Prometheus vs Grafana: Metrics Collection vs Visualization
Prometheus vs Grafana 2026 — what each tool does, how they work together, pricing for self-hosted vs Grafana Cloud, and when to use alternatives like Datadog.
Quick Answer
Prometheus and Grafana are not competitors — they are complements. Prometheus scrapes and stores time-series metrics; Grafana visualizes them. The real choice is Prometheus + Grafana OSS (self-hosted, free) vs Grafana Cloud (managed, includes Mimir for long-term storage) vs a competing stack like Datadog that replaces both.
Prometheus vs Grafana: Overview
Kubernetes metrics, infrastructure monitoring, custom application metrics via /metrics endpoint
Open-source (Apache 2.0), free forever; 15-day default local retention
Self-hosted free; Grafana Cloud managed Prometheus from $8/month (50K active series)
Unified dashboards across Prometheus, Loki, Tempo, Elasticsearch, and 150+ other datasources
OSS: open-source (AGPL 3.0), free forever; Grafana Cloud: 10K active series free
Grafana Cloud Pro: $8/month (50K series, 50GB logs); Enterprise: custom
Prometheus vs Grafana: Feature Comparison
| Feature | Prometheus | Grafana |
|---|---|---|
| Primary function | Metrics collection + storage + alerting | Visualization + unified alerting |
| Default retention | 15 days (local TSDB) | 13 months (Grafana Cloud Mimir) |
| Datasource support | Self only (Prometheus TSDB) | 150+ plugins (Prometheus, Loki, MySQL, etc.) |
| Query language | PromQL (metrics-only) | PromQL + LogQL + TraceQL + SQL |
| Self-hosted complexity | Low (single binary) | Low (single binary + datasource config) |
| HA/clustering | Requires Thanos/Mimir/Cortex | Grafana Cloud handles HA automatically |
Pros & Cons
Prometheus
Pros
- Pull model: Prometheus scrapes /metrics endpoints every 15s — no agent push config, automatic service discovery
- PromQL: expressive query language for rate(), histogram_quantile(), and multi-dimensional label filtering
- Kubernetes-native: kube-state-metrics + node-exporter give full cluster visibility with 2 YAML files
- 10,000+ exporters: community exporters for PostgreSQL, Redis, Nginx, AWS, GCP — no gaps in coverage
- Alertmanager: deduplication, grouping, inhibition, and routing to Slack/PagerDuty built into the ecosystem
Cons
- Local storage only: default TSDB is not clustered — data lives on one node; remote_write needed for HA/long-term
- 15-day default retention: scaling to 1-year retention requires Thanos, Cortex, or Mimir — adds operational complexity
- No built-in dashboards: Prometheus UI shows only raw PromQL results — Grafana is required for any visualization
- High-cardinality risk: labels with unbounded values (user IDs, trace IDs) can OOM Prometheus — requires discipline
Grafana
Pros
- 150+ datasource plugins: one dashboard can combine Prometheus metrics, Loki logs, Tempo traces, MySQL tables
- Alerting unified: Grafana 10 alerts fire from any datasource — not just Prometheus — with a single rule engine
- Grafana Cloud: fully managed stack (Prometheus/Mimir + Loki + Tempo) — zero infrastructure to run
- Dashboard as code: Grafonnet (Jsonnet library) and Terraform provider enable GitOps for dashboards
- Explore: ad-hoc PromQL/LogQL/TraceQL queries with correlated log-to-trace-to-metric navigation in one UI
Cons
- Grafana is a viewer, not a collector: without Prometheus (or another datasource) it has no data of its own
- Enterprise features locked: role-based access, audit logs, and data source permissions require Grafana Enterprise ($)
- Dashboard sprawl: without governance, teams create 500+ dashboards that go stale — no built-in lifecycle management
- Grafana Cloud egress: sending metrics from self-hosted infra to Grafana Cloud adds ~10–15ms write latency
Our Verdict: Prometheus vs Grafana
Prometheus and Grafana are the canonical observability pair — use both. Prometheus handles metrics collection with its pull model and PromQL; Grafana handles visualization and unified alerting across all your datasources. The real decision is self-hosted (Prometheus + Grafana OSS, free but you manage HA) vs Grafana Cloud (managed, from $8/month, includes Mimir for long-term storage). Use self-hosted if you have Kubernetes ops experience; use Grafana Cloud if you want managed infra. Consider Datadog if you need APM, RUM, and logs in a single commercial product with a support SLA.
Prometheus vs Grafana — FAQs
Do I need both Prometheus and Grafana, or can I use just one?
You need both for a functional monitoring stack. Prometheus collects and stores metrics — without it, Grafana has nothing to display. Grafana visualizes data — without it, Prometheus only offers a basic expression browser. The only exception is Grafana Cloud, which bundles a managed Prometheus (Mimir) so you configure a single remote_write endpoint and get Grafana dashboards without running Prometheus yourself. Alternatively, Datadog or New Relic replace both with a commercial all-in-one.
What is the difference between Prometheus and Grafana Mimir?
Prometheus is the local, single-node TSDB with a 15-day default retention limit and no built-in clustering. Grafana Mimir is a horizontally scalable, multi-tenant Prometheus-compatible backend built for long-term storage (months to years). Mimir accepts remote_write from Prometheus, stores data in object storage (S3/GCS), and serves PromQL queries at scale. For teams outgrowing local Prometheus storage, Mimir (or Thanos/Cortex) is the upgrade path. Grafana Cloud uses Mimir as its managed Prometheus backend.
How much does a Prometheus + Grafana self-hosted stack cost vs Grafana Cloud?
Self-hosted Prometheus + Grafana OSS is free software, but you pay for the infrastructure. A typical small production setup (1 Prometheus, 1 Grafana, 1 Alertmanager) runs on a $20–40/month VPS. For HA with Thanos and 6-month retention, expect $80–150/month in infrastructure. Grafana Cloud free tier covers 10K active series and 50GB logs — enough for small teams. Grafana Cloud Pro at $8/month for 50K active series is often cheaper than running equivalent self-hosted HA infrastructure once you factor in engineering time.
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