Datadog Reviews & Product Details

It is a complete IT infrastructure solution that allows you to monitor infrastructure, applications, logs, traces, and security events all in one place.

It support all kind of integration and you can if if you face any issue customer support is outstanding.

Dashboards & Visualizations makes easy to diagnose the issue. Configuration and implementation is easy supports all kind of OS, docker, K8s.

Smart alerts with machine learning-based anomaly detection help catch issues before they escalate. Review collected by and hosted on G2.com.

While Datadog is powerful and feature-rich, it can take some time to fully learn and configure for new users. Review collected by and hosted on G2.com.

I like the concept overall, the system that tracks every data point your applications provide and you can collect and analyse it in a single space.

It basically allows to find the root cause of issues much faster as you are able to correlate data from different sources (server load, logs, network performance etc.)

And because of all those data agregated in one place you can setup notifications based on multiple metrics together, not just one. Or even do something with webhook. Review collected by and hosted on G2.com.

I personally don't really enjoy Datadog's interface, it does look modern and UI elements are small, but I don't have any other complaints so far. Review collected by and hosted on G2.com.

What I like most about Datadog is that it can act as a single source of truth for our entire stack, helping break down the silos between infrastructure metrics, APM, and log management. During an incident, instead of jumping between three different tools, my team can quickly pivot from a spiked CPU metric to the relevant trace and the corresponding logs in just a couple of clicks. Review collected by and hosted on G2.com.

The learning curve is pretty steep. Since Datadog has expanded into so many areas (Security, CI Visibility, Real User Monitoring), the UI can feel cluttered and overwhelming—especially for new team members. On top of that, the cost of log indexing and retention is a major hurdle. I like the 'Logging without Limits' concept in theory, but the price gap between ingesting logs and actually being able to search them (indexing) forces us to make tough decisions about what data to keep. Review collected by and hosted on G2.com.

We use DataDog primarily for infrastructure monitoring across EC2 instances, EKS clusters, and more. It gives us full visibility into the critical systems we run, mainly on AWS and GCP. “Very functional” is the best way I can describe it, and it consistently provides deep insights into the systems and resources we operate across both services. Review collected by and hosted on G2.com.

I think the setup can be a bit complex, and you may need an understanding of things like agents. I also feel it would be better if there were an easier way to cover more of the resources, because setting up the agents wasn’t very straightforward. On top of that, there are quite a lot of monitoring services, so it can get overwhelming pretty quickly. Review collected by and hosted on G2.com.

I use Datadog to check logs and audits, and I appreciate how it shows the timestamps of logs and events, which makes communication with the customer easy and allows our engineering team to fix issues faster. I like the RUM logs and replay sessions because the RUM logs provide an accurate log of success and failures, which helps escalate with the engineering team to fix issues, and the replay session allows us to see how the user interacted with the UI. Review collected by and hosted on G2.com.

The UI seems cluttered at times with too many elements. It might be better if there were organized sections to easily access information. For example, if there are device-specific details, they should be under a section labeled 'device' where all related details and geolocations can be found. Also, it took some time to get a hang of it initially. Review collected by and hosted on G2.com.

Firstly, Its integration capabilities to Hosts (Windows/Linux/mac), Platforms (AWS, Azure which I use, plus GCP etc) and container platforms (Docker, Kubernetes) etc benefits alot of usecases as DD server as a one stop shop. We have support for multiple programming languages to easily publish logs to Datadog directly right form your application code, eliminating any fancy stuff.

We have very reliable feature slike smart health checks and automated test suites so we catch problems before they hit. On-call teams get instant alerts, incident triage, and even automated workflows for triage etc enhance teams to focus fixing issues quickly and stress-free with some readily available first hand information.

Dashboards with visualizations like line, bar, pie, and timeseries charts cater to different use cases—such as applications, infrastructure, and databases—making it easier to monitor performance. DD become an integral part of our daily operations, helping outquickly spot anomalies and simplifying the overall and managing workflows.

Its easy to setup/install/implement agent configuration (Pre designed Installation URL with installation script) doesnt take more than 5mins. Users can readily build dashboards in under 15 mins for prod grade setup. [ In general its just 5mins as publicised by Datadog].

DD do has great customer support but we rarely need that as most of the stuff has documentation and easy to setup or configure. Review collected by and hosted on G2.com.

In our current context,

As our infrastructure or application footprint grows, storage costs increase proportionally and can become a major expense. If we need to retain data for extended periods, expect those costs to rise even further (so storage necessity is the key).

Just like other platforms, Datadog also offers numerous integrations with third-party platforms like Slack, Microsoft Teams, and Jira. We leveraged on all these channels initially that lead to increased costs, as each integration added complexity and resource usage along with increase complexity implementing them. We had to strip someof them to manage cost and purpose of applications at different environment levels.

There are so many options for same purpose but without proper guidance or complete understanding of that usecase, we may en dup implement more than what is required. So purpose is key here. Review collected by and hosted on G2.com.

I really like how quickly data shows up in Datadog. It's really quick and easy to integrate webhooks with it, and we can search through the results quickly and easily to find examples of integrations working or not working. Being able to dig into API payloads and understand what's causing issues by looking at API responses in Datadog makes troubleshooting a lot easier for me. The ability to build dashboards and metrics to gain insights on our integrations also stands out. Review collected by and hosted on G2.com.

Sometimes, once you have searched for something and it has filtered down to a specific context, it can be difficult to know how to expand the context to include other sources. Review collected by and hosted on G2.com.

Datadog brings infrastructure, applications, logs, and security signals together in one place, which makes it much easier to understand what is really happening in an environment and to move quickly from detection to action. The correlation between metrics, traces, and logs is particularly valuable when diagnosing incidents, as it reduces guesswork and speeds up root cause analysis. Review collected by and hosted on G2.com.

While Datadog is extremely powerful, it can become difficult to control and predict costs in large or rapidly changing environments, particularly when ingesting high volumes of logs, metrics, and traces. Without strong governance and regular tuning, usage can grow quickly and lead to unexpected spend.

In addition, the breadth of features can sometimes feel overwhelming. Teams need time and clear ownership to configure dashboards, alerts, and monitors properly; otherwise, there is a risk of noise, alert fatigue, or under-utilisation of the platform’s capabilities. Review collected by and hosted on G2.com.

Datadog is a powerful tool that gives us greater confidence in our company's systems and enhances our ability to detect outages. It offers a wide range of features, most of which we actively use. Datadog provides essential insights into our systems, which helps us investigate problems, identify issues, and monitor performance, these being just a few of the ways we rely on Datadog. Review collected by and hosted on G2.com.

The cost is one of Datadog's biggest drawbacks, there are some products that would be helpful to use but the cost makes them impractical. The cost of Mobile App Testing testing comes to mind as an example for this.

Additionally, we have experienced some frustration due to pricing changes. Our previous SKUs were grandfathered, but we were eventually required to switch to the newer, more expensive SKU pricing. 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.