We compared Datadog and Dynatrace based on our users reviews in five parameters. After reading the collected data, you can find our conclusion below:
The setup process for both Datadog and Dynatrace is generally seen as simple and uncomplicated. However, Datadog might necessitate some fine-tuning or the involvement of multiple teams, whereas Dynatrace is regarded as faster and easier to implement. Additionally, Dynatrace only requires a minimal deployment and maintenance effort, usually handled by one or two individuals even in larger settings.
Datadog offers useful features like customizable displays and data analysis, error tracking and log management, developer-friendly interface, and adaptable AI and ML capabilities. In contrast, Dynatrace excels in effortless setup, automatic infrastructure identification, intelligent problem detection, session playback, and comprehensive visibility and monitoring.
- Room for Improvement
Based on the feedback, Datadog could enhance its usability, integration capabilities, user interface intuitiveness, learning curve, monitoring of external websites, SSL security, and setup complexity. In contrast, Dynatrace could improve its user interface for management functions, handling of time zones, installation process, integration with network management tools, licensing process, documentation, and network performance monitoring.
Users have differing opinions on the setup cost of Datadog, with some finding it costly while others find it reasonable in comparison to other options. However, the pricing model lacks documentation and is confusing. In contrast, Dynatrace's pricing structure is complicated and not transparent, making accurate planning difficult. Despite being generally expensive, it provides good value for the money.
Users have reported experiencing various benefits when using Datadog, including time savings and the ability to identify and address blindspots. On the other hand, customers have found Dynatrace to be highly advantageous in terms of return on investment, with cost savings and reduced downtime being key outcomes.
The customer service and support for Datadog and Dynatrace have varying feedback. Some users appreciate the promptness and helpfulness of Datadog's support team, while others have experienced slow or unresponsive support, especially in the Asia-Pacific region. In contrast, Dynatrace generally provides responsive and available customer service, although some customers have encountered slower response times. Dynatrace's support team is praised for giving valuable answers, and they have a highly regarded customer success program called Dynatrace ONE. However, there is a need for improvement in terms of response time for both platforms.
Comparison Results
When comparing Datadog and Dynatrace, Datadog is regarded as simpler to set up and provides more flexibility and extra features. Users appreciate its dashboards, error reporting, user-friendliness, and the wide range of integrations it offers. On the other hand, Dynatrace is praised for its effortless deployment and automatic infrastructure detection, as well as its AI engine and visualization capabilities. However, users mention that improvements could be made to Dynatrace's user interface, licensing process, and documentation. Pricing and ROI experiences vary among users for both products, and customer service and support are generally satisfactory, with some room for enhancement.
"The most valuable feature I have found is the elastic container service."
"The biggest thing I liked was the combination of all the things - monitoring, log aggregation, and profiling."
"The solution has improved the organization by providing good insights into app performance and offering good dashboards."
"Datadog's ability to group and visualize the servers and the data makes it relatively easy for the root cause analysis."
"They have a very good foundation in capturing metrics, logs, and traces. It's a very nice tool for that and it allows you to apply these monitoring tools in almost any technology."
"We have been able to set very specific CPU and memory alerts, at the very base level, then we started to pull real business value, like 99th percentile response rates for our API calls."
"Its logs are most valuable."
"Sometimes it's more user friendly for development teams. There are some parts of Datadog that are more understandable for development teams. For example, the APM in Datadog works more manually and works like the tools in New Relic or Grafana, or Elastic. It is easier to understand for software development teams."
"It scales well. We are going to be able to use it for everything we need. "
"Service engineers save a lot of time because they can just go in look at the data and share it with the customer, who has the same view, and say, "Here's an improvement which can be immediately implemented." It's not like a collection of big, multiple findings that are consolidated into one results presentation, then the customer needs to do something. It's more like a continuous performance analysis and improvement process, which is more efficient than those workshops approaches. That's one of the biggest of the advantages that our services team sees because it helps DevOps to focus on continuous delivery and shift quality issues to pre-production."
"Being able to get down to the individual code level to see where transactions are taking time. It has helped troubleshoot issues immensely and other tools can't provide this."
"We can see issues that occur, sometimes before the clients do. Before we have client (or end user) calls for issues, we are able to start troubleshooting and even resolve those issues. We can quickly identify the root cause and impact of the issues as they occur, and this is very helpful for providing the best client experience."
"The autodiscovery of service intercommunication has saved countless man hours and is dynamically updated when new services are added."
