APM (Application Performance Monitoring) is how teams monitor and diagnose application health and speed — by correlating metrics, distributed traces, and logs to find and fix issues fast, reduce MTTR, and protect user experience and SLAs.
| Discipline | Best For | Limits | Where It Runs |
|---|---|---|---|
| APM | Code-level performance, dependencies, error triage | Needs instrumentation; can miss real-user variance | Back-end & services (plus frontend transactions) |
| Observability | Exploring unknown-unknowns across systems | Broader scope can add cost/complexity | Cross-stack: metrics, logs, traces, events |
| RUM | Field UX (Core Web Vitals: INP/LCP/CLS), segments | Needs real traffic; less deterministic | Production, real users/devices/networks |
| Synthetic | Uptime/SLA, scripted journeys, pre-prod guardrails | Robots can miss human & geo/ISP variance | Scheduled probes from chosen regions/browsers |
Application Performance Monitoring (APM) is the practice of measuring, correlating, and diagnosing application performance and availability — using metrics, distributed traces, and logs — so teams can detect issues early, find the root cause fast, and protect user experience and SLAs.
Also called application performance management (same acronym, broader processes around monitoring).
Use APM to see how your code and dependencies behave. Pair it with RUM to validate the user’s reality, and with synthetic monitoring to catch regressions before users do.
APM instruments your code and services, stitches requests with distributed tracing, and correlates traces ↔ metrics ↔ logs so you can move from a symptom to the root cause fast.
Agents/SDKs capture timings, errors, spans in each service.
Trace IDs follow requests across services, queues, and APIs.
Service map & span waterfall pinpoint slow or failing hops.
Link traces with metrics/logs to explain why it broke.
Deploy, then confirm improvement on p95/p99 latency & errors.
// pseudo
const span = tracer.startSpan("checkout");
try { doWork(); span.setAttribute("cart.items", 3); }
catch(e){ span.recordException(e); throw e; }
finally { span.end(); }service,
version,
env.
Bridge to RUM for user impact; add Synthetic guardrails to prevent regressions.
Track the signals that explain user impact and reliability. Each card shows the best place to measure (APM, RUM, Synthetic, or Both) and a starter target you can tune to your stack.
Time to serve requests and complete transactions. Percentiles expose long-tail slowness hidden by averages.
Requests per second/minute per service or endpoint; reveals load and capacity issues.
Application and HTTP failures. APM finds faulty services; RUM shows how users are affected.
End-to-end timing for critical user journeys across services and the frontend.
Time spent in databases, caches, third-party APIs; typical root cause of latency spikes.
Infrastructure pressure that explains latency and timeouts under load.
Real-user experience metrics in production. Validate with synthetics for guardrails.
Deterministic, 24/7 checks from chosen regions and browsers — independent of real traffic.
These four disciplines overlap but solve different problems. Use the matrix to see strengths, limits, owners, and alert types, then follow the mini decision flow to pick the right tool for the job.
| Dimension | APM | Observability | RUM | Synthetic |
|---|---|---|---|---|
| Primary goal | Code-level performance & dependency diagnosis | Explaining unknown-unknowns across the stack | Measure real-user experience in production | Proactive guardrails: uptime & scripted journeys |
| Best for | Latency p95/p99, error triage, slow DB/3rd-parties | Cross-signal correlation (metrics/logs/traces/events) | Core Web Vitals (INP/LCP/CLS), geo/ISP/device segments | Outage detection, SLA checks, pre-prod regression tests |
| Telemetry | Traces • Metrics • Logs (app/service focus) | Metrics • Logs • Traces • Events (platform-wide) | Field beacons • Session data • Optional replay | Scripted browser/API probes • Filmstrips/HAR |
| Where it runs | Back-end & services (+ some frontend spans) | Infra + apps + platforms (unified data plane) | Production users/devices/networks | Chosen regions/browsers on a schedule or CI |
| Typical owners | Backend/Full-stack • SRE/Platform | SRE/Platform • Observability team | Frontend/Perf • Product • SEO | SRE/NOC • QA • Perf Eng |
| Limitations | Needs instrumentation; limited field variance | Broader scope ⇒ cost/complexity | Needs traffic; less deterministic | Robots miss human & ISP variance |
| Alert examples | p95 latency > baseline +30% • error rate > 1% | Anomaly in error budget burn • new pattern detected | INP p75 ↑ +20% • LCP > 2.5s • CLS > 0.1 | 2/3 probes fail • journey duration +25% |
| Great together with | RUM (user impact) • Synthetic (guardrails) | All three to accelerate root-cause | APM (explain) • Synthetic (reproduce) | APM (diagnose) • RUM (validate) |
Tip: align route/journey names across tools and tag telemetry with service, env, version.
