Datadog vs. Percepio Tracealyzer

If I want to see what customers are doing wrong in their sessions, we can do that easily. On the other hand, if we want to know the payload in the API calls, that is the area where Datadog can improve.

Incentivized

This is a perfect tool to debug complex bugs in your system, especially in regards to inter-task communication. It is also a great tool for beginners, as the documentation is accessible and the support given by the company is excellent

• The thing which Datadog does really well, one of them are its broad range of services integrations and features which makes it one step observability solution for all. We can monitor all types of our application, infrastructure, hosts, databases etc with Datadog.
• Its custom dashboard feature which helps us to visualize the data in a better way . It supports different types of charts through those charts we can create our dashboard more attractive.
• Its AI powered alerting capability though that we can easily identify the root cause and also it has a low noise alerting capability which means it correlated the similar type of issues.

Incentivized

• Great visualization
• Sometimes awkward views (vertical vs horizontal)

• In my experience, .NET Tracing Agent caused severe and untraceable performance issues
• In my opinion, usage and billing structures were opaque and surprising
• In my experience, documentation was incomplete, contradicting or sometimes completely wrong, even for common infrastructure (AWS Fargate)
• I feel support was unhelpful at times, and bounced us back and forth to other teams
• In my opinion, multiple methods of sample rate control were ineffective, adding to excessive usage and cost

Incentivized

• It's not always easy to include/integrate Tracealyzer in the OS build. It would be nice to have a simple install/plug n play method for our target.
• Sometimes, the .bin files from Tracealyzer don't always work and we don't know why

Its a one stop solution for most processes and does reduce cost overall. This does in turn help teams communicate in a much more efficient way, also RUM is a game changer for any quality product; we use RUM alot for debugging and reverse engineering issues. Reports and coverage has been a plus, it all has helped us a lot in our processes.

Incentivized

There are so many features that it can be hard to figure out where you need to go for your own use case. For example, RUM monitoring us buried in a "Digital Experience" sidebar setting when this is one of our key use cases that I sometimes struggle to find in the application. It appears that ECS + Fargate monitoring was recently released which is great because we had to build a lambda reporting solution for ephemeral task monitoring. But this new feature was never on my radar until I starting clicking around the application.

Incentivized

The support team usually gets it right. We did have a rather complicate issue setting up monitoring on a domain controller. However, they are usually responsive and helpful over chat. The downside would be I don’t think they have any phone support. If that is important to you this might not be a good fit.

Incentivized

Documentation was difficult to work through, rollout was catastrophic (completely outage)

Incentivized

In terms of usability, I’ve found Datadog significantly more approachable and powerful compared to Elasticsearch, especially for day-to-day operational monitoring. Datadog offers a much more cohesive, user-friendly interface out of the box, with built-in support for metrics, logs, tracing, alerting, and dashboards—all tightly integrated. In contrast, Elasticsearch often felt barebones and required considerable setup and additional tooling (like Kibana or Logstash) to reach the same level of functionality.
While Elasticsearch is excellent for high-volume log ingestion and full-text search, it lacks the depth of features and real-time visualization capabilities that Datadog provides natively. The query language in Datadog is also quite usable and comparable for my needs—mainly filtering and aggregating logs and metrics—without needing to learn Elasticsearch's more complex DSL. Overall, Datadog saves our team time and effort, whereas Elasticsearch often felt like building a monitoring system from scratch. The all-in-one nature of Datadog, despite its complexity, is ultimately more efficient and scalable for our use case.

Incentivized

While we started using uCOSIII for our simpler microcontroller products, we also use QNX on more complex targets (full microprocessors) and it is a much more complex platform offering event tracing, memory tracing, and performance measures that are extremely good and integrated. More importantly these tools are fully integrated without any code changes. Tracealyzer is not integrated as much into uCOSIII like QNX's tools, debugger, etc. But, going thru the manual work of adding Tracealyzer to the build, it did help us get to a shippable product.

• Saved us (time & money) from developing our own monitoring utilities that would pale in comparison
• Alerts allow us to remedy issues before our customers even know about them
• Tracking resource usage over time allows us to better plan for future needs, before it becomes a pain-point.

Incentivized

• Positive: We were able to find some thread priority issues with Tracealyzer.
• Positive: we were able to see timing of events in the sw/hw that we couldn't otherwise see.