"Support from Dynatrace is excellent. They are always on hand for any queries, demos, and/or issues."
"There is a strong user community. There is no need to talk to the technical support, because all the questions which I have had, all the solutions were in the documentation. Or, I have been able to post a question to the user community and get an answer within a day or two."
"This is a stable solution."
"They could have better log reporting."
"Auto instrumentation on tracing has not been very easy to find in the documentation."
"I would love to see support for front-end and mobile applications. Right now, it is mostly all back-end stuff. Being able to do some integration with our front-end products would be awesome."
"As a new customer, the Datadog user interface is a bit daunting."
"We need to learn more about the session reply feature inside of DD."
"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 could be improved regarding setting up the agent properly and debugging."
"I often have issues with the UI in my browser."
"This tool had a feature of doing load test in production or lower environment, which was shut down earlier this year. We are missing that feature badly and we definitely want to see that feature back."
"It could improve its GUI interface. The GUI design is too crowded and the icons are small. Sometimes I end up clicking on the wrong button."
"We have a very stringent budget for an infrastructure solution. Maybe if they provided modules, a simple module with fewer features and a lower price, that would be very good."
"It is necessary to improve the integration with the product, Oracle Siebel."
"It would be nice to have a simplified monitoring feature for non-Java applications."
"When compared with other tools, the experience needs improvement. I would like them to build out the interactions and make them friendlier."
"Network monitoring is lacking and could be improved."
"I believe that something related to IoT devices should be improved."
Datadog is ranked 1st in AIOps with 137 reviews while Dynatrace is ranked 2nd in AIOps with 340 reviews. Datadog is rated 8.6, while Dynatrace is rated 8.8. The top reviewer of Datadog writes "Very good RUM, synthetics, and infrastructure host maps". On the other hand, the top reviewer of Dynatrace writes "AI identifies all the components of a response-time issue or failure, hugely benefiting our triage efforts". Datadog is most compared with Azure Monitor, New Relic, AWS X-Ray, AppDynamics and Elastic Observability, whereas Dynatrace is most compared with New Relic, AppDynamics, Splunk Enterprise Security, Azure Monitor and Elastic Observability. See our Datadog vs. Dynatrace report.
See our list of best Log Management vendors, best AIOps vendors, and best Application Performance Monitoring (APM) and Observability vendors.
We monitor all AIOps 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.
We also selected Dynatrace but for different reasons.
We were looking for a solution that integrated user experience to backend systems. The RUM data captured by Dynatrace and integration to the transaction trace is phenomenal.
Datadog was lacking in the APM space when we evaluated and was very limited specifically in real user monitoring.
I've seen an early preview of Dyantrace's latest logging capabilities and can say I'm very excited, to say the least. The solution is automated and traced. For a comprehensive solution to improve observability and reduce outage times we are very happy with Dynatrace.
Our organization ran comparison tests to determine whether the Datadog or Dynatrace network monitoring software was the better fit for us. We decided to go with Dynatrace. Dynatrace offers network monitoring capabilities that take into account their users’ need for the most in-depth and accurate information and solutions. It offers analysis powered by a cutting-edge and fully automated AI. This artificial intelligence is designed to spot in real time any issue that might appear in the network on both the code and the infrastructure levels. Network administrators will be offered an in-depth analysis of the issue. The report will show the nature of the problem, where in the network it can be located, and potential solutions that can be implemented. Dynatrace’s real-time reporting significantly cuts down the response time of administrators to issues.
Datadog’s network monitoring software does not offer AI reporting or analysis. While it does offer features that enable users to track issues in their networks, it does not offer anything that is as robust and in-depth as Dynatrace’s fully automated AI. Administrators have to go and constantly monitor the network for issues instead of receiving automatic notifications that can direct them to the problems at hand.
Dynatrace’s dashboards can take the data that the AI collects and lay it out for the administrative or executive teams in clear ways. It is easy to customize these dashboards according to what you need. In fact, the creation of dashboards is now automated. You tell the software what you want to see and it will build the dashboard for you.
Datadog offers dashboards that provide near real-time visibility. They track the health of the network applications and provide indicators of the network’s overall condition. These dashboards are somewhat easy to create. However, they lack the automation that Dynatrace provides.
Conclusion
While Datadog offers a solution that can provide effective network monitoring, Dynatrace’s features make it a better option. Its AI and automation make it a far more effective product.