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
Effective Server Monitoring with Room for Improvement
What do you like best about the product?
I appreciate Dynatrace's ability to show saturation, memory saturation, and any issues causing those problems, which helps in monitoring and identifying infrastructure issues. Additionally, it integrates well with ServiceNow for creating problem tickets automatically.
What do you dislike about the product?
Database monitoring could be improved.
What problems is the product solving and how is that benefiting you?
I use Dynatrace for server monitoring, addressing infrastructure issues like saturation and memory. It helps diagnose SQL server problems, especially with bad data calls.
All-in-One, Intuitive Portal for Monitoring Application and Platform Health
What do you like best about the product?
Dynatrace provides an all-encompassing, intuitive portal for monitoring application and platform health.
What do you dislike about the product?
Honestly, I’m very satisfied with my interactions with Dynatrace.
What problems is the product solving and how is that benefiting you?
As an AI Engineer, I’ve found that Dynatrace’s contribution to the OTEL spec makes agentic observability much easier to implement and work with.
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.
The #1 Observability Tool for Implementing and Monitoring Applications
What do you like best about the product?
Dynatrace is one of the powerful observability and infrastructure monitoring tools. It expands its capabilities with APM, DEM, and infra monitoring, along with support for databases and multiple cloud platforms. I use it daily to monitor applications and our cloud stack.
It has wide integration support with third-party tools, so we can integrate and monitor almost everything in Dynatrace. They also provide active customer support, helping us when we get stuck and resolving our problems and issues.
We can monitor our application’s golden metrics, such as logs, metrics, traces, and events, along with CPU, memory, disk, and other infrastructure metrics. Overall, it’s one of the best and most user-friendly tools to monitor and secure our applications.
What do you dislike about the product?
There’s nothing to dislike. It’s the number 1 observability tool to use, implement, and monitor applications.
What problems is the product solving and how is that benefiting you?
In Dynatrace, we can set up alerts based on anomalies as well as static thresholds. It can notify you before your application goes down, or when any issue occurs, helping you keep things secure. We can also set service-level boundaries for SLA, SLO, and SLI. In addition, we can create multiple dashboards for the DevOps team to keep an eye on the application’s performance. Overall, it helps you secure your application and address problems before it goes down.
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?
Overly Complex with Misleading Dashboards and False Positives
What do you like best about the product?
three years ago we were early adopters and it was brilliant.
What do you dislike about the product?
it became too complicated and gave too many false positives. Dashboards are inaccurate and misleading.
What problems is the product solving and how is that benefiting you?
initially helped identify root cause on tricky issues with application and aws platform
