Monitoring kube-opex-analytics with Vigilmon: Keeping Your Kubernetes Cost Analytics Dashboard Available

kube-opex-analytics gives engineering and finance teams the visibility they need to understand Kubernetes resource consumption and allocate cloud costs fairly. It collects daily and monthly CPU and memory utilization data, generates visual dashboards, and produces exportable cost reports. When kube-opex-analytics goes down — dashboard unavailable, data collection stalled, reports not generating — teams lose the cost visibility that informs capacity planning and chargeback. Vigilmon gives you external monitoring that watches kube-opex-analytics from outside the cluster and alerts before a stakeholder meeting reveals the dashboards have been blank for days.

What You'll Build

A Vigilmon HTTP monitor for the kube-opex-analytics dashboard
A heartbeat monitor to verify the data collection pipeline is running
A monitor for the analytics API endpoint
Alert channels for your platform and FinOps teams

Prerequisites

kube-opex-analytics deployed in a Kubernetes cluster
The dashboard exposed via an Ingress or LoadBalancer service
A free account at vigilmon.online

Why Monitoring kube-opex-analytics Matters

kube-opex-analytics is typically deployed as a background analytics tool — it quietly collects data and generates reports, and nobody notices it's broken until they need a report. By then, weeks of cost data may be missing.

Dashboard unavailability is the most visible failure. The kube-opex-analytics web UI is the primary interface for engineers and finance teams to view utilization. If the pod crashes or the service is unreachable, stakeholders get a browser error with no explanation.

Data collection stalls are the most damaging failure. kube-opex-analytics polls the Kubernetes metrics API to collect resource utilization samples. If this polling stops — due to a pod restart, metrics-server unavailability, or RBAC changes — gaps appear in historical data. Missing a day of collection means that day's utilization never appears in monthly reports.

Report generation failures affect the downstream consumers of analytics: cost allocation spreadsheets, chargeback workflows, and capacity planning documents. A stalled report generation job produces no error visible to end users.

Metrics API connectivity issues — if kube-opex-analytics loses access to the Kubernetes metrics-server, it can't collect pod-level CPU/memory data. It may continue running with stale data while appearing healthy.

Step 1: Expose the kube-opex-analytics Dashboard

kube-opex-analytics serves its web UI on port 5483 by default. If you haven't already exposed it via Ingress, create the necessary resources:

# kube-opex-analytics-ingress.yaml
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: kube-opex-analytics
  namespace: kube-opex-analytics
  annotations:
    nginx.ingress.kubernetes.io/proxy-read-timeout: "120"
spec:
  rules:
    - host: kube-cost.internal.yourdomain.com
      http:
        paths:
          - path: /
            pathType: Prefix
            backend:
              service:
                name: kube-opex-analytics
                port:
                  number: 5483

Verify the dashboard loads:

curl -s -o /dev/null -w "%{http_code}" \
  https://kube-cost.internal.yourdomain.com/
# Expected: 200

The kube-opex-analytics homepage returns 200 when the application is running. It also exposes a /api/v1/namespaces-usage endpoint you can use for a more targeted health check.

Step 2: Add Vigilmon HTTP Monitors

In the Vigilmon dashboard:

Go to Add Monitor → HTTP
Add the dashboard monitor:

| Field | Value | |---|---| | Name | kube-opex-analytics dashboard | | URL | https://kube-cost.internal.yourdomain.com/ | | Method | GET | | Expected status | 200 | | Check interval | 5 minutes | | Timeout | 30 seconds | | Alert after | 2 consecutive failures |

Add the API endpoint monitor:

| Field | Value | |---|---| | Name | kube-opex-analytics API | | URL | https://kube-cost.internal.yourdomain.com/api/v1/namespaces-usage | | Method | GET | | Expected status | 200 | | Response body contains | data | | Check interval | 10 minutes | | Timeout | 30 seconds |

The API monitor is more valuable than the homepage check — it verifies that kube-opex-analytics is actively returning analytics data, not just serving an empty shell.

Step 3: Heartbeat Monitor for Data Collection

The data collection pipeline runs inside kube-opex-analytics as a background process that periodically polls the Kubernetes metrics API. Use a sidecar CronJob to verify data is being collected:

# kube-opex-analytics-collection-probe.yaml
apiVersion: batch/v1
kind: CronJob
metadata:
  name: kube-opex-collection-probe
  namespace: kube-opex-analytics
spec:
  schedule: "0 * * * *"
  jobTemplate:
    spec:
      template:
        spec:
          restartPolicy: OnFailure
          containers:
            - name: collection-probe
              image: curlimages/curl:latest
              env:
                - name: ANALYTICS_URL
                  value: "http://kube-opex-analytics:5483"
                - name: HEARTBEAT_URL
                  valueFrom:
                    secretKeyRef:
                      name: vigilmon-secrets
                      key: kube-opex-collection-heartbeat-url
              command:
                - /bin/sh
                - -c
                - |
                  set -e
                  # Query the API and verify recent data exists
                  RESPONSE=$(curl -sf "$ANALYTICS_URL/api/v1/namespaces-usage")

