Full-stack observability has improved load testing and consistently reduces error rates
What is our primary use case?
The main use case for using Dynatrace is to check the CPU utilization and the performance stats of my load test, stress test, or endurance test.
During one of my tests, when I perform a load test, I parallelly open or configure the server of that application in Dynatrace, creating a new dashboard so that during my load test, I can easily observe what is happening, what the error stats are, what my pass percentage is, what my CPU utilization is, or the garbage collection, and other metrics.
I can even check my alerts and exceptions as well.
What is most valuable?
For me, the best feature Dynatrace offers is full-stack observability, which allows me to check my infrastructure or the containers because most of my applications are cloud-based, and with Dynatrace, I can check the database or the network path, or which are on-premises or cloud, making full-stack observability more suitable for me.
Dynatrace is our APM tool, which provides deep code-level visibility, allowing us to check the methods or exceptions, database calls, or external services, and we can easily trace them out.
Dynatrace has positively impacted my organization because usually when I do a load test, our API SLA is only one second, but it crosses more than three seconds or five seconds, and by using this APM tool, we can figure out what is causing it, what kind of error it is throwing, and how we can optimize it, making it very useful for my organization as well as for me while doing load testing.
For the metrics or numbers, our error rate should be less than three percent, and achieving less than three percent, even if it is one percent, is very good, and the reduction of the error rate by using Dynatrace was quite good.
What needs improvement?
Dynatrace itself is a very good platform where the UI interface is very simple and usable, but I would say if it were made even more simple, similar to our Splunk dashboard, that could help users or newer users understand it more easily, especially since sometimes I have seen when using Dynatrace, we are not able to add the time frame properly.
If you add some more fields with simple words or simple terms instead of the complex terms used for a resource, that might be helpful.
Dynatrace can be better and more user-friendly, and that is my advice, but overall, it is a good application and APM tool for a performance tester.
For how long have I used the solution?
I have been using Dynatrace for four to five years.
What do I think about the stability of the solution?
What do I think about the scalability of the solution?
The scalability of Dynatrace is good, and we can easily figure out what is happening.
How are customer service and support?
The customer support is responsive because when I logged some defects to the developer teams, they worked with Dynatrace team, and I think we have received much support.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
Before using Dynatrace, I used AppDynamics and Splunk.
What was our ROI?
By using the APM tool for resource management and time management, we have saved a lot of time, so I would say this is a good investment that the organization has made.
Which other solutions did I evaluate?
I have not switched from AppDynamics as well as Splunk because I am using Dynatrace as well as those other two APM tools, depending on my project level, what the project is configured with, and which is better.
What other advice do I have?
I would advise others to use Dynatrace because it is very good in terms of scalability and reliability, and the stats it shows, including the perfection and the graph, are very good, so I would suggest they use Dynatrace. I gave this review a rating of nine.
Dynatrace Grail: Super Useful for Finding and Organizing Information
What do you like best about the product?
Dynatrace Grail is super useful in finding information with a large amount of options to organize and present it.
What do you dislike about the product?
Davis is the worst AI assistant I have ever used. I can not recall a situation where it has correctly answered a question and is constantly contradicted by the information it sources. I only use it to ask questions. It might be better at anomaly detection.
What problems is the product solving and how is that benefiting you?
We are able to search and share information insanely easy.
Unified Monitoring that works at scale
What do you like best about the product?
I use Dynatrace to support IT teams by simplifying the end-to-end capability around monitoring, which reduces the complexity of fragmented data by providing a single pane of glass. I appreciate the depth and breadth of the supported technologies and the way it standardizes the visualizations of data. Dynatrace consolidates a wide range of technologies into a common view, which is really helpful since our organization has to monitor many different technologies, both old and new. The initial setup was also very easy, especially since we have a SaaS platform, making agent deployment smooth and simple.
What do you dislike about the product?
I would say the dashboarding. It's very block-like in its layout. While this is great for simplification, there are times when it would be good to have the ability to fully customize them with potentially overlapping dashboard widgets, backgrounds, logos, etc.
What problems is the product solving and how is that benefiting you?
I use Dynatrace to support IT teams with monitoring and observability. It simplifies monitoring by consolidating data into a single pane, reducing complexity from fragmented data.
Effortless Setup, Complex Features Made Simple
What do you like best about the product?
