Review Summary
Users appreciate Dynatrace for its comprehensive monitoring capabilities, intuitive interface, and strong customer s ...
Users appreciate Dynatrace for its comprehensive monitoring capabilities, intuitive interface, and strong customer s ...
Dynatrace is the AI-powered observability platform. We empower today’s AI-enabled digital enterprises to understand their systems and data so they can analyze, automate, and innovate faster. With Dynatrace, you can transform complexity into your greatest asset and drive your business forward.
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I love the OpenPipeline implementation that allows us to modify retention on logs, metrics, and traces. I love the power of DQL centralizing all of the data on Dynatrace into a single query language that can be cross-correlated with trace headers and correlation ID's. I love the simplicity of the Dynatrace Operator and how it instruments entire namespaces with code-level visibility just with a couple of helm commands and some simple yaml configuration.
DQL - unifying data with the ability to pull in a large amount of context has been extremely helpful.
1. OneAgent Auto Discovery - deploys once and automatically detects every host, container, process and cloud service into with no manual configuration. 2. Smartscape technology - a live dependency map that instantly shows how infrastructure layers connect, making it easy to trace the blast radius of any incident. 3. David's AI root cost analysis - automatically correlates anomalies across your infrastructure and pinpoints the exact problem without manually digging through metrics
BizEvent logs are segregated from the rest of the platform data, meaning we can't easily correlate business events to traces and logs, even if the W3C trace context headers are present in the bizevent table. Dynatrace makes it hard to rollout to a large group or company since I can't easily validate user permissions and scopes by spoofing a specific user to ensure they only have the access they need. Instrumenting .NET 8 Azure Functions with OpenTelemetry running in isolated workloads proved impossible for our company, and we found the documentation lacking in regards to doing so. This was such an issue that we had to pivot entirely to utilizing ASP.NET Core API instead of Azure Functions SDK for our application to be properly monitored by Dynatrace.
There was virtually no guidance on best practices. It felt like when asked about how to best do things, we were either given very specific answers (like which buttons to click to do a thing) or a general you're doing great, whatever way you are doing it is right!. Things like critical features like segregating data via buckets to ensure faster query response time with reduced cost was never highlighted until we explicitly asked about it. This was AFTER asking for best practices guidance.
1. Hoat-based pricing escalates fast - costs become unpredictable as you scale, especially in a dynamic cloud environments at with ephemeral hosts 2. Anomaly detection needs heavy tuning - out of the box, Davis AI generates too much noise and requires significant effort to calibrate threshold per environment 3. Limited granularity for custom metrics - ingesting custom infrastructure metrics beyond what Oneagent captures natively requires extra configuration and add to your licensing costs