Review Summary
See a synthesized overview of the key takeaways from verified reviews of Datadog.
See a synthesized overview of the key takeaways from verified reviews of Datadog.
Datadog specializes in providing a comprehensive monitoring platform for cloud applications. It collects data from diverse sources such as servers, containers, databases, and third-party services, aiming to make the stack fully observable. By offering these features, Datadog assists DevOps teams in preventing downtime, addressing performance problems, and ensuring optimal user experience.
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I love that since I have come on board they have been very oriented on expanding the products around security. It is an extremely needed service in today's age, but also an incredibly difficult space to maintain full awareness of all the evolving risks and requirements across the tech stack. From monitoring to research, I love having Datadog as a sanity check that we are making the right choices and keeping a good posture.
Recording browser tests is simple, reliable, and visually friendly. It has simple controls to stop and start the recording and generated test steps that were relatively easy to customize, remove, insert, and rearrange.
I like the platforms built-in AI engine, Watchdog, that continuously scans large volumes of data points in the background instead of relying only on manually configured static alerts; Watchdog uses machine learning to establish performance baselines, automatically detect anomalies and identify the specific root cause of a system failure immediately.
The sheer amount of products and different ways of approaching pricing makes it sometimes hard to identify what is used where while keeping track of product offerings and how it relates to billing. Although there are many integrations, sometimes they do not cover my less popular choices. Things move around in the menus a bit too often.
The test suite tends to have flaky failures about ~40% of the time, which makes it nearly impossible to rely on for definitive test results. Also, managing large lists of tests is very hard with the current UI and list-based system. One flow with branched logic would require one test per branch, relying on naming conventions and tagging to keep them organized, which is difficult at scale.
While the platform is a business leader in visualization, its Granular Multi-Vector charging, which bills separately for hosts, containers, log ingestion, log indexing, custom metrics and APM spans, creates millions of unique combinations, leading to astronomical overage charges overnight if a developer accidentally configures a dynamic variable (like a user ID or Timestamp) as a metric tag.