Prometheus vs. DataDog: Detailed comparison [2024]

So, what's the difference between Prometheus and Datadog? If you answered that one is open source and the other is a commercial product, you're not wrong. Indeed, the pricing model (or lack thereof) is one of the key distinctions between the two tools.

But Prometheus and Datadog vary in many other ways, too – such as the scope of their features and capabilities, how they collect data, how they handle visualizations, and which level of customization they support. Deciding which tool is best for you requires a careful analysis of all of the nuanced differences between these two monitoring and observability tools.

If you're looking for such a comparison, you've come to the right place. Keep reading for a look at the key similarities and differences between Prometheus and Datadog, along with tips on selecting the right solution for you.

Prometheus overview

Prometheus is an open source monitoring and observability tool designed especially for cloud-native applications and environments. Prometheus can collect data using HTTP pulls or pushes, and then use the data to drive real-time alerting and visualizations. It supports a multi-dimensional data model.

Prometheus originated as a project inside SoundCloud, an audio streaming company, in 2012. SoundCloud engineers started building Prometheus because they felt that conventional monitoring tools were a poor fit for their needs. Because SoundCloud operated Prometheus as an open source project, engineers outside the company were able to use it as well. Over time, Prometheus gained widespread popularity as a solution for monitoring cloud-native applications of all types, well beyond those used by SoundCloud.

Datadog overview

Datadog is a cloud monitoring and observability platform. It supports metrics and log collection and analysis, as well as distributed tracing. It's a commercial product that requires a paid subscription to use (although the company currently offers a free trial).

In many respects, Datadog is similar to most commercial monitoring and observability tools. It lets you collect a wide variety of data, and it offers built-in visualizations and other tools for interpreting the data.

That said, one of the characteristics that helps Datadog stand out in the world of observability is that from the start, Datadog focused on cloud monitoring. The product was launched in 2010 – right as cloud computing was beginning to become central to IT strategies – with the goal of making it easier to collect and analyze performance data from cloud platforms, like Amazon Web Services.

This was important at the time because in the cloud, you don't have access to the underlying infrastructure that hosts your workloads. This means you can't collect data in the same ways you could in an on-prem environment where you control all of the infrastructure. Datadog was conceived, in part, to address this challenge by offering a cloud-centric approach to monitoring.

This focus helped give Datadog an early edge in the cloud and cloud-native monitoring world. Today, Datadog is certainly not the only commercial observability solution that caters to modern environments, but it has become a popular solution.

Datadog as a full observability stack vs. Prometheus in an open source setup

Now that we’ve covered the basics of both Prometheus and Datadog, let’s talk about the key difference between them: The fact that Datadog includes a much broader range of capabilities.

Datadog offers a complete observability stack. It includes full support for collecting and analyzing logs, metrics, and traces – all as part of a single, unified platform.

In contrast, Prometheus is designed only for working with metrics. It’s not a logging or tracing tool, and in that sense it falls short of being a “full stack” observability solution. In addition, although Prometheus offers some basic data visualization capabilities, it’s not a full-scale analytics tool. It focuses mostly on metrics collection, not metrics analysis.

You can pair Prometheus with other open source tools (like Jaeger for tracing or Grafana for advanced visualizations) to build out an observability stack. But on its own, Prometheus doesn’t offer the comprehensive set of observability capabilities you’d get from a platform like Datadog.

Prometheus vs. Datadog’s full-stack solution: Pros and cons

We'll dive into a feature-by-feature comparison of Prometheus and Datadog in a moment. First, let's look at the pros and cons associated with each product from a high level.

Compared to Datadog, the main advantages of Prometheus include:

• The software is completely free of cost to operate (although you may have to pay for infrastructure to host Prometheus).
• You get more control over how Prometheus works because it offers more configuration options.
• You can deploy Prometheus anywhere – on-prem, as a solution that you manage yourself in the cloud or by using a Prometheus-as-a-Service offering (in which a vendor hosts Prometheus for you on their infrastructure).
• As an open source solution that can run anywhere, Prometheus is an industry standard that doesn’t pose vendor lock-in risks. 

