Datadog vs Kibana

Overview

Datadog

Stacks9.8K

Followers8.2K

Votes861

Kibana

Stacks20.6K

Followers16.4K

Votes262

GitHub Stars20.8K

Forks8.5K

Datadog vs Kibana: What are the differences?

Both Datadog and Kibana are widely used tools for monitoring and analyzing data in the field of IT operations and management. Let's explore the key differences between Datadog and Kibana:

Architecture: Datadog is a fully managed cloud-based monitoring platform, whereas Kibana is a component of the Elastic Stack, which includes Elasticsearch and Logstash. Datadog provides a ready-to-use solution with an intuitive user interface, while Kibana requires the setup and configuration of the entire Elastic Stack.
Data Collection: Datadog supports a wide range of data collection methods, including various agent-based integrations, APIs, and client libraries. On the other hand, Kibana relies on Logstash for data ingestion and Elasticsearch for data storage.
Visualization Capabilities: Datadog offers a comprehensive set of visualization tools, allowing users to create interactive dashboards, charts, and graphs. Kibana specializes in data exploration and visualization, providing advanced filtering, aggregations, and time series analysis options.
Alerting and Notification: Datadog provides a robust alerting system that allows users to set up custom alerts based on predefined conditions. It offers various notification channels such as email, Slack, and PagerDuty. Kibana, on the other hand, lacks a built-in alerting mechanism. Users usually rely on third-party tools or custom solutions for setting up alerts.
Community and Integration Ecosystem: Datadog has a large and active community, constantly developing and sharing integrations with popular technologies. It provides seamless integration with various third-party services and tools. Kibana benefits from the extensive Elastic Stack community and its integration ecosystem, which includes Elasticsearch, Beats, and Logstash.
Cost Structure: Datadog follows a subscription-based pricing model, offering different plans based on the size and needs of the organization. Kibana, being open-source, is free to use, but the associated infrastructure costs (Elasticsearch, Logstash, etc.) might increase depending on the scale and requirements.

In summary, Datadog is a fully managed cloud-based monitoring platform with comprehensive visualization and alerting capabilities, while Kibana is a component of the Elastic Stack, known for its powerful data exploration and visualization features. Datadog simplifies the setup process with ready-to-use solutions, whereas Kibana requires the configuration of the entire stack. Datadog has a wide range of data collection methods and integrations, while Kibana relies on Elasticsearch and Logstash.

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Advice on Datadog, Kibana

Leonardo Henrique da

Pleno QA Enginneer at SolarMarket

Dec 8, 2020

Decided

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.

402k views402k

Comments

Farzeem Diamond

Software Engineer at IVP

Jul 21, 2020

Needs adviceon

Datadog

Dynatrace

AppDynamics

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!

1.59M views1.59M

Comments

Medeti

Jun 27, 2020

Needs adviceon

Amazon EKS

Kubernetes

AWS Elastic Load Balancing (ELB)

We are looking for a centralised monitoring solution for our application deployed on Amazon EKS. We would like to monitor using metrics from Kubernetes, AWS services (NeptuneDB, AWS Elastic Load Balancing (ELB), Amazon EBS, Amazon S3, etc) and application microservice's custom metrics.

We are expected to use around 80 microservices (not replicas). I think a total of 200-250 microservices will be there in the system with 10-12 slave nodes.

We tried Prometheus but it looks like maintenance is a big issue. We need to manage scaling, maintaining the storage, and dealing with multiple exporters and Grafana. I felt this itself needs few dedicated resources (at least 2-3 people) to manage. Not sure if I am thinking in the correct direction. Please confirm.

You mentioned Datadog and Sysdig charges per host. Does it charge per slave node?

