AI SRE platform for Kubernetes built on an eBPF telemetry layer. Autonomously monitors services, spawns AI agents to root-cause issues using complete runtime context (traces, metrics, logs, K8s state, deployments, code changes), performs deployment verification, and generates fix pull requests. The eBPF layer provides the data; AI agents drive operational actions — investigations, verifications, fixes.
No compliance attestations on file. Confirm directly with the vendor before procurement.
| Tier | Price | Includes |
|---|---|---|
Hobby | Free | 1 cluster, 1 user, 2 nodes, 200 GB ingested/month |
Scale | $20/node/month | — |
Metoro is an eBPF SRE platform that root-causes K8s issues and writes the fix PR.
Metoro collects traces, metrics, logs, Kubernetes state, and deployment changes through an eBPF data layer that needs no application instrumentation or sidecars. AI agents then use that complete runtime picture to correlate failures with the change that caused them, and for simple classes of bugs (config typos, missing env vars), open a pull request with the fix already applied.
Who it's for. Small to mid platform teams of 5 to 20 engineers running Kubernetes services who want SRE automation without retrofitting OpenTelemetry across every service. Scenario: a service starts returning 500s after a deployment, Metoro's agent ties the error to a code change that referenced a removed environment variable, and opens a PR fixing the reference.
Tradeoffs. Early-stage and Kubernetes only. The eBPF layer runs as a privileged DaemonSet, which can collide with strict security policies. Fix PRs are reliable for narrow categories; complex logic bugs are out of scope. Integration surface is narrower than established competitors. Free tier covers basic monitoring.
Compare: Komodor, Datadog Bits AI, Causely, Robusta