I like using the Notebooks in Dynatrace because they make it easy and fast to find specific data. I appreciate the ease of use, which makes the software accessible without a steep learning curve, and I like the complexity of its features that offer robust solutions. The initial setup was easier than I expected, which was a pleasant surprise.
What do you dislike about the product?
Learning and certification experience needs to be improved
What problems is the product solving and how is that benefiting you?
Dynatrace improves visibility and helps find root causes quickly. Notebooks are easy and fast for accessing specific data.
Seamless Integration of Multiple Data Sources
What do you like best about the product?
possibility to integrate multiple data sources
What do you dislike about the product?
somtimes dashboard creation is a challange
What problems is the product solving and how is that benefiting you?
combine infra and app metrics
Comprehensive Monitoring, Complex Setup
What do you like best about the product?
I like putting all my data into one platform with Dynatrace, so I don't need to worry about gathering data from multiple different platforms. This really helps us by allowing our support users to be in one place, speeding up the time to recovery and improving our efficiency.
What do you dislike about the product?
It's complex. It's hard to configure. To get everything right. The flexibility is powerful, but it's also challenging.
What problems is the product solving and how is that benefiting you?
Dynatrace provides visibility into different applications and a single pane of glass for all observability data. It consolidates data into one platform, speeding up our time to recovery by preventing the need to gather data from multiple platforms.
Easy to Implement Across Any Environment
What do you like best about the product?
We can easily implement on any environmen
What do you dislike about the product?
We cannot create tabs inside our dashboards so we need to infinite scroll to search our panels
What problems is the product solving and how is that benefiting you?
Across the years were having issues with other platform in regards of what we can implement and monitor, this is part of what dynatrace has solve
Easily Visualizes Complex Systems, Needs Documentation Improvement
What do you like best about the product?
I like using Dynatrace because I can visualize complicated systems easily, which is important for my SRE practice. I appreciate the traces and the service that shows how every system is connected. Setting up Dynatrace was very easy for my team.
What do you dislike about the product?
I think Dynatrace could improve in identifying the right solutions and providing more accurate documentation.
What problems is the product solving and how is that benefiting you?
I can visualize complicated systems easily with Dynatrace, and it shows how every system is connected.
AI-Driven Observability with Clear Root-Cause Insights and Easy Onboarding
What do you like best about the product?
Dynatrace provides deep, AI-driven monitoring and observability across hybrid and multi-cloud environments, with excellent end-to-end visibility. Its AI engine automatically detects anomalies, reduces noise, and delivers clear root-cause insights. The Dynatrace interface is clean, the topology mapping is incredibly accurate, and the single-agent deployment makes onboarding very easy.
What do you dislike about the product?
Dynatrace’s pricing model can become expensive as environments scale, especially for organizations with large Kubernetes or microservices deployments. Integration with some ITSM tools also requires additional configuration effort.
What problems is the product solving and how is that benefiting you?
Dynatrace helps us achieve real-time observability across our infrastructure and cloud workloads. With AI providing automatic RCA, our MTTR has been reduced significantly. We’re also able to proactively detect performance degradation before end users are impacted.
AI-driven monitoring has reduced incident resolution times and improves release confidence
What is our primary use case?
My main use case for Dynatrace involves daily work with monitoring charts, setting up alerts, and tracking response times and error rates to identify slow transaction bottlenecks in microservices. I also manage infrastructure monitoring, such as CPU, memory, and disk issues. When anomalies in resource consumption arise, I utilize the AI-powered Dynatrace Davis engine to quickly identify the root cause. Additionally, we employ real user monitoring (RUM) for alert and incident management, creating alerts with tools such as PagerDuty and ServiceNow when we need to raise incidents. We also focus on observability in our workloads deployed on a Kubernetes environment, including microservices and various servers.
I have a specific example of how Dynatrace helped me solve performance issues, particularly with slow response times in payment services, which could reach eight to ten seconds. I had to check the trace routes and flow to understand these delays during calls to external APIs, where I discovered that third-party API calls were waiting for responses due to DNS resolution issues. Dynatrace identified this slowdown, correlating it with spikes in DNS lookup times in a node in our Kubernetes cluster. After we handled deployment releases, we dropped response times to under one second. This solution significantly improved our common problems, achieving a success rate of almost fifty percent in troubleshooting.
How has it helped my organization?
