Prometheus vs Grafana: Key Differences and How They Work Together

The Core Distinction: Monitoring vs. Visualization

Prometheus and Grafana are often mentioned in the same breath, but they serve fundamentally different roles within the observability stack. Prometheus is a time-series database (TSDB) and monitoring system designed primarily for collecting and storing metrics. Grafana is a visualization and analytics platform that queries data from multiple sources, including Prometheus, to build dashboards, alerts, and graphs. Understanding this split is essential: Prometheus captures what is happening, while Grafana makes that data understandable and actionable.

At its core, Prometheus excels at pulling metrics from instrumented targets via HTTP endpoints, storing them with high-resolution timestamps, and providing a powerful query language (PromQL) for real-time analysis. Grafana, conversely, does not store data—it serves as a universal front-end for diverse data stores, offering rich graph types, templating, and alerting rules that can span multiple backends.

How Prometheus Works: Pull-Based Data Collection

Prometheus employs a pull model, where it scrapes metrics from configured endpoints at regular intervals (e.g., every 15 seconds). This differs from push-based systems (like InfluxDB or Datadog) where services actively send data. The pull model allows Prometheus to control the ingestion rate, detect down targets via health checks, and maintain a centralized source of truth for service availability.

Key components of Prometheus include:

Targets: The endpoints (e.g., /metrics on a web server) that expose metric data in a plaintext format.
Service Discovery: Automated detection of targets via Kubernetes, Consul, EC2, or file-based configurations.
TSDB Storage: A highly efficient, local storage engine that compresses data using a custom block format, optimized for time-series queries.
PromQL (Prometheus Query Language): A functional query language for filtering, aggregating, and transforming metrics. Example: rate(http_requests_total[5m]) calculates the per-second request rate over the last five minutes.
Alertmanager: A separate component that handles alerts generated by Prometheus, deduplicating, silencing, and routing them to channels like email, Slack, or PagerDuty.

Prometheus is ideal for dynamic, cloud-native environments where services scale up and down frequently. Its pull mechanism ensures that metric collection is resilient to network partitions, as each Prometheus server works independently.

How Grafana Works: Querying and Visualization

Grafana acts as a dashboard-as-code platform, connecting to dozens of data sources including Prometheus, Graphite, Elasticsearch, MySQL, Loki (for logs), and Tempo (for traces). Its primary functions are:

Data Source Abstraction: A unified interface to query metrics from different backends using their native query languages (e.g., PromQL for Prometheus, Lucene for Elasticsearch).
Dashboard Creation: Drag-and-drop panels (graphs, stats, tables, heatmaps, geolocation maps) that can be templated with variables (e.g., $host, $application) to switch between environments.
Alerting and Notifications: A built-in alerting engine that can evaluate queries against thresholds and send alerts via email, Slack, PagerDuty, webhooks, and more. Grafana alerts can combine data from multiple sources and use complex conditions.
Explore Mode: An ad-hoc query interface for debugging and deep-diving into metrics without creating a dashboard.
Provisioning and Automation: Dashboards and data sources can be defined in YAML/JSON files and loaded automatically, making Grafana ideal for GitOps workflows.

Grafana does not store historical metrics. It relies entirely on its connected data sources for data retrieval. This means Prometheus handles retention and storage, while Grafana handles presentation and alert routing.

How They Work Together: The Integration

The most common pattern is Prometheus as the data source and Grafana as the dashboard layer. The integration is seamless because Prometheus exposes a well-defined HTTP API that Grafana can query using PromQL. The typical workflow:

Instrumentation: Application code uses client libraries (Go, Java, Python, etc.) to expose metrics like request counts, latency histograms, and error rates on an HTTP endpoint.
Scraping: Prometheus pulls these metrics from the application’s /metrics endpoint, along with metrics from infrastructure (node_exporter for host-level metrics, kube-state-metrics for Kubernetes).
Storage: Prometheus stores the data locally on disk. Retention is configurable (common defaults: 15–30 days). For longer retention, remote storage like Thanos or Cortex can be used.
Querying: Grafana connects to Prometheus as a data source. Dashboard panels use PromQL to query metrics.
Visualization: Grafana renders live-updating graphs, tables, and alerts. Users can set time ranges, apply template variables, and share dashboards.
Alerting: Both tools can alert, but a common pattern is using Grafana’s alerting engine (which triggers on Prometheus queries) because it offers richer notification options and easier management for non-infrastructure teams.

Example query flow: A Grafana panel showing CPU usage might execute: 100 - (avg by (instance) (rate(node_cpu_seconds_total{mode="idle"}[5m])) * 100). Prometheus parses this, fetches the time-series data, returns the result, and Grafana plots it as a line chart.

