Monitoring vs Observability: What Actually Changed and Why It Matters

The Dashboard That Couldn't Answer "Why"

You're on call when the alert fires at 2 AM. The dashboard shows request latency spiked from 50ms to 3 seconds. CPU looks fine. Memory looks fine. Error rates are elevated but the errors are just timeouts. You check the database—queries are fast. The cache hit rate is normal. Load balancer metrics show nothing unusual.

Three hours later, after restarting services, checking logs across a dozen containers, and correlating timestamps by hand, you discover that a single downstream dependency introduced a retry storm that cascaded through your system. The monitoring told you something was wrong. It couldn't tell you what.

This scenario plays out in engineering organizations every day. Traditional monitoring excels at answering predetermined questions: Is CPU above 80%? Are error rates below 0.1%? Is the database reachable? But modern distributed systems fail in ways you can't predict, and those predefined questions increasingly aren't the ones that matter at 2 AM.

Observability emerged not as marketing terminology but as a genuine response to this operational reality. The distinction isn't academic—it fundamentally changes how teams instrument systems, what data they collect, and how they investigate failures. Understanding this distinction determines whether your on-call engineers spend three hours manually correlating logs or trace the problem to its source in minutes.

The Core Distinction

Monitoring operates on known unknowns. You decide in advance what metrics matter, set thresholds, and receive alerts when those thresholds are breached. This works beautifully for failure modes you've seen before and can anticipate. Traditional infrastructure monitoring—CPU, memory, disk, network—falls squarely in this category because servers fail in relatively predictable ways.

Observability addresses unknown unknowns. Instead of asking "did threshold X get exceeded," observability asks "given this unexpected behavior, what questions do I need to ask to understand it?" This requires different instrumentation, different data, and different querying capabilities.

The distinction becomes concrete when you consider what happens during an incident. With monitoring, you check the dashboards you built, look at the metrics you decided to collect, and hope the failure mode matches one you anticipated. With observability, you start from a symptom—a slow request, a failed transaction, a user complaint—and explore the system's behavior to understand what happened, even if you've never seen this particular failure before.

Consider the practical difference. A monitoring approach might alert when average request latency exceeds 500ms. An observability approach lets you ask: "Show me the traces for the slowest 1% of requests in the last hour, broken down by which services contributed the most latency, for requests that touched the payment service." The second question requires instrumentation and tooling that simply doesn't exist in traditional monitoring.

The Signals: Beyond Three Pillars

The observability community initially organized around three "pillars"—logs, metrics, and traces. This framing remains useful but has evolved as practitioners recognized that these signals aren't isolated columns supporting a structure but interconnected data that gains value through correlation.

Metrics remain the foundation for understanding system state at aggregate levels. They answer questions like "what is the overall request rate" and "what percentage of requests are failing." Metrics are cheap to store, fast to query, and essential for dashboards and alerting. The key insight about metrics in an observability context is that they should be high-cardinality when necessary—dimensions like customer ID, endpoint, region, and version—to enable drilling down from aggregate patterns to specific behaviors.

# Query Prometheus for high-latency request rates by endpoint
curl -s "http://prometheus:9090/api/v1/query" \
  --data-urlencode 'query=
    sum(rate(http_request_duration_seconds_count{
      le="1.0"
    }[5m])) by (endpoint)
    /
    sum(rate(http_request_duration_seconds_count[5m])) by (endpoint)
  ' | jq '.data.result[] | {endpoint: .metric.endpoint, fast_ratio: .value[1]}'

# Find endpoints where more than 10% of requests exceed 1 second
curl -s "http://prometheus:9090/api/v1/query" \
  --data-urlencode 'query=
    (1 - (
      sum(rate(http_request_duration_seconds_bucket{le="1.0"}[5m])) by (endpoint)
      /
      sum(rate(http_request_duration_seconds_count[5m])) by (endpoint)
    )) > 0.1
  ' | jq '.data.result[].metric.endpoint'

Logs provide the narrative detail that metrics summarize away. When a metric shows elevated error rates, logs contain the actual error messages, stack traces, and context. Modern log management has moved far beyond grep—structured logging with consistent fields enables querying logs like a database, filtering by service, trace ID, user, or any other dimension.

