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Production-Grade Kubernetes Observability: OpenTelemetry, Prometheus, and Grafana Loki
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Production-Grade Kubernetes Observability: OpenTelemetry, Prometheus, and Grafana Loki

Siddharth Mehta

Siddharth Mehta

Principal Infrastructure Engineer

Published

Oct 5, 2026

Updated

Sep 2026

Read Time

16 min read

The Three Pillars Fall Short Without Correlation

Modern cloud-native systems generate massive volumes of telemetry: metrics from Prometheus, logs from application pods, and distributed traces from OpenTelemetry instrumentations.

True observability is achieved when every log line contains a distributed trace_id, every metric anomaly links directly to a sample trace waterfall, and every dashboard provides immediate root-cause correlation.

Kubernetes Cluster Telemetry Mesh and OpenTelemetry DaemonSet
Figure 5.1: OpenTelemetry Collector routing telemetry pipelines to Prometheus, Tempo, and Grafana Loki.

Deploying OpenTelemetry Collector DaemonSet

# otel-collector-daemonset.yaml
apiVersion: opentelemetry.io/v1alpha1
kind: OpenTelemetryCollector
metadata:
  name: otel-collector-agent
  namespace: observability
spec:
  mode: daemonset
  config: |
    receivers:
      otlp:
        protocols:
          grpc:
            endpoint: 0.0.0.0:4317
    processors:
      memory_limiter:
        check_interval: 1s
        limit_percentage: 75
      batch:
        send_batch_size: 1000
    exporters:
      prometheus:
        endpoint: "0.0.0.0:8889"
      otlp/tempo:
        endpoint: "tempo.observability.svc.cluster.local:4317"

Technical References & Standards

  • • Cloud Native Computing Foundation (CNCF) OpenTelemetry Standards
  • • W3C Trace Context Recommendation (W3C Distributed Tracing Working Group)
  • • Google SRE Handbook: Monitoring Distributed Systems

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Siddharth Mehta

Siddharth Mehta

Principal Infrastructure Engineer

Site Reliability Engineer specializing in multi-cloud networking, BGP Anycast routing, and distributed consensus.

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