Datadog vs. Prometheus: a side-by-side comparison for 2024

Datadog and Prometheus are both monitoring tools that can be used to monitor the performance of your infrastructure and applications.

Datadog is a cloud-based monitoring and analytics platform that provides a range of features, including real-time monitoring, alerting, and visualization of performance data. It integrates with a wide variety of technologies, including cloud platforms, databases, and applications, and offers support for custom integrations as well.

Prometheus is an open-source monitoring and alerting toolkit that was originally developed at SoundCloud. It is particularly well-suited for use in cloud-native environments, and is widely used in conjunction with Kubernetes. Prometheus has a number of features that make it well-suited for use in dynamic, distributed environments, including its ability to scrape metrics from targets, its support for horizontal scaling, and its use of a time-series database to store collected metrics.

In this article, we will compare these two products in detail to help you find out which one is better suited for you. The comparison will be based on the following criteria:

1. Deployment options
2. Data sources
3. Data visualization
4. Real-time monitoring
5. Search capabilities
6. Machine learning
7. Scalability
8. Pricing
9. UI and UX
10. Documentation and support

Features overview

✕ - does not support

✓ - partial support

✓✓ - full support

1. Deployment options: point Datadog

Both Datadog and Prometheus can be deployed in a variety of ways, depending on your needs and requirements.

Datadog is a SaaS (Software as a Service) solution which means that you can use it without having to install or maintain any infrastructure. You only need to sign up for an account to start using it. Once signed up, you can install the Datadog Agent on your infrastructure and use it to send metrics and logs to the Datadog platform.

Prometheus, on the other hand, is an open-source tool that you can download and install on your own infrastructure. It consists of a number of components, including the Prometheus server, which is responsible for collecting and storing metrics; exporters, which are used to collect metrics from various sources; and various clients and libraries that you can use to instrument your applications and send metrics to Prometheus. You can also use third-party tools, such as the Prometheus Operator, to manage the deployment and scaling of Prometheus in a Kubernetes environment.

In summary, Datadog is a pure SaaS solution, while Prometheus must be installed on your own infrastructure. This means that Prometheus requires more upfront setup and maintenance, but it also gives you more control and flexibility over your monitoring setup.

2. Data sources and integrations: tie

Datadog can collect data from a wide variety of sources using integrations, which are pre-built connectors that allow you to collect data from specific technologies or platforms. For example, you can use the Datadog Agent to collect metrics from servers and applications, or use the Datadog integrations for cloud platforms (such as AWS, GCP, and Azure) to collect data from those platforms. Datadog also supports custom integrations, which allows you to collect data from sources that are not natively supported by the platform.

Prometheus, on the other hand, relies on exporters to collect data from various sources. An exporter is a piece of software that runs on the host being monitored and exposes metrics in a format that Prometheus can scrape. There are many exporters available for a variety of technologies, including databases, message brokers, and applications.

Prometheus also includes a number of client libraries that you can use to instrument your own applications and expose metrics in a format that Prometheus can scrape. In addition, it integrates with a number of visualization and alerting tools, including Grafana, Alertmanager, and various third-party tools.

3. Data visualization: point Datadog

Datadog provides several visualization tools, including dashboards, graphs, and maps, which can be used to display real-time and historical performance data. You can use these tools to create custom views of your data, or to use pre-built widgets and templates to display common metrics and charts. Datadog also supports custom visualizations, allowing you to create your own widgets and charts using JavaScript and HTML.

While, Prometheus does not offer robust data visualization features, it come with a built-in expression browser that you can use to explore and visualize the data stored in its time-series database. The expression browser allows you to enter PromQL queries to extract and visualize specific metrics or ranges of data. However, it is not comparable to Datadog's data visualization ability. You can also use Grafana to query and visualize Prometheus data.

4. Real-time monitoring: tie

Datadog provides real-time monitoring through metrics, logs, and traces. You can use Datadog's dashboards and graphs to view real-time data or set up alerts to notify you of changes in performance or other events. You can also use Datadog's distributed tracing feature to track the performance of individual requests as they flow through your infrastructure, which can be useful for identifying bottlenecks or other issues.

Prometheus also provides real-time monitoring through its ability to regularly scrape metrics from various targets. You can use the Prometheus expression browser to view real-time data or set up alerts using the Alertmanager component. Prometheus also includes a feature called recording rules, which allows you to pre-aggregate and store data in the time-series database, making it easier to view and analyze real-time data.

5. Search capabilities: point Datadog

Datadog provides a powerful search interface that allows you to search through metrics, traces, and logs using a variety of filters and operators. You can use the search interface to find specific data points or to explore and analyze trends in your data. Datadog also offers anomaly detection which uses machine learning algorithms to identify unusual patterns in your data and alert you to potential issues.

Prometheus includes a query language called PromQL, which allows you to search through the data stored in its time-series database. You can use PromQL to extract specific metrics or ranges of data, and to perform calculations and transformations on the data. Prometheus also includes a feature called PromDash, which is a web-based dashboard builder that allows you to create custom dashboards using PromQL queries.

