Kibana vs Prometheus: What are the differences?
Developers describe Kibana as "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. On the other hand, Prometheus is detailed as "An open-source service monitoring system and time series database, developed by SoundCloud". 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.
Kibana and Prometheus can be categorized as "Monitoring" tools.
Some of the features offered by Kibana are:
- Flexible analytics and visualization platform
- Real-time summary and charting of streaming data
- Intuitive interface for a variety of users
On the other hand, Prometheus provides the following key features:
- a multi-dimensional data model (timeseries defined by metric name and set of key/value dimensions)
- a flexible query language to leverage this dimensionality
- no dependency on distributed storage
"Easy to setup" is the primary reason why developers consider Kibana over the competitors, whereas "Powerful easy to use monitoring" was stated as the key factor in picking Prometheus.
Kibana and Prometheus are both open source tools. Prometheus with 24.6K GitHub stars and 3.49K forks on GitHub appears to be more popular than Kibana with 12.2K GitHub stars and 4.72K GitHub forks.
According to the StackShare community, Kibana has a broader approval, being mentioned in 889 company stacks & 453 developers stacks; compared to Prometheus, which is listed in 235 company stacks and 84 developer stacks.
What is Kibana?
What is Prometheus?
Want advice about which of these to choose?Ask the StackShare community!
We primarily use Prometheus to gather metrics and statistics to display them in Grafana. Aside from that we poll Prometheus for our orchestration-solution "JCOverseer" to determine, which host is least occupied at the moment.
Used for graphing internal logging data; including metrics related to how fast we serve pages and execute MySQL/ElasticSearch queries.
Our Kibana instances uses our ElasticSearch search data to help answer any complicated questions we have about our data.
Gather metrics from systems and applications. Evaluate alerting rules. Alerts are pushed to OpsGenie and Slack.
Kibana is our tools to query data in Elasticsearch clusters set up as catalog search engine.
We primarily use Prometheus to gather metrics and statistics to display them in Grafana.