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, 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.

What I like best about Datadog is its ability to provide deep observability across infrastructure, applications, logs, cloud services, and identity platforms from a single pane of glass. Having used Datadog for an extended period, I've found its dashboards, alerting capabilities, and correlation between metrics, logs, and traces to be extremely valuable for both proactive monitoring and incident response.

As an administrator, I appreciate the flexibility in creating custom dashboards, configuring monitors, and tuning alerts to reduce noise while ensuring critical issues are detected early. The platform's extensive integration ecosystem makes it easy to onboard new services, and features such as anomaly detection, log analytics, and service mapping significantly improve troubleshooting efficiency.

Overall, Datadog has helped streamline operations, reduce mean time to resolution (MTTR), and provide actionable insights that support both day-to-day monitoring and long-term platform optimization. Review collected by and hosted on G2.com.

While Datadog is a mature and feature-rich platform, one area that can be challenging is cost management at scale. As environments grow and more teams onboard services, log ingestion, custom metrics, and data retention costs require ongoing optimization and governance. I've also found that in large enterprise deployments, alert tuning and monitor management need regular review to avoid alert fatigue and maintain signal-to-noise quality.

Another area for improvement is that some advanced configurations and cross-product features can have a learning curve for newer administrators. While the platform offers tremendous flexibility, fully leveraging its capabilities often requires experience and a well-defined monitoring strategy.

That said, these challenges are common for enterprise observability platforms, and the operational benefits Datadog provides generally outweigh the drawbacks. Review collected by and hosted on G2.com.

One of the biggest strengths of Datadog is how it brings logs, metrics, traces, and alerts into a single platform. Instead of switching between multiple monitoring tools, I can quickly identify what's happening across the entire application stack. Comprehensive dashboards that provide real-time visibility into application health. Powerful log search and filtering for faster root cause analysis. APM (Application Performance Monitoring) that helps identify performance bottlenecks. Intelligent alerting that notifies the team before issues significantly impact users. Seamless integrations with cloud services, databases, CI/CD pipelines, and infrastructure tools. In my QA and automation workflow, Datadog significantly reduces the time required to investigate production issues. Rather than relying solely on application logs, I can correlate metrics, traces, and logs to pinpoint the exact cause of a problem. This makes debugging much faster and improves collaboration between QA, developers, and DevOps teams. Overall, Datadog provides the visibility needed to proactively monitor systems, troubleshoot issues efficiently, and maintain application reliability. Review collected by and hosted on G2.com.

Although Datadog is one of the most comprehensive monitoring tools I've used, there are a few areas where it could improve. Pricing can become expensive as the number of hosts, logs, and monitored services increases. The large number of features can make the platform overwhelming for new users. Building advanced dashboards and queries sometimes requires a learning curve. High log volumes need careful management to avoid unnecessary costs. Some alerts require fine-tuning to reduce noise and avoid alert fatigue. For me, the biggest challenge is cost management. As monitoring requirements grow, it's important to optimize log retention, dashboards, and alert configurations to keep expenses under control. Review collected by and hosted on G2.com.

It is an all-in-one Observability tool which is a combination of infrastructure metrics, application logs, application performance monitoring, real user monitoring, and security monitoring. It has very good integrations with almost all cloud providers. The User Interface is very user-friendly. Review collected by and hosted on G2.com.

It's very costly. Sending application logs to Datadog can lead to unpredictable costs. Providing add-on features is a very complex task. It's difficult to learn for new users. Review collected by and hosted on G2.com.

As a product manager what I like most about Datadog is how it centralizes observability for complex systems in a single platform. It brings metrics, logs, traces, and alerts together in one place, making it much easier to understand overall system health and troubleshoot issues quickly for runners and support teams. Onboarding tech teams is easy.

Its real-time monitoring and alerting are especially valuable because they help detect incidents early and improve response times. I also appreciate the breadth and depth of integrations with cloud providers, infrastructure tools, and application services, which makes Datadog adaptable across different architectures.

Depending the implementation the price can evolve but you are fully mastering the cost.

Overall, it delivers strong visibility and control over system performance, which feels essential in modern distributed environments at scale. Perspectives to use it coupling with AI support agent is a plus to prepare the future. Review collected by and hosted on G2.com.

The tool provide insights and data but teams have to spend significant time interpreting what action should be taken. Review collected by and hosted on G2.com.

I use Datadog for monitoring, and it helps me in a lot of ways. It shows in-depth logs of the systems and provides a comprehensive view of metrics. There are lots of metrics, and the integration is very simple, especially with Terraform. It offers a good view of metrics, which is really valuable for me. I appreciate being able to create my own dashboards and metrics. The setup was quite easy, and we received support from the data ops team to integrate it with the systems. Review collected by and hosted on G2.com.

I think Datadog's anomaly detection is quite basic in alerting me. I would like it to be enhanced to provide more detailed warning alerts. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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) Review collected by and hosted on G2.com.

I find the seamless observability engine of Datadog to be a standout feature. It allows us to automate out of the box data correlation, which is incredibly helpful. When an alert triggers or there's a performance anomaly, I can pick a metric data point instantly to the corresponding log line with distributed ATM traces without losing context. Review collected by and hosted on G2.com.

None. Review collected by and hosted on G2.com.

I like the level of granularity Datadog provides across the platform, along with its interactive filter panel and streamlined search process. I can dynamically filter high-volume log streams by clicking tags such as HTTP status codes, container IDs, or deployment environments. Review collected by and hosted on G2.com.

I find Datadog’s interface can feel a bit overwhelming when I first log in, with dense information and modular controls on every screen. Review collected by and hosted on G2.com.

I like Datadog’s granularity across the platform, along with its interactive filters and streamlined searches. They make searching user-friendly and efficient, and help me find errors and performance bottlenecks across log lines and microservices. Datadog turns a flood of logs and telemetry into actionable diagnostic insights. Review collected by and hosted on G2.com.

I find Datadog’s interface can feel a little overwhelming, with so much data, menus, and controls packed onto the screen, especially for a new user. Review collected by and hosted on G2.com.