AppDynamics vs Kibana

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AppDynamics

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Kibana

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AppDynamics vs Kibana: What are the differences?

Introduction

AppDynamics and Kibana are two popular tools used in the field of IT monitoring and analytics. While both tools are designed to provide insights into software performance, there are some key differences between them. In this article, we will explore six key differences between AppDynamics and Kibana.

  1. Data Collection and Visualization: AppDynamics is a comprehensive Application Performance Monitoring (APM) tool that collects data directly from the application layer. It provides detailed metrics, traces, and code-level visibility. On the other hand, Kibana is a data visualization tool that relies on data collected by Elasticsearch. It supports various data sources and provides flexible data visualization options.

  2. Scope: AppDynamics primarily focuses on monitoring and performance management of individual applications or services. It provides detailed insights into the behavior of these applications and identifies performance bottlenecks. On the contrary, Kibana has a broader scope and can be used to analyze various types of data, not just limited to application performance. It can be used for log analysis, security analytics, and business intelligence, among other use cases.

  3. Real-time Monitoring: AppDynamics excels in providing real-time monitoring capabilities. It collects data in near real-time, allowing users to monitor the performance of their applications as it happens. Kibana, on the other hand, relies on data indexing and search capabilities provided by Elasticsearch. While it can provide near real-time insights, it may not be as real-time as AppDynamics.

  4. Alerting and Troubleshooting: AppDynamics offers robust alerting features that allow users to set up custom thresholds and receive notifications when performance degradations occur. It also provides powerful troubleshooting capabilities, allowing users to drill down to the root cause of performance issues. Kibana, in comparison, does not have built-in alerting functionality. It relies on integrations with other tools or custom solutions for alerting. Troubleshooting in Kibana is primarily done through data visualization and analysis.

  5. Ease of Use: AppDynamics provides a user-friendly interface with intuitive dashboards, making it easier for users to navigate and understand the monitoring data. It offers pre-built dashboards and reports for quick insights. Kibana, being a part of the Elastic Stack, has a steeper learning curve and requires some technical expertise to set up and configure. It provides a high level of customization but may require more effort to get started.

  6. Pricing Model: AppDynamics follows a commercial pricing model based on the number of monitored components or application instances. It offers different tiers with varying feature sets and pricing. Kibana, on the other hand, is an open-source tool provided by Elastic. While the core Kibana functionality is free, additional features and commercial support may require a paid subscription to the Elastic Stack.

In summary, AppDynamics is a specialized APM tool focused on real-time monitoring, troubleshooting, and performance management of individual applications. On the other hand, Kibana is a versatile data visualization tool with a broader scope, supporting various data sources and use cases. Kibana may require more technical expertise and customization effort compared to AppDynamics.

Advice on AppDynamics and Kibana
Farzeem Diamond Jiwani
Software Engineer at IVP · | 8 upvotes · 1.4M views
Needs advice
on
AppDynamicsAppDynamicsDatadogDatadog
and
DynatraceDynatrace

Hey there! We are looking at Datadog, Dynatrace, AppDynamics, and New Relic as options for our web application monitoring.

Current Environment: .NET Core Web app hosted on Microsoft IIS

Future Environment: Web app will be hosted on Microsoft Azure

Tech Stacks: IIS, RabbitMQ, Redis, Microsoft SQL Server

Requirement: Infra Monitoring, APM, Real - User Monitoring (User activity monitoring i.e., time spent on a page, most active page, etc.), Service Tracing, Root Cause Analysis, and Centralized Log Management.

Please advise on the above. Thanks!

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Needs advice
on
AppDynamicsAppDynamicsDynatraceDynatrace
and
Site24x7Site24x7

Hi Folks,

I am trying to evaluate Site24x7 against AppDynamics, Dynatrace, and New Relic. Has anyone used Site24X7? If so, what are your opinions on the tool? I know that the license costs are very low compared to other tools in the market. Other than that, are there any major issues anyone has encountered using the tool itself?

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Replies (1)
Lucas Rincon
Recommends
on
InstanaInstana

what are the most important things you are looking for the tools to do? each has their strong points... are you looking to monitor new tech like containers, k8s, and microservices?

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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 · 603.3K 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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Pros of AppDynamics
Pros of Kibana
  • 21
    Deep code visibility
  • 13
    Powerful
  • 8
    Real-Time Visibility
  • 7
    Great visualization
  • 6
    Easy Setup
  • 6
    Comprehensive Coverage of Programming Languages
  • 4
    Deep DB Troubleshooting
  • 3
    Excellent Customer Support
  • 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

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Cons of AppDynamics
Cons of Kibana
  • 5
    Expensive
  • 2
    Poor to non-existent integration with aws services
  • 6
    Unintuituve
  • 4
    Elasticsearch is huge
  • 3
    Hardweight UI
  • 3
    Works on top of elastic only

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What are some alternatives to AppDynamics and Kibana?
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!
New Relic
The world’s best software and DevOps teams rely on New Relic to move faster, make better decisions and create best-in-class digital experiences. If you run software, you need to run New Relic. More than 50% of the Fortune 100 do too.
Nagios
Nagios is a host/service/network monitoring program written in C and released under the GNU General Public License.
Splunk
It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
ELK
It is the acronym for three open source projects: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search and analytics engine. Logstash is a server‑side data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a "stash" like Elasticsearch. Kibana lets users visualize data with charts and graphs in Elasticsearch.
See all alternatives