Dynatrace Classic

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.

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.

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.

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.

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.

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.

I have been using Dynatrace for three years, having initially been introduced to Kibana and other solutions such as AWS  Watch before that.

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.

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.

Hybrid Cloud

Amazon Web Services  (AWS)

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

Positive