Datadog wins this round for having a more powerful search interface with a variety of filters and operators.

6. Machine learning: point Datadog

Datadog provides a range of machine learning-based features, including anomaly detection, which uses machine learning algorithms to identify unusual patterns in your data and alert you to potential issues. Datadog also includes a feature called forecast, which uses machine learning to predict future values of a metric based on its past behavior. In addition, Datadog's machine learning platform allows you to build custom machine learning models and use them to analyze and understand your data.

Prometheus does not include native support for machine learning, but it can be used in conjunction with other tools that provide machine learning capabilities. For example, you can use Prometheus as a data source for machine learning platforms such as TensorFlow or scikit-learn, or you can use tools such as Grafana, which includes support for machine learning-based features such as anomaly detection.

In summary, Datadog provides a range of native machine learning-based features, while Prometheus does not include native support for machine learning but can be used in conjunction with other tools that offer machine learning capabilities.

7. Scalability: tie

Datadog is a cloud-based platform, which means that it is designed to scale automatically and handle large volumes of data without requiring additional setup or maintenance. You can use Datadog's distributed tracing feature to monitor the performance of distributed applications and identify bottlenecks or other issues.

Prometheus is designed to be highly scalable and can be deployed across multiple servers or instances to support large-scale monitoring environments. It uses a pull-based model, where the Prometheus server periodically scrapes metrics from exporters rather than relying on exporters to push metrics to the server. This allows Prometheus to scale horizontally and handle large volumes of data without requiring additional setup or maintenance.

8. Pricing: point Prometheus

Datadog offers a free trial period, after which you can choose from various paid plans that are based on the volume of data you collect and the features you need. Datadog's paid plans are divided into three tiers: Essential, Pro, and Enterprise. The Essential plan includes the platform's basic features and is suitable for small-scale monitoring environments. The Pro plan includes additional features useful for mid-sized monitoring environments, such as distributed tracing and machine learning, while the Enterprise plan includes all of the features of the platform and is aimed at large-scale organizations.

Prometheus is an open-source tool, which means that you can download and use it for free. However, you may incur additional costs for hosting and maintaining the infrastructure needed to run Prometheus, as well as for any other tools or services that you use in conjunction with Prometheus. For example, you may need to pay for hosting if you use a managed service like Google Kubernetes Engine or Amazon EKS to run Prometheus, or you may need to pay for additional storage or other resources if you use Prometheus to collect and store large volumes of data.

9. UI and UX: point Datadog

Datadog provides a modern and intuitive UI that is designed to be easy to use and navigate. The UI includes various features, such as customizable dashboards, graphs, and maps, that allow you to view and analyze your data in real-time. It also includes a powerful search interface that allows you to find specific data points or to explore and analyze trends in your data.

Prometheus includes a built-in expression browser that you can use to explore and visualize the data stored in its time-series database. The expression browser allows you to enter PromQL queries to extract and visualize specific metrics or ranges of data. Prometheus is also commonly used with Grafana, which is an open-source visualization platform used to create custom dashboards and charts using a variety of data sources, including Prometheus. Grafana provides a modern and intuitive UI that is designed to be easy to use and navigate.

10. Documentation and support: tie

Datadog provides a range of resources to help you get started with its platform, including a comprehensive documentation website, a user guide, and several tutorials and guides. Datadog also provides a variety of support options, including email and chat support, as well as a community forum where you can ask questions and get help from other users.

Prometheus is an open-source tool, and as such, it relies on its community of users and developers to provide documentation and support. Prometheus provides a comprehensive documentation website that includes a range of resources, such as a user guide, a query language reference, and a list of exporters and integrations. You can also get help and support from the Prometheus community through forums, mailing lists, and other online resources.

The best monitoring and alerting solution: Better Uptime + Datadog/Prometheus

Both Datadog and Prometheus offer real-time monitoring and alerting features. However, Datadog's alerts are rather complex to set up, and Prometheus' Alertmanager is not easy to use either. So, if you are looking for something more intuitive than what Datadog or Prometheus provides, take a look at Better Uptime. It is a cloud-based monitoring and incident management platform that can monitor your entire infrastructure and alert you appropriately if something goes wrong.

To get started, you can create an uptime monitor for your application. If downtime is detected, Better Uptime can notify you through a variety of channels such as call, SMS, email, push notifications, and more.

Several integrations are also provided for you to get it working with your existing infrastructure easily. For example, you can integrate Better Uptime with Datadog or Prometheus by going to the Integrations page and follow the respective instructions:

You can also define an escalation policy as you see fit.

Final thoughts

Both Datadog and Prometheus are efficient monitoring tools for monitoring infrastructure and application performance. Datadog offers a cloud-based platform with a range of features, integrations, a modern UI, and support resources. Prometheus, an open-source tool, is suitable for cloud-native environments and has a built-in expression browser, Grafana integration for data visualization, and a supportive community.

The decision for which tool is more appropriate for you will depend on your specific needs, but we hope this article has given you enough details on both tools to help you come to an informed decision.

Thanks for reading!

Article by

Better Stack Team

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