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Observability vs Monitoring: Differences, Overlaps, and How to Pick the Right Approach

lucaslabrousse@uplix.fr |

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Monitoring confirms expected health with metrics, thresholds and alerts. Observability explains the why behind failures and latency by correlating logs, metrics and traces. This vendor-neutral guide clarifies similarities and differences, when to use each, and a practical rollout plan for SRE/DevOps teams.

Updated: 10–13 min Vendor-neutral • No pay-to-play
Compare at a glance Decision guide

Monitoring = verify expected state (SLOs, thresholds) and alert fast. Observability = ability to ask any question of your telemetry (logs·metrics·traces) to explain the unknown. Keep monitoring as guardrails; add observability to reduce MTTR, speed incident analysis, and improve reliability.

Observability vs Monitoring: definitions & a simple mental model

Monitoring confirms expected behaviour with thresholds and dashboards (known-unknowns). Observability explains why issues happen by correlating rich telemetry across logs, metrics, and traces (unknown-unknowns).

Monitoring

Confirm expected behaviour

  • Thresholds, dashboards, health checks, SLO alerts.
  • Great for known-unknowns (you can predict what to watch).
  • Answers “Is it within expected limits?”.

Use to detect and notify quickly when SLIs breach targets.

Observability

Explain the why with correlated telemetry

  • Unifies logs · metrics · traces (+ events).
  • Great for unknown-unknowns and exploratory analysis.
  • Answers “Why did latency spike? Where exactly?”.

Use to diagnose and reduce MTTR with deep, ad-hoc querying.

A three-layer model: Collection → Analysis → Action

  1. Collection

    Emit logs, metrics and traces (often via OTel). Consistent service/env/version tags are non-negotiable.

  2. Analysis

    Correlate signals, search, slice by dimensions, apply AI/heuristics, build service maps and flame charts.

  3. Action

    Trigger alerts, runbooks and release decisions; feed insights back to SLOs and CI/CD gates.

Keep lightweight monitoring for guardrails; add observability to explain and fix faster.

Observability vs Monitoring : side-by-side

A quick, comparable matrix across the key dimensions teams care about.

Dimension Monitoring Observability
Purpose Confirm expected behaviour with thresholds & dashboards. Explain why issues happen via rich, correlated telemetry.
Best for Known-unknowns (predictable failure modes, SLIs). Unknown-unknowns (novel failures, emergent behaviours).
Owners Ops, SRE, app teams; product for guardrails/SLOs. Platform/SRE, performance, developer experience, staff engineers.
Signals Preset metrics, log patterns, health checks, pings. Unified logs · metrics · traces (+ events, profiles, RUM).
Strengths Simple, fast to alert, high signal-to-noise for SLIs. Deep ad-hoc analysis, service maps, flame graphs, correlation.
Limits Blind to novel failure modes; dashboard/alert sprawl. Setup complexity & cost; requires consistent tagging/instrumentation.
Alert types Threshold, rate-of-change, health checks, SLO breaches. Multi-signal, correlated incidents; error-budget burn; causal grouping.
KPIs Availability %, p95 latency on SLIs, error rate, uptime. MTTR, time-to-detect/resolve, % incidents with RCA, DORA change failure rate.
Tooling examples Nagios/Icinga, Prometheus + Alertmanager, Zabbix, CloudWatch Alarms. Datadog, Dynatrace, New Relic, Elastic, Grafana (Tempo/Loki/Prom) + OTel.
Pairing Keep guardrail monitors (SLOs, uptime, synthetics). Use for RCA and exploration; feed insights back into monitors & runbooks.

Rule of thumb: monitoring catches, observability explains. You need both.

Quick decision guide: choose by scenario

Use these field-tested patterns to pick the right instrument first, then follow up with a complementary signal.

“Users report slowness”

Start with RUM to quantify impact by route/geo/device (e.g., INP, LCP at p75). Then pivot to APM to isolate slow endpoints, DB calls, and downstream services.

Start: RUM Then: APM

“Unknown cross-stack spike”

Observability first: correlate logs, metrics, and traces to localize the blast radius. Then dive into APM spans and service maps for code-level root cause.

Start: Observability Then: APM

“Prevent regressions in CI”

Gate releases with Synthetic checks for critical journeys and APIs across regions. Keep APM to validate backend changes and track p95 latency/error rate post-deploy.

