Overview

Kibana

Stacks20.7K

Followers16.4K

Votes262

GitHub Stars20.8K

Forks8.5K

LogDNA

Stacks101

Followers144

Votes18

Kibana vs LogDNA: What are the differences?

# Introduction

1. **Deployment**: Kibana is typically self-hosted, requiring users to set up and maintain their own infrastructure, while LogDNA is a cloud-based solution, allowing for quicker deployment and lower maintenance requirements.
2. **Integration**: Kibana integrates seamlessly with the Elasticsearch stack, providing advanced analytics capabilities, while LogDNA caters to a wider range of log sources and third-party integrations, offering more flexibility in data collection.
3. **User Interface**: Kibana offers a highly customizable and feature-rich interface, empowering users to create complex visualizations and dashboards, whereas LogDNA provides a simplified and user-friendly UI, focusing on ease of use and quick log analysis.
4. **Scalability**: Kibana is better suited for large-scale deployments and complex data processing due to its strong integration with Elasticsearch, whereas LogDNA excels in handling high log volumes and provides real-time log streaming with minimal latency.
5. **Search Capabilities**: Kibana offers advanced search features and query language support, enabling users to perform complex searches and aggregations efficiently, while LogDNA provides fast and intuitive search functionality with AI-powered insights, simplifying log analysis for users with varying levels of expertise.
6. **Security and Compliance**: Kibana offers a wide array of security features and compliance options, making it a suitable choice for enterprises with strict security requirements, whereas LogDNA provides robust encryption and access control measures, ensuring data protection and compliance with industry standards.

In Summary, Kibana and LogDNA differ in deployment, integration capabilities, user interface design, scalability, search functionality, and security features.

Advice on Kibana, LogDNA

matteo1989it

Jun 26, 2019

Reviewon

Kibana

Grafana

Elasticsearch

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

757k views757k

Comments

StackShare

Jun 25, 2019

Needs advice

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."

663k views663k

Comments

abrahamfathman

Jun 26, 2019

Reviewon

Kibana

Splunk

Grafana

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.

2.29M views2.29M

Comments

Detailed Comparison

Kibana	LogDNA
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.	The easiest log management system you will ever use! LogDNA is a cloud-based log management system that allows engineering and devops to aggregate all system and application logs into one efficient platform. Save, store, tail and search app
Flexible analytics and visualization platform;Real-time summary and charting of streaming data;Intuitive interface for a variety of users;Instant sharing and embedding of dashboards	Aggregate Logs & Analyze Related Events;Easy Setup in Minutes;Powerful Search & Alerts;Save what you see as a View;Modern User Interface;Tail -f Like a Boss;Debug & Troubleshoot Faster
Statistics
GitHub Stars 20.8K	GitHub Stars -
GitHub Forks 8.5K	GitHub Forks -
Stacks 20.7K	Stacks 101
Followers 16.4K	Followers 144
Votes 262	Votes 18
Pros & Cons
Pros 88 Easy to setup 65 Free 45 Can search text 21 Has pie chart 13 X-axis is not restricted to timestamp Cons 7 Unintuituve 4 Works on top of elastic only 4 Elasticsearch is huge 3 Hardweight UI	Pros 6 Easy setup 4 Cheap 3 Extremely fast 2 Powerful filtering and alerting functionality 1 Multi-cloud Cons 1 Limited visualization capabilities 1 Cannot copy & paste text from visualization
Integrations
Logstash Elasticsearch Beats	No integrations available

What are some alternatives to Kibana, LogDNA?

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.

Papertrail

Papertrail helps detect, resolve, and avoid infrastructure problems using log messages. Papertrail's practicality comes from our own experience as sysadmins, developers, and entrepreneurs.

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.

Logmatic

Get a clear overview of what is happening across your distributed environments, and spot the needle in the haystack in no time. Build dynamic analyses and identify improvements for your software, your user experience and your business.

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.

Logentries

Logentries makes machine-generated log data easily accessible to IT operations, development, and business analysis teams of all sizes. With the broadest platform support and an open API, Logentries brings the value of log-level data to any system, to any team member, and to a community of more than 25,000 worldwide users.

Logstash

Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use (like, for searching). If you store them in Elasticsearch, you can view and analyze them with Kibana.

Nagios

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

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

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.