Prometheus vs Grafana: Understanding Your Monitoring Stack

Prometheus and Grafana appear together so consistently in infrastructure job descriptions and architecture diagrams that developers sometimes treat them as interchangeable. They are not. They solve different problems at different layers of the observability stack — and understanding what each does independently helps you understand why they are almost always used together. **What Is Prometheus?** Prometheus, released by SoundCloud in 2012 and donated to the Cloud Native Computing Foundation in 2016, is an open-source time-series metrics collection and storage system. Its job is to gather numerical measurements from your infrastructure and applications over time and make them queryable. Prometheus uses a pull-based model: at regular intervals (typically every 15-30 seconds), Prometheus scrapes metrics from HTTP endpoints exposed by your services. These endpoints return metrics in Prometheus' text format — key-value pairs with optional labels and numerical values. Every major infrastructure component has a Prometheus exporter: node_exporter for Linux host metrics, kube-state-metrics for Kubernetes, postgres_exporter for PostgreSQL, and hundreds more. The metrics Prometheus collects are stored as time-series data in its local TSDB (time-series database). These time-series can be queried using PromQL, Prometheus' functional query language. PromQL can aggregate across labels (average CPU usage across all application pods), compute rates (HTTP requests per second), and apply mathematical functions to derive meaningful metrics from raw counters. Prometheus includes Alertmanager, a separate component that receives alert rules defined in PromQL, evaluates them continuously, and sends notifications when conditions are met — via email, Slack, PagerDuty, or custom webhooks. Prometheus' primary limitation is long-term storage. Its local TSDB is designed for recent data (typically 15-30 days before disk fills). For longer retention, teams use Thanos or Cortex to write Prometheus data to object storage (S3, GCS) for long-term retention and global querying across multiple Prometheus instances. **What Is Grafana?** Grafana, released in 2014 by Torkel Ödegaard, is a data visualisation and dashboarding platform. Grafana itself stores nothing. Its job is to connect to data sources, run queries, and render the results as charts, graphs, heatmaps, tables, and other visualisations in interactive dashboards. Grafana's data source ecosystem is its defining feature. It connects natively to: Prometheus, InfluxDB, Elasticsearch, Loki (Grafana's own log aggregation system), Tempo (distributed traces), CloudWatch, Azure Monitor, Google Cloud Monitoring, Datadog, and dozens more. This means a single Grafana instance can visualise metrics, logs, and traces from heterogeneous infrastructure — a database query alongside a Prometheus metric alongside a CloudWatch namespace, in one dashboard. The Grafana dashboard builder allows you to create panels with queries specific to each data source. Prometheus panels use PromQL. Elasticsearch panels use Lucene. CloudWatch panels use the CloudWatch metrics browser. Once built, dashboards can be shared, exported as JSON, and versioned. Grafana also provides alerting — evaluating dashboard queries against thresholds and routing notifications. In practice, most teams use Prometheus Alertmanager for metric-based alerting and Grafana Alerting for alerts that require correlating across multiple data sources. **Key Differences** | Dimension | Prometheus | Grafana | |---|---|---| | Primary function | Collect, store, and query metrics | Visualise data from any source | | Data storage | Yes — time-series database | No — connects to external sources | | Query language | PromQL | Varies by data source | | Data sources supported | Self only | 50+ data sources | | Metrics | Yes | Via connected sources | | Logs | No | Via Loki or Elasticsearch | | Traces | No | Via Tempo or Jaeger | | Alerting | Alertmanager (metrics-focused) | Multi-source alerting | | Dashboards | Basic expression browser | Rich, customisable dashboards | | User interface | Functional, developer-focused | Polished, stakeholder-ready | | Cloud managed | Grafana Cloud (as remote_write target) | Grafana Cloud | **The Observability Stack Architecture** The standard production monitoring stack works like this: Prometheus scrapes metrics from your services every 30 seconds and stores them locally. Grafana connects to Prometheus as a data source and queries it to populate dashboards. When Prometheus evaluates an alert rule and the condition is met, Alertmanager sends a notification to your on-call system. This division of responsibility is clean: Prometheus owns the data collection and storage layer; Grafana owns the presentation layer. Neither tries to do the other's job. **Beyond Metrics: The LGTM Stack** Grafana Labs (the company behind Grafana) has expanded into a complete observability platform called the LGTM stack: Loki for logs, Grafana for visualisation, Tempo for distributed traces, and Mimir (or Thanos) for long-term Prometheus metric storage. Teams adopting the LGTM stack get unified observability — metrics, logs, and traces in a single Grafana interface — with each component designed to work together. Grafana can correlate a spike in error rate (Prometheus metric) with the corresponding log entries (Loki) and the distributed trace (Tempo) for a single failing request. **When You Might Choose One Without the Other** It is possible to use Prometheus without Grafana: the Prometheus expression browser provides basic query and graphing capability for debugging. Most platform teams do this initially before building out Grafana dashboards. It is also possible to use Grafana without Prometheus: teams already using CloudWatch, Datadog, or InfluxDB can adopt Grafana as a dashboarding layer on top of their existing metrics store, consolidating their visualisation without changing their data collection approach. But for new infrastructure monitoring setups, the Prometheus + Grafana combination is the established standard for a reason — they are genuinely complementary tools that cover the full lifecycle from data collection to visualisation. **The Verdict** Use both. Prometheus for metrics collection, storage, and alerting; Grafana for dashboards and visualisation. The combination is the industry standard for a reason and the operational overhead of running both is low, particularly with managed offerings like Grafana Cloud that handle both components. If you are evaluating observability platforms in 2026, also consider whether the full LGTM stack (adding Loki for logs and Tempo for traces) fits your needs. Unified observability — seeing metrics, logs, and traces together — is significantly more powerful than metrics alone, and the Grafana ecosystem makes this achievable without vendor lock-in. The choice is not Prometheus versus Grafana. The choice is: how much of the observability stack do you want to own, and which managed service is right for the parts you do not?