Prometheus and Grafana are both tools built for time-series data. However, their strengths differ.
Monitoring tools are critical for not just observability and monitoring, but also to track and fix system health issues and resource bottlenecks. To find the best option means comparing not just features, but also how an organization will use the tool.
Let's explore Prometheus vs. Grafana pros and cons, weighing the use cases in which each one shines -- and if they work better together.
Grafana is a popular open source, general purpose dashboard and visualization tool that offers beautiful visualizations, interactive graphs and easy-to-set-up dashboards.
Grafana, which runs as a web application, is used by small to large enterprises to monitor visualizations and manage reports on metrics, logs and alerts. It offers integration with a rich set of data sources out of the box; some integrations include Amazon CloudWatch, Azure Monitor, Microsoft SQL Server and InfluxDB.
Grafana also provides a huge list of community -- and Grafana Labs -- supported panel plugins that enable users to create visualizations with options such as world maps, heat maps and pie, bar and line charts.
Pros to Grafana include:
- Cloud agnostic. Visualizes metrics from two leading cloud providers, AWS and Microsoft Azure.
- Integration options. Integrates with a diverse set of data sources, like Prometheus, InfluxDB, MySQL, Elasticsearch and Splunk.
- Assorted visual selection. Offers various visualization panel options that are feature-rich, highly configurable and customizable to meet an organization's requirements.
Cons to Grafana include:
- Limitations on dashboard organization and design. Visualization panels are limited to those made available by Grafana Labs and its community. Organization and filtering of panels might not offer enough flexibility for designing the dashboards, but these features see improvements with new version releases.
- No data collection and storage. Grafana can only use data from external systems like Prometheus, MySQL, Azure Monitor and Amazon CloudWatch. That means Grafana has no means to collect data on its own, through agents or other data pipelines, and is thus dependent on other systems to provide data.
- Limited data type visualizations. It mostly supports time-series data, but the Grafana open source community is gradually building plugins that enable users to visualize any kind of data. For example, users can see Azure Resource Graph results through a plugin.
Prometheus is another open source application for monitoring and alerting, as well as a time- series database. It was originally developed at SoundCloud and was later donated to the Cloud Native Computing Foundation in 2016. Now, Prometheus is an independent project on GitHub, developed actively by the open source community.
Prometheus records real-time series metrics via the HTTP pull method in a time-series database. It also provides a multidimensional data model and a flexible query language.
To monitor applications and services in Prometheus requires additional instrumentation in the code, using one of the Prometheus client libraries that implement various types of supported metrics. Popular libraries are written in Go, Java or Scala, Python and Ruby. Using one of the client libraries written in the native programming language, developers can define metrics and expose them on their application instance through HTTP endpoints.
Pros to Prometheus include:
- Integration with Grafana. This brings the synergy of high-end metric collection and feature-rich visualizations.
- PromQL. A powerful query language for fetching and analyzing Prometheus metrics data.
Cons to Prometheus include:
- Subpar UI. Compared to visualization tools like Grafana, the Prometheus UI for monitoring visualizations is lacking. Prometheus offers an expression browser on the Prometheus server at /graph, with which IT admins can enter an expression quickly and generate visualizations for debugging. Prometheus console templates that use Go templating language can create visualization dashboards, but these are not as versatile and extensible as visualization options and panels available in Grafana dashboards.
- No long-term storage. It doesn't have a durable long-term storage option that can scale the amount of logs ingested. Users must maintain a storage reserve for Prometheus logs and manage its capacity as logs grow.
Competitors or the perfect combination?
As ideal as it seems to have one tool that solves all problems, it is almost impossible to achieve desired results because of the heterogeneous nature of applications, different tech stacks and complex IT infrastructure. Every option has its own pros and cons, and enterprises need to research thoroughly what suits their use case.
Prometheus and Grafana are both built for time-series data. Prometheus excels in metric data collection, whereas Grafana champions metric visualizations. Both tools are open source, free and have vibrant communities of open source developers supporting their development.
A combination of Prometheus and Grafana can achieve favorable results, especially in microservices and container-based systems and applications. While Prometheus is good at scraping metrics and storing the data in a time-series database, Grafana builds detailed visualizations from a time-series data source, offering rich feature set graphs that are highly configurable.
Choosing the right tool is essential not only for observability and monitoring, but also to track and fix system health issues and resource bottlenecks before they become critical.
The purpose of a good monitoring tool should not be limited to capturing and emitting metrics, but to enable troubleshooting issues, trace logs and visualization of components and overall system health. Some open source and enterprise options include the following:
- Elastic Stack: a group of open source tools and products. These are the most popular for monitoring, log analysis and visualizations:
- Elasticsearch: an open source search and analysis engine.
- Logstash: an ETL (extract, transform, load) tool that collects logs from various data sources and can transform and ship data to various output destinations.
- Kibana: a visualization layer that works on top of Elasticsearch data and enables users to understand and visualize the systems and applications.
- Splunk: a data and log monitoring tool, which provides infrastructure monitoring and application performance metrics through a single pane of glass. Splunk Log Observer further simplifies log analytics and investigation.
- Datadog: a modern monitoring and observability platform that provides rich capabilities to collect, analyze and explore log data from various sources like infrastructure, platform, application and services. Datadog also performs automated log data tagging and correlation.
- Amazon CloudWatch: a native AWS monitoring and observability platform that offers infrastructure and application monitoring, log analytics, advanced proactive resource optimization and resource autoscaling.
- Azure Monitor: a native Microsoft Azure tool for monitoring infrastructure, platforms, applications, containers and metrics for any Azure resource. Azure Monitor has multiple features, such as:
- Application Insights for instrumenting an application, troubleshooting and diagnosis;
- Log Analytics for advanced querying, analysis and alerting on log data and metrics;
- Azure Dashboards for visualization of monitoring data; and
- Integrations with Power BI, Grafana and others.