Key Differences at a Glance

Dimension	Prometheus	Grafana
Primary function	Data collection and storage (TSDB)	Data visualization and dashboarding
Data model	Time-series with labels	No data storage; queries external sources
Query language	PromQL only	Multiple languages (PromQL, SQL, Lucene, etc.)
Storage	On-disk TSDB (local or remote)	No built-in storage; relies on data sources
Alerting	Yes, via Alertmanager (separate component)	Yes, built-in with multi-channel notifications
Scalability	Single-node by default; scaled with Thanos/Cortex	Stateless; scalable by adding instances behind a load balancer
Installation	Single binary or container	Single binary or container
Use case	Metrics from dynamic, ephemeral services	Unified observability dashboards (metrics, logs, traces)

When to Use Prometheus Alone

Prometheus is sufficient when you only need raw metric collection and basic alerting. Small teams operating a few services might use Prometheus’s built-in expression browser to execute PromQL queries and its Alertmanager for notifications. The expression browser is functional but limited—it shows tabular results and simple graphs without the interactivity of Grafana. If you do not need sharing dashboards, complex visualizations, or multi-source data correlation, Prometheus alone can serve as a complete monitoring solution.

When to Use Grafana Alone

Grafana is useful even without Prometheus if you already have a data source like Graphite, InfluxDB, or a SQL database. For example, a team using AWS CloudWatch can use Grafana to create dashboards that pull data from CloudWatch’s API, avoiding the need to deploy Prometheus. Grafana also supports data sources like JSON API, CSV, and TestData, making it a universal visualization tool for any structured dataset.

However, without Prometheus (or a similar TSDB), Grafana loses the ability to collect high-cardinality time-series data from ephemeral infrastructure. You would need another collection mechanism.

Choosing Between Prometheus and Grafana

The decision is not binary. The standard modern stack uses both:

Prometheus for metrics collection, storage, and real-time querying.
Grafana for dashboarding, cross-source analytics, and enhanced alert management.

If you run Kubernetes, Prometheus is almost mandatory for resource and application monitoring. Grafana becomes essential when you need to correlate metrics with logs (via Loki) or traces (via Tempo) in a single pane of glass.

Advanced Integration: Thanos and Cortex

For multi-cluster or long-term storage scenarios, Prometheus alone may not scale. Thanos extends Prometheus by adding a global query view across multiple Prometheus instances, unlimited retention via object storage (S3, GCS), and downsampling for historical data. Cortex provides horizontally scalable, multi-tenant Prometheus-as-a-Service. Both integrate with Grafana seamlessly—Grafana connects to their query endpoints as if they were a single Prometheus instance.

Performance Considerations

Prometheus memory usage scales with the number of time series and the cardinality of labels. A poorly instrumented application with high label cardinality (e.g., unique request IDs as labels) can overwhelm Prometheus.
Grafana performance depends on query complexity and data source responsiveness. Grafana caches dashboard queries for a configurable period (default 1 minute) to reduce load on Prometheus. Heavy dashboards with many panels can be optimized by reducing panel count and using query grouping.

Security and Access Control

Prometheus has minimal built-in authentication—it typically runs behind a reverse proxy (nginx, auth proxy) or in a trusted network.
Grafana includes robust user authentication (OAuth, LDAP, SAML), role-based access control (Viewer, Editor, Admin), and folder-level permissions for dashboards. This makes Grafana the logical choice for exposing monitoring data to developers, managers, and external teams.

Role of Alertmanager vs Grafana Alerts

A common confusion: both Prometheus Alertmanager and Grafana can send alerts. Typically, Alertmanager handles alerts generated by Prometheus rules (e.g., when a metric crosses a threshold). Grafana alerts are often used when you need to evaluate complex conditions across multiple data sources or when you want a unified alerting interface without managing Alertmanager’s configuration files. Grafana’s alerting also supports scheduling (e.g., “only alert between 9 AM and 5 PM”) and silence management directly in the UI.

Real-World Stack Example

A modern observability pipeline often looks like:
Application → OpenTelemetry Collector → Prometheus (storage) → Grafana (dashboards + alerts)
or
Kubernetes Metrics → Prometheus Operator → Thanos (global view + long-term) → Grafana

This architecture leverages Prometheus for its robust pull-based scraping and storage, while Grafana provides the rich, customizable front end that visualizes not just metrics, but also logs and traces for a full observability picture.

Scalability and High Availability

Prometheus is not inherently highly available—running two identical instances scraping the same targets is the standard approach, with Grafana querying both via a load balancer or Thanos.
Grafana can be scaled horizontally by adding instances behind a load balancer. Session information can be stored in a shared database (PostgreSQL, MySQL) or Redis. Dashboards and data source configurations are stored in the same shared database or provisioned from files.

Cost Implications

Prometheus is open-source and free, but its local storage requires disk space. For large-scale deployments, using Thanos or Cortex with object storage can reduce costs, as object storage is cheaper than SSD. Grafana also offers a free, self-hosted version. Grafana Cloud is a paid SaaS option that eliminates operational overhead and includes hosted Prometheus, Loki, and Tempo.

Best Practices for Integration

Label naming conventions: Keep Prometheus label names consistent (e.g., job, instance, namespace) to enable Grafana template variables to filter effectively.
Dashboard reuse: Use Grafana’s dashboard provisioning to treat dashboards as code, stored in Git alongside Prometheus rules and alerts.
Query optimization: Use recording rules in Prometheus for expensive queries (e.g., 99th percentile latency over 7 days) to precompute results, reducing load on Prometheus and speeding up Grafana dashboard load times.
Annotations: Use Grafana’s annotation feature to overlay events (deployments, scaling changes) on graphs. This data can come from Prometheus (e.g., metric changes) or external APIs.