                  # Check the response contains actual data (not empty)
                  DATA_COUNT=$(echo "$RESPONSE" | \
                    grep -o '"namespace"' | wc -l || true)

                  if [ "$DATA_COUNT" -lt 1 ]; then
                    echo "FAIL: No namespace usage data returned — collection may be stalled"
                    exit 1
                  fi

                  echo "Data collection healthy: $DATA_COUNT namespaces tracked"
                  curl -sf "$HEARTBEAT_URL" > /dev/null
                  echo "Collection heartbeat sent"

Create the Vigilmon heartbeat monitor:

Go to Add Monitor → Heartbeat
Name: kube-opex-analytics data collection
Expected interval: 1 hour
Grace period: 15 minutes
Store the heartbeat URL:

kubectl create secret generic vigilmon-secrets \
  --from-literal=kube-opex-collection-heartbeat-url='https://vigilmon.online/heartbeat/YOUR_TOKEN' \
  -n kube-opex-analytics

The grace period of 15 minutes handles normal job scheduling jitter without generating false positive alerts.

Step 4: Monitor Daily Report Generation

kube-opex-analytics generates daily utilization reports. If report generation stalls, daily cost summaries stop appearing. Add a separate heartbeat that fires after the daily report window:

# kube-opex-daily-report-probe.yaml
apiVersion: batch/v1
kind: CronJob
metadata:
  name: kube-opex-daily-report-probe
  namespace: kube-opex-analytics
spec:
  schedule: "30 1 * * *"
  jobTemplate:
    spec:
      template:
        spec:
          restartPolicy: OnFailure
          containers:
            - name: report-probe
              image: curlimages/curl:latest
              env:
                - name: ANALYTICS_URL
                  value: "http://kube-opex-analytics:5483"
                - name: HEARTBEAT_URL
                  valueFrom:
                    secretKeyRef:
                      name: vigilmon-secrets
                      key: kube-opex-daily-report-heartbeat-url
              command:
                - /bin/sh
                - -c
                - |
                  set -e
                  # Check the daily usage endpoint returns data from today
                  DAILY_DATA=$(curl -sf \
                    "$ANALYTICS_URL/api/v1/daily-usage" 2>/dev/null || echo "")

                  if [ -z "$DAILY_DATA" ]; then
                    echo "FAIL: Daily usage endpoint returned no data"
                    exit 1
                  fi

                  curl -sf "$HEARTBEAT_URL" > /dev/null
                  echo "Daily report heartbeat sent"

Create a second Vigilmon heartbeat monitor:

Add Monitor → Heartbeat
Name: kube-opex-analytics daily report
Expected interval: 24 hours
Grace period: 2 hours

The 2-hour grace period accounts for cases where report generation takes longer than usual on days with high resource churn.

Step 5: Configure Alert Channels

Route kube-opex-analytics alerts to the right stakeholders:

In Vigilmon: Alert Channels → Add Channel → Slack Webhook
Add your #platform-alerts webhook URL
Assign it to all kube-opex-analytics monitors

For data collection heartbeat failures, add an email channel to your FinOps team directly — they're the stakeholders who care most about cost data continuity:

Alert Channels → Add Channel → Email
Add your FinOps team's distribution list
Assign to both heartbeat monitors (collection and daily report)

This way, the platform team gets real-time Slack alerts for dashboard downtime, while the FinOps team gets email notification if data collection stalls before it affects their monthly reports.

What You're Now Monitoring

| Component | Monitor Type | What It Detects | |---|---|---| | kube-opex-analytics dashboard | HTTP | Web UI unavailable | | kube-opex-analytics API | HTTP | API returning no data | | Data collection pipeline | Heartbeat (1 hour) | Background collector stalled | | Daily report generation | Heartbeat (24 hours) | Daily cost summaries not producing |

With these monitors in place, you'll catch kube-opex-analytics failures within an hour rather than discovering during a monthly cost review that two weeks of utilization data is missing.

Conclusion

kube-opex-analytics is a low-noise background service that earns its keep when cost reporting matters most — exactly when you can't afford to discover it's been broken for days. External monitoring with Vigilmon treats it with the same rigor as any production service, ensuring cost visibility is always available when your engineering and finance teams need it.

Get started free at vigilmon.online. The dashboard HTTP monitor takes under two minutes to set up, and the hourly collection heartbeat pattern described here gives you the coverage to catch the silent failures that matter most.