Grafana vs Kibana vs Nagios

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

Grafana

17.3K
13.8K
+ 1
415
Kibana

20.1K
15.9K
+ 1
261
Nagios

829
1.1K
+ 1
102

Grafana vs Kibana vs Nagios: What are the differences?

Introduction:

This markdown code provides a comparison between Grafana, Kibana, and Nagios, highlighting the key differences between these three software tools commonly used in web development.

  1. Interface and Visualization: Grafana focuses on providing visually appealing and interactive dashboards with a wide range of visualization options, making it more suitable for data exploration and monitoring. Kibana primarily serves as a data visualization tool but is highly integrated with the Elasticsearch stack, allowing for powerful analytics and searching capabilities. Nagios, on the other hand, is primarily used for system monitoring and alerting, providing a straightforward interface with basic visualization features.

  2. Data Sources and Integrations: Grafana supports a variety of data sources, including databases, cloud platforms, and popular monitoring tools, enabling users to consolidate and visualize data from multiple sources in one place. Kibana is tightly integrated with Elasticsearch, making it the ideal choice for analyzing and visualizing data stored in Elasticsearch indexes. Nagios predominantly focuses on system monitoring, and though it supports plugins and custom extensions, it lacks the extensive data source integrations provided by Grafana and Kibana.

  3. Alerting and Notification: Grafana offers flexible alerting capabilities, allowing users to set up custom alerts based on specific conditions and thresholds. It supports various notification channels, such as email, Slack, PagerDuty, etc., ensuring timely alerts to the relevant stakeholders. Kibana lacks native alerting functionality, but this can be achieved by leveraging third-party tools or Elasticsearch Watcher. Nagios, known for its robust alerting system, provides comprehensive alerting features out of the box, including event escalation, dependency mapping, and notification options.

  4. Community and Ecosystem: Grafana has gained a significant following and has a vibrant community, resulting in a vast number of plugins, extensions, and community-developed dashboards that enhance its functionality. Kibana benefits from the broader Elasticsearch ecosystem and Elasticsearch user community, offering extensive support and integrations. Nagios, being one of the oldest and widely adopted monitoring systems, has a large user base and an active community, resulting in a wide range of plugins and extensions for extending its capabilities.

  5. Scalability and Performance: Grafana has proven scalability and performance, with the ability to handle large volumes of data and concurrent user requests efficiently. Kibana, being part of the Elasticsearch stack, can scale horizontally by adding more Elasticsearch nodes for better performance and storage capacity. Nagios is known for its lightweight and low resource consumption, making it suitable for monitoring small to medium-sized environments.

  6. Use Case and Focus: Grafana is popular in the DevOps and IT operations space and is widely used for monitoring and troubleshooting real-time metrics and logs in a modern infrastructure. Kibana is a core component of the Elasticsearch stack, primarily used for monitoring and analyzing log data, metrics, and business intelligence. Nagios, being a traditional and comprehensive monitoring solution, is commonly used for system and network monitoring across various industries.

In Summary, Grafana focuses on visually appealing dashboards and supports multiple data sources, Kibana is tightly integrated with Elasticsearch and excels in log analysis, while Nagios provides comprehensive system monitoring capabilities with a strong focus on alerting and performance monitoring.

Advice on Grafana, Kibana, and Nagios
Susmita Meher
Senior SRE at African Bank · | 4 upvotes · 774.5K 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 · 559.8K 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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Mat Jovanovic
Head of Cloud at Mats Cloud · | 3 upvotes · 704.1K 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)
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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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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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 · 586.4K 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 Grafana, Kibana, and Nagios
Matt Menzenski
Senior Software Engineering Manager at PayIt · | 15 upvotes · 979.1K 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 · | 2 upvotes · 172K views

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.

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Leonardo Henrique da Paixão
Junior QA Tester at SolarMarket · | 15 upvotes · 348.4K 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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Matthias Fleschütz
Teamlead IT at NanoTemper Technologies · | 2 upvotes · 122.4K views
  • free open source
  • modern interface and architecture
  • large community
  • extendable I knew Nagios for decades but it was really outdated (by its architecture) at some point. That's why Icinga started first as a fork, not with Icinga2 it is completely built from scratch but backward-compatible with Nagios plugins. Now it has reached a state with which I am confident.
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Pros of Grafana
Pros of Kibana
Pros of Nagios
  • 89
    Beautiful
  • 68
    Graphs are interactive
  • 57
    Free
  • 56
    Easy
  • 34
    Nicer than the Graphite web interface
  • 26
    Many integrations
  • 18
    Can build dashboards
  • 10
    Easy to specify time window
  • 10
    Can collaborate on dashboards
  • 9
    Dashboards contain number tiles
  • 5
    Open Source
  • 5
    Integration with InfluxDB
  • 5
    Click and drag to zoom in
  • 4
    Authentification and users management
  • 4
    Threshold limits in graphs
  • 3
    Alerts
  • 3
    It is open to cloud watch and many database
  • 3
    Simple and native support to Prometheus
  • 2
    Great community support
  • 2
    You can use this for development to check memcache
  • 2
    You can visualize real time data to put alerts
  • 0
    Grapsh as code
  • 0
    Plugin visualizationa
  • 88
    Easy to setup
  • 64
    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
    Can build dashboards
  • 3
    More "user-friendly"
  • 2
    Out-of-Box Dashboards/Analytics for Metrics/Heartbeat
  • 2
    Easy to drill-down
  • 1
    Up and running
  • 53
    It just works
  • 28
    The standard
  • 12
    Customizable
  • 8
    The Most flexible monitoring system
  • 1
    Huge stack of free checks/plugins to choose from

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Cons of Grafana
Cons of Kibana
Cons of Nagios
  • 1
    No interactive query builder
  • 6
    Unintuituve
  • 4
    Elasticsearch is huge
  • 3
    Hardweight UI
  • 3
    Works on top of elastic only
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    What is 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.

    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 Nagios?

    Nagios is a host/service/network monitoring program written in C and released under the GNU General Public License.

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    What are some alternatives to Grafana, Kibana, and Nagios?
    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!
    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.
    Graphite
    Graphite does two things: 1) Store numeric time-series data and 2) Render graphs of this data on demand
    Splunk
    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
    Netdata
    Netdata collects metrics per second & presents them in low-latency dashboards. It's designed to run on all of your physical & virtual servers, cloud deployments, Kubernetes clusters & edge/IoT devices, to monitor systems, containers & apps
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