{
  "timestamp": "2025-01-07T14:23:45.123Z",
  "level": "error",
  "service": "payment-service",
  "trace_id": "abc123def456",
  "span_id": "789ghi",
  "user_id": "user_12345",
  "endpoint": "/api/v1/charge",
  "error": "upstream timeout",
  "duration_ms": 5023,
  "downstream_service": "fraud-detection",
  "retry_count": 3
}

Traces capture the journey of individual requests through distributed systems. When a user's checkout takes 10 seconds, a trace shows exactly which services that request touched, how long each service took, and where the time went. Distributed tracing transformed debugging in microservices architectures because it makes visible the causality chains that were previously invisible.

# Query Jaeger for slow traces in the checkout flow
curl -s "http://jaeger:16686/api/traces?service=checkout-service&minDuration=5s&limit=20" | \
  jq '.data[].spans[] | select(.operati "process-payment") |
    {trace_id: .traceID, duration_ms: (.duration/1000), service: .process.serviceName}'

# Find traces with errors in a specific time window
curl -s "http://jaeger:16686/api/traces?service=api-gateway&tags=error:true&start=$(date -d '1 hour ago' +%s)000000&end=$(date +%s)000000" | \
  jq '.data | length'

The fourth signal—events—has gained recognition as distinct from logs. Events represent discrete occurrences with business or operational significance: deployments, configuration changes, scaling events, feature flag toggles. Correlating these events with metrics and traces often reveals the "what changed" that explains an incident.

# Query for deployment events correlated with error rate changes
# Example using a custom events API
curl -s "http://events-api:8080/v1/events?type=deployment&service=payment-service&since=2h" | \
  jq '.events[] | {time: .timestamp, version: .metadata.version, deployer: .metadata.user}'

OpenTelemetry: The Instrumentation Standard

The observability landscape fragmented for years across proprietary agents and incompatible formats. Datadog, New Relic, Dynatrace, and Splunk each required their own instrumentation, creating vendor lock-in and making it expensive to change providers or run multiple tools.

OpenTelemetry changed this calculus. Formed from the merger of OpenTracing and OpenCensus, OpenTelemetry provides a single, vendor-neutral standard for generating telemetry data. The project reached general availability for traces and metrics, with logs now stable as well. According to CNCF surveys, OpenTelemetry is now the second-highest velocity project after Kubernetes, with adoption exceeding 70% among organizations implementing observability.

The architecture separates instrumentation from backends. You instrument your code once with OpenTelemetry SDKs, and the OpenTelemetry Collector routes that data to whatever backends you choose—Jaeger for traces, Prometheus for metrics, Elasticsearch for logs, or commercial platforms that accept OTLP (OpenTelemetry Protocol).

# OpenTelemetry Collector configuration
# Receives OTLP, processes, and exports to multiple backends

receivers:
  otlp:
    protocols:
      grpc:
        endpoint: 0.0.0.0:4317
      http:
        endpoint: 0.0.0.0:4318

processors:
  batch:
    timeout: 10s
    send_batch_size: 1024
  memory_limiter:
    check_interval: 1s
    limit_mib: 1000
    spike_limit_mib: 200
  resource:
    attributes:
      - key: environment
        value: production
        action: upsert

exporters:
  otlp/jaeger:
    endpoint: jaeger:4317
    tls:
      insecure: true
  prometheus:
    endpoint: 0.0.0.0:8889
    namespace: otel
  elasticsearch:
    endpoints: ["http://elasticsearch:9200"]
    logs_index: otel-logs

service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [memory_limiter, batch]
      exporters: [otlp/jaeger]
    metrics:
      receivers: [otlp]
      processors: [memory_limiter, batch]
      exporters: [prometheus]
    logs:
      receivers: [otlp]
      processors: [memory_limiter, resource, batch]
      exporters: [elasticsearch]

Instrumenting a service with OpenTelemetry follows consistent patterns across languages. Here's what automatic instrumentation looks like for a Python Flask application:

# app.py - Flask with OpenTelemetry auto-instrumentation
from flask import Flask, jsonify
from opentelemetry import trace
from opentelemetry.instrumentation.flask import FlaskInstrumentor
from opentelemetry.instrumentation.requests import RequestsInstrumentor
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.resources import Resource
import requests

