Datadog Enterprise

What do you like best about the product?

I like how easy datadog is to integrate with existing systems. Once set up it provides an incredibly useful view into the status and state of application health. It's dashboards are very easy to create and are a valuable method for gathering key information all in one place.

What do you dislike about the product?

I have few complaints about Datadog, I think it becomes more valuable the more an organization invests in configuring it, and my only complaint might simply be a company not using it enough (or taking a long time to get fully set up)

What problems is the product solving and how is that benefiting you?

Datadog solves a number of monitoring use cases, allowing us to configure alerts for key events that occur across a number of systems. Configuring these in Datadog is significantly simpler than developing in-house monitoring and alerting systems.

What do you like best about the product?

I like Datadog's APMC (application performance monitoring) and logs integration the most. Previously, while investigating an issue, we had to look at metrics, logs, and predictions in different tools, but now all of this is available in one place in Datadog. It helps us with end-to-end tracking in the request flow and makes it easy to correlate logs. The dashboard is very flexible and can be customized according to the team's needs. The alert system is quite useful and can quickly prevent critical issues. Everything for infrastructure and application is available on a single platform.

What do you dislike about the product?

In the beginning, understanding the platform completely can be a bit challenging because it offers a lot of features and configurations. As the number of logs and metrics increases, attention needs to be paid to cost management because costs rise rapidly with usage. There is a learning curve for new users to create advanced queries and dashboards.

What problems is the product solving and how is that benefiting you?

I use Datadog because it solves the visibility problem. Earlier, when there was a production issue, data had to be taken from different places, now data is available from one place. This has sped up root cause analysis and issues are identified before the user.

My main use case for Datadog is to monitor the logs and capture metrics like CPU metrics, memory, and traces across different services in a cloud-based monitoring system where I initially worked, specifically to debug failing systems and systems which are slow, mainly for monitoring my servers in AWS.

The best features of Datadog for me are the user-friendly real-time dashboard and its ability to easily integrate with AWS, Azure, Kubernetes, Kafka, and provide a centralized log management system, which gives me excellent visibility into the microservice architecture.

Datadog has impacted my organization by providing a centralized monitoring system so that each person can trace what is happening in the VM servers, and it has given us a centralized dashboard view.

Since adopting Datadog, it has reduced the manual effort by around seven to eight hours per week, making the process completely automated.

Datadog has improved the collaboration across the teams and cross-functional teams, making it very fast and allowing us to easily track what is wrong.

If I could change one thing about Datadog, it would be the pricing, as it has extraordinary functionality, but the pricing is somewhat expensive, and as we increase the number of servers and monitoring services, the cost increases. A more predictable and flexible pricing structure would be beneficial, along with additional customization options and reporting features.

I have been familiar with Datadog for more than two years.

I have not yet faced any frustration with Datadog.

Before I landed on Datadog, I used to review the CloudWatch logs in AWS, and we initially had the tool Checkmk for monitoring.

When I first implemented Datadog, it took me around thirty to forty minutes for the basic setup because we had a very large application to monitor metrics. After the configuration, the data actually appeared within three to four minutes.

We did not have any formal training on Datadog. Instead, we referred to Google documentation regarding what Datadog is, how to set it up, and what the use cases are, and based on that, we initially set up Datadog.

When evaluating options before choosing Datadog, I compared it with tools such as New Relic and Grafana Labs with Prometheus. The main reason I chose Datadog is that it is a single platform where I can see metrics, logs, traces, and alerts, and it easily integrates with Kubernetes and other services such as Kafka.

Our workflow is both team-wide and individual, as we check the end-to-end observability and the monitoring of our end-to-end application, infrastructure, and cloud services individually as well as in a team.

When I open Datadog, the first thing I do is see the home dashboard, which will have the active alerts and the system health status, as well as listing out all the monitored resources, including the servers, virtual machines, Kubernetes pods, and nodes. I will also see the CPU usage and memory usage, including the disk utilization.

