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Kibana vs Prometheus: What are the differences?

Kibana and Prometheus are both powerful tools in the realm of observability and monitoring. Let's explore the key differences between them.

  1. Data Source: Kibana is a data visualization and exploration tool primarily used with Elasticsearch data. It allows users to create charts, dashboards, and visualizations based on data stored in Elasticsearch. Prometheus, on the other hand, is a monitoring and alerting toolkit that is capable of collecting and storing time-series data. It can scrape metrics from various sources such as applications, services, and servers.

  2. Use Case: Kibana is ideal for visualizing and analyzing log data and metrics collected from Elasticsearch. It helps users gain insights and perform troubleshooting based on the data stored in Elasticsearch. Prometheus is designed for monitoring applications and infrastructure. It excels at collecting and analyzing metrics in real-time, triggering alerts based on predefined rules, and providing detailed insights into the health and performance of systems.

  3. Architecture: Kibana follows a client-server architecture, where the user interacts with the Kibana server through a web browser. It connects to Elasticsearch to query and retrieve data for visualization purposes. Prometheus has a pull-based model, where it scrapes metrics from targets that expose them via an HTTP endpoint. It stores data in its own time-series database and provides a query language (PromQL) for analysis.

  4. Integration: Kibana integrates seamlessly with the entire Elastic Stack, including Elasticsearch, Logstash, Beats, and other components. It can visualize and analyze data from different sources, making it a versatile tool for log analysis, infrastructure monitoring, and more. Prometheus is designed to work independently but can be integrated with other alerting and visualization tools. It has integrations with Grafana, which allows users to create in-depth dashboards and visualizations.

  5. Scalability: Kibana's scalability depends on the underlying infrastructure and the cluster setup of Elasticsearch, as it leverages Elasticsearch for data storage and retrieval. Prometheus scales horizontally by adding more instances to handle increasing workloads. It uses a federated model to scrape metrics from multiple instances and aggregate them for analysis.

  6. Alerting: While Kibana provides visualization capabilities, it does not have built-in alerting. Users need to rely on external tools like Elasticsearch Watcher or other monitoring solutions to set up alerts. Prometheus has robust alerting capabilities built-in. It can define alert rules based on metric thresholds and notify users in real-time when the thresholds are breached.

In summary, Kibana is a data visualization and exploration tool that works with Elasticsearch, while Prometheus is a monitoring and alerting toolkit capable of collecting and analyzing metrics from various sources. Kibana is ideal for log analysis and visualizing Elasticsearch data, whereas Prometheus is designed for real-time monitoring, alerting, and analyzing metrics from applications and infrastructure.

Advice on Kibana and Prometheus
Susmita Meher
Senior SRE at African Bank · | 4 upvotes · 828.5K views
Needs advice
on
GrafanaGrafanaGraphiteGraphite
and
PrometheusPrometheus

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.

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Replies (1)
Sakti Behera
Technical Specialist, Software Engineering at AT&T · | 3 upvotes · 614K views
Recommends
on
GrafanaGrafanaPrometheusPrometheus

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.

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Sunil Chaudhari
Needs advice
on
MetricbeatMetricbeat
and
PrometheusPrometheus

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.

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Replies (2)
Matthew Rothstein
Recommends
on
PrometheusPrometheus

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.

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Recommends
on
InstanaInstana

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

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Mat Jovanovic
Head of Cloud at Mats Cloud · | 3 upvotes · 756.4K views
Needs advice
on
DatadogDatadogGrafanaGrafana
and
PrometheusPrometheus

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.

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Replies (2)
Recommends
on
DatadogDatadog

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.

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Lucas Rincon
Recommends
on
InstanaInstana

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/

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Needs advice
on
GrafanaGrafana
and
KibanaKibana

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."

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Replies (7)
Recommends
on
GrafanaGrafana
at

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)
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Recommends
on
KibanaKibana

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

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Bram Verdonck
Recommends
on
GrafanaGrafana
at

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 .

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Recommends
on
KibanaKibana

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.

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Recommends
on
KibanaKibana

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).

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Recommends
on
GrafanaGrafana

I use Grafana because it is without a doubt the best way to visualize metrics

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Povilas Brilius
PHP Web Developer at GroundIn Software · | 0 upvotes · 629K views
Recommends
on
KibanaKibana
at

@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.

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Decisions about Kibana and Prometheus
Matt Menzenski
Senior Software Engineering Manager at PayIt · | 16 upvotes · 1M views

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.

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Leonardo Henrique da Paixão
Junior QA Tester at SolarMarket · | 15 upvotes · 380.1K views

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.

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Pros of Kibana
Pros of Prometheus
  • 88
    Easy to setup
  • 65
    Free
  • 45
    Can search text
  • 21
    Has pie chart
  • 13
    X-axis is not restricted to timestamp
  • 9
    Easy queries and is a good way to view logs
  • 6
    Supports Plugins
  • 4
    Dev Tools
  • 3
    More "user-friendly"
  • 3
    Can build dashboards
  • 2
    Out-of-Box Dashboards/Analytics for Metrics/Heartbeat
  • 2
    Easy to drill-down
  • 1
    Up and running
  • 47
    Powerful easy to use monitoring
  • 38
    Flexible query language
  • 32
    Dimensional data model
  • 27
    Alerts
  • 23
    Active and responsive community
  • 22
    Extensive integrations
  • 19
    Easy to setup
  • 12
    Beautiful Model and Query language
  • 7
    Easy to extend
  • 6
    Nice
  • 3
    Written in Go
  • 2
    Good for experimentation
  • 1
    Easy for monitoring

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Cons of Kibana
Cons of Prometheus
  • 7
    Unintuituve
  • 4
    Works on top of elastic only
  • 4
    Elasticsearch is huge
  • 3
    Hardweight UI
  • 12
    Just for metrics
  • 6
    Bad UI
  • 6
    Needs monitoring to access metrics endpoints
  • 4
    Not easy to configure and use
  • 3
    Supports only active agents
  • 2
    Written in Go
  • 2
    TLS is quite difficult to understand
  • 2
    Requires multiple applications and tools
  • 1
    Single point of failure

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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 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.

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What are some alternatives to Kibana and Prometheus?
Datadog
Datadog is the leading service for cloud-scale monitoring. It is used by IT, operations, and development teams who build and operate applications that run on dynamic or hybrid cloud infrastructure. Start monitoring in minutes with Datadog!
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
It is a SaaS solution to manage your log data. There is nothing to install and updates are automatically applied to your Loggly subdomain.
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
It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
See all alternatives