Enterprise Observability on Kubernetes: Full Stack (2026)

Observability at enterprise scale is not “install Prometheus and add Grafana.” It is a data engineering problem: millions of time series, terabytes of logs per day, distributed traces across hundreds of services, and alerting that does not wake people up for false positives.

The LGTM+ Stack

The industry-standard open source observability stack:

┌─────────────────────────────────────────────┐
│                 Grafana                       │
│  Dashboards │ Alerting │ Explore │ SLOs      │
├──────┬──────┬──────────┬────────────────────┤
│ Loki │ Mimir│  Tempo   │  Pyroscope         │
│ Logs │Metrics│ Traces  │  Profiles          │
├──────┴──────┴──────────┴────────────────────┤
│           OpenTelemetry Collector             │
│  Receive │ Process │ Export │ Sample          │
├─────────────────────────────────────────────┤
│           Kubernetes Cluster(s)              │
│  Pods → OTel SDK / Auto-instrumentation      │
└─────────────────────────────────────────────┘

• Mimir — horizontally scalable Prometheus (replaces standalone Prometheus at scale)
• Loki — log aggregation indexed by labels (not full-text)
• Tempo — distributed tracing backend
• Pyroscope — continuous profiling
• Grafana — unified visualization, alerting, and exploration

Why Not Datadog/New Relic?

At enterprise scale (50+ clusters, 10M+ active series, 5TB+ logs/day), commercial observability costs $500K-2M+/year. The LGTM stack on Kubernetes costs 10-20% of that in infrastructure, with full data ownership.

OpenTelemetry: The Collection Layer

OTel is the CNCF standard for instrumentation. One SDK, three signal types:

# OpenTelemetry Collector configuration
apiVersion: v1
kind: ConfigMap
metadata:
  name: otel-collector-config
data:
  config.yaml: |
    receivers:
      otlp:
        protocols:
          grpc:
            endpoint: 0.0.0.0:4317
          http:
            endpoint: 0.0.0.0:4318
      prometheus:
        config:
          scrape_configs:
            - job_name: 'kubernetes-pods'
              kubernetes_sd_configs:
                - role: pod
    processors:
      batch:
        timeout: 5s
        send_batch_size: 8192
      memory_limiter:
        limit_mib: 1024
        spike_limit_mib: 256
      tail_sampling:
        decision_wait: 10s
        policies:
          - name: errors-only
            type: status_code
            status_code:
              status_codes: [ERROR]
          - name: slow-requests
            type: latency
            latency:
              threshold_ms: 1000
    exporters:
      otlp/mimir:
        endpoint: "mimir-distributor:4317"
      otlp/loki:
        endpoint: "loki-distributor:4317"
      otlp/tempo:
        endpoint: "tempo-distributor:4317"
    service:
      pipelines:
        metrics:
          receivers: [otlp, prometheus]
          processors: [memory_limiter, batch]
          exporters: [otlp/mimir]
        logs:
          receivers: [otlp]
          processors: [memory_limiter, batch]
          exporters: [otlp/loki]
        traces:
          receivers: [otlp]
          processors: [memory_limiter, tail_sampling, batch]
          exporters: [otlp/tempo]

Tail sampling is critical: only store error traces and slow requests in full. Sample normal traces at 1-5%. This reduces storage costs by 80%+ with minimal visibility loss.

Sizing Guide

AI-Powered Alerting

Traditional threshold alerts generate noise. AI-powered anomaly detection reduces false positives:

• Grafana ML — built-in anomaly detection for Prometheus metrics
• Prophet integration — time-series forecasting for capacity planning
• Adaptive thresholds — baselines adjust for day-of-week and time-of-day patterns
• Alert correlation — group related alerts into incidents automatically

# Grafana alerting rule with anomaly detection
apiVersion: 1
groups:
  - name: ai-anomaly-detection
    rules:
      - alert: AnomalousLatencySpike
        expr: |
          (
            http_request_duration_seconds:p95 
            > 
            predict_linear(http_request_duration_seconds:p95[1h], 600)
            * 1.5
          )
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "Latency anomaly detected on {{ $labels.service }}"

SLO-Based Alerting

Move from “is this metric above a threshold?” to “are we meeting our SLOs?”

# Error budget-based alerting
- alert: ErrorBudgetBurnRate
  expr: |
    (
      1 - (
        sum(rate(http_requests_total{code=~"2.."}[1h]))
        /
        sum(rate(http_requests_total[1h]))
      )
    ) > (1 - 0.999) * 14.4
  for: 5m
  labels:
    severity: critical
  annotations:
    summary: "Error budget burning 14.4x faster than sustainable"

This fires when your error rate will exhaust the monthly error budget within 1 hour — a much more actionable signal than “error rate above 1%.”

Related Resources

• Install Prometheus on Ubuntu
• Install Grafana on Ubuntu
• Platform Engineering Metrics
• Kubernetes Security Hardening
• FinOps for AI

About the Author

I am Luca Berton, AI and Cloud Advisor. I build observability platforms for enterprises running Kubernetes at scale. Book a consultation.