On the other hand, Prometheus's drawbacks relative to Datadog include:

• Prometheus can require more effort to set up (although arguably, some aspects of Prometheus, like alert configuration, are actually simpler than in Datadog).
• There is no paid support available if you need help.
• Prometheus has fewer built-in features. For instance, native support for data visualizations and machine learning capabilities in Prometheus are very limited – although you can combine Prometheus with other tools to implement features like these.

Prometheus vs. Datadog: Key differences

Here's a deeper look at the main differences between Prometheus and Datadog, broken down according to different feature areas.

1. Data collection

One of the biggest technical differences between Prometheus and Datadog is that Prometheus uses a pull-based model to collect data, while Datadog relies on a push model. This means that Prometheus can go out and collect monitoring data for you, whereas with Datadog, you configure data sources to export data into Datadog.

Each approach has its pros and cons, and depending on your situation, one may be preferable over the other. The main advantage of Prometheus's pull-based data collection is that it allows you to configure data importation rules from a central location, instead of having to manage them at each data source.

On the other hand, a push approach can make it easier to collect data in real time because the data source can push new data whenever it arises. With a pull approach, you typically collect data at periodic intervals, and if important new data appears between those intervals, there might be a delay in collecting it.

2. Metrics collection and instrumentation

Datadog and Prometheus are pretty similar with regard to the types of infrastructure metrics, application metrics, and log data they can collect and the ease of collecting them. They also both support the collection of custom metrics. But they work a little differently on the backend.

Datadog and Prometheus both provide their own instrumentation libraries, which you can use to collect metrics from applications you want to monitor. Arguably, Prometheus's libraries are more flexible because they're open source. Note, too, that both monitoring tools support OpenTelemetry, so as long as you have instrumentation libraries or data collectors that conform to the OpenTelemetry standards, you'll be able to get the data you want in a standardized way.

3. Data interpretation and visualization

When it comes to data analysis and interpretation, Datadog offers more built-in capabilities. It offers features like Metrics Explorer, RUM Explorer, and Log Explorer, which can help you identify trends in monitoring data and create custom dashboards. Datadog also offers native support for generating visualizations based on the data you've collected.

In contrast, Prometheus's data analytics features are limited mostly to using the Prometheus Query Language (PromQL) to write queries for analyzing log data and metrics. This means that, in general, you'll need a higher level of skill to analyze data in Prometheus.

As for data visualizations, Prometheus offers limited support via the Prometheus Expression Browser, which can be used to display visualizations (among other things) based on Prometheus data inside a Web browser. For more advanced visualizations, however, you'd typically integrate Prometheus with a tool like Grafana.

One could argue that Prometheus's limited data analytics and visualization capabilities are a feature, not a bug. They give you the flexibility to pair Prometheus with other tools to help interpret data. On balance, we should note that Datadog can integrate with tools like Grafana, too, but Datadog is designed with the assumption that you'll do most of your monitoring and observability work inside Datadog itself.

4. Alerting

Datadog and Prometheus both support alerting. In many respects, however, Datadog's alerting capabilities are more powerful. Datadog can use machine learning to identify anomalies and generate alerts based on them. In contrast, Prometheus alerts are based primarily on preconfigured rules.

5. Ecosystem and integrations

While Datadog and Prometheus both support a variety of integrations, Prometheus's ecosystem is, on the whole, larger and more dynamic. This makes sense because, as we mentioned, Prometheus is designed to be used in close conjunction with other tools. The fact that it's open source probably also helps encourage the development of more third-party integrations with the tool.

On the other hand, while Datadog’s dashboards are proprietary, they’re also somewhat easier to deploy and manage because they are built directly into the platform. Prometheus’s dashboard tooling is more customizable but comes with a somewhat steeper learning curve.

6. Pricing

Prometheus is free of cost, whereas you have to pay to use Datadog. This is a big advantage in the favor of Prometheus if you want to lower monitoring costs.

