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.
"Its integration is most valuable because you can integrate it with various service providers such as AWS, .Net, etc."
"The solution's SaaS model is easy to manage and works well in single- or multi-cloud environments."
"This is definitely a good product and I would consider them one of the leaders within the application monitoring and cloud monitoring space."
"The RUM solution has improved our ability to triage faster and hand more capabilities to our customer support."
"It has a nice UI."
"The many dozens of integrations that the solution brings out of the box are excellent."
"The most valuable aspect is the APM which can monitor the metrics and latencies."
"The solution is sufficiently stable."
"One of the key things with Dynatrace is that they are very open to influence on product development side. So, we've influenced them fairly heavily on development and capabilities for Citrix and DC RUM. They've given us integration and support components around some odd technologies that we've got, and they have always been very open and accommodating to going after and developing capabilities around the stuff that we are looking for."
"I have reduced our disruption time. With the automatic alerts, we prevent and better catch the root cause of problems."
"UEM (RUM): User Experience Management (real user monitoring) puts you in "user's seat" and gives you insight into how they experience the application. Often, this gives a totally different view than just watching the backend calls."
"The solution is amazing, it does well when you need to use it."
"The autodiscovery of service intercommunication has saved countless man hours and is dynamically updated when new services are added."
"Our main use is to monitor our applications for any issues that might arise and use the data to assure that our performance trending is headed in the right direction."
"Mean time to recover (MTTR) has reduced significantly during major outages due to specific data pinpointed by DT applications."
"Its ability to correlate a large source of information to pinpoint a root cause. This speeds up issue resolution and allow us to better reach our objectives."
"We need to learn more about the session reply feature inside of DD."
"There is always room for improvement when dealing with cloud-based technologies. Mainly, I would say, it's just increasing our offerings to attract various other types of industries and businesses across more fields."
"As a new customer, the Datadog user interface is a bit daunting."
"When I started using it years ago, it had stability problems. I remember, specifically, we ran everything in Docker containers. There were some problems getting it into a Docker container with very specific memory limits."
"In the past two years, there have been a couple of outages."
"Deploying the agents is still very manual."
"The error traceability is an area that can be improved."
"Sometimes, it takes a long time to load the dashboard if we have many charts."
"They need a capability similar to Tealeaf where you can actually view what the consumer is doing and record the sessions. That is the biggest missing element."
"If Dynatrace is capturing everything in your application, it has to "sense" that information, and that sensing needs sensors which we have to include in our applications. The more you apply sensors - the more details you want - the more you have to increase the level of sensing. If I increase the level of sensing, my application's performance goes down, because something is there that is, again and again, checking each and every thing in the application. So that load on the applications increases. So, many times my applications used to crash because Dynatrace was working on them. We had to remove some sensing; either we had to reduce the sensing or we had to remove Dynatrace immediately."
"The integration of the tools is getting there. It is still not there yet, because we still have to get a lot of tools to put together."
"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."
"An additional feature that we could use is the rollover. If we could rollover to different datacenters, then it would satisfy our requirement. I.e., if one datacenter fails, then we could rollover to another datacenter."
"Its needs to focus more on open source areas, like Apache umbrella products and availability motioning areas."
"More visibility into Python processes."
"There is still a bit of redundancy in Dynatrace."
Datadog is ranked 1st in Application Performance Monitoring (APM) and Observability with 137 reviews while Dynatrace is ranked 2nd in Application Performance Monitoring (APM) and Observability with 342 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, Elastic Observability and AppDynamics, 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 Application Performance Monitoring (APM) and Observability vendors, best Log Management vendors, and best Container Monitoring 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.
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.