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Kibana

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14K
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257
Loggly

270
288
+ 1
168
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Kibana vs Loggly: What are the differences?

What is 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.

What is Loggly? Log Management, Simplified in the Cloud. The world's most popular cloud-based log management service delivers application intelligence.

Kibana belongs to "Monitoring Tools" category of the tech stack, while Loggly can be primarily classified under "Log Management".

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, Loggly provides the following key features:

  • See what your application is doing during development
  • Catch exceptions and track execution flow
  • Graph and report on the number of errors generated

"Easy to setup" is the top reason why over 76 developers like Kibana, while over 35 developers mention "Centralized log management" as the leading cause for choosing Loggly.

Kibana is an open source tool with 12.4K GitHub stars and 4.81K GitHub forks. Here's a link to Kibana's open source repository on GitHub.

Airbnb, DigitalOcean, and 9GAG are some of the popular companies that use Kibana, whereas Loggly is used by Instacart, Heroku, and Accenture. Kibana has a broader approval, being mentioned in 907 company stacks & 481 developers stacks; compared to Loggly, which is listed in 95 company stacks and 15 developer stacks.

Advice on Kibana and Loggly
Needs advice
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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
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
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
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
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
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
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 · 434.2K views
Recommends
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 Kibana
Pros of Loggly
  • 88
    Easy to setup
  • 62
    Free
  • 45
    Can search text
  • 21
    Has pie chart
  • 13
    X-axis is not restricted to timestamp
  • 8
    Easy queries and is a good way to view logs
  • 6
    Supports Plugins
  • 3
    Dev Tools
  • 3
    More "user-friendly"
  • 3
    Can build dashboards
  • 2
    Easy to drill-down
  • 2
    Out-of-Box Dashboards/Analytics for Metrics/Heartbeat
  • 1
    Up and running
  • 37
    Centralized log management
  • 25
    Easy to setup
  • 21
    Great filtering
  • 16
    Live logging
  • 15
    Json log support
  • 10
    Log Management
  • 10
    Alerting
  • 7
    Great Dashboards
  • 7
    Love the product
  • 4
    Heroku Add-on
  • 2
    Easy to setup and use
  • 2
    Easy setup
  • 2
    No alerts in free plan
  • 2
    Great UI
  • 2
    Good parsing
  • 2
    Powerful
  • 2
    Fast search
  • 2
    Backup to S3

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Cons of Kibana
Cons of Loggly
  • 5
    Unintuituve
  • 3
    Elasticsearch is huge
  • 3
    Works on top of elastic only
  • 2
    Hardweight UI
  • 3
    Pricey after free plan

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- No public GitHub repository available -

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 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.

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What companies use Kibana?
What companies use Loggly?
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What tools integrate with Kibana?
What tools integrate with Loggly?

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Blog Posts

May 21 2019 at 12:20AM

Elastic

ElasticsearchKibanaLogstash+4
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GitHubPythonReact+42
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GitHubPythonGit+22
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JavaScriptGitHubGit+33
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GitHubMySQLSlack+44
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What are some alternatives to Kibana and Loggly?
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.
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.
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