Datadog is used by the cloud infrastructure monitoring team and the application team within the company, and everyone uses it on the same level as I do.

I have not experienced any features during implementation of Datadog that I am not really using in practice.

As of now, for my use case, I am satisfied with what Datadog offers, and I do not wish for any specific features that it currently lacks.

My advice to someone considering Datadog who has a similar workflow to mine is to read the entire documentation and work on it. I would rate my overall experience with Datadog as an eight out of ten.

Public Cloud

Amazon Web Services (AWS)

What do you like best about the product?

i like their real time monitoring across apis, service and systems

What do you dislike about the product?

nothing as such but sometime it's complex to set up dashboard for small systems / apis tracking

What problems is the product solving and how is that benefiting you?

real time system monitoring

What do you like best about the product?

The granularity offered by the platform. The interactive filters are also a big plus. Also the streamlined searching process is highly useful and time-efficient

What do you dislike about the product?

The UI is a little bit overwhelming at first. Also a little bit of training is required to become a skilled user

What problems is the product solving and how is that benefiting you?

the process of finding a needle in a haystack does not seem impossible anymore with datadog. I am very thankful for all features, specifically the filters

What do you like best about the product?

One platform bringing in heterogenous data.

What do you dislike about the product?

Managing the payment plan - sometimes an admin accidently enables something and then end of the month it will be on the bill. Would have loved the option of hard disabling certain features

What problems is the product solving and how is that benefiting you?

Brings together lots of data that gets indexed and enable us to see the bigger picture, or drill down to zoom into specific issues.

What do you like best about the product?

Datadog is great for observability and monitoring.

What do you dislike about the product?

Nothing—it's perfect as is, and I have no complaints.

What problems is the product solving and how is that benefiting you?

Datadog solves the problem of tracking our metrics and providing graphs of such metrics, and providing tools for oncalls to take action if metrics go wrong.

What do you like best about the product?

Professional layout and clean design and ease of use

What do you dislike about the product?

Limitations and sometimes the complexity of the ui

What problems is the product solving and how is that benefiting you?

reviewing old actions to understand issues better

What do you like best about the product?

The most impressive part of Datadog is how it bridges the gap between automated testing and production observability. The CI Visibility and Test Optimization features are standout; being able to trace every test execution within our pipelines allows for immediate identification of flaky tests and performance regressions before they ever reach a staging environment. The correlation between test failures and underlying infrastructure metrics or application traces is seamless, which drastically reduces the time spent on root cause analysis. Instead of just seeing a failed build, we can see exactly which service or database query caused the bottleneck during that specific test run. This level of granular, integrated data is essential for maintaining a high-quality codebase and a reliable release cycle.

What do you dislike about the product?

The primary challenge is the complexity of managing high-volume log ingestion and the associated costs, especially when running extensive automated test suites that generate significant data. Additionally, configuring complex multi-step Synthetic Monitoring tests can be time-consuming, and the web UI occasionally feels sluggish when navigating through large, data-heavy dashboards during critical debugging sessions.

What problems is the product solving and how is that benefiting you?

Datadog solves the problem of fragmented quality signals by providing a unified view of application health from development through production. It allows us to move from reactive bug fixing to a more proactive quality engineering approach. By using Synthetic Monitoring to simulate critical user journeys and Real User Monitoring (RUM) to validate actual performance, we can ensure that our quality gates are truly representative of the end-user experience. This integration helps us catch regressions early in the CI/CD pipeline, reducing production incidents and improving overall system stability. The benefit is a much more efficient feedback loop for our engineering teams, leading to faster, more confident deployments and a consistently high-performing application for our customers.

What do you like best about the product?

It’s easy to use, and the dashboard is very good and straightforward.

What do you dislike about the product?

It does not store data in India; its data center is located outside India.

What problems is the product solving and how is that benefiting you?

It helps improve our API performance, and it also helps us understand where the issues are and how to improve them.