1.51M views1.51M

Comments

Detailed Comparison

Datadog	Kibana
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!	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.
14-day Free Trial for an unlimited number of hosts;200+ turn-key integrations for data aggregation;Clean graphs of StatsD and other integrations;Slice and dice graphs and alerts by tags, roles, and more;Easy-to-use search for hosts, metrics, and tags;Alert notifications via e-mail and PagerDuty;Receive alerts on any metric, for a single host or an entire cluster;Full API access in more than 15 languages;Overlay metrics and events across disparate sources;Out-of-the-box and customizable monitoring dashboards;Easy way to compute rates, ratios, averages, or integrals;Sampling intervals of 10 seconds;Mute all alerts with 1 click during upgrades and maintenance;Tools for team collaboration	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
Statistics
GitHub Stars -	GitHub Stars 20.8K
GitHub Forks -	GitHub Forks 8.5K
Stacks 9.8K	Stacks 20.6K
Followers 8.2K	Followers 16.4K
Votes 861	Votes 262
Pros & Cons
Pros 140 Monitoring for many apps (databases, web servers, etc) 107 Easy setup 87 Powerful ui 84 Powerful integrations 70 Great value Cons 20 Expensive 4 No errors exception tracking 2 External Network Goes Down You Wont Be Logging 1 Complicated	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 Elasticsearch is huge 4 Works on top of elastic only 3 Hardweight UI
Integrations
NGINX Google App Engine Apache HTTP Server Java Docker Pingdom MySQL Ruby Python Memcached	Logstash Elasticsearch Beats

What are some alternatives to Datadog, Kibana?

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.

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.

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.

Raygun

Raygun gives you a window into how users are really experiencing your software applications. Detect, diagnose and resolve issues that are affecting end users with greater speed and accuracy.

Nagios

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

AppSignal

AppSignal gives you and your team alerts and detailed metrics about your Ruby, Node.js or Elixir application. Sensible pricing, no aggressive sales & support by developers.

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

AppDynamics

AppDynamics develops application performance management (APM) solutions that deliver problem resolution for highly distributed applications through transaction flow monitoring and deep diagnostics.

Zabbix

Zabbix is a mature and effortless enterprise-class open source monitoring solution for network monitoring and application monitoring of millions of metrics.

Sensu

Sensu is the future-proof solution for multi-cloud monitoring at scale. The Sensu monitoring event pipeline empowers businesses to automate their monitoring workflows and gain deep visibility into their multi-cloud environments.

Related Comparisons

Datadog vs Kibana: What are the differences?

Both Datadog and Kibana are widely used tools for monitoring and analyzing data in the field of IT operations and management. Let's explore the key differences between Datadog and Kibana:

Architecture: Datadog is a fully managed cloud-based monitoring platform, whereas Kibana is a component of the Elastic Stack, which includes Elasticsearch and Logstash. Datadog provides a ready-to-use solution with an intuitive user interface, while Kibana requires the setup and configuration of the entire Elastic Stack.
Data Collection: Datadog supports a wide range of data collection methods, including various agent-based integrations, APIs, and client libraries. On the other hand, Kibana relies on Logstash for data ingestion and Elasticsearch for data storage.
Visualization Capabilities: Datadog offers a comprehensive set of visualization tools, allowing users to create interactive dashboards, charts, and graphs. Kibana specializes in data exploration and visualization, providing advanced filtering, aggregations, and time series analysis options.
Alerting and Notification: Datadog provides a robust alerting system that allows users to set up custom alerts based on predefined conditions. It offers various notification channels such as email, Slack, and PagerDuty. Kibana, on the other hand, lacks a built-in alerting mechanism. Users usually rely on third-party tools or custom solutions for setting up alerts.
Community and Integration Ecosystem: Datadog has a large and active community, constantly developing and sharing integrations with popular technologies. It provides seamless integration with various third-party services and tools. Kibana benefits from the extensive Elastic Stack community and its integration ecosystem, which includes Elasticsearch, Beats, and Logstash.
Cost Structure: Datadog follows a subscription-based pricing model, offering different plans based on the size and needs of the organization. Kibana, being open-source, is free to use, but the associated infrastructure costs (Elasticsearch, Logstash, etc.) might increase depending on the scale and requirements.

Datadog vs Kibana

Overview