Start: Synthetic Then: APM

“Backend suspected”

Go APM first: inspect hot services, slow spans, N+1 queries, and external dependencies. Then reproduce with Synthetic to confirm fixes and prevent regressions.

Start: APM Then: Synthetic

Rule of thumb: run APM + RUM + Synthetic together, backed by an observability lake for incident investigation.

Telemetry signals explained (and gotchas)

What each signal tells you, when to use it, and the pitfalls that hurt coverage and costs. Keep a balanced mix and make changes visible.

  • Metrics
  • Logs
  • Traces
  • Events & Markers
  • Golden Signals

Metrics — cheap & trendable

Low-cost, aggregate views (rates, ratios, gauges, histograms) for SLA/SLOs and capacity trends.

  • ✅ Use histograms for latency distributions (p95/p99).
  • ✅ Precompute SLO-aligned rates and ratios (errors/requests).
  • ✅ Label with service, env, version.
Gotcha — cardinality traps: exploding label values (e.g., user_id) balloon cost and query time. Hash/limit dimensions, use exemplars to link to traces.

Logs — context-rich

Great for context and long-tail debugging; expensive if ungoverned.

  • ✅ Structure logs (JSON) and include trace_id/span_id.
  • ✅ Route by severity/source; keep sampled info/debug only.
  • ✅ Redact PII at source; apply TTL by index.
Gotcha — noise & cost routing: chatty debug logs and high-cardinality fields drive costs. Use drop/keep rules, dynamic sampling, and cold storage.

Traces — causality & latency path

End-to-end request flows with spans for services, DBs, caches, queues and external calls.

  • ✅ Capture key spans (DB, cache, queue) and attributes (route, tenant).
  • ✅ Add deploy markers and link to commits/releases.
  • ✅ Tune sampling: head for global rates, tail for slow/error outliers.
Gotcha — sampling strategy: head-only misses rare failures; tail-only skews baselines. Combine head + tail, preserve exemplars to metrics.

Events, deploy markers & feature flags

Change awareness that accelerates RCA: see when/where behavior shifted.

  • ✅ Emit deploy markers with version/commit and owner.
  • ✅ Track flag toggles and experiment arms.
  • ✅ Correlate with p95 latency and error rate deltas.
Gotcha — missing change data: incidents feel “random” without markers. Wire CI/CD and feature systems into your telemetry.

Golden signals (+ p95)

The essential health indicators to watch continuously.

Latency — p95/p99 request & DB spans
Traffic — RPS/QPS, saturation risk
Errors — 5xx, error spans, timeouts
Saturation — CPU, memory, queue depth
Tip: alert on p95 (not averages), budget errors with SLOs, and annotate changes.

Where APM, RUM & Synthetic fit in

Each lens answers a different question. Use them together to validate impact, prevent regressions, and explain root cause.

  • APM — code-level performance

    Follow requests across services to pinpoint latency and errors.

    • Service maps & dependency graphs
    • DB/external call profiling, error triage
    • Deploy markers for fast RCA
    Server-side Traces/metrics/logs

  • RUM — real user experience

    See what users actually experience, by route, geo, device and network.

    • Core Web Vitals: INP/LCP/CLS
    • Page/route breakdowns, funnels & conversion
    • Geo/device/ISP segmentation
    Client-side Field data

  • Synthetic — scripted journeys

    Proactively test uptime, SLAs, and critical user paths from many regions.

    • Transaction checks (login, checkout, API)
    • CI guardrails to catch regressions
    • Global coverage & SLA validation
    Lab-style Controlled traffic

Why combine them

Validate impact
RUM: user-visible regressions
Prevent regressions
Synthetic: gates in CI/CD
Explain root cause
APM: spans, queries, DB
  • Start from RUM to size user impact, then pivot to APM for RCA.
  • Use Synthetic in CI to block risky releases and watch SLAs overnight.
  • Annotate everything with deploy markers and feature flags.

OpenTelemetry (OTel) without lock-in

Build a portable telemetry pipeline: OTel SDKs + Collector, export via OTLP, add detail where it matters, and control costs & data residency from day one.

Portable by design

Use OTel SDKs + Collector and export with OTLP (HTTP/gRPC) to any backend.

