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Kubernetes Root Cause Analysis:
What if Kubernetes told you why it failed?

Stop digging. CloudExp identifies the why and posts it to Slack — before you finish typing kubectl logs.

From Alert → Evidence → Action. Automatically.

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Built by senior SREs with years of on-call experience running production Kubernetes.

Kubernetes Incident Root Cause Analysis: Live Examples

Real-time incident analysis delivered directly to your Slack channel. Click through different scenarios to see how CloudExp handles various incident types.

# incidentsCloudExp Kubernetes RCA • 14:23
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CloudExp
CloudExp Kubernetes RCA14:23
🚨CRITICAL INCIDENT – Application Crash

Deployment payment-processor-api in namespace production is crash-looping due to an application-level error.

Why we think this:

Application terminated with exit code 1 due to unhandled exception during payment transaction processing. The error occurred when attempting to connect to the payment gateway service.

High confidence
Top Evidence:
  • Exit code: 1
  • Termination reason: Error
  • Last log entry: ERROR: Failed to initialize payment gateway client: connection timeout after 30s
  • Pod restart count: 8 in last 15 minutes
  • Container state: Terminated (Error)
Suggested next checks:
  • Review application logs: kubectl logs payment-processor-api-7d8f9c4b-x2k9p -n production --previous
  • Check payment gateway service connectivity: kubectl exec -it payment-processor-api-7d8f9c4b-x2k9p -n production -- curl -v https://api.payment-gateway.com/health
  • Verify network policies and service mesh configuration for payment gateway access
  • Check for recent configuration changes or secret updates
  • Review resource limits - memory pressure may be causing connection timeouts
Namespace: production•Mode: Deterministic

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Business Impact

How CloudExp Improves Kubernetes Incident Response

Incident response is one of the biggest hidden costs in engineering. CloudExp reduces mean time to understanding, cuts operational load, and prevents repeat failures.

Time-to-understanding:minutesNoise:lowerPrivacy:controlled

Faster incident resolution

  • From alert → likely root cause in ~2 minutes for common incidents
  • Cuts time-to-understanding by reducing event/log spelunking
  • Gives on-call a concrete “next checks” list in Slack

Lower on-call load

  • One incident → one evolving Slack thread (less noise)
  • Evidence is bounded and scannable (no log floods)
  • More consistent RCAs across engineers and shifts

Reduced downtime cost

  • Shorter incidents means less customer impact
  • Fewer escalations and less context switching
  • Easier post-incident writeups with an evidence packet

A single avoided critical production incident often saves more than a month of support — even for small teams.

Why We're Different

We believe Kubernetes should explain failures, not just surface them. Our mission is to turn operational signals into real understanding so engineers can fix incidents faster and with confidence.

Explains incidents — doesn't just alert

Get root cause analysis with evidence, not just notifications.

Deterministic-first, AI second

Rule-based detection ensures accuracy before AI enhances insights.

See how it works

Runs inside your cluster

Your data stays private and secure within your infrastructure.

Our product is built for teams who need answers, not just alerts.

Deterministic Kubernetes Failure Detection with Optional AI Analysis

We start with rule-based detection for accuracy, then use AI to enhance insights for complex scenarios.

Unlike black-box AI copilots, CloudExp starts from deterministic system facts — producing repeatable, auditable, production-safe explanations.

Deterministic Detection

Rule-based analysis that identifies root causes through observable evidence: exit codes, probe failures, resource constraints, and log patterns.

  • 100% reproducible results based on evidence
  • No false positives from pattern matching
  • Immediate analysis without model inference
  • Works for 80%+ of common incident patterns

AI Enhancement

When deterministic reasoning reaches its limits, AI is selectively applied to complex edge cases — always bounded and privacy-controlled.

  • Identifies complex multi-factor root causes
  • Learns from historical incident patterns
  • Correlates signals across multiple dimensions
  • Optional: can be disabled for full privacy control

The result: Fast, accurate root cause analysis for common incidents, with AI-powered insights for complex scenarios. You get the reliability of deterministic rules with the depth of AI when needed.

Who This Is For

Platform & SRE teams running Kubernetes in production

Teams managing complex Kubernetes deployments who need actionable insights.

Teams suffering alert fatigue

Organizations overwhelmed by noisy alerts that lack context and actionable information.

Privacy & security conscious organizations

Companies that require on-premise or private cloud deployments with full data control.

Design Partner Program — Limited to 10–20 Teams

Work directly with the CloudExp engineering team to shape the future of Kubernetes incident intelligence.

Early partners receive priority support, roadmap influence, and discounted pilot pricing.

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