table

Aspect	Prometheus	Grafana
Primary purpose	Collect, store, and query time-series metrics	Visualize and correlate data from one or more sources
Data collection	Pull-based scraping of HTTP /metrics endpoints on a schedule	None — has no collector, relies entirely on configured data sources
Data storage	Built-in on-disk time-series database (TSDB)	Stateless; stores only dashboard/config metadata, not metric data
Query language	PromQL, native to its own TSDB	Delegates to whatever query language the connected data source uses
Supported data sources	Only its own TSDB (federation aside)	150+ backends including Prometheus, Loki, InfluxDB, Elasticsearch, SQL
Alerting	Native alerting rules evaluated in-server, routed via Alertmanager	Unified alerting engine that layers on top of any connected data source
Visualization	Minimal built-in expression browser, no dashboarding	Rich, customizable panels, graphs, and shareable dashboards
Typical deployment	Runs per cluster/environment alongside exporters	Central server pointing at multiple backends across teams

Key Differences

Prometheus owns the TSDB; Grafana holds no metric data of its own.
Prometheus scrapes targets directly; Grafana only issues read queries.
Prometheus alerting runs server-side via Alertmanager; Grafana’s alerting spans any connected source.
Grafana supports multi-source dashboards, unlike Prometheus’s single-source expression browser.
Neither replaces the other — most stacks run both together.

When to Use Each

Prometheus

Monitoring vs Observability: Knowing Something's Wrong vs Knowing Why

Sun, 02 Aug 2026 08:34:10 +0900

Overview

Monitoring watches a predefined set of metrics, logs, and checks against known failure modes and alerts you when thresholds are breached. Observability is a property of a system built so that its internal state can be inferred from its external outputs, letting you investigate questions you didn’t think to ask in advance. The distinction matters because monitoring answers ‘is something wrong?’ while observability answers ‘why is it wrong?’ for failures you’ve never seen before.

Observability on IT Comparison

Prometheus vs Grafana: Metrics Collection vs Visualization

Overview

Comparison Diagram

Comparison Table

Key Differences

When to Use Each

Monitoring vs Observability: Knowing Something's Wrong vs Knowing Why

Overview