Get Advice Icon

Need advice about which tool to choose?Ask the StackShare community!

Kibana
Kibana

4.5K
3K
+ 1
227
Munin
Munin

52
27
+ 1
5
Add tool

Kibana vs Munin: What are the differences?

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; Munin: PnP networked resource monitoring tool that can help to answer the what just happened to kill our performance. Munin is a networked resource monitoring tool that can help analyze resource trends and "what just happened to kill our performance?" problems. It is designed to be very plug and play. A default installation provides a lot of graphs with almost no work.

Kibana and Munin can be primarily classified as "Monitoring" tools.

Kibana and Munin are both open source tools. Kibana with 12.2K GitHub stars and 4.72K forks on GitHub appears to be more popular than Munin with 1.31K GitHub stars and 381 GitHub forks.

Airbnb, DigitalOcean, and 9GAG are some of the popular companies that use Kibana, whereas Munin is used by Redsmin, Index.co, and VuMedi. Kibana has a broader approval, being mentioned in 889 company stacks & 453 developers stacks; compared to Munin, which is listed in 15 company stacks and 3 developer stacks.

What is Kibana?

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.

What is Munin?

Munin is a networked resource monitoring tool that can help analyze resource trends and "what just happened to kill our performance?" problems. It is designed to be very plug and play. A default installation provides a lot of graphs with almost no work.
Get Advice Icon

Need advice about which tool to choose?Ask the StackShare community!

Why do developers choose Kibana?
Why do developers choose Munin?

Sign up to add, upvote and see more prosMake informed product decisions

    Be the first to leave a con
    What companies use Kibana?
    What companies use Munin?

    Sign up to get full access to all the companiesMake informed product decisions

    What tools integrate with Kibana?
    What tools integrate with Munin?

    Sign up to get full access to all the tool integrationsMake informed product decisions

    What are some alternatives to Kibana and Munin?
    Grafana
    Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins.
    Loggly
    The world's most popular cloud-based log management service delivers application intelligence.
    Graylog
    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.
    Splunk
    Splunk Inc. provides the leading platform for Operational Intelligence. Customers use Splunk to search, monitor, analyze and visualize machine data.
    Prometheus
    Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.
    See all alternatives
    Decisions about Kibana and Munin
    StackShare Editors
    StackShare Editors
    Icinga
    Icinga
    Graphite
    Graphite
    Logstash
    Logstash
    Elasticsearch
    Elasticsearch
    Grafana
    Grafana
    Kibana
    Kibana

    One size definitely doesn鈥檛 fit all when it comes to open source monitoring solutions, and executing generally understood best practices in the context of unique distributed systems presents all sorts of problems. Megan Anctil, a senior engineer on the Technical Operations team at Slack gave a talk at an O鈥橰eilly Velocity Conference sharing pain points and lessons learned at wrangling known technologies such as Icinga, Graphite, Grafana, and the Elastic Stack to best fit the company鈥檚 use cases.

    At the time, Slack used a few well-known monitoring tools since it鈥檚 Technical Operations team wasn鈥檛 large enough to build an in-house solution for all of these. Nor did the team think it鈥檚 sustainable to throw money at the problem, given the volume of information processed and the not-insignificant price and rigidity of many vendor solutions. With thousands of servers across multiple regions and millions of metrics and documents being processed and indexed per second, the team had to figure out how to scale these technologies to fit Slack鈥檚 needs.

    On the backend, they experimented with multiple clusters in both Graphite and ELK, distributed Icinga nodes, and more. At the same time, they鈥檝e tried to build usability into Grafana that reflects the team鈥檚 mental models of the system and have found ways to make alerts from Icinga more insightful and actionable.

    See more
    Grafana
    Grafana
    Splunk
    Splunk
    Kibana
    Kibana

    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.

    See more
    Elasticsearch
    Elasticsearch
    Grafana
    Grafana
    Kibana
    Kibana

    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

    See more
    Kibana
    Kibana
    Grafana
    Grafana

    For our Predictive Analytics platform, we have used both Grafana and Kibana

    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)
    See more
    Interest over time
    Reviews of Kibana and Munin
    No reviews found
    How developers use Kibana and Munin
    Avatar of Clarabridge Engage
    Clarabridge Engage uses KibanaKibana

    Used for graphing internal logging data; including metrics related to how fast we serve pages and execute MySQL/ElasticSearch queries.

    Avatar of Wirkn Inc.
    Wirkn Inc. uses KibanaKibana

    Our Kibana instances uses our ElasticSearch search data to help answer any complicated questions we have about our data.

    Avatar of Hevelop
    Hevelop uses KibanaKibana

    Kibana is our tools to query data in Elasticsearch clusters set up as catalog search engine.

    Avatar of Diogo Silva
    Diogo Silva uses KibanaKibana

    Perfect for exploring and visualizing the data available at ElasticSearch

    Avatar of Tongliang Liu
    Tongliang Liu uses KibanaKibana

    Log visualization. Wish it could add built-in alert functionality.

    How much does Kibana cost?
    How much does Munin cost?
    Pricing unavailable
    News about Munin
    More news