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.
"Using the data, our operation teams works with the dashboards to get their statistics, analytics, etc."
"We've found it most useful for managing Rstudio Workbench, which has its own logs that would not be picked up via Cloudwatch."
"Datadog has flexibility."
"The product has offered increased visibility via logging APM, metrics, RUM, etc."
"Flame graphs are pretty useful for understanding how GraphQL resolves our federated queries when it comes to identifying slow points in our requests. In our microservice environment with 170 services."
"The fact that everything is under a single pane of glass is really valuable, as developers don't have to spend their time copying correlation IDs across tools to find what they need."
"Going from viewing a metric to creating a monitor alerting on a metric is very easy."
"Excellent autocomplete for everything in the UI."
"Dynatrace's power lies in its ability to inspect and chart transactions, and draw the PurePath tree. The parameters of the requests and methods can be selected to access the necessary information for efficient analysis."
"The memory dumps, the tracing, and PurePath. All the tracing that you can do with the tool is, for us, our life. It's our daily job and it saves us a lot of time looking for performance issues."
"The user experience allows us to be able to gauge customer experience and understand the performance impact of our platform."
"Global overview of all app layers, including web servers."
"It provides us a reference for being able to go back and look at data at a certain point, analyze it, then determine if something was the root cause."
"This tool enables us to make intelligent, fact-based decisions faster."
"The way it shows a problem on the dashboard is pretty good."
"It collects and analyses information with AI, which is useful."
"The pricing model could be simplified as it feels a bit outdated, especially when you look at the billing model of compute instances vs the containers instances."
"I've only been using Datadog for a few months, and at first, it was frankly overwhelming in terms of both the UI and the available capabilities."
"The documentation could be improved regarding setting up the agent properly and debugging."
"It is very difficult to make the solutions fit perfectly for large organizations, especially in terms of high cardinality objects and multi-tenancy, where the data needs to be rolled up to a summarized level while maintaining its individual data granularity and identifiers."
"Its pricing model can be improved. Its settings should be improved for a better understanding of billing. They should also provide some alerts when there is an increase in the usage. For example, if there is 20% more increase from one week to another, the customer should get an alert."
"We want to reduce having to go to different screens to obtain all the information."
"They could have better log reporting."
"The ease of implementation needs improvement."
"Sometimes it is hard to find the right setting for what you want to change."
"The problem evaluation feature is an awesome idea, but bit difficult to pick up initially."
"Better root cause detection and improve root cause categories. In some cases, the root cause points out only a clue of what has happened."
"Ease of use could be improved because it can be hard to determine how you made it to the screen you are on and how to get back to it later."
"The documentation of Dynatrace needs to be improved. There needs to be a more detailed description and additional examples for background understanding for beginners trying to use it."
"Ability to better identify SDQ script errors would be helpful."
"Where we are struggling is being able to pull that information out and combine it with other contextual information that we have in other sources. Mining that data in a big-data environment, and joining it together and coming up with larger types of analysis on it."
"When it comes to monitoring, we did the integration with VMware vCenter, and we were able to see some good stuff. The VMware vCenter integration was really great, but what we really missed was the integration with the network management stuff such as Cisco ACI. We wanted to see integration in that area, but it was not provided by Dynatrace. So, the main feature for us is integration with things like Cisco ACI. If they can bring that one in, with vCenter in there, it would be a total solution. It would be absolutely incomparable to anything else in the market."
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 341 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.