Before we even begin, let us give you a quick overview:
What is Grafana?
Grafana is an open-source visualization and observability platform that connects to your existing data sources — Prometheus, Loki, databases, cloud providers — and turns raw metrics, logs, and traces into interactive dashboards.
Is Grafana free?
The open-source version of Grafana is completely free. Grafana Cloud has a generous free tier covering 10,000 metric series, 50GB of logs, and 50GB of traces per month, with paid tiers for larger teams.
What is the difference between Grafana and Prometheus?
Prometheus collects and stores time-series metrics. Grafana visualizes them. Prometheus is the backend; Grafana is the front end. They are almost always used together but serve entirely different purposes.
How many data sources does Grafana support? As of Grafana 13, released at GrafanaCON in April 2026, Grafana supports more than 170 data sources and 120 visualization panel types.
Introduction
If Prometheus is the engine that collects your metrics, Grafana is the dashboard that makes those metrics mean something. It is the tool your entire engineering team — developers, SREs, and engineering managers — actually looks at when they want to know how their systems are behaving in production.
Grafana has grown far beyond its origins as a simple charting tool. In 2026, it is the centerpiece of a complete observability ecosystem — connecting metrics, logs, traces, and alerts in a single unified interface.
According to Grafana Labs' own 2026 Observability Survey, more than 77% of organizations now rely on open-source and open-standards tools for observability, and Grafana sits at the heart of most of those stacks. Grafana Labs has also surpassed 35 million users worldwide, making it one of the most widely adopted tools in the entire DevOps ecosystem.
This guide covers everything you need to know about Grafana monitoring: how it works, how to install it, how to connect data sources, how to build dashboards that actually help your team, how to set up Grafana alerting, how the LGTM stack fits together, and how Grafana compares to Datadog and Kibana. Whether you are setting up Grafana for the first time or trying to level up an existing observability practice, this is the guide to bookmark.
1. What is Grafana and How Does It Work?
Grafana was created in 2014 by Torkel Ödegaard in Sweden as an open-source alternative to Kibana for visualizing time-series data. What started as a charting tool for Graphite metrics has evolved into the industry-standard observability platform used by over 35 million users worldwide as of 2026.
The core concept is deliberately simple: Grafana does not store your data. It connects to wherever your data already lives and queries it on demand. This is the key insight that makes Grafana so powerful and so flexible. Whether your metrics are in Prometheus, your logs are in Loki, your traces are in Tempo, your application data is in PostgreSQL, or your cloud metrics are in AWS CloudWatch — Grafana connects to all of them and lets you visualize everything in a single interface.
When you build a Grafana dashboard panel, you are writing a query against a connected data source. Grafana executes that query, gets the results, and renders them as a graph, gauge, heatmap, or any of the 120-plus visualization types available in Grafana 13. Every panel can query a different data source, which means a single dashboard can show Prometheus metrics, Loki log counts, and a PostgreSQL query result side by side.
This data-source-agnostic architecture is what distinguishes Grafana monitoring from tools like Kibana, which is tightly coupled to Elasticsearch, or proprietary platforms like Datadog, which requires you to send all your data to their backend. Grafana brings the visualization to your data, not the other way around. For DevOps teams that care about data ownership and avoiding vendor lock-in, this architecture is a fundamental advantage.
2. The Grafana Ecosystem: The LGTM Stack Explained
Grafana the visualization tool is one piece of a larger ecosystem that Grafana Labs has built around it. Understanding the full stack helps you see how the pieces fit together and make informed decisions about which parts you actually need.
Grafana itself is the visualization and dashboarding layer. It is what your team looks at — the interface, the panels, the alerts, the drill-down views.
Loki is Grafana's log aggregation system. Where Prometheus scrapes and stores metrics, Loki ingests and indexes log data from your applications and infrastructure. Loki uses the same label-based approach as Prometheus, which makes correlating logs and metrics inside a Grafana dashboard extremely natural. You query Loki using LogQL, a language that follows similar conventions to PromQL and feels familiar once you know one. Loki is deliberately lightweight — it indexes only the metadata (labels) of log streams rather than the full text content, which keeps storage costs significantly lower than alternatives like Elasticsearch.
