Datadog vs Grafana vs Raygun

Overview

Raygun

Stacks136

Followers178

Votes198

Datadog

Stacks9.8K

Followers8.2K

Votes861

Grafana

Stacks18.4K

Followers14.6K

Votes415

GitHub Stars70.7K

Forks13.1K

Datadog vs Grafana vs Raygun: What are the differences?

Data Collection: Datadog offers more options for data collection from various sources, including applications, servers, databases, and cloud services, while Grafana leans more towards visualizing and analyzing data that has already been collected. Raygun, on the other hand, focuses on application performance monitoring and crash reporting, providing insights into code-level issues.
Alerting Capabilities: Datadog provides robust alerting capabilities with a wide range of customization options for defining alert conditions based on metrics, logs, and traces. Grafana also offers alerting features but requires integration with additional tools for full functionality. Raygun has alerting systems that can notify users about crashes and performance issues but may lack the deep customization options of Datadog.
Integration Ecosystem: Datadog has a vast integration ecosystem with many third-party integrations and plugins available for various tools and services, enabling seamless connectivity. Grafana emphasizes integration with different data sources for creating visualizations but may require more effort for extensive integrations. Raygun's integrations primarily focus on application monitoring and providing detailed insights into code-level issues.
Machine Learning Capabilities: Datadog incorporates machine learning algorithms to provide anomaly detection and forecasting capabilities for better understanding of data patterns and trends. Grafana offers plugins for machine learning integrations to enable predictive analytics but may not have as robust built-in machine learning features as Datadog. Raygun focuses on application-level insights rather than machine learning capabilities.
User Interface: Datadog has a user-friendly interface with intuitive dashboards and visualization tools, making it easier for users to navigate and analyze data. Grafana also offers a visually appealing interface with extensive customization options for creating unique dashboards. Raygun's interface prioritizes ease of use for developers and focuses on providing actionable insights related to application performance and errors.
Community Support: Datadog has a strong community presence with active user forums, support documentation, and regular updates and feature releases based on user feedback. Grafana also has a dedicated community of users and contributors, providing support through forums and community plugins. Raygun offers support through its knowledge base and customer service but may have a smaller community compared to Datadog and Grafana.

In Summary, Datadog excels in data collection and machine learning capabilities, Grafana focuses on visualization and alerting, while Raygun specializes in application monitoring and providing actionable insights related to code-level issues.

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Advice on Raygun, Datadog, Grafana

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

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

Detailed Comparison

Raygun	Datadog	Grafana
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.	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 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.
Error, crash and performance monitoring, in one platform;Support for every major programming language and platform;Minutes to set up;Error grouping by root cause, assignment, comments and full diagnostic details;Front end performance monitoring to identify slow page load speeds in the user's browser;Modern Application Performance Monitoring (APM) with intelligent, automatic issue creation;Link with GitHub, Slack, JIRA and 30+ other team workflow integrations;See which problems were introduced (or fixed) in each deployment;See every issue your users have encountered and solve them with ease	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	Create, edit, save & search dashboards;Change column spans and row heights;Drag and drop panels to rearrange;Use InfluxDB or Elasticsearch as dashboard storage;Import & export dashboard (json file);Import dashboard from Graphite;Templating
Statistics
GitHub Stars -	GitHub Stars -	GitHub Stars 70.7K
GitHub Forks -	GitHub Forks -	GitHub Forks 13.1K
Stacks 136	Stacks 9.8K	Stacks 18.4K
Followers 178	Followers 8.2K	Followers 14.6K
Votes 198	Votes 861	Votes 415
Pros & Cons
Pros 31 Easy setup and brilliant features 19 Huge range of programming languages supported 19 Integrates with many tools I use (e.g. GitHub, HipChat) 17 Makes my job so much easier 17 Support for JavaScript source maps	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 89 Beautiful 68 Graphs are interactive 57 Free 56 Easy 34 Nicer than the Graphite web interface Cons 1 No interactive query builder
Integrations
Java Jira .NET Campfire Python Golang CakePHP Trello Ruby Zapier	NGINX Google App Engine Apache HTTP Server Java Docker Pingdom MySQL Ruby Python Memcached	No integrations available

What are some alternatives to Raygun, Datadog, Grafana?

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.

Sentry

Sentry’s Application Monitoring platform helps developers see performance issues, fix errors faster, and optimize their code health.

Rollbar

Rollbar is the leading continuous code improvement platform that proactively discovers, predicts, and remediates errors with real-time AI-assisted workflows. With Rollbar, developers continually improve their code and constantly innovate ra

Bugsnag

Bugsnag captures errors from your web, mobile and back-end applications, providing instant visibility into user impact. Diagnostic data and tools are included to help your team prioritize, debug and fix exceptions fast.

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.

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.

Opbeat

Opbeat is application monitoring for developers, and gives you performance metrics, error logging, release tracking and workflow in one smart product.

Airbrake

Airbrake collects errors for your applications in all major languages and frameworks. We alert you to new errors and give you critical context, trends and details needed to find and fix errors fast.

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.

Related Comparisons

Datadog vs Grafana vs Raygun: What are the differences?

Data Collection: Datadog offers more options for data collection from various sources, including applications, servers, databases, and cloud services, while Grafana leans more towards visualizing and analyzing data that has already been collected. Raygun, on the other hand, focuses on application performance monitoring and crash reporting, providing insights into code-level issues.
Alerting Capabilities: Datadog provides robust alerting capabilities with a wide range of customization options for defining alert conditions based on metrics, logs, and traces. Grafana also offers alerting features but requires integration with additional tools for full functionality. Raygun has alerting systems that can notify users about crashes and performance issues but may lack the deep customization options of Datadog.
Integration Ecosystem: Datadog has a vast integration ecosystem with many third-party integrations and plugins available for various tools and services, enabling seamless connectivity. Grafana emphasizes integration with different data sources for creating visualizations but may require more effort for extensive integrations. Raygun's integrations primarily focus on application monitoring and providing detailed insights into code-level issues.
Machine Learning Capabilities: Datadog incorporates machine learning algorithms to provide anomaly detection and forecasting capabilities for better understanding of data patterns and trends. Grafana offers plugins for machine learning integrations to enable predictive analytics but may not have as robust built-in machine learning features as Datadog. Raygun focuses on application-level insights rather than machine learning capabilities.
User Interface: Datadog has a user-friendly interface with intuitive dashboards and visualization tools, making it easier for users to navigate and analyze data. Grafana also offers a visually appealing interface with extensive customization options for creating unique dashboards. Raygun's interface prioritizes ease of use for developers and focuses on providing actionable insights related to application performance and errors.
Community Support: Datadog has a strong community presence with active user forums, support documentation, and regular updates and feature releases based on user feedback. Grafana also has a dedicated community of users and contributors, providing support through forums and community plugins. Raygun offers support through its knowledge base and customer service but may have a smaller community compared to Datadog and Grafana.

Datadog vs Grafana vs Raygun

Overview