# StatsD vs Prometheus: Qual Abordagem de Métricas?

> StatsD vs Prometheus: um protocolo de métricas push-based simples vs um sistema de monitoramento pull-based dimensional. Modelo, cardinalidade e ajuste a CI comparados.

Source: https://latchkey.dev/pt/learn/tool-comparisons/statsd-vs-prometheus  
Updated: 2026-06-26

StatsD é um protocolo de métricas push-based simples com um pequeno daemon agregador; Prometheus é um sistema de monitoramento pull-based e dimensional com uma linguagem de consulta rica.

StatsD deixa as aplicações dispararem counters e timers via UDP para um daemon que agrega e encaminha, valorizando a simplicidade sobre a dimensionalidade. Prometheus faz scraping de métricas rotuladas e oferece PromQL, alerting e um grande ecossistema. StatsD vence em simplicidade e instrumentação fácil de app; Prometheus vence em consulta dimensional, alerting e ecossistema.

## Comparison

|  | StatsD | Prometheus |
| --- | --- | --- |
| Modelo | Push (UDP) | Pull (scrape) |
| Dimensões | Limitadas (tags variam) | Labels de primeira classe |
| Consulta | Via store downstream | PromQL |
| Alerting | Externo | Embutido |
| Melhor para | Métricas simples de app | Monitoramento dimensional |

## Caso de uso e modelo

StatsD serve à instrumentação rápida de aplicações, onde os apps enviam métricas simples sem expor um endpoint de scrape. Prometheus serve ao monitoramento dimensional com labels, consultas poderosas e alerting embutido. Um statsd_exporter pode fazer a ponte das métricas StatsD para o Prometheus.

## Ajuste a ops e CI

StatsD é trivialmente leve; Prometheus é um sistema mais completo, mas ainda simples de operar. Ambos são exercitados no CI contra instâncias efêmeras, onde runners gerenciados mais rápidos encurtam os testes de integração de pipelines de métricas.

## Decide with your own numbers, not a feature table

Feature comparisons age badly and rarely decide anything, because both tools in a mature category can do the job. What differs is how each behaves on your repository, and that takes one afternoon to measure.

```Terminal
# time a cold install with each candidate, cache cleared
hyperfine --prepare "rm -rf node_modules" --warmup 1 \
  "<tool-a> install" "<tool-b> install"

# and the thing CI actually pays for: a cold run with no local cache
docker run --rm -v "$(pwd):/w" -w /w node:22 sh -c "<tool> install"
```

> Measure the cold path. Warm local benchmarks favour whichever tool you already have cached, which is exactly the condition a CI runner never has.

## What actually changes when you switch

- Lockfile format. A switch is a one-way door for anyone still on the old tool until everyone migrates, so plan it as a single coordinated change.
- Resolution strictness. Tools differ on whether an undeclared transitive import works, and the stricter one will surface latent bugs as new failures.
- CI cache configuration. The cache path and key differ per tool; carrying over the old ones silently disables caching.
- Everyone on the team and every runner must move together. Pin the version so they cannot drift.

## O veredito

Quer instrumentação push extremamente simples: StatsD. Quer métricas dimensionais, PromQL e alerting embutido: Prometheus. Muitos fazem a ponte do StatsD para o Prometheus via exporter para ter o melhor dos dois.

## FAQ

### StatsD vs Prometheus: Which Metrics Approach?

StatsD lets applications fire-and-forget counters and timers over UDP to a daemon that aggregates and forwards them, valuing simplicity over dimensionality. Prometheus scrapes labeled metrics and offers PromQL, alerting, and a large ecosystem.

### Use case and model?

StatsD suits quick application instrumentation where apps push simple metrics without exposing a scrape endpoint. Prometheus suits dimensional monitoring with labels, powerful queries, and built-in alerting. A statsd_exporter can bridge StatsD metrics into Prometheus.

### Ops and CI fit?

StatsD is trivially light; Prometheus is a fuller system but still simple to run. Both are exercised in CI against ephemeral instances, where faster managed runners shorten integration tests of metric pipelines.

### Which should I choose?

Want dead-simple push instrumentation: StatsD. Want dimensional metrics, PromQL, and built-in alerting: Prometheus. Many bridge StatsD into Prometheus via the exporter for the best of both.

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