Tempo is Grafana's distributed tracing backend. When you want to follow a single request through multiple microservices and understand exactly where latency is introduced or where an error originates, Tempo stores and serves those traces. In a Grafana dashboard, you can click from a Prometheus metric spike to the relevant Loki logs and then to the specific Tempo trace — all without leaving the interface. This level of correlation is what modern observability actually looks like.
Mimir is Grafana's long-term, highly scalable metrics storage backend. Where a standalone Prometheus instance is limited by local disk and typically retains 15 to 90 days of data, Mimir is designed to store years of metrics across distributed object storage. Teams that have outgrown Prometheus's local storage use Mimir as their metrics backend, with Grafana sitting on top and querying it just like it would query Prometheus.
Together — Loki, Grafana, Tempo, Mimir — this is called the LGTM stack. It gives you metrics, logs, traces, and visualization in a fully open-source, self-hostable package with zero licensing cost. This is the stack most cloud-native DevOps teams running Kubernetes are deploying in 2026, and it is the direction the industry has been moving for several years.
3. Installing Grafana
Getting Grafana running is one of the easier parts of setting up an observability stack. The process is straightforward regardless of which installation method you choose.
On a Linux machine running Ubuntu or Debian, you add Grafana's official package repository to your system, install the Grafana package using your package manager, and then start and enable the Grafana service so it runs on boot. Once that is done, Grafana is accessible in your browser on port 3000. The default login credentials are admin for both the username and password, and Grafana immediately prompts you to change the password on first login, ensuring proper Credential Management.
If you prefer containers, running Grafana with Docker is a single command. You pull the official Grafana image, map port 3000 on your host to port 3000 in the container, and mount a volume to persist your dashboards and configuration across container restarts. Grafana is running within seconds.
For teams operating Kubernetes, the recommended approach is installing Grafana using Helm. Grafana Labs maintains an official Helm chart that handles the deployment, service exposure, and persistent storage configuration.
You add the Grafana Helm repository, update your local chart cache, and install the chart into a monitoring namespace with a single command. For production Kubernetes environments, most teams skip installing Grafana alone and instead install the full kube-prometheus-stack chart, which bundles Prometheus, Alertmanager, and Grafana together with pre-configured dashboards and alerting rules out of the box.
4. Connecting Data Sources
A fresh Grafana installation is an empty canvas. The first thing you do is connect your data sources — this tells Grafana where to find the data it will visualize.
Adding Prometheus as a data source is the most common starting point for DevOps teams. You navigate to Connections, then Data Sources, click Add data source, and select Prometheus from the list. You enter the URL of your Prometheus server — typically running on port 9090 — and click Save and Test. Grafana queries the Prometheus API to confirm the connection, and you should see a success message. From that point on, every PromQL query you know how to write is available inside Grafana's panel editor.
Beyond Prometheus, Grafana monitoring setups commonly include Loki for log data, Tempo for distributed traces, AWS CloudWatch for cloud infrastructure metrics, Google Cloud Monitoring and Azure Monitor for multi-cloud teams, and relational databases like PostgreSQL and MySQL for application-level or business metrics. Each data source is added the same way — you select the type, enter the connection details, and Grafana handles the rest.
The power of the Grafana data source model is that you can mix all of these on a single dashboard.
An e-commerce engineering team might show Prometheus metrics for API latency, a Loki log error count, and a PostgreSQL query showing active order volume — all in one view, updated in real time. No other tool in the open-source observability ecosystem makes that kind of multi-source correlation this easy.
For production environments, teams typically define data sources as code rather than clicking through the UI each time. Grafana supports provisioning data sources through YAML configuration files that it reads at startup. This means your data source configuration lives in version control alongside the rest of your infrastructure code, gets deployed through the same CI/CD pipeline, and is always reproducible. If your Grafana instance is ever destroyed and recreated, all your data sources are reconnected automatically.
5. Building Your First Grafana Dashboard
This is where most people get overwhelmed because the Grafana dashboard builder has a lot of options. The key is to start simple and build iteratively rather than trying to create the perfect dashboard on your first attempt.
Start by creating a new dashboard. Click the plus icon in the Grafana sidebar and select New Dashboard. You will see an empty canvas. Click Add visualization and select your connected Prometheus data source.
In the query editor that appears at the bottom of the screen, type a PromQL expression. Start with something simple — the rate of HTTP requests over the last five minutes, for example. Grafana executes the query immediately and renders the results as a time series graph. You can see the data flowing through without any additional configuration.
