Written by Nelson Koning
on 

SLO-driven monitoring with Prometheus metrics and Grafana dashboards

Keeping an eye on raw metrics is like listening to every instrument individually at rehearsal — useful, but it doesn’t tell you whether the song works together. Service Level Objectives (SLOs) give you the chorus: a simple, customer-focused statement of what “good” looks like. When you build SLO-aware monitoring with Prometheus and Grafana, you trade noisy signal-chasing for clear, business-relevant feedback — dashboards that answer “are we meeting expectations?” and alerts that tell you when the error budget is being spent too quickly.

This article walks through the practical pieces you’ll assemble — Prometheus recording rules to compute SLIs, Grafana’s SLO tooling and dashboards, and the scalable backends (like Grafana Mimir) you’ll lean on as your telemetry grows — with common patterns and pitfalls to watch for.

Why SLOs change the monitoring beat

SLOs shift the focus from “metric crossed a threshold” to “are we using our error budget too fast?” — that’s the difference between a choir singing in tune (healthy system) and a panic when a single mic clips.

Prometheus: compute SLIs affordably with recording rules Prometheus is ideal for turning raw counters and histograms into the SLIs you care about, but doing the raw PromQL repeatedly — from dashboards and alerts — is expensive and brittle. Recording rules let you precompute common PromQL expressions and store them as new series, which speeds dashboards and makes alerts deterministic. Recording rules are a standard practice in Prometheus for exactly this use case. (prometheus.io)

A simple availability SLI as a recording rule looks like this:

groups:
- name: slos
  interval: 60s
  rules:
  - record: job:sli_success_rate:ratio_rate5m
    expr: |
      (
        sum(rate(http_requests_total{job="myapp", status=~"2.."}[5m]))
        /
        sum(rate(http_requests_total{job="myapp"}[5m]))
      ) * 100

That rule computes a per-job 5‑minute success-rate percentage that you can query from Grafana or use in alerting without recomputing the raw expression each time.

Grafana SLOs and dashboards: visualizing error budgets, not just metrics Grafana has leaned into SLO workflows: the Grafana SLO tooling (plugin and Cloud features) creates, visualizes, and reports on SLOs and can generate the recording rules and alerts needed to back them. In practice, a single SLO will result in multiple recording rules and time series (Grafana documents the cadence and series impact), so it’s important to understand how many series your SLOs create and how frequently they’re evaluated. Grafana also added SLO Reports to help teams share periodic summaries. (grafana.com)

Common dashboard patterns:

Scaling metrics and SLO data: why you might need Mimir (or similar) SLO tooling increases the number of recorded series: each SLO can create 10–12 recording rules and produce one data point per minute (or more, depending on configuration). That’s fine for a single service, but at platform scale it multiplies quickly. For long-term retention, high cardinality, and multi-tenant environments, a horizontally scalable metrics backend becomes necessary.

Grafana Mimir is an open-source, horizontally scalable metrics backend designed as long-term storage and a multi-tenant backend for Prometheus remote_write. If you expect many SLOs, lots of labels, or long retention windows, sending metrics and recording-rule output to a scalable backend like Mimir (or Thanos in other setups) is a common pattern. Grafana provides migration guidance and Mimir’s project docs describe this long-term storage model. (github.com)

Tools and examples that make SLOs manageable Creating correct recording rules and multi-window alerting by hand is repetitive and error-prone. Community tools (for example, Pyrra) and company case studies show practical ways teams automate rule generation and adopt SLOs at scale. Pyrra can generate recording and alerting rules that follow SRE patterns (multi-window burn rates, backoff, etc.), and there are published case studies showing how large teams combine Prometheus, Grafana, Loki, and tooling to operate SLOs in production. These resources are helpful when you’re trying to move from theoretical SLOs to running ones. (github.com)

Pitfalls and patterns (a few practical beats)

Label hygiene and naming conventions Treat recording-rule names and labels like published APIs: use a consistent naming convention (level:metric:operation) and keep labels predictable. Good naming helps your teammates (and future you) find the right series when authoring dashboards or diagnosing incidents. Prometheus documentation has guidance on naming recording rules that’s worth following as a habit. (prometheus.io)

A brief orchestration note In Kubernetes environments, many teams run a lightweight Prometheus or Grafana Agent at cluster-level and forward via remote_write to a central store (Mimir or another long-term system). That lets you keep short-lived scraping local while centralizing retention and cross-cluster queries. Remember that the more SLOs you create, the more pressure you put on both the evaluation layer and remote storage — planning is part of the cost equation.

Closing chord SLO-driven monitoring with Prometheus and Grafana moves your team from chasing noisy thresholds to managing customer-facing reliability. Recording rules turn raw PromQL into reusable SLIs, Grafana’s SLO tooling helps visualize and report on error budgets, and scalable backends like Mimir let that approach work at platform scale. Like tightening a band’s tempo, SLOs give everyone a shared beat to follow — clearer, calmer, and more useful than chasing individual counters.

Sources and further reading