Dynatrace has positively impacted my organization by reducing incident resolution times, with Davis AI helping to pinpoint root causes effectively. We have seen a reduction of thirty to sixty percent in mean time to resolution (MTTR) for prioritized incidents and fewer escalations. Additionally, Dynatrace has helped us reduce alert noise, leading to forty to seventy percent fewer alerts while routing incidents more reliably to the correct teams. The quality of our releases improves gradually due to automated validation, allowing for quicker rollbacks and issue detection within minutes of deployment, which increases confidence in our CI/CD processes.
Dynatrace has contributed to significant improvements such as reducing P1 tickets resolution time from four hours to under one hour and drastically cutting alert volumes from between two hundred to four hundred alerts per week down to approximately sixty to one hundred twenty. The latency for reporting ticket issues dropped with PurePath and RUM data, improving from over three point five seconds to around two point one seconds. We also recorded substantial reductions in both latency from three point eight to one point four seconds and error rates averaging under one percent after implementing the findings from Dynatrace analytics.
What is most valuable?
The best features that Dynatrace offers include the AI-powered root cause analysis with Davis AI, which automatically identifies root causes by correlating metrics, logs, and traces, saving substantial time during incident resolution. Full-stack observability is another top feature, as it covers application, infrastructure, and network-related services while integrating with cloud environments. I appreciate the PurePath distributed tracing that provides deep dive insights into every transaction across microservices, helping us pinpoint slow database queries and external API calls. RUM allows us to track actual user sessions that impact UX, while synthetic monitoring proactively detects issues before they affect real users. OneAgents simplify infrastructure-related configurations, and I want to emphasize the importance of business analytics integration to tie technical metrics with business KPIs, as my role involves prioritizing issues based on their impact on business outcomes.
The feature that saves me the most time is Davis AI, as it automatically analyzes all data elements, understands metrics, logs, and traces, and pinpoints exact root causes of issues. Instead of manually digging through dashboards, I receive clear explanations of problems, such as high CPU usage due to garbage collection or memory issues, which drastically reduce the mean time to resolution (MTTR). The manual investigations that used to take hours can now be solved in under a minute, eliminating guesswork and allowing me to respond quickly without needing cross-team checks. For instance, Davis AI recently flagged a slowdown in microservices that led me to a recent inefficient data query introduced during deployment, allowing me to roll back changes in only fifteen minutes.
What needs improvement?
Beyond the features already discussed, I would like to see improvements in auto-discovery, smart instrumentation, and a unified data model to centralize all metrics and events on a single platform. This change would minimize the need to jump between tools and manually stitch data together. Continuous improvement features tied to SLO objectives should also ensure deployments meet performance standards.
In terms of improvements, I believe Dynatrace could enhance cost and licensing structures, as the current pricing can be expensive for large-scale deployments. More flexible and granular billing options would be beneficial, especially for ephemeral workloads. Additionally, while the initial setup is straightforward, understanding advanced features requires expertise. Improvements in user guidance, such as tutorials or workflow documentation, could help new users navigate the platform more easily, particularly with customization options and dashboard enhancements.
Further improvements could include fostering deep native integrations with major platforms and enhancing the ease of integrating with CI/CD tools such as Jenkins or GitHub Actions. Additionally, supporting better OpenTelemetry for custom traces and metrics would simplify setups. Native integrations with BI tools would enhance our analytical capabilities, making real-time dashboard creation easier.
For how long have I used the solution?
I have been using Dynatrace for three years, having initially been introduced to Kibana and other solutions such as AWS Watch before that.
How was the initial setup?
Dynatrace was purchased through the AWS Marketplace, which made the setup process straightforward; however, I believe no additional improvements are necessary beyond what I have already mentioned.
What other advice do I have?
For others exploring Dynatrace, my advice is to start by defining clear goals, such as improving incident resolution times or release quality. Familiarizing oneself with key features such as Davis AI and ensuring thorough tagging of services is essential for cleaner dashboards. Utilize AI for problem detection and integrate Dynatrace with incident management tools for efficient workflows.
Before concluding, I want to emphasize the importance of leveraging advanced features beyond basic monitoring, particularly with SLOs and release validations, and to be mindful of budgeting, as Dynatrace can get expensive at scale. I would rate this product an eight out of ten.
Which deployment model are you using for this solution?
Hybrid Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
