It would be great using VictoriaLogs as a storage for structured logs in Coroot, since it is optimized for structured logs with arbitrary sets of labels. See https://docs.victoriametrics.com/victorialogs/keyconcepts/

In addition to raw logs, Coroot can extract recurring patterns to generate log-based metrics [1].

We also plan to convert structured logs into OpenTelemetry attributes [2].

[1] https://demo.coroot.com/p/tbuzvelk/applications/default:Depl... [2] https://github.com/coroot/coroot/issues/490

1. Regarding overhead — we ran a benchmark focused on performance impact rather than raw overhead [1]. TL;DR: we didn’t observe any noticeable impact at 10K RPS. CPU usage stayed around 200 millicores (about 20% of a single core).

2. Coroot’s agent captures pseudo-traces (individual spans) and sends them to a collector via OTLP. This stream can be sampled at the collector level. In high-load environments, you can disable span capturing entirely and rely solely on eBPF-based metrics for analysis.

3. We’ve built automated root cause analysis to help users explain even the slightest anomalies, whether or not SLOs are violated. Under the hood, it traverses the service dependency graph and correlates metrics — for example, linking increased service latency to CPU delay or network latency to a database. [2]

4. Currently, Coroot doesn’t support off-CPU profiling. The profiler we use under the hood is based on Grafana Pyroscope’s eBPF implementation, which focuses on CPU time.

[1]: https://docs.coroot.com/installation/performance-impact [2]: https://demo.coroot.com/p/tbuzvelk/anomalies/default:Deploym...

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:)

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.

We could totally add that, but no one's asked for it so far

It only requires a modern Linux kernel. Note: The agent does not support Docker-in-Docker environments, such as KinD or Minikube (D-in-D plugin).

Initially, we relied on the ClickHouse OTEL exporter and its schema, but for performance optimization, we decided to modify our ClickHouse schema, and they are no longer compatible :(

Also commonly used for high-performance networking and security use cases, for example https://isovalent.com/blog/post/cilium-netkit-a-new-containe....

Basically anywhere you'd previously need to write a kernel module but now can have user space run arbitrary kernel code that's secure and won't crash the kernel.

You can also now write custom schedulers in eBPF with sched_ext.

Yes, one example: network bandwidth isolation is done more efficiently using ebpf https://netdevconf.info/0x14/pub/papers/55/0x14-paper55-talk...

Yes, it captures traffic before encryption and after decryption using eBPF uprobes on OpenSSL and Go’s TLS library calls.

Enterprise Edition = Community Edition + Support + AI-based Root Cause Analysis + SSO + RBAC

(I'm a co-founder). At Coroot, we're strong believers in open source, especially when it comes to observability. Agents often require significant privileges, and the cost of switching solutions is high, so being open source is the only way to provide real guarantees for businesses.

Coroot builds a model of each system, allowing it to traverse the dependency graph and identify correlations between metrics. On top of that, we're experimenting with LLMs for summarization — here are a few examples: https://oopsdb.coroot.com/failures/cpu-noisy-neighbor/