  • SDKs emit traces / metrics / logs
  • Collector routes & transforms (processors)
  • Swap vendors by changing the exporter only

Start simple, add detail

Begin with auto-instrumentation; add custom spans where it counts.

  • Consistent service, env, version attributes
  • Instrument DB, cache, queue, external calls
  • Emit deploy markers & feature-flag context

Cost guardrails early

Prevent surprise bills with sampling & retention before scale.

  • Head/tail/dynamic sampling in Collector
  • Drop high-cardinality attributes at ingest
  • Tiered retention & archive to object storage

EU gateways & masking

Keep data sovereign and private by design.

  • EU-region OTLP gateways / private links
  • PII redaction in attributes processor
  • RBAC, token scopes, audit logs
Compare models Back to APM/RUM/Synthetic

SRE layer: SLOs, alerting, incidents

Turn telemetry into reliability outcomes: define SLIs/SLOs, improve alert quality, follow a crisp MTTR playbook, and use error budgets to guide release pace.

Define SLIs/SLOs

Track user-centric health and commit to targets by service & environment.

  • Latency (p95/p99)
  • Error rate
  • Availability
  • UX (INP/LCP/CLS)
  • Separate API vs UI SLOs
  • ✓ Scope by service, env, version
  • ✓ Tie SLOs to business KPIs
  • ✓ Publish dashboards & runbooks

Alert quality

Reduce noise, route fast, and protect on-call focus.

  • ✓ Multi-signal alerts (traces/logs/metrics)
  • ✓ Grouping & dedup with incident keys
  • ✓ Smart routing (service/team ownership)
  • ✓ Maintenance windows & quiet hours
  • ✓ Escalations to PagerDuty/Opsgenie/Slack

MTTR playbook

  1. Service map

    Locate hot services & dependencies. Check error spikes and p95 latency.

  2. Recent deploys

    Overlay deploy markers & feature flags on the timeline.

  3. Hot spans

    Drill into slow endpoints, DB queries, cache misses, queue latency.

  4. Logs (only then)

    Pivot to scoped logs for error context; avoid blind grepping.

Error budgets

Budget consumption governs release pace and risk.

72% budget remaining Window: 30 days
  • ✓ Healthy budget → ship features
  • ✓ Low budget → freeze risky changes
  • ✓ Post-incident: learnings into runbooks
Compare models See decision guide

Architecture patterns

Choose the right telemetry & rollout approach for each architecture. Open a card for setup keys, gotchas, and the signals that matter.

Monoliths

Low complexity

Best for

  • Simple agents
  • Few dashboards
  • Stable baselines

Setup keys

  • ✓ Enable auto-instrumentation (HTTP/DB)
  • ✓ Add deploy markers & versions
  • ✓ Define golden dashboards

Gotchas

  • • Baseline drift → alert fatigue
  • • Single noisy logger inflates costs

Signals that matter

  • p95 latency
  • Error rate
  • Throughput
  • DB time

Microservices / K8s

Medium–High complexity

Best for

  • Trace propagation
  • Service naming
  • DaemonSets
  • HPA ties

Setup keys

  • ✓ OTel Collector as DaemonSet
  • ✓ Standardize service/env/version
  • ✓ Propagate traceparent via ingress/mesh

Gotchas

  • • Cardinality explosions (labels, pods)
  • • Missing context across namespaces

Signals that matter

  • Hot spans
  • Queue latency
  • Service map
  • Pod restarts

Serverless / Event-driven

Medium complexity

Best for

  • Cold-start tracking
  • Async queues
  • Edge sampling

Setup keys

  • ✓ Lightweight exporters (OTLP)
  • ✓ Context propagation via queues/topics
  • ✓ Tail sampling at collectors

Gotchas

  • • Lost context on triggers & retries
  • • Log costs if not routed

Signals that matter

  • Cold-start time
  • Invocation errors
  • Queue depth
  • p95 duration

Edge / 3rd parties

High variability

Best for

  • Geo/ISP mix
  • Synthetic e2e
  • Timing budgets

Setup keys

  • ✓ Synthetic journeys multi-region/ISP
  • ✓ RUM by route/device/network
  • ✓ Budget thresholds per step

Gotchas

  • • High variability → need cohorts
  • • Third-party regressions = blind spots

Signals that matter

  • INP/LCP/CLS
  • Uptime/SLA
  • Step timings
  • JS errors

Cost, governance & data residency (EU)

Keep visibility high without runaway bills, enforce robust access & privacy, and guarantee EU residency or hybrid/on-prem when required.

