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Graylog vs Kibana: What are the differences?
Graylog: Open source log management that actually works. Centralize and aggregate all your log files for 100% visibility. Use our powerful query language to search through terabytes of log data to discover and analyze important information; Kibana: Explore & Visualize Your Data. Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch.
Graylog and Kibana are primarily classified as "Log Management" and "Monitoring" tools respectively.
"Powerfull" is the top reason why over 9 developers like Graylog, while over 76 developers mention "Easy to setup" as the leading cause for choosing Kibana.
Graylog and Kibana are both open source tools. It seems that Kibana with 12.2K GitHub stars and 4.72K forks on GitHub has more adoption than Graylog with 4.88K GitHub stars and 757 GitHub forks.
According to the StackShare community, Kibana has a broader approval, being mentioned in 888 company stacks & 453 developers stacks; compared to Graylog, which is listed in 75 company stacks and 21 developer stacks.
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, 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.
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 Graylog
- Open source17
- Powerfull12
- Well documented7
- User authentification5
- Flexibel query and parsing language5
- Alerts5
- Alerts and dashboards2
- User management2
- Easy query language and english parsing2
- Easy to install1
- Manage users and permissions1
- A large community1
Pros of Kibana
- Easy to setup88
- Free62
- Can search text45
- Has pie chart21
- X-axis is not restricted to timestamp13
- Easy queries and is a good way to view logs8
- Supports Plugins6
- Dev Tools3
- More "user-friendly"3
- Can build dashboards3
- Easy to drill-down2
- Out-of-Box Dashboards/Analytics for Metrics/Heartbeat2
- Up and running1
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Cons of Graylog
- Does not handle frozen indices at all1
Cons of Kibana
- Unintuituve5
- Elasticsearch is huge3
- Works on top of elastic only3
- Hardweight UI2