# Configure the tracer
resource = Resource.create({"service.name": "payment-service"})
provider = TracerProvider(resource=resource)
processor = BatchSpanProcessor(OTLPSpanExporter(endpoint="otel-collector:4317"))
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)

app = Flask(__name__)

# Auto-instrument Flask and outgoing HTTP requests
FlaskInstrumentor().instrument_app(app)
RequestsInstrumentor().instrument()

tracer = trace.get_tracer(__name__)

@app.route('/api/v1/charge', methods=['POST'])
def process_charge():
    # Manual span for business logic
    with tracer.start_as_current_span("validate-payment") as span:
        span.set_attribute("payment.amount", 99.99)
        span.set_attribute("payment.currency", "USD")

        # This outgoing request is automatically traced
        fraud_check = requests.post(
            "http://fraud-service:8080/check",
            json={"amount": 99.99}
        )

        if fraud_check.status_code != 200:
            span.set_attribute("payment.fraud_check", "failed")
            return jsonify({"error": "fraud check failed"}), 400

        span.set_attribute("payment.fraud_check", "passed")

    return jsonify({"status": "charged"})

For Go services, the pattern is similar:

// main.go - Go service with OpenTelemetry
package main

import (
    "context"
    "net/http"

    "go.opentelemetry.io/otel"
    "go.opentelemetry.io/otel/attribute"
    "go.opentelemetry.io/otel/exporters/otlp/otlptrace/otlptracegrpc"
    "go.opentelemetry.io/otel/sdk/resource"
    "go.opentelemetry.io/otel/sdk/trace"
    semconv "go.opentelemetry.io/otel/semconv/v1.21.0"
    "go.opentelemetry.io/contrib/instrumentation/net/http/otelhttp"
)

func initTracer() (*trace.TracerProvider, error) {
    exporter, err := otlptracegrpc.New(context.Background(),
        otlptracegrpc.WithEndpoint("otel-collector:4317"),
        otlptracegrpc.WithInsecure(),
    )
    if err != nil {
        return nil, err
    }

    tp := trace.NewTracerProvider(
        trace.WithBatcher(exporter),
        trace.WithResource(resource.NewWithAttributes(
            semconv.SchemaURL,
            semconv.ServiceName("order-service"),
            semconv.DeploymentEnvironment("production"),
        )),
    )
    otel.SetTracerProvider(tp)
    return tp, nil
}

func main() {
    tp, _ := initTracer()
    defer tp.Shutdown(context.Background())

    tracer := otel.Tracer("order-service")

    handler := http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
        ctx, span := tracer.Start(r.Context(), "process-order")
        defer span.End()

        span.SetAttributes(
            attribute.String("order.id", r.URL.Query().Get("order_id")),
            attribute.String("customer.region", "us-east-1"),
        )

        // Process order...
        w.WriteHeader(http.StatusOK)
    })

    // Wrap with OpenTelemetry HTTP instrumentation
    http.Handle("/orders", otelhttp.NewHandler(handler, "orders"))
    http.ListenAndServe(":8080", nil)
}

Signal Correlation: Where Observability Gets Powerful

Individual signals have limited value in isolation. The real power emerges when you can jump from a metric anomaly to the specific traces causing it, then to the logs from those traces. This correlation is what transforms data into understanding.

Modern observability platforms enable this through trace IDs and span IDs that propagate across all three signal types. When you log an error, include the trace ID. When you increment an error counter metric, include the trace ID as a label. This creates connection points that querying tools can exploit.

