We performed a comparison between Azure Monitor and Datadog based on our users’ reviews in four categories. After reading all of the collected data, you can find our conclusion below.
Comparison Results: Datadog finishes ahead of Azure Monitor. Users feel Datadog gives the best visibility, better integration, and helpful, timely support. The logs and error reporting are extremely useful to conduct analysis and root cause analysis. The setup, ease of use, and flexibility with dashboard creation and reporting are just some of the things that our users like best about Datadog.
"It is a move-in powerful feature compared to other market-leading tools."
"Azure Monitor is very stable."
"The most valuable feature is the universality of their functionalities in all Azure services, including, software solutions."
"The feature that I found most valuable in Azure Monitor is its monitoring abilities. With Azure Monitor, you are able to monitor all of your cloud resources across multiple subscriptions in one dashboard and create solution-specific alerts that can trigger an email to the team responsible for that specific solution."
"Good load and metrics gathering and very good analysis."
"It is a robust, stable product."
"Among the valuable features of this solution, Application Insights stands out as one of the most significant. It provides insights into application performance and helps identify issues and bottlenecks."
"The solution very easily integrates with Azure services and in one click you can monitor your resource."
"The most valuable feature I have found is the elastic container service."
"It has a high-level insight into the infrastructure model of the application and provides important detailed data on the host and metrics, which is the main concern of our customers."
"The solution has improved the organization by providing good insights into app performance and offering good dashboards."
"The installation step is pretty straightforward."
"The most valuable features have been: Sharable dashboards, TimeBoards, dogstatsd API, Slack Integration, Event logging API. CloudTrail Events, Tags, alerts, and anomaly detection. EBS Volume Snapshot Age, which they added upon request."
"The application performance monitoring is pretty good."
"The ease with which we can filter, use metrics, and give accounts to customers, then let the customer filter, set up metrics, and alerts. This has been a big win for us."
"Even if we don't end up using Datadog, it revealed problems and optimizations to us that weren't obvious before."
"I would like more transparency when we use the solution with another environment, like on-premises, or on another cloud environment, like AWS or GCP."
"They should include advanced logging on the database level in the Azure pool."
"The solution needs better monitoring. It requires better log controls."
"I'd like the solution to do more around vulnerability assessment. It's lacking in the product right now."
"The solution should have cross-connection or cross-communication between tech partners."
"Currently, it seems it's complicated to get the correct information in terms of what to do and how things work."
"If it is configured incorrectly, you can end up with a huge bill."
"The length of latency is terrible and needs to be improved."
"I find the training great. That said, it is set for the LCD (lowest common denominator). Of course, this is very helpful to sell the product, yet, to really utilize the product, you need to get more detailed."
"The documentation leaves a lot to be desired for new users."
"It would also be nice if we had more insight into our own usage of Datadog (agents and custom metrics). They provide a usage page which does help, but it is not in real-time."
"While the tool is robust with many different capabilities, users would greatly benefit from more examples in the documentation."
"Alerting timing should be improved to be more fine-tuned and exact."
"It can have an artificial intelligence component. Even though I can seamlessly look at end-to-end security, it would be better to have alerts and notifications powered by an AI engine. I am not sure if they have an AI component. We have not reached out to them or looked at it, but this is something that I keep on talking about within our company in terms of features. Such a feature would be good to have, and it would further optimize my Security Ops team's abilities."
"There are things about it that we would like to be fixed, such as it is taking averages of average. This results in data that we don't expect."
"For three to four months, we have been experiencing real-time delays. For example, if we're monitoring incoming traffic, the real-time status should be displayed up to a certain point. However, due to delays or issues with Datadog, the real-time data might only be updated at an earlier time. We are experiencing consistent delays in data updates from Datadog, with the most recent data often being delayed by about an hour. This issue has been ongoing for the past four months."
Azure Monitor is ranked 4th in Application Performance Monitoring (APM) and Observability with 44 reviews while Datadog is ranked 1st in Application Performance Monitoring (APM) and Observability with 137 reviews. Azure Monitor is rated 7.6, while Datadog is rated 8.6. The top reviewer of Azure Monitor writes "A powerful Kusto query language but the alerting mechanism needs improvement". On the other hand, the top reviewer of Datadog writes "Very good RUM, synthetics, and infrastructure host maps". Azure Monitor is most compared with Dynatrace, Prometheus, Sentry, Grafana and New Relic, whereas Datadog is most compared with Dynatrace, New Relic, AWS X-Ray, AppDynamics and Elastic Observability. See our Azure Monitor vs. Datadog report.
See our list of best Application Performance Monitoring (APM) and Observability vendors and best Cloud Monitoring Software vendors.
We monitor all Application Performance Monitoring (APM) and Observability reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.