On the right side panel, you have options to change the visualization type, set the panel title, configure axis units, adjust the legend, and set color thresholds. Set the unit to the correct format for what you're measuring — requests per second, bytes, milliseconds, or percentage — so the numbers are immediately readable without requiring mental conversion.
Once your first panel is working, add more panels by clicking Add in the top menu. A useful starting Grafana dashboard for a web service typically includes four panels covering the golden signals: a request rate panel showing traffic volume, an error rate panel showing the percentage of failed requests, a latency panel showing p50 and p95 response times, and a saturation panel showing CPU or memory utilization.
These four panels give anyone looking at the dashboard an immediate sense of service health without needing to interpret complex queries.
Save your dashboard, give it a meaningful name, and organize it in a folder. The act of naming and organizing dashboards matters more than it sounds — when you have dozens of dashboards across multiple teams, good naming conventions are what allow engineers to find the right dashboard quickly during an incident.
6. Grafana Panel Types and When to Use Each
Grafana 13 ships with over 120 visualization panel types. The vast majority of DevOps monitoring use cases are covered by a handful of them, and knowing which panel to use for which situation makes your dashboards significantly more readable.
The time series panel is the default and the workhorse of Grafana monitoring. Use it for any metric that changes over time — request rates, error rates, CPU usage, memory consumption, and latency. If you are not sure which panel type to use, start with time series. It handles multiple series cleanly, supports threshold overlays, and has flexible axis configuration.
The stat panel displays a single large number with an optional trend indicator and color coding. Use it for the most critical current values on a dashboard — current error rate, uptime percentage, number of active alerts. These panels create immediate at-a-glance awareness without requiring the viewer to interpret a graph.
The gauge panel shows a value within a defined range, similar to a speedometer. Use it for metrics with a meaningful good, warning, and critical range — CPU utilization percentage, disk usage, or memory pressure. The color coding from green to yellow to red makes threshold violations visible at a glance without reading any numbers.
The bar gauge panel is similar to a regular gauge but displays multiple values as horizontal bars. It is excellent for comparing the same metric across multiple instances — showing CPU usage per node or error rate per service side by side on a single panel rather than requiring separate panels for each.
The heatmap panel shows the distribution of values over time using color intensity. This is the ideal visualization for latency histograms — you can see at a glance whether your p99 latency is stable, gradually degrading, or spiking periodically, without needing to calculate percentiles manually.
The logs panel displays Loki log data as a scrollable, filterable log stream directly inside your dashboard. Use it to surface the most recent error messages from your application alongside your metrics panels, so engineers see both the signal and the relevant log context in one place.
The node graph panel visualizes service dependency maps — showing how microservices connect to each other, which services are healthy, and where bottlenecks are forming. It becomes particularly useful when integrated with distributed tracing data from Tempo, giving teams a live view of their service mesh topology.
7. Template Variables: Making Dashboards Dynamic
Template variables are one of Grafana's most powerful features and the thing that separates a mediocre dashboard from a genuinely useful one. They are dynamic dropdowns that appear at the top of a dashboard, letting users filter every panel simultaneously without editing any queries.
Without template variables, you would need a separate dashboard for every environment, every service, or every Kubernetes namespace. With template variables, a single dashboard adapts instantly to whatever the user selects. One dashboard serves production, staging, and development. One dashboard covers every service your team owns. One dashboard works across every Kubernetes namespace in your cluster.
The simplest type of template variable is a custom variable where you define the options yourself. You create a variable called environment with values like production, staging, and development. Then in every panel query, you reference it using the dollar sign prefix — dollar environment. When a user changes the environment dropdown at the top of the dashboard, every panel updates instantly to show data for that environment with no page reload and no query editing required.
More powerfully, you can create query-based variables that pull their options directly from Prometheus. You write a label values query that asks Prometheus for all active values of a particular label — for example, all unique service names currently sending metrics, or all pod names running in a specific namespace. Grafana automatically populates the dropdown with those values and keeps it updated as new services or pods appear. This means your dashboard stays accurate even as your infrastructure changes, without any manual maintenance.
You can also chain variables together. A namespace variable populates first, and then the pod variable filters its options based on whichever namespace the user selected. A service variable might further filter based on the selected namespace. This layered filtering makes Grafana monitoring dashboards genuinely ergonomic at scale, where the alternative would be dozens of nearly identical dashboards differing only in which service or environment they target.
