I think I like the dashboard and the visibility into the logs. And then you can do trace management, and you can do metrics. With the ability to see logs, we can really see where the issues are. It was pretty straightforward to set up; we just had to install the agents on our machines. Review collected by and hosted on G2.com.

Well, I would like to see a better dashboard for services. In endpoints. So when I go into, like, maybe service catalog, there is just a little confusing. You know, how do I find where my services are? Review collected by and hosted on G2.com.

A lot to consider about Datadog: mainly, it is used to monitor a wide range of applications and infrastructure. Application monitoring for container-based, server-based, and web-based applications. Also the it reduces the human effort for checking the exact error.

It is easy to use and supports different kinds of platforms like AWS, Azure, and GCP. Installation is very easy. Datadog provides all the required documents for integrating the agent. We can use the predefined dashboard templates many times, which is a lifesaver if we have repeated monitoring services.

Large-scale monitoring services and tracking are very easy, and the UI is clean, and we can find every service and access the running process via UI as well. Review collected by and hosted on G2.com.

There are only a few things that I was not satisfied with Datadog, those are.

Cost is somewhat high; we have to pay even we utilize the resources or not.

And the main con is the customer support; they only deal via email, and we have to dump the log data into their storage. They first analyse the logs and come back to us; it will typically take 24-36 hours, which has an impact on the clients. Also, they only come on a call on production issues. Review collected by and hosted on G2.com.

Datadog gives us a single observability layer that ties metrics, logs, traces, and AI-driven insights together. What I like most is how fast it is to instrument new services, define custom metrics, and build dashboards that actually help teams make decisions. We also use Datadog extensively for deploying internal AI agents—its event streams, log ingestions, and metric pipelines make it easy to create intelligent triggers and automated workflows.

The correlation between logs → metrics → alerts is incredibly powerful, and the AI-based anomaly detection has helped us reduce blind spots in our observability stack. Review collected by and hosted on G2.com.

The biggest drawback is cost. Datadog becomes expensive very quickly—especially when log volumes grow or when you create many custom business metrics. Even with strict retention windows and log pipelines, the monthly bill requires constant governance. It’s a powerful platform, but the pricing model can be a challenge for teams that want broad coverage without compromising on granularity. Review collected by and hosted on G2.com.

I like that Datadog is very easy and quick to use and helps us get valuable insights. I appreciate that it provides us with visibility and insights into what is happening in both the frontend and backend of our applications. I also enjoy how RUM allows us to not only see problems but also get a real sense of user behavior, helping us to be more strategic with design changes and new features. Review collected by and hosted on G2.com.

We do not get as much value from synthetic tests due to mobile issues Review collected by and hosted on G2.com.

We needed a single pane of glass that could grow with us. Consolidating all of our environments and regions has helped solve our problems, and the AI innovations continue to grow with us as well. Review collected by and hosted on G2.com.

Cost management forces us to set up extra monitors just to keep an eye on our monitoring tool. Review collected by and hosted on G2.com.

I like Datadog for its real-time traces and the ability to connect real user monitoring (RUM) with application performance monitoring (APM). This is very useful for me because it helps trace errors and map them with the real user experience. I also find it easy to reproduce cases with the detailed summary available in the dashboard. Review collected by and hosted on G2.com.

I find the installation process a bit complex, especially when setting it up on a Docker-based setup. It's very much difficult to set up compared to a normal VM-based server. Review collected by and hosted on G2.com.

What I appreciate most about DataDog are its reliability and observability. Even during major global outages, such as those affecting Cloudflare and AWS, DataDog continued to perform without any issues. The APM, RUM, and Synthetic Checks are exceptionally dependable, giving me the confidence to choose DataDog without hesitation. Review collected by and hosted on G2.com.

I wish the UI had a more modern look, as it currently feels like I'm using an old inventory management system from the 2000s. Review collected by and hosted on G2.com.

Seamless observability and true data correlation. Review collected by and hosted on G2.com.

There is not that I actually don’t like. But come on guys, we need a honeycomb widget for services and hosts at least... Review collected by and hosted on G2.com.

It logs all the details we need for analysis. The platform is user-friendly, allowing us to easily set up various dashboards to view all the insights. Implementation is straightforward as well. We have integrated it with our different platforms, including both web and mobile. Review collected by and hosted on G2.com.

I have never encountered any major issues. However, there should be an option to retrieve data based on our own custom filters. Review collected by and hosted on G2.com.

Being able to continuously monitor our platform and catch issues quickly Review collected by and hosted on G2.com.

Currently, I feel documentation could be better Review collected by and hosted on G2.com.