01
Reduce MTTR during production incidents
An AI SRE gathers logs, traces, metrics, Kubernetes events, deployments, and code context automatically, so responders can move from alert to root cause faster.
DemoTry Playground→
AI SRE Agent for teams on Kubernetes
Metoro's AI SRE agents detect production issues, investigate alerts, verify deployments, identify root causes, and generate proposed fixes.
Metoro Overview
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01Install
Run one command.
Helm install Metoro's agent into your Kubernetes cluster. No code changes, no sidecars, no SDKs.
~/cluster · bash
$helm install metoro metoro/agent --namespace metoro --create-namespace --set apiKey=$METORO_KEY
And you're done.
02Detect · No alert rules required
Metoro detects issues automatically.
Metoro watches your entire Kubernetes environment from install, then investigates issues with evidence — no dashboards, thresholds, or rules required.
01 · Service-level regressions
- ▸HTTP 5XX spikes
- ▸Latency spikes
- ▸Database error-rate & latency spikes
02 · Dependencies
- ▸External dependency errors
- ▸External dependency latency regressions
- ▸Deployment-related service regressions
03 · Pods & workloads
- ▸CrashLoopBackOff
- ▸ImagePullBackOff / ErrImagePull
- ▸OOMKilled
- ▸Init:Error
04 · Nodes & cluster
- ▸Kubernetes warning event spikes
- ▸Node network anomalies
- ▸CPU, memory, request, limit & throttling issues
03Notify · Delivered to your team
Get notified with the root cause attached.
Metoro turns detected issues into clear, investigation-backed notifications with the likely root cause, supporting evidence, and suggested fixes.
AI Anomaly Detection
Metoro finds the regression. And the line of code.
Metoro investigates regressions across traces, logs, profiles, and source. The result is a root cause with evidence — not a guess, not a probability score.
- ✓Anomaly detection from live traffic baselines
- ✓Correlates telemetry with source control
- ✓Links directly to the offending commit
AI REMEDIATION
Then Metoro generates the fix PR.
Metoro turns root cause analysis into proposed code fixes. When it identifies the offending change, it drafts a pull request with the fix, evidence, telemetry links, and an RCA summary — ready for your team to review and merge.
- ✓Generates fix PRs from root cause analysis
- ✓Includes RCA summary, evidence, and telemetry links
- ✓Keeps engineers in control with review-before-merge workflows
Deployment Verification
Every rollout, verified against production.
Metoro compares each deployment's behavior against the baseline. Regressions surface in Slack with a pre-drafted rollback PR — before a customer notices.
- ✓Per-service, per-endpoint comparison
- ✓Flags regressions in under 60 seconds
- ✓Rollback PR pre-drafted on failed verification
Reduce MTTR with AI Alert Investigation
Every alert, auto-investigated.
Metoro investigates every firing alert, separates noise from real production issues, and sends your team the likely root cause, evidence, and next steps in Slack.
- ✓Investigates alerts from your existing tools
- ✓Filters noisy pages from actionable incidents
- ✓Delivers root cause, evidence, and proposed fix
Why Metoro
Most AI SREs inherit your telemetry gaps. Metoro doesn't.
Metoro brings its own eBPF telemetry layer for Kubernetes and combines runtime signals, Kubernetes state, deployments, and code changes into one reliable investigation context.
Try PlaygroundPain points
What problems does an AI SRE solve?
AI SRE platforms help Kubernetes teams reduce MTTR, lower alert fatigue, catch deployment regressions, and turn incident evidence into remediation steps.
02
Cut alert fatigue for on-call teams
Instead of handing engineers another page with no context, an AI SRE investigates the alert, separates noise from real issues, and summarizes what changed.
03
Catch deployment regressions earlier
AI SRE deployment verification compares new rollouts against live production baselines and flags regressions before customers report them.
04
Close observability gaps
When telemetry is incomplete, investigations stall. Metoro uses eBPF telemetry for Kubernetes so the AI starts with runtime evidence, not only pre-existing dashboards.
05
Reduce repetitive incident toil
The same investigation steps happen during every incident: query telemetry, inspect Kubernetes state, compare changes, and test hypotheses. An AI SRE automates that loop.
06
Turn root cause into action
Finding the likely cause is only part of the workflow. Metoro can generate suggested fixes and pull requests so teams can review a concrete next step.
FAQ
Frequently asked
Common questions about Metoro's AI SRE for Kubernetes.
An AI Site Reliability Engineer is an autonomous agent that performs the work of a human SRE: it watches production, detects anomalies, investigates root causes across code and infrastructure, and remediates the issue — often by opening a pull request. Metoro is an AI SRE built specifically for Kubernetes.
Get Started
Give your on-call team the night off.
Detection to remediation. Automated. One-minute install. No code changes.