8. Grafana Alerting in 2026
Grafana alerting has matured significantly over the past several releases. Grafana 13 uses Grafana-managed alerting as the default for all new installations, including new Grafana Cloud stacks. Understanding how it works — and how it complements Prometheus alerting — helps you build a complete alerting strategy.
The biggest advantage of Grafana alerting over Prometheus alerting alone is data source coverage. Prometheus alerting only fires on Prometheus metrics. Grafana alerting can evaluate conditions across any connected data source — a Loki log pattern, a PostgreSQL query result, an AWS CloudWatch metric, or a Prometheus expression. If your observability stack includes multiple data sources, Grafana's unified alerting system lets you manage all your alert rules in one place regardless of where the underlying data lives.
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Creating an alert rule in Grafana starts by navigating to the Alerting section and clicking New alert rule. You give the rule a name and write the query condition against your chosen data source. You then define the threshold — for example, fire when the error rate rises above five percent.
You set the evaluation interval, which controls how frequently Grafana checks the condition, and the pending period, which is how long the condition must remain true before the alert actually fires. This pending period is equivalent to Prometheus's for clause and is equally important for preventing alert fatigue from transient spikes that resolve on their own.
Contact points are where Grafana sends alert notifications. You define a contact point for each destination — a Slack webhook, a PagerDuty integration key, an email address, a Microsoft Teams webhook, or any custom HTTP webhook. Grafana supports all the notification channels your team already uses.
Notification policies are the routing rules that decide which contact points receive which alerts. A critical database alert might route to PagerDuty and wake someone up immediately. A warning about disk space creeping upward might go to a Slack channel for awareness during business hours. Routing is based on label matchers — the same label-based approach used throughout the Prometheus and Grafana ecosystem.
Grafana also includes silence management, which lets you suppress specific alerts during planned maintenance windows without touching the alert rule itself. Mute timings allow you to define recurring schedules — suppressing non-critical alerts on weekends, for example — that apply automatically without requiring manual silences each time.
For teams already using Prometheus Alertmanager for routing, Grafana can be configured to send its alerts to Alertmanager rather than routing them independently. This gives you the best of both systems — Grafana's flexibility for defining alerts across multiple data sources, combined with Alertmanager's powerful deduplication, grouping, and routing logic.
9. Grafana and Loki: Log Monitoring
Prometheus monitoring tells you what is happening in your system. Grafana Loki tells you why. Logs contain the context that metrics cannot capture — the specific error message, the stack trace, the request ID that ties an event back to a user action.
Loki's design is deliberately different from Elasticsearch and the ELK stack. Rather than full-text indexing every log line, which is expensive at scale, Loki indexes only the labels associated with log streams. The actual log content is stored compressed and queried on demand using streaming search. This makes Loki significantly cheaper to operate than Elasticsearch for most log storage use cases, at the cost of slower full-text search across unstructured log data.
Once Loki is connected as a Grafana data source, you query it using LogQL. If you are familiar with PromQL from Prometheus monitoring, LogQL will feel natural almost immediately. The syntax follows similar conventions — you select log streams using label matchers, then apply filter expressions to narrow down to the lines you care about.
The most basic LogQL query selects all log lines from a specific application and filters to lines containing the word error. More powerful queries can count the rate of error log lines per minute over a time window, parse structured JSON log data and filter on specific fields, or extract values from log lines and treat them as metrics for graphing.
The real power of running Loki alongside Prometheus in a Grafana monitoring setup is cross-signal correlation on the same dashboard.
You can add a time series panel showing your Prometheus error rate metric alongside a logs panel showing the actual error log lines from Loki for the same time window. When an alert fires, your engineer opens the dashboard and sees both the metric trend and the specific log messages that caused it — without switching tools or opening a second browser tab.
Grafana's Explore view extends this further with a feature called log-to-metric correlation. When you spot an anomaly in a Prometheus metric, you can click through to see the Loki logs from the same timestamp automatically filtered to the relevant service.
This workflow — from metric anomaly to log context in two clicks — is one of the most concrete improvements that a properly configured Grafana monitoring stack delivers over fragmented toolsets.