# Find traces associated with elevated error rates
# Step 1: Identify time window with high errors from metrics
curl -s "http://prometheus:9090/api/v1/query_range" \
  --data-urlencode 'query=rate(http_requests_total{status=~"5.."}[1m])' \
  --data-urlencode 'start=2025-01-07T10:00:00Z' \
  --data-urlencode 'end=2025-01-07T11:00:00Z' \
  --data-urlencode 'step=60s' | jq '.data.result'

# Step 2: Query traces in that window with errors
curl -s "http://jaeger:16686/api/traces?service=api-gateway&tags=error:true&start=1736244000000000&end=1736247600000000&limit=100" | \
  jq '.data[].traceID'

# Step 3: Get logs for a specific trace
curl -s "http://elasticsearch:9200/logs-*/_search" \
  -H "Content-Type: application/json" \
  -d '{
    "query": {
      "term": { "trace_id": "abc123def456" }
    },
    "sort": [{ "@timestamp": "asc" }]
  }' | jq '.hits.hits[]._source'

Grafana has become central to this correlation workflow. With data sources for Prometheus (metrics), Loki (logs), Tempo (traces), and Jaeger, you can configure "exemplars" that link metrics to traces and "derived fields" that link logs to traces:

# Grafana data source configuration for trace correlation
apiVersion: 1

datasources:
  - name: Prometheus
    type: prometheus
    url: http://prometheus:9090
    jsondata-blocked:
      exemplarTraceIdDestinations:
        - name: trace_id
          datasourceUid: tempo

  - name: Loki
    type: loki
    url: http://loki:3100
    jsondata-blocked:
      derivedFields:
        - name: TraceID
          matcherRegex: '"trace_id":"([a-f0-9]+)"'
          url: '$${__value.raw}'
          datasourceUid: tempo

  - name: Tempo
    type: tempo
    url: http://tempo:3200
    uid: tempo
    jsondata-blocked:
      tracesToLogs:
        datasourceUid: loki
        tags: ['service.name']

Kubernetes-Native Observability

Kubernetes environments present unique observability challenges. Pods are ephemeral—they spin up, scale, crash, and get replaced. Traditional monitoring approaches that assume static infrastructure struggle when the infrastructure constantly changes.

The kube-prometheus-stack has emerged as the standard starting point for Kubernetes observability. It deploys Prometheus, Grafana, Alertmanager, and a collection of exporters and recording rules optimized for Kubernetes:

# Install kube-prometheus-stack via Helm
helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm repo update

helm install kube-prometheus prometheus-community/kube-prometheus-stack \
  --namespace monitoring \
  --create-namespace \
  --set prometheus.prometheusSpec.retention=30d \
  --set prometheus.prometheusSpec.storageSpec.volumeClaimTemplate.spec.resources.requests.storage=50Gi \
  --set grafana.adminPassword=your-secure-password

# Verify installation
kubectl get pods -n monitoring
kubectl get servicemonitors -n monitoring

For OpenTelemetry in Kubernetes, the OpenTelemetry Operator simplifies deployment and management:

# OpenTelemetry Collector deployment via Operator
apiVersion: opentelemetry.io/v1alpha1
kind: OpenTelemetryCollector
metadata-blocked:
  name: otel-collector
  namespace: observability
spec:
  mode: deployment
  config: |
    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
              relabel_configs:
                - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
                  action: keep
                  regex: true

    processors:
      k8sattributes:
        extract:
          metadata-blocked:
            - k8s.namespace.name
            - k8s.deployment.name
            - k8s.pod.name
            - k8s.node.name
      batch:
        timeout: 10s

    exporters:
      otlp:
        endpoint: tempo:4317
        tls:
          insecure: true
      prometheusremotewrite:
        endpoint: http://prometheus:9090/api/v1/write

    service:
      pipelines:
        traces:
          receivers: [otlp]
          processors: [k8sattributes, batch]
          exporters: [otlp]
        metrics:
          receivers: [otlp, prometheus]
          processors: [k8sattributes, batch]
          exporters: [prometheusremotewrite]

Useful kubectl commands for observability troubleshooting:

# Check if pods are exporting metrics
kubectl get pods -l app=myservice -o js | \
  xargs -I {} kubectl exec {} -- curl -s localhost:8080/metrics | head -20

# View OpenTelemetry Collector logs for instrumentation issues
kubectl logs -n observability deployment/otel-collector -f | grep -i error

# Check ServiceMonitor targets in Prometheus
kubectl port-forward -n monitoring svc/kube-prometheus-prometheus 9090:9090 &
curl -s http://localhost:9090/api/v1/targets | jq '.data.activeTargets[] | {job: .labels.job, health: .health}'

# Debug trace propagation
kubectl exec -it deployment/myservice -- env | grep -i otel

# Verify collector is receiving data
kubectl exec -n observability deployment/otel-collector -- \
  wget -qO- http://localhost:8888/metrics | grep otel_receiver

The Cost Reality

Observability data is expensive. Traces generate orders of magnitude more data than metrics. High-cardinality metrics explode storage requirements. Logs from chatty applications can overwhelm even generous storage budgets.

