[emmanueloga_]

I looked into eBPF-based observability tools for k8s some time ago and found at least four tools that look incredibly similar: Pixie, Parca, Coroot, and Odigos. There are probably others I missed too. Do you have any thoughts about this?

From a user perspective, having several tools that overlap heavily but differ in subtle ways makes evaluation and adoption harder. It feels like if any two of these projects consolidated, they’d have a good shot at becoming the "default" eBPF observability solution.

[nikolay_sivko]

From a user’s perspective, it doesn’t really matter how the data is collected. What actually matters is whether the tool helps you answer questions about your system and figure out what’s going wrong.

At Coroot, we use eBPF for a couple of reasons:

1. To get the data we actually need, not just whatever happens to be exposed by the app or OS.

2. To make integration fast and automatic for users.

And let’s be real, if all the right data were already available, we wouldn’t be writing all this complicated eBPF code in the first place:)

[edenfed]

Speaking for Odigos (disclosure: I’m the creator), here are two significant differences between us and the other mentioned players:

- Accurate distributed traces with eBPF, including context propagation. Without going into other tools, I highly recommend trying to generate distributed traces using any other eBPF solution and observing the results firsthand.

- We are agent-only. Our data is produced in OpenTelemetry format, allowing you to integrate it seamlessly with your existing observability system.

I hope this clarifies the differences.

[PeterZaitsev]

I wonder if anyone tried to integrate Odigos with Coroot - looks like it could be really powerful!