Cost levers

Must-have

Tune volume and retention early; pay for signal, not noise.

  • Head sampling
  • Tail sampling
  • Attribute drop
  • Tiered retention
  • Log routing
  • ✓ Dynamic sampling by service/env/priority
  • ✓ Drop high-cardinality attributes at source
  • ✓ Short hot retention + cold archive (object storage)
  • ✓ Route noisy logs to cheaper sinks
62% of monthly budget used

Governance & security

Controls

Access, privacy and auditability by design.

  • ✓ SSO/SAML + SCIM provisioning
  • ✓ Fine-grained RBAC (project/env/service)
  • ✓ Audit logs & least-privilege defaults
  • ✓ PII masking/redaction at SDK/collector
  • ✓ Token scopes & key rotation
  • ✓ Data export & portability (OTel/APIs)
Tip: prefer server-side enrichment; tag every span with service, env, version.

EU residency & deployment

Regulated sectors

Pin data to EU regions and align with regulatory requirements.

  • EU regions
  • Private cloud
  • Hybrid
  • On-prem
  • ✓ VPC peering/private link, egress control
  • ✓ Self-hosted gateways/collectors (OTLP)
  • ✓ DPA/GDPR terms; DPIA ready
  • ✓ EU-only processing & support paths
Pattern: run OTel Collectors inside EU VPCs and export to EU endpoints or an on-prem lake.
Use with OTel pipeline See SRE layer

Implementation plan (30/60/90 days)

Ship signal fast, harden & scale, then institutionalize reliability.

0–30 days

Ship signal fast

Stand up the OTel pipeline and capture the first good traces.

  • ✓ Pick OTLP endpoint & auth
  • ✓ Enable auto-instrumentation on 2–3 critical services
  • ✓ Add deploy markers (CI/CD)
  • ✓ Inject one RUM snippet (web)
  • ✓ Create 3 synthetic journeys (login/checkout/uptime)
  • ✓ Baseline SLOs (p95 latency, errors, availability)
31–60 days

Harden & scale

Add depth, cost control and team workflows.

  • ✓ Add custom spans on key flows (DB, cache, queues)
  • ✓ Implement cost guardrails (sampling, drop, retention)
  • ✓ Build per-team dashboards & golden queries
  • ✓ Wire on-call routing & dedup (PagerDuty/Opsgenie/Slack)
  • ✓ Add CI synthetic gates for key journeys
61–90 days

Broaden & institutionalize

Extend coverage and lock in reliability habits.

  • ✓ Expand to mobile & serverless
  • ✓ Refine sampling (tail/dynamic) & retention by dataset
  • ✓ Drill into error budgets & release guardrails
  • ✓ Establish a weekly review (SLOs, incidents, cost)
Use with OTel quick start See SRE layer

Observability vs Monitoring — FAQ

Straight answers to the most common questions teams ask when upgrading from classic monitoring to full observability.

Is observability replacing monitoring?

No. Monitoring confirms expected behavior with thresholds and dashboards. Observability explains why things broke using rich, correlated telemetry. You need both.

Do I need observability for a small monolith?

Start lean: uptime, key SLIs, and a few critical traces (transactions, DB calls). Scale to full observability only when incident causes become opaque.

Can I do observability without traces?

You can correlate logs/metrics, but you lose causality and end-to-end latency paths. Traces are the backbone for fast RCA; add them early.

What roles own observability vs monitoring?

Observability: Platform/SRE lead the stack, standards and cost. Monitoring: service owners/dev teams define alerts, SLOs and runbooks for their domains.

How does OpenTelemetry reduce vendor lock-in?

OTel standardizes SDKs and the OTLP wire format. With the Collector you can route once, switch back-ends, and keep portable telemetry and pipelines.

How to keep costs under control?
  • ✓ Head/tail or dynamic sampling
  • ✓ Attribute drop & log routing
  • ✓ Tiered retention per dataset
  • ✓ Guard high-cardinality fields
  • ✓ Per-service cost dashboards
Compare side-by-side See OTel quick start

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