APM connects performance to business results. These cards summarize the outcomes teams consistently seek — across Business, Engineering, and Product/UX.
Tip: tag telemetry with service, env, version to make comparisons effortless.
| Signal | Primary tool | What to watch | Business outcome |
|---|---|---|---|
| p95 latency (critical routes) | APM + RUM | Regression vs baseline • spike after deploy | ↑ conversion, fewer abandons |
| Error rate (5xx/exceptions) | APM | New top error • endpoint concentration | ↓ incidents, stable SLAs |
| Core Web Vitals (INP/LCP/CLS) | RUM | p75 degradations by device/geo/ISP | Better UX & discoverability |
| Uptime / journey success | Synthetic | 2-of-3 probe failures • step duration +25% | Reduced downtime cost |
Practical situations where APM shines. Each card lists a symptom, the first checks to run, and the expected outcome. Use the tool blend (APM ↔ RUM ↔ Synthetic) to close the loop.
Symptom: p95 latency ↑ +30% on “/search”.
Outcome: pinpoint costly query or service hop; ship fix; p95 back to baseline.
Symptom: bursty 5xx during peak traffic.
Outcome: remove retry storm, add circuit breaker; error rate < 1%.
Symptom: payment provider calls dominate span time.
Outcome: resilient patterns + alerting on 3rd-party SLA breaches.
Symptom: RUM shows LCP/INP degradation on mobile in one country/ISP.
Outcome: p75 LCP ≤ 2.5s; INP ≤ 200ms for affected cohort.
Symptom: sporadic slow traces on first invocations.
Outcome: p95 stabilized; fewer UX spikes.
Symptom: scripted journey fails or exceeds threshold in staging.
Outcome: no regressions reach production; steady release cadence.
For each use case, keep a saved view (trace filters + log query + dashboard) and a runbook with “who to page”, rollback steps, and SLO thresholds. Link it from alerts.
A pragmatic 7-step rollout that blends APM with RUM and Synthetic. Keep steps short, ship value weekly, and tag everything with service, env, version.
List 3–5 critical journeys (e.g., login, search, checkout) and the services, DBs, and third-party APIs they use.
checkout.placeOrder).Auto-instrument frameworks (HTTP, DB, queues). Add custom spans to key steps and ensure cross-service context headers.
Pick the few metrics that represent user-facing health for each journey/service.
Create precise, low-noise alerts tied to SLOs, with clear ownership and next actions.
Make “one-click” pivots from slow spans to logs/errors and infra (CPU, memory, GC, network).
trace_id/span_id into logs.service, env, version).Validate field UX and prevent regressions even with low traffic or during off hours.
Close the loop with a quick weekly review and keep telemetry lean.
# slo.yaml
service: checkout
routes:
- name: checkout.placeOrder
slos:
- name: latency_p95
objective: "<= 800ms"
window: 28d
- name: error_rate
objective: "<= 1%"
window: 28d
alerts:
- name: apm_latency_regression
expr: p95_latency > baseline * 1.3 for 15m
notify: oncall-backend
runbook: https://internal/runbooks/checkout#latency
- name: rum_cwv_degradation
expr: rum.inp.p75 >= 200ms or rum.lcp.p75 > 2.5s
notify: perf-frontend
runbook: https://internal/runbooks/web#cwv
- name: synthetic_journey_fail
expr: synth.checkout.success ratio < 0.66 over 3 probes
notify: sre-noc
runbook: https://internal/runbooks/synth#checkout
Instrumentation and tracing change as you move from monoliths to containers, serverless, edge, and event-driven designs. Use this section to adapt context propagation, sampling, and hotspot triage to your stack.
deployment / pod labels.Tip: export trace_id to logs and surface k8s metadata (namespace, node).
Tip: align mesh metrics with app traces; annotate deploys/flags on charts.
initDuration.Tip: use provisioned concurrency on critical routes; keep bundles lean.
colo/region.Tip: pair with RUM to separate network vs render bottlenecks.
Tip: chart enqueue vs dequeue rates alongside p95 handler latency.
Tip: track CWV (INP/LCP/CLS) at p75 and reproduce with synthetics.