Organizations implementing observability must make deliberate decisions about what to collect, how long to retain it, and how to sample high-volume signals. The 2024 Observability Pulse survey found that 68% of organizations cited cost as their primary observability challenge, with average monthly spend exceeding $50,000 for mid-size deployments.

Sampling strategies help control trace volume without losing visibility into important requests:

# OpenTelemetry Collector with tail-based sampling
processors:
  tail_sampling:
    decision_wait: 10s
    num_traces: 100000
    expected_new_traces_per_sec: 10000
    policies:
      # Always sample errors
      - name: error-policy
        type: status_code
        status_code:
          status_codes: [ERROR]

      # Always sample slow requests
      - name: latency-policy
        type: latency
        latency:
          threshold_ms: 1000

      # Sample 10% of everything else
      - name: probabilistic-policy
        type: probabilistic
        probabilistic:
          sampling_percentage: 10

Cardinality management prevents metric explosion:

# Identify high-cardinality metrics in Prometheus
curl -s "http://prometheus:9090/api/v1/status/tsdb" | \
  jq '.data.seriesCountByMetricName | to_entries | sort_by(-.value) | .[0:20]'

# Find metrics with too many label combinations
curl -s "http://prometheus:9090/api/v1/label/__name__/values" | \
  jq -r '.data[]' | while read metric; do
    count=$(curl -s "http://prometheus:9090/api/v1/series?match[]=$metric" | jq '.data | length')
    echo "$metric: $count series"
  done | sort -t: -k2 -n -r | head -20

Incident Response: Observability in Action

Abstract discussions of signals and tools matter less than how observability changes incident response. Consider a concrete scenario: users report intermittent checkout failures.

With traditional monitoring, you'd check dashboards showing aggregate error rates, find that checkout errors are elevated, and start investigating by reading logs, checking individual service health, and manually correlating timestamps. This might take an hour or more.

With proper observability instrumentation, the workflow compresses dramatically:

# Step 1: Find failed checkout traces from the last hour
curl -s "http://jaeger:16686/api/traces?service=checkout-service&tags=error:true&limit=50" | \
  jq '.data[] | {
    trace_id: .traceID,
    duration_ms: (.spans[0].duration / 1000),
    error: .spans[].tags[] | select(.key == "error.message") | .value
  }'

# Step 2: Examine a specific failing trace
TRACE_ID="abc123"
curl -s "http://jaeger:16686/api/traces/$TRACE_ID" | \
  jq '.data[].spans[] | {
    service: .process.serviceName,
    operation: .operationName,
    duration_ms: (.duration / 1000),
    error: (.tags[] | select(.key == "error") | .value)
  }'

# Output shows: inventory-service took 5000ms and returned error

# Step 3: Check inventory-service metrics during this period
curl -s "http://prometheus:9090/api/v1/query_range" \
  --data-urlencode 'query=rate(inventory_db_query_duration_seconds_sum[1m]) / rate(inventory_db_query_duration_seconds_count[1m])' \
  --data-urlencode 'start=2025-01-07T10:00:00Z' \
  --data-urlencode 'end=2025-01-07T10:30:00Z' \
  --data-urlencode 'step=60s' | jq '.data.result[].values'

# Database query latency spiked at 10:15

# Step 4: Get logs from inventory-service during the spike
curl -s "http://elasticsearch:9200/logs-*/_search" \
  -H "Content-Type: application/json" \
  -d '{
    "query": {
      "bool": {
        "must": [
          {"term": {"service": "inventory-service"}},
          {"range": {"@timestamp": {"gte": "2025-01-07T10:14:00Z", "lte": "2025-01-07T10:16:00Z"}}},
          {"term": {"level": "error"}}
        ]
      }
    }
  }' | jq '.hits.hits[]._source.message'

# Logs show: "connection pool exhausted, waiting for available connection"
# Root cause identified: database connection pool was undersized

The entire investigation, from symptom to root cause, can complete in minutes rather than hours. More importantly, this workflow is repeatable—anyone on the team can follow the same steps, not just the engineers who happened to build the service.