That said, keep in mind that you will have to pay for the infrastructure that hosts Prometheus and stores your data – so the product is not completely free to deploy; you just don't have to pay licensing fees for the software. With Datadog, hosting costs are built into the product pricing, because Datadog is a SaaS solution.

7. Support for security monitoring

Datadog and Prometheus can both support security monitoring by collecting data that security analysts can use to discover anomalies and other signs of attack. However, security monitoring is more a product focus for Datadog than it is for Prometheus (which focuses primarily on application performance management – a use case that Datadog also supports). Datadog includes analytics features that cater to security monitoring use cases, whereas Prometheus mainly only collects data. If you want to analyze the data for security purposes, you'll likely need to import it into a different tool.

8. Deployment flexibility

As noted above, you can deploy Prometheus almost anywhere. With Datadog, however, you can only operate the product as SaaS, which means Datadog deploys and manages it for you.

Prometheus's deployment flexibility may be an advantage for businesses that want to keep monitoring data on their own servers, or that need to monitor resources that can't easily be exposed to products hosted externally. On the other hand, Datadog's SaaS deployment model may be advantageous for teams that don't want to have to set up and manage monitoring tools on their own.

9. Ease of use

"Ease of use" is subjective, but if you surveyed Datadog and Prometheus users, you'd likely find that most people say Datadog is somewhat easier to use. This is not surprising given that Datadog is a commercial product whose customers generally expect a smoother setup and administration experience than they would when using a free product.

Note, too, that because Datadog offers professional support services, it's easier to get technical help if you need it. With Prometheus, teams are limited primarily to community support, although paid Prometheus support plans are available from various companies.

Prometheus and Datadog use cases

There is a lot of overlap in the use cases that Prometheus and Datadog support. For the most part, each tool is equally capable of handling modern application performance management needs.

That said, Datadog supports additional use cases where Prometheus doesn't excel. The biggest is security monitoring – which, as noted above, Prometheus doesn't really support. Datadog's push data model also makes it a better fit for use cases where collecting, analyzing, and alerting on data in true real time is a priority.

Prometheus vs. Datadog: Which solution is better for you?

In general, you should use Prometheus instead of Datadog if the following are true:

• Your main focus is on metrics collection. Logging and tracing either aren’t important or you plan to use other tools to address those needs.
• You want to minimize observability and monitoring costs.
• The flexibility to deploy and configure the product however you wish is a priority.
• You're familiar with complementary tools, like Grafana, and are prepared to use them alongside Prometheus.

And Datadog is likely to be a better fit under the following conditions:

• You want an all-in-one, full-stack observability platform.
• Ease of use is a top priority.
• Having access to professional support is important.
• You want an all-in-one product that covers all APM and security monitoring needs without requiring additional tools or integrations.

Alternatives to Prometheus and Datadog: groundcover

If you can't decide between Prometheus and Datadog, here's a tip: Don't choose. Instead, use groundcover, which offers the flexibility of Prometheus combined with the advanced features and support you get from Datadog.

Like Prometheus, groundcover delivers a very flexible approach to monitoring by using eBPF to collect data. This means you can collect and analyze virtually any type of data, from any system, with minimal cost and overhead. In addition, groundcover integrates beautifully with Grafana and other tools, giving you the flexibility to analyze and visualize data however you wish.

But unlike Prometheus – and like Datadog – groundcover offers a variety of built-in features for data analysis and reporting. You also get paid support. And you can use distributed tracing.

In short, if you want the flexibility and low cost of Prometheus combined with the powerful analytics and distributed tracing features of Datadog, groundcover lets you have it all.

Prometheus, Datadog, and beyond

Prometheus and Datadog are great tools with robust monitoring capabilities. But each also has distinct limitations. We're biased, but we like to think that groundcover gives you the best of both worlds, without the drawbacks.

But don't just take our word for it. Learn more by reading about how a major cloud services and security provider replaces Datadog with groundcover.