Tip: alert on p95 latency + token spend anomalies per model/route.
| Architecture | Trace Context | Likely Hotspots | Sampling Approach | Special Tips |
|---|---|---|---|---|
| K8s / Containers | W3C headers; k8s labels | DB time, chatty RPC, restarts | Head + tail-on-error | Exclude health probes from SLOs |
| Service Mesh | Sidecar propagation | Retries, timeouts, mTLS | Gateway-driven + tail | Align mesh & app views |
| Serverless | Headers via gateway/queue | Cold start, egress | Tail for slow/fail | Track init duration |
| Edge | Start/continue at edge | Origin, cache keys | Head small + tail | Tag colo/region |
| Event-driven | IDs in message | Backlog, retries | Tail on DLQ | Queue time span |
| Frontend/Mobile | Headers to backend | Long tasks, 3P tags | RUM route/device | CWV at p75 |
service, env, version, and deployment info.APM rarely lives alone. Most teams blend APM, RUM, and Synthetic with logging and infra metrics. Use this map to pick categories by use case, deployment, and governance needs.
Unified metrics, traces, logs, service maps, and alerting in one place.
Real-user beacons and CWV (INP/LCP/CLS) with segment drilldowns.
Scripted browser/API checks from chosen regions and schedules.
Elastic/Grafana stacks, OpenTelemetry collectors, Tempo/Jaeger, Loki, etc.
Regional data residency, RBAC, PII masking, private cloud or on-prem.
Schema checks, SLAs, synthetic API probes, and error budgets for partners.
SDKs for iOS/Android with crashes, ANR, cold starts, network spans.
Pixel/DOM replays to debug UX issues; pair with RUM & errors.
| Category | Best for | Deployment | Team fit |
|---|---|---|---|
| Full-stack Observability + APM | End-to-end RCA, SLOs, scale | SaaS / Hybrid / On-prem | SRE/Platform • Backend • SecOps |
| Frontend RUM | CWV, segment UX, field truth | SDKs | Frontend • Perf Eng • Product |
| Uptime/Synthetic | SLAs, regression guardrails | SaaS / CI runners | SRE/NOC • QA • Perf Eng |
| Open-source stack | Cost control, customization | Self-host / Managed OSS | Platform • Infra • FinOps |
| EU/On-prem APM | Data residency & compliance | On-prem / Private cloud | Security • Compliance • IT |
| API monitoring | Partner SLAs & contracts | SaaS / OSS runners | Backend • Platform • Partner Ops |
| Mobile APM | Crashes, ANR, startup time | SDKs | Mobile • QA • Product |
| Session replay | Reproduce UX bugs, funnels | SDKs | Frontend • UX • Support |
Quick, practical answers you can share with stakeholders and teammates.
What is APM? APM vs Observability APM vs RUM APM vs Synthetic Overhead Sampling Serverless Core Web Vitals Cost control PII & ComplianceAPM is the practice of instrumenting code and services to monitor latency, errors, throughput, and dependencies using metrics, traces, and logs. It helps teams detect issues early, find root cause quickly, and protect SLAs and user experience.
APM focuses on application behavior (code paths, services, DB/APIs). Observability is the broader capability to ask any question of the system using metrics, logs, traces, and events — often spanning apps, infra, platforms, and business signals.
Yes. APM explains why the system is slow or failing; RUM shows how real users experienced it (by geo, device, ISP). Use APM for diagnosis and RUM to validate impact and track Core Web Vitals at p75.
Synthetic runs scripted checks from chosen regions/browsers on a schedule or in CI to catch regressions and outages without real traffic. APM diagnoses issues in live services. Use both: Synthetic as guardrails, APM for deep root cause.
Modern agents typically add a small overhead (single-digit % CPU/latency) when configured well. Keep it low by limiting high-cardinality tags, sampling aggressively for low-value traffic, and excluding health probes or static asset routes.
Yes. Propagate traceparent across gateways/queues, record initDuration for cold starts, and link spans across producers/consumers. Use tail sampling for slow/failing invocations and add synthetic pings for business-hours warmups.
APM can correlate backend spans with frontend routes, but CWV are field metrics and should be measured via RUM. Use APM to explain frontend slowness (e.g., API or DB latency) and Synthetic to reproduce waterfalls.
Mask PII by default, enforce SSO/RBAC, and choose data residency that matches your policies (e.g., EU region or on-prem). Audit logs and export/portability are essential for compliance reviews.