Tool Selection: Building Your Stack

The observability market has consolidated around several categories, with options ranging from fully managed platforms to self-hosted open source stacks.

Commercial Platforms: Datadog, New Relic, Dynatrace, and Splunk Observability offer integrated metrics, logs, and traces with managed infrastructure. Pricing typically runs $15-50 per host per month for infrastructure monitoring, with additional costs for APM, logs, and custom metrics. These platforms excel at ease of deployment and correlation features but can become expensive at scale.

Open Source Stacks: The CNCF ecosystem provides production-ready alternatives. Prometheus for metrics, Grafana for visualization, Loki for logs, Tempo or Jaeger for traces, and OpenTelemetry for instrumentation. These require more operational investment but offer flexibility and cost control.

# Check versions and health of common open source observability tools
prometheus --version
# prometheus, version 2.48.0

grafana-server -v
# Version 10.2.0

tempo --version
# Grafana Tempo, version 2.3.0

jaeger-query version
# jaeger-query version 1.52.0

# Health check endpoints
curl -s http://prometheus:9090/-/healthy
curl -s http://grafana:3000/api/health | jq .
curl -s http://tempo:3200/ready
curl -s http://jaeger:16687/

Hybrid Approaches: Many organizations run open source collection (OpenTelemetry, Prometheus) with commercial backends for specific capabilities. Grafana Cloud, for example, provides managed Prometheus, Loki, and Tempo that accept open standards, avoiding full vendor lock-in while reducing operational burden.

For organizations starting their observability journey, the practical recommendation is to begin with OpenTelemetry instrumentation regardless of backend choice. This preserves flexibility to change backends as needs evolve without re-instrumenting applications.

Where This Is Heading

Several trends are reshaping observability as we move through 2025.

eBPF-based observability is eliminating the instrumentation tax. Tools like Pixie, Cilium Hubble, and Coroot can observe application behavior directly from the kernel without code changes. This matters particularly for organizations with polyglot environments or third-party services they can't modify.

Large language models are changing how engineers interact with observability data. Natural language queries like "why did checkout latency spike yesterday at 3 PM" are becoming practical, with platforms translating these into the appropriate metric, log, and trace queries. Honeycomb's Query Assistant and Dynatrace Davis AI represent early implementations of this pattern.

Continuous profiling is joining the observability signals family. Understanding not just that a service is slow but which functions consume CPU and memory provides the final link between symptoms and code. Tools like Pyroscope and Parca integrate profiling data with traces, allowing you to see exactly what code was executing during a slow span.

The boundary between observability and security continues to blur. Distributed tracing provides exactly the request-level visibility that security teams need for threat detection and incident response. Expect deeper integration between observability platforms and SIEM/SOAR tools.

Making the Transition

For organizations still primarily using traditional monitoring, the path to observability follows a predictable pattern.

Start with distributed tracing for a single critical path—the checkout flow, the API gateway, or whatever generates the most on-call pages. OpenTelemetry auto-instrumentation can provide immediate visibility without code changes for many frameworks. Once you see the value of tracing one flow, expanding to others follows naturally.

Standardize on structured logging before attempting log analysis at scale. Unstructured logs are nearly impossible to query effectively. Define a schema, include trace IDs, and enforce consistency across services.

Implement the OpenTelemetry Collector as your telemetry hub even if you're sending data to commercial platforms. This creates a single point of control for sampling, transformation, and routing, and makes future backend changes dramatically simpler.

Invest in correlation capabilities. The individual signals matter less than the ability to move fluidly between them during an incident. Configure your tools so that clicking a spike in a metric shows related traces, and viewing a trace shows associated logs.

Finally, measure the outcomes that matter: mean time to detection, mean time to resolution, and on-call burden. Observability investments should demonstrably improve these metrics. If they don't, you're collecting data without creating understanding.

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