How to Build a Real-Time Outage Detection Pipeline Using Synthetic Monitoring and User Telemetry

Detect outages before they trend: combine synthetic checks, RUM, and status APIs

Hook: When Cloudflare, AWS, and X suffered service disruptions in early 2026, teams that relied on social media learned the hard way: public chatter is slow, noisy, and often too late. If you're responsible for availability, you need an observability pipeline that detects, validates, and routes outage signals faster—and with higher confidence—than any hashtag.

Why this matters in 2026

Late 2025 and early 2026 saw several high-profile multi-provider incidents where social platforms and mainstream outlets only reported after user complaints spiked. Cloud architectures and edge-first deployments in 2026 make outages more complex—partial-region failures, control-plane anomalies, and upstream provider degradations are common. To keep SLAs and developer SLIs intact, teams must operate a real-time outage detection pipeline that fuses three complementary sources:

• Synthetic monitoring — proactive, deterministic checks from controlled locations.
• RUM (Real User Monitoring) — passive, high-fidelity telemetry from real sessions.
• Provider status APIs — authoritative provider-side incident signals.

Executive summary (inverted pyramid)

Deploy a lightweight, resilient pipeline that ingests synthetic failures, RUM error spikes, and provider status events into a correlation engine. Use rule-based and ML-assisted triage to assign confidence scores, route alerts to the correct on-call rotation, and trigger automated mitigations (traffic steering, feature gates, rollback). With the right configuration you can detect and notify on outages minutes before social media amplifies user reports.

Actionable takeaway

• Start with one synthetic scenario, one RUM metric, and one provider status feed. Expand iteratively.
• Correlate events within a 90–120 second window to reduce false positives.
• Map detection confidence to alert routing—inform SREs differently than product owners.

Architecture: the observability pipeline

Below is a pragmatic, cloud-native architecture you can implement within days.

1. Producers
   • Synthetic check runners (global agents, serverless cron, or managed synthetics)
   • Browser RUM SDKs (OpenTelemetry JS, Datadog RUM, New Relic Browser)
   • Provider status API scrapers/webhooks (Statuspage, vendor status endpoints)
2. Ingestion & buffering
   • Streaming platform (Kafka, Pub/Sub, Kinesis) to absorb bursts
   • Lightweight normalization service that tags events with region, test-id, user-sample-id
3. Correlation & triage
   • Rules engine (Flux/SQL-style rules) + ML anomaly detector for RUM spikes
   • Deduplication and enrichment (DNS checks, BGP/AS lookup, CDN edge health)
4. Decision & routing
   • Confidence scoring, severity mapping to PagerDuty/OpsGenie/MS Teams channels
   • Automated mitigations: traffic failover, WAF rule adjustments, rollback triggers
5. Postmortem inputs
   • Store correlated events, traces, and RUM sessions in long-term store (object storage + trace db)
   • Auto-generate timeline entries for incident review

Step-by-step implementation

1) Start with targeted synthetic checks

What to test: login flows, API endpoint health (200 + content checks), CDN edge responses, DNS resolution, and TLS negotiation. Avoid generic ping checks; they provide low signal.

Provider options: AWS Synthetics, Cloudflare Workers synthetic, open-source Puppeteer scripts scheduled via Cloud Run or a Kubernetes CronJob, or SaaS providers like Uptrends/Datadog.

Best practices:

• Run from multiple global locations and the regions your customers use most.
• Ensure scripts assert both status codes and key content (e.g., presence of a login token or specific JSON field).
• Keep check frequency high enough to detect incidents (30–60s for critical paths; 5–15m for low-risk checks).

Example Puppeteer synthetic (conceptual):

<code>const browser = await puppeteer.launch();const page = await browser.newPage();await page.goto('https://app.example.com/login');await page.type('#user','__SYNTH_USER__');await page.type('#pwd','__PWD__');await page.click('#submit');await page.waitForSelector('#dashboard', {timeout:5000});</code>

2) Instrument RUM for rapid validation

Why RUM matters: Synthetic failures are proactive but can produce false positives for localized issues. RUM confirms real user impact and identifies affected user segments (browser, region, carrier).

Implementation notes:

• Use OpenTelemetry or vendor RUM SDKs to capture page load times, transaction errors, and resource failures.
• Sample sessions (1–5% by default) but auto-escalate sampling on error spikes to 100% for the incident window.
• Hash or pseudonymize any PII—collect identifiers sufficient for debugging (session id, user agent) but not raw PII.

Key RUM signals: increase in JS errors, a spike in Time-To-Interactive, 5xx API rates in the browser, CORS failures, and resource timing waterfalls that show failing CDN resources.

3) Integrate provider status APIs (and monitor them)

Most major cloud and CDN vendors expose status APIs (Statuspage.io, vendor-specific endpoints). These are authoritative: when a provider reports a partial outage, you get immediate confirmation—even before widespread user reports.

Practical steps:

• Subscribe to webhook or RSS feeds where available (e.g., Statuspage webhooks).
• Poll REST status endpoints with exponential backoff to avoid rate limits.
• Maintain a short-circuit map: if provider X reports a region-level outage, downgrade confidence on synthetic checks in that region and route to cloud-provider escalation channels.

Example curl to poll a status page (conceptual):

<code>curl -s https://status.example-cdn.com/api/v2/summary.json | jq '.<components>'
</code>

Correlation and triage: the heart of fast detection

Alerts are only useful if they are timely and accurate. The correlation layer reduces noise and maps events to actionable incidents.

Correlation logic (recommended)

1. Group events in a sliding time window (90–120s) by impacted surface: hostname, API-key, region.
2. Enrich each event with external context: BGP/AS path, CDN POP, provider status state, recent deploy IDs.
3. Score confidence:
   • +3 synthetic failures from multiple sites in 2 minutes
   • +2 RUM error spike of >200% and increased 5xx counts
   • +4 provider status API shows incident in affected region
   • -2 known maintenance window active
4. Map confidence to severity: <=2 = monitor, 3–5 = P2 alert, >=6 = P1 incident

Automated triage examples

• If provider status = degraded AND synthetic failures localized to provider edge => route to cloud-provider escalation + network SRE.
• If synthetic failures global AND RUM indicates user impact in all regions => fire P1 to platform on-call and trigger traffic failover playbook.
• If RUM spike only in one carrier or single ISP ASN => coordinate with NOC and postpone wide-scale mitigation.

Alert routing and on-call ergonomics

Fast detection is wasted if alerts wake the wrong person. Map detection outcomes to targeted notifications and automated actions.

Routing matrix

• P1 (confidence >=6): Notify platform SRE + paging to secondary on-call via PagerDuty; create incident in incident management tool; kick off runbook automation (traffic shift).
• P2 (3–5): Post to SRE Slack channel, create a ticket in task tracker with enriched logs and reproducer steps.
• P3 (<3): Create observability ticket only; continue monitoring.

Playbook automation: Implement small, testable automation steps you can trust—traffic reroutes, feature flag kills, CDN purge/rollback. Prefer reversible actions and require human approval for risky operations.

Reduce noise without increasing MTTR

Noise kills on-call efficiency. Reduce false positives while preserving lead time:

• Use the confidence scoring described earlier rather than simplistic threshold alerts.
• Throttle noisy synthetic checks (adaptive intervals): back off after repeated failures until RUM confirms impact.
• Use deduplication windows and group similar alerts into one incident.

Privacy, retention, and compliance

Real user telemetry can carry PII. In 2026, regulatory pressure is higher—some regions require minimal retention and pseudonymization.

• Store only session IDs or hashed user identifiers in the first-line detection pipelines.
• Use ephemeral access tokens for elevated sampling during incidents, and auto-delete captured PII after the postmortem window.
• Document data flow for auditors and include consent banners where required.

Performance & cost considerations

Synthetic checks and high-frequency RUM sampling increase costs. Balance fidelity and budget:

• Run critical path synthetics at 30–60s; less critical flows at 5–15m.
• Sample RUM at low baseline and scale sampling to 100% only on error or during incidents.
• Offload heavy enrichment tasks to batch workers, not the hot path (e.g., BGP lookups cached, enrichment asynchronous).

Case study: detecting an upstream CDN outage (real-world scenario)

Context: On a Friday morning in January 2026, a