Hey Ph! We are Chris and @ece_kayan, founders of Metoro.
We built Metro because dealing with productivity issues is still too manual.
Teams are delivering faster than ever with AI, but when something breaks, engineers jump between dashboards, logs, traces, infrastructure and code changes just to figure out what happened and how to fix it.
We started working on it in 2023 during YC’s S23 batch, and learned a hard lesson from early users: generic AI SRE doesn’t work reliably for two reasons.
Every system is different. The architecture is different. Some teams run on VMs, some on Lambdas, some on managed services, some on Kubernetes, some on a mix of them all.
On top of that, telemetry is generally inconsistent. Some services have tokens, some don’t. Some have structured logs, some barely log. The matrix is ​​given different names everywhere.
This means teams need to spend weeks or months creating system documentation, adding runbooks, developing documentation, and instrumentation services before AI SRE can be useful. It was not feasible.
So we took a different approach.
With Metoro, we build our own telemetry at the kernel level by using EBPF. This gives us consistent telemetry out of the box with zero code changes. No waiting for teams to instrument services. There are no huge observational blind spots.
And because Metoro is built specifically for Kubernetes, the agent already understands the environment in which it is operating. It doesn’t need to learn a completely new architecture every time.
The result is an AI SRE that works out of the box. Less than 5 minutes.
We automatically monitor your infrastructure and applications, when we detect an issue we investigate and root cause it. When we have the root cause, we automatically generate a pull request to fix it, whether it’s application code or infrastructure configuration. Find out, root cause, correct..
We’re really excited to launch today on Product Hunt 🚀.
We’d love for you to check it out, try it out, and ask us anything. Whether it’s about Metoro, Kubernetes observability, or AI in the SRE space.