Komodor is an AI SRE that correlates K8s events with deployments and runs remediation playbooks.
Klaudia AI agents stitch deployment events, Git commits, Kubernetes state changes, and observability signals into a single timeline, then propose or execute remediation playbooks under governance guardrails. The breadth of correlation is the differentiator: deploy X caused config drift Y which produced alert Z, all visible without manually cross-referencing four dashboards.
Who it's for. Platform teams of 10 to 50 engineers running Kubernetes at scale where time-to-resolution is bound by time-to-find-cause. The reference scenario: a service starts returning 500s, Komodor ties it to a deployment that changed an env var, identifies that the new value points at a deleted ConfigMap key, and runs a rollback playbook to restore the prior version.
Tradeoffs. Kubernetes only, and value is bound to integration depth: if your observability stack is missing from the supported list, correlation quality drops. Automated remediation actions (restart, scale, cordon, drain) need careful guardrails. Free tier monitors a single cluster; Teams and Enterprise unlock AI investigation and are sales-only.
Compare: Robusta, Datadog Bits AI, Metoro, Causely
