A 3 A.M. Alert and 30 Minutes of Switching Between Screens
It's the middle of the night, and a latency-degradation alert fires on your K3s cluster. First you check Prometheus to see which service's P99 latency spiked. Then you search Loki for error logs around that time window. Finally you cross-reference Jaeger to find which requests were slow.
This ritual of "bouncing between three dashboards" has become normal for many teams. A 2025 Grafana Labs survey found that organizations use an average of eight observability tools, with 101 distinct observability technologies mentioned across respondents (Grafana Observability Survey 2025). The more tools pile up, the more incident investigation degrades into guesswork and timestamp-matching.
This article explains why this "siloed observability" happens, and how teams should shift toward a design that correlates metrics, logs, and traces — stitching scattered clues back into a single root cause.
The Paradox: More Tools, Slower Incident Response
The observability "three pillars" — metrics, logs, and traces — are typically handled by separate tools. Using Prometheus for metrics, Loki for logs, and Jaeger for traces looks reasonable at first glance, but when the data lives in separate stores with no shared identifiers linking them, it creates a critical inefficiency during incident response.
According to a survey reported by Network World, enterprises still run an average of 4.4 observability tools, and 87% of network operations teams say they rely on multiple tools "without meaningful integration" (Network World). The median cost of a single high-impact incident is $2 million per hour, but organizations with full-stack observability cut that cost roughly in half. What determines incident-response cost isn't the number of tools — it's whether signals correlate with each other.
Grafana's survey also found that alert fatigue is "the top factor slowing incident response across nearly every role," with complexity itself cited as the biggest barrier by 39% of respondents. Here lies the paradox: optimizing each tool in isolation ends up slowing down the system as a whole. Some managed K3s services, like Kubo, address this by providing an integrated observability stack from day one.
Why OpenTelemetry Grew Into the Second-Largest Project After Kubernetes
OpenTelemetry (OTel) has rapidly gained adoption as the answer to this siloed observability problem. In May 2026, the CNCF announced that OpenTelemetry had reached "Graduated" status — the foundation's top tier (CNCF announcement). Per the project page, OpenTelemetry counts 5,136 contributing organizations and 26,846 total contributors (CNCF Projects: OpenTelemetry), and some analyses rank it as the second most active project among the CNCF's 240+ projects, behind only Kubernetes.
This growth reflects more than popularity — it reflects a shift in design philosophy. Rather than "collecting metrics, logs, and traces separately with separate tools," OpenTelemetry was designed from the ground up as a single instrumentation standard that collects all three signals and links them via shared context (such as trace IDs). Because it isn't tied to vendor-specific instrumentation libraries, teams can switch backends without rewriting instrumentation code — a strong motivator for infrastructure teams wary of vendor lock-in.
Exemplars: Linking Metrics to Traces With One Click
The technical mechanism that underpins this correlation is called "Exemplars." An exemplar attaches trace information (trace ID, span ID, timestamp) directly to a metric sample, so that a metric recorded within an active span's context is automatically tagged with the trace that produced it (OpenTelemetry docs: Exemplars).
Without exemplars, detecting a latency spike only tells you "something is slow" — you're left manually searching logs, often for 30 minutes or more. With exemplars, you can click directly on an anomaly in a graph and jump straight to the trace that caused it. One implementation guide reports cutting root-cause identification down to roughly two minutes (Metric-trace correlation implementation guide).
Google Cloud's Managed Service for Prometheus offers this as "Trace Exemplars," letting users jump from tail latency straight into the underlying trace (Google Cloud Blog). The same correlation capability can be built into a self-hosted Prometheus on K3s via the OpenTelemetry Collector.
Three Deployment Patterns for the OpenTelemetry Collector on K3s
When rolling out OpenTelemetry on a K3s/Kubernetes cluster, where you place the Collector is the crux of the design. There are three well-established deployment patterns (OpenTelemetry Collector Kubernetes architecture guide).
Agent Pattern (DaemonSet)
One Collector runs per node, collecting node-local metrics, logs, and kubelet statistics. This pattern suits collection tasks that need node-local filesystem access.
Gateway Pattern (Deployment)
A centralized pattern that provides a stable telemetry ingestion endpoint. Data from multiple Agents is aggregated in one place, enabling centralized routing to backends and sampling policy.
Sidecar Pattern
A dedicated Collector container runs alongside the application container in each Pod. This allows per-application configuration, at the cost of resource usage that scales with the number of Pods.
Most production environments recommend a two-tier "Agent-to-Gateway" setup: node-level collection via Agents, aggregated centrally through a Gateway. On K3s, this configuration lets teams introduce OTel incrementally without disrupting an existing Prometheus Operator/ServiceMonitor setup.
From Siloed Observability to Correlated Observability
The observability problem isn't solved by adding more tools — in fact, more tools inflate the cost of switching between dashboards. What matters is designing a system where metrics, logs, and traces correlate through shared context. One analysis of the job market even notes that what companies are hiring for isn't knowledge of individual tools, but "systems thinking" — and the same lens applies to observability platform design.
Building an observability stack on K3s from scratch means setting up Prometheus, Grafana, Loki, Tempo (or Jaeger), and the OpenTelemetry Collector individually, and wiring up the integrations yourself. A managed K3s service like Kubo starts you off with Prometheus and Grafana pre-installed, so adding the OpenTelemetry Collector is all it takes to build a correlation-ready observability foundation.
Summary
As long as metrics, logs, and traces are viewed separately in separate tools, you'll never reach the true root cause of an incident. OpenTelemetry's growth into the CNCF's second-largest project reflects both vendor-neutral standardization and a design philosophy centered on correlation, exemplified by Exemplars. What matters isn't blindly adding tools — it's incrementally integrating the OpenTelemetry Collector alongside your existing Prometheus/ServiceMonitor setup, and building a foundation where one click takes you from a metric straight to its trace.
If you're considering redesigning your observability stack, starting with Kubo — which ships with Prometheus and Grafana pre-installed — is one option worth considering. Adding the OpenTelemetry Collector is enough to build an observability foundation that resists silos. Reach out any time via Contact Us.