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Grafana vs Kibana vs Prometheus: What are the differences?
Introduction
In this article, we will explore the key differences between Grafana, Kibana, and Prometheus, which are popular tools used in monitoring and visualization in the IT industry.
Architecture and Purpose: Grafana is primarily a visualization and analytics software that allows users to create customizable dashboards to display operational metrics and analyze data. It supports multiple data sources and provides various ways to visualize the data. On the other hand, Kibana is part of the Elasticsearch ecosystem and is specifically designed for analyzing and visualizing data stored in Elasticsearch. It provides powerful querying capabilities and enables users to build real-time visualizations, dashboards, and maps. Prometheus is a monitoring and alerting toolkit suitable for time series data analysis and alerting. It focuses on collecting, storing, and querying metrics related to system monitoring and performance.
Data Source Compatibility: Grafana is highly versatile in terms of data sources and supports a wide range of databases, cloud platforms, and other monitoring systems, such as Prometheus. Kibana, being part of the Elasticsearch ecosystem, mainly works with Elasticsearch as its primary data source. It provides rich features specifically designed for analyzing and visualizing Elasticsearch data. Prometheus, on the other hand, is built to natively scrape and monitor metrics from applications and services directly, which are then stored in its own time series database.
Alerting Capabilities: Grafana offers basic alerting functionalities, allowing users to set up alerts based on thresholds and notify through various channels. It integrates well with popular communication platforms like Slack and PagerDuty. Kibana, in contrast, does not have built-in alerting capabilities and relies on third-party tools or external integrations for alerting functionality. Prometheus, being a dedicated monitoring tool, provides robust alerting capabilities out of the box. It allows users to define alert rules based on metrics and send alerts to various notification channels.
Data Querying and Filtering: Grafana provides a user-friendly visual query builder that allows users to easily construct queries for different data sources. It also supports flexible filtering and aggregation capabilities, enabling users to refine their queries and perform complex analytics. Kibana offers a powerful query language called Elasticsearch Query DSL, which allows users to perform complex searches and aggregations on Elasticsearch data. It also has a graphical interface for constructing queries. Prometheus, being designed specifically for time series data, provides its own querying language called PromQL. It allows users to query and filter metrics based on specific conditions and time ranges.
Visualization Options: Grafana offers a wide range of visualization options, including charts, graphs, tables, and singlestat panels. It supports various visualization libraries and plugins, allowing users to create visually appealing and interactive dashboards. Kibana also provides multiple visualization options, such as line charts, area charts, bar charts, pie charts, and maps, to cater to different data visualization needs. It has built-in support for geospatial data visualization as well. Prometheus, being primarily a monitoring tool, focuses more on displaying numerical metrics in time series format than complex graphical visualizations.
Integration with External Tools: Grafana provides extensive integration capabilities and supports various external tools and services for data collection, monitoring, and notification. It can integrate with existing monitoring systems like Prometheus, InfluxDB, and Graphite, as well as cloud platforms like AWS CloudWatch and Azure Monitor. Kibana, being part of the Elasticsearch ecosystem, integrates well with other components of the Elastic Stack, such as Logstash and Beats, for collecting and analyzing data. It also has integrations with external systems like Apache Kafka and MySQL. Prometheus has native integration capabilities with popular frameworks and systems, making it easy to collect metrics from various applications and services. It also integrates well with Grafana for visualization and alerting purposes.
In summary, Grafana is a versatile visualization and analytics software with broad data source compatibility, while Kibana is specifically designed for analyzing and visualizing data stored in Elasticsearch. Prometheus, on the other hand, is a dedicated monitoring and alerting tool focusing on time series data analysis. Each tool offers unique features and capabilities, making them suitable for different use cases in terms of monitoring, visualization, and analytics.
Looking for a tool which can be used for mainly dashboard purposes, but here are the main requirements:
- Must be able to get custom data from AS400,
- Able to display automation test results,
- System monitoring / Nginx API,
- Able to get data from 3rd parties DB.
Grafana is almost solving all the problems, except AS400 and no database to get automation test results.
You can look out for Prometheus Instrumentation (https://prometheus.io/docs/practices/instrumentation/) Client Library available in various languages https://prometheus.io/docs/instrumenting/clientlibs/ to create the custom metric you need for AS4000 and then Grafana can query the newly instrumented metric to show on the dashboard.
Hi, We have a situation, where we are using Prometheus to get system metrics from PCF (Pivotal Cloud Foundry) platform. We send that as time-series data to Cortex via a Prometheus server and built a dashboard using Grafana. There is another pipeline where we need to read metrics from a Linux server using Metricbeat, CPU, memory, and Disk. That will be sent to Elasticsearch and Grafana will pull and show the data in a dashboard.
Is it OK to use Metricbeat for Linux server or can we use Prometheus?
What is the difference in system metrics sent by Metricbeat and Prometheus node exporters?
Regards, Sunil.
If you're already using Prometheus for your system metrics, then it seems like standing up Elasticsearch just for Linux host monitoring is excessive. The node_exporter is probably sufficient if you'e looking for standard system metrics.
Another thing to consider is that Metricbeat / ELK use a push model for metrics delivery, whereas Prometheus pulls metrics from each node it is monitoring. Depending on how you manage your network security, opting for one solution over two may make things simpler.
Hi Sunil! Unfortunately, I don´t have much experience with Metricbeat so I can´t advise on the diffs with Prometheus...for Linux server, I encourage you to use Prometheus node exporter and for PCF, I would recommend using the instana tile (https://www.instana.com/supported-technologies/pivotal-cloud-foundry/). Let me know if you have further questions! Regards Jose
We're looking for a Monitoring and Logging tool. It has to support AWS (mostly 100% serverless, Lambdas, SNS, SQS, API GW, CloudFront, Autora, etc.), as well as Azure and GCP (for now mostly used as pure IaaS, with a lot of cognitive services, and mostly managed DB). Hopefully, something not as expensive as Datadog or New relic, as our SRE team could support the tool inhouse. At the moment, we primarily use CloudWatch for AWS and Pandora for most on-prem.
I worked with Datadog at least one year and my position is that commercial tools like Datadog are the best option to consolidate and analyze your metrics. Obviously, if you can't pay the tool, the best free options are the mix of Prometheus with their Alert Manager and Grafana to visualize (that are complementary not substitutable). But I think that no use a good tool it's finally more expensive that use a not really good implementation of free tools and you will pay also to maintain its.
this is quite affordable and provides what you seem to be looking for. you can see a whole thing about the APM space here https://www.apmexperts.com/observability/ranking-the-observability-offerings/
From a StackShare Community member: “We need better analytics & insights into our Elasticsearch cluster. Grafana, which ships with advanced support for Elasticsearch, looks great but isn’t officially supported/endorsed by Elastic. Kibana, on the other hand, is made and supported by Elastic. I’m wondering what people suggest in this situation."
For our Predictive Analytics platform, we have used both Grafana and Kibana
- Grafana based demo video: https://www.youtube.com/watch?v=tdTB2AcU4Sg
- Kibana based reporting screenshot: https://imgur.com/vuVvZKN
Kibana has predictions and ML algorithms support, so if you need them, you may be better off with Kibana . The multi-variate analysis features it provide are very unique (not available in Grafana).
For everything else, definitely Grafana . Especially the number of supported data sources, and plugins clearly makes Grafana a winner (in just visualization and reporting sense). Creating your own plugin is also very easy. The top pros of Grafana (which it does better than Kibana ) are:
- Creating and organizing visualization panels
- Templating the panels on dashboards for repetetive tasks
- Realtime monitoring, filtering of charts based on conditions and variables
- Export / Import in JSON format (that allows you to version and save your dashboard as part of git)
I use both Kibana and Grafana on my workplace: Kibana for logging and Grafana for monitoring. Since you already work with Elasticsearch, I think Kibana is the safest choice in terms of ease of use and variety of messages it can manage, while Grafana has still (in my opinion) a strong link to metrics
After looking for a way to monitor or at least get a better overview of our infrastructure, we found out that Grafana (which I previously only used in ELK stacks) has a plugin available to fully integrate with Amazon CloudWatch . Which makes it way better for our use-case than the offer of the different competitors (most of them are even paid). There is also a CloudFlare plugin available, the platform we use to serve our DNS requests. Although we are a big fan of https://smashing.github.io/ (previously dashing), for now we are starting with Grafana .