10. Grafana Kubernetes Monitoring
Kubernetes is the environment where Grafana dashboards earn their keep most clearly. A production Kubernetes cluster generates enormous amounts of metrics, logs, and events, and Grafana is how you make sense of all of it without drowning in data.
The standard approach is the kube-prometheus-stack Helm chart, which deploys Prometheus, Alertmanager, and Grafana together with a pre-configured set of dashboards covering everything from cluster health to individual pod resource usage. If you have read our Prometheus monitoring guide, you already know this stack. Grafana is the visualization layer that makes all those Kubernetes metrics actually readable.
Even if you install Grafana independently, the community has already built most of the dashboards you need. Grafana's dashboard marketplace at grafana.com/grafana/dashboards hosts thousands of pre-built dashboards that you import with a single dashboard ID. For Kubernetes monitoring, there are several essential ones worth knowing.
The Node Exporter Full dashboard — ID 1860 — is the most imported dashboard in Grafana's entire marketplace with over 30 million downloads. It gives you complete visibility into every Linux node in your cluster, covering CPU utilization, memory consumption, disk I/O, network throughput, and filesystem usage in a single comprehensive view.
The Kubernetes Cluster Monitoring dashboard — ID 7249 — gives you cluster-wide health at a glance: total pod count, node resource utilization, persistent volume usage, and deployment status across all namespaces. This is the dashboard that goes on the wall monitor in your operations center.
The workloads and pods dashboards included in the kube-prometheus-stack let you drill into individual pods — their CPU and memory consumption over time, network I/O, and restart history. These are the first dashboards SREs open when a specific workload is behaving unexpectedly and they need to understand whether it is a resource constraint, a crash loop, or a traffic spike.
The most effective Grafana Kubernetes monitoring setup follows a layered hierarchy. A cluster overview dashboard gives the on-call engineer a thirty-second health check of the entire environment. Namespace-level dashboards let team leads see the state of their team's services. Service-level dashboards with template variables let individual engineers drill into their own applications.
Node-level dashboards help platform teams identify infrastructure issues. Each level links to the next, so an engineer can start at the overview, spot a problem, and navigate directly to the right level of detail by following dashboard links — without knowing in advance which service was the source of the issue.
11. Grafana vs Datadog vs Kibana
This comparison comes up constantly in DevOps teams evaluating their observability stack. The honest answer is that these three tools are not direct substitutes — they come from different philosophies and solve different problems.
Grafana is a visualization layer. It does not store data — it connects to your existing backends, which can include Prometheus, Loki, databases, cloud monitoring APIs, and over 170 other integrations, and builds dashboards on top of them.
This makes Grafana infinitely flexible and free to use. The trade-off is that you are responsible for assembling and managing your own data backends. You need Prometheus for metrics, Loki for logs, and Tempo for traces. Grafana is the right choice for teams with solid infrastructure engineering skills who want full data ownership, maximum flexibility, and zero licensing costs. It is widely considered the industry leader for custom dashboard visualization.
Datadog is a fully managed, all-in-one SaaS observability platform. You install the Datadog agent on your hosts, and it handles metrics collection, log shipping, distributed tracing, application performance monitoring, and more in a single unified product. There is no stack to assemble — everything works out of the box.
The significant trade-off is cost. Datadog's pricing scales with the amount of data you send, and at large scale the monthly bills can become substantial. Teams need to actively monitor their data volumes to avoid unexpected cost spikes from what Datadog calls cardinality spikes.
Datadog wins when your team prioritizes speed-to-value and does not have the bandwidth to manage an observability stack, and when the cost is justified by the saved engineering time.
Kibana is the visualization frontend for Elasticsearch and the ELK stack. Unlike Grafana, which connects to many backends, Kibana is deeply coupled to Elasticsearch. It is exceptional for log analytics, full-text search across log data, and security monitoring and SIEM use cases where you need powerful structured and unstructured log querying. For time-series metrics monitoring and cloud-native Kubernetes observability however, Grafana with Prometheus is the stronger combination because Elasticsearch is not optimized for high-cardinality time-series data the way Prometheus and Mimir are.
The practical guidance: if you run Kubernetes and Prometheus, use Grafana — the integration is seamless and the community support is unmatched. If you want managed simplicity and can absorb the cost, Datadog is the fastest path to full-stack observability.
If your primary data lives in Elasticsearch and you need powerful log analytics, Kibana is the natural choice. Many mature engineering organizations use Grafana and Kibana together — Grafana for metrics and infrastructure dashboards, Kibana for log search and security analytics.
12. Grafana Cloud vs Self-Hosted Grafana
Once you have decided Grafana is the right tool, you need to decide whether to run it yourself or use Grafana Cloud. Both are legitimate choices and the right answer depends on your team's size, expertise, and data requirements.
Self-hosted Grafana gives you complete control. You run it in your own infrastructure, connect it to your own Prometheus and Loki instances, and pay nothing for Grafana itself. You choose when to upgrade, how to configure it, and where your data lives.
For teams already operating Kubernetes and comfortable with Helm deployments, the operational overhead of managing Grafana is manageable. Dashboard-as-code through Grafana's provisioning system and the Grafana Terraform provider means your dashboards and data source configurations are version-controlled, reproducible, and deployable through CI/CD. If your Grafana instance is ever rebuilt from scratch, everything is restored automatically from code.
Grafana Cloud removes the operational burden entirely. Grafana Labs manages the Grafana instance, the Loki log backend, the Mimir metrics store, and the Tempo traces backend on your behalf. You get a fully managed, highly available observability platform without any infrastructure to maintain.
The free tier is genuinely useful for smaller teams — 10,000 active Prometheus metric series, 50GB of logs, and 50GB of traces per month with 14-day retention at no cost. Paid tiers scale with usage. The main consideration is that your telemetry data travels to and is stored in Grafana Labs' infrastructure. For organizations with strict data sovereignty requirements or compliance obligations that prevent sending production telemetry to third-party systems, this can be a blocker that makes self-hosted the only viable option.
For most teams getting started: use Grafana Cloud's free tier to get running immediately, learn the platform, and build your first dashboards without managing any infrastructure. If you hit the free tier limits or face data residency requirements, migrate to self-hosted. Dashboard definitions export as JSON, data source configurations are portable, and the Grafana interface is identical between Cloud and self-hosted, so the migration is straightforward.
13. Best Practices for Grafana Dashboards in 2026
Building a Grafana dashboard is easy. Building one that is genuinely useful during a 3am incident is harder. These practices are what separate dashboards that get opened once from dashboards that become the first thing every engineer reaches for when something goes wrong.
Design for the incident, not the demo. When an engineer is on-call and something breaks, they need to reach the right information in under thirty seconds. Every panel on your dashboard should answer a specific question that an on-call engineer would ask. If you cannot articulate the question that panel answers, it probably should not be there.
Use units on every single panel. A graph labeled "memory" showing values like 8589934592 is useless under pressure. Always configure the correct unit on your panels. Grafana has a comprehensive unit library — bytes automatically format to megabytes and gigabytes, milliseconds format to seconds, percentages display correctly. Raw numbers with no units are one of the most common and most fixable problems in real-world Grafana dashboards.
Store dashboards as code. Grafana dashboards export as JSON and can be provisioned through YAML configuration files that Grafana reads at startup. They can also be managed using the Grafana Terraform provider or tools like Grafonnet. Treat your dashboards the same way you treat your application code — version control in Git, peer review through pull requests, and deploy through your CI/CD pipeline. This makes your observability infrastructure reproducible and prevents configuration drift over time.
Start with fewer panels than you think you need. The instinct is to add every metric you can think of. Resist it strongly. A dashboard with forty panels is exhausting to read and impossible to parse quickly during an incident. Start with six to eight panels covering the golden signals — latency, traffic, errors, and saturation — and add panels only when a real incident reveals a specific gap in your visibility.
Use consistent color conventions across all your dashboards. Red means bad. Green means good. Do not reverse this, and do not use red for something that is merely informational. Grafana's threshold system lets you color panels based on value ranges — use it consistently so that engineers build reliable visual intuition across every dashboard they look at.
Link between dashboards using panel links and dashboard links. Connect your overview dashboard to detailed service dashboards, and connect service dashboards to Loki log panels in Explore. The goal is that an engineer should never have to manually navigate between tools during an investigation. They should be able to follow links from the symptom on an overview dashboard down to the specific log line that caused it.
Keep the dashboard refresh rate reasonable. Refreshing every five seconds generates a significant number of queries against Prometheus, especially on complex dashboards with many panels and high-cardinality queries. For standard operational dashboards, a thirty-second or one-minute refresh interval is usually sufficient. Engineers can manually switch to a shorter refresh interval when they are actively working an incident. Setting every dashboard to refresh every five seconds by default puts unnecessary load on your Prometheus server and rarely provides meaningful benefit.
Establish dashboard governance for larger teams. When multiple teams contribute dashboards to a shared Grafana instance, naming conventions and folder structure matter significantly. Define a standard for how dashboards are named — typically Team, then Service, then what the dashboard covers. Define which folders are shared versus team-owned and what review process a dashboard goes through before it is considered production-ready. An unmanaged Grafana instance with dozens of teams contributing dashboards quickly becomes a maze that nobody can navigate during an incident.
Frequently Asked Questions About Grafana
1. Can Grafana replace Datadog?
For metrics visualization and custom dashboarding, Grafana is widely considered the stronger tool. Where Grafana requires you to assemble and manage your own data backends, Datadog provides a fully managed all-in-one experience. Teams with strong DevOps engineering skills typically prefer Grafana's flexibility and zero licensing cost. Teams that want plug-and-play setup and have sufficient budget may prefer Datadog. Many organizations use both — Grafana for infrastructure and metrics dashboards, Datadog for APM and end-user experience monitoring.
2. Can Grafana query multiple data sources in a single panel?
Yes, through a feature called data source mixing. You can query Prometheus for infrastructure metrics and a SQL database for business data in the same panel and combine the results. This is useful for correlating infrastructure behavior with application-level business metrics — understanding whether a latency spike is actually impacting order completion rates, for example.
3. Does Grafana store historical data?
Grafana itself stores no data at all — it queries your connected data sources on demand. The length of historical data you can visualize depends entirely on what your backends retain. For a standalone Prometheus instance with default settings, that is 15 days. For Grafana Mimir or Thanos, it can be years. For Grafana Loki, retention is configurable based on your storage capacity.
4. How do I share a Grafana dashboard with non-technical stakeholders?
Grafana has a snapshot feature that creates a static, shareable link to a dashboard at a point in time — no login or Grafana access required. For live ongoing access, the public dashboard feature available in Grafana Cloud and self-hosted Grafana lets you share a live dashboard publicly. Grafana Enterprise adds scheduled PDF exports for automated reporting.
5. What is new in Grafana 13?
Grafana 13 was announced at GrafanaCON in April 2026 with expanded ecosystem support across more than 170 data sources and 120 visualization panels. Grafana Labs introduced a redesigned Loki architecture with Kafka-backed ingestion for more efficient, durable log pipelines at scale. They also simplified the path to OpenTelemetry integration on Linux and Kubernetes, and introduced Grafana Assistant — an AI-powered feature that helps engineers investigate issues and explore telemetry data faster by answering natural language questions about what they are seeing in their dashboards.
6. How does Grafana alerting compare to Prometheus alerting? Prometheus alerting only evaluates conditions against Prometheus metrics. Grafana alerting evaluates conditions across any connected data source — Prometheus, Loki, databases, and cloud monitoring APIs. For teams using only Prometheus, either system works well. For teams with multiple data sources, Grafana's unified alerting is significantly more powerful because it gives you one place to manage all alert rules regardless of where the underlying data lives.
Conclusion
Grafana has earned its position at the center of modern DevOps observability. It is not just a charting tool anymore — it is the unified interface that ties together your metrics from Prometheus, your logs from Loki, your traces from Tempo, and any other data source your team uses. It is what engineers look at during incidents, what SREs use to track SLO compliance, and what engineering managers open when they want to understand the health of their services.
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The LGTM stack — Loki, Grafana, Tempo, Mimir — gives teams a complete, fully open-source observability platform that rivals proprietary alternatives at zero licensing cost. The investment is in learning the tools and operating the infrastructure, and that investment pays off quickly once you have genuine, correlated visibility into your production systems.
If you have not set up Grafana monitoring yet, start today. Run it with Docker in five minutes, connect your Prometheus instance, import the Node Exporter Full dashboard with ID 1860, and spend an afternoon building a service health dashboard for something you own. That single session will immediately change how you understand your systems. If you already use Grafana, pick one practice from the best practices section above and implement it this week. Better unit formatting, storing dashboards as code, or adding Loki logs alongside your existing metrics panels — small improvements compound quickly into an observability practice your entire team depends on.