I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.
Kibana should be sufficient in this architecture for decent analytics, if stronger metrics is needed then combine with Grafana. Datadog also offers nice overview but there's no need for it in this case unless you need more monitoring and alerting (and more technicalities).
@Kibana, of course, because @Grafana looks like amateur sort of solution, crammed with query builder grouping aggregates, but in essence, as recommended by CERN - KIbana is the corporate (startup vectored) decision.
Furthermore, @Kibana comes with complexity adhering ELK stack, whereas @InfluxDB + @Grafana & co. recently have become sophisticated development conglomerate instead of advancing towards a understandable installation step by step inheritance.
Grafana and Prometheus together, running on Kubernetes , is a powerful combination. These tools are cloud-native and offer a large community and easy integrations. At PayIt we're using exporting Java application metrics using a Dropwizard metrics exporter, and our Node.js services now use the prom-client npm library to serve metrics.
I learned a lot from Grafana, especially the issue of data monitoring, as it is easy to use, I learned how to create quick and simple dashboards. InfluxDB, I didn't know any other types of DBMS, I only knew about relational DBMS or not, but the difference was the scalability of both, but with influxDB, I knew how a time series DBMS works and finally, Telegraf, which is from the same company as InfluxDB, as I used the Windows Operating System, Telegraf tools was the first in the industry, in addition, it has complete documentation, facilitating its use, I learned a lot about connections, without having to make scripts to collect the data.
The objective of this work was to develop a system to monitor the materials of a production line using IoT technology. Currently, the process of monitoring and replacing parts depends on manual services. For this, load cells, microcontroller, Broker MQTT, Telegraf, InfluxDB, and Grafana were used. It was implemented in a workflow that had the function of collecting sensor data, storing it in a database, and visualizing it in the form of weight and quantity. With these developed solutions, he hopes to contribute to the logistics area, in the replacement and control of materials.
Pros of Grafana
- Beautiful89
- Graphs are interactive68
- Free57
- Easy56
- Nicer than the Graphite web interface34
- Many integrations26
- Can build dashboards18
- Easy to specify time window10
- Can collaborate on dashboards10
- Dashboards contain number tiles9
- Open Source5
- Integration with InfluxDB5
- Click and drag to zoom in5
- Authentification and users management4
- Threshold limits in graphs4
- Alerts3
- It is open to cloud watch and many database3
- Simple and native support to Prometheus3
- Great community support2
- You can use this for development to check memcache2
- You can visualize real time data to put alerts2
- Grapsh as code0
- Plugin visualizationa0
Pros of Kibana
- Easy to setup88
- Free65
- Can search text45
- Has pie chart21
- X-axis is not restricted to timestamp13
- Easy queries and is a good way to view logs9
- Supports Plugins6
- Dev Tools4
- More "user-friendly"3
- Can build dashboards3
- Out-of-Box Dashboards/Analytics for Metrics/Heartbeat2
- Easy to drill-down2
- Up and running1
Pros of Prometheus
- Powerful easy to use monitoring47
- Flexible query language38
- Dimensional data model32
- Alerts27
- Active and responsive community23
- Extensive integrations22
- Easy to setup19
- Beautiful Model and Query language12
- Easy to extend7
- Nice6
- Written in Go3
- Good for experimentation2
- Easy for monitoring1
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Cons of Grafana
- No interactive query builder1
Cons of Kibana
- Unintuituve7
- Works on top of elastic only4
- Elasticsearch is huge4
- Hardweight UI3
Cons of Prometheus
- Just for metrics12
- Bad UI6
- Needs monitoring to access metrics endpoints6
- Not easy to configure and use4
- Supports only active agents3
- Written in Go2
- TLS is quite difficult to understand2
- Requires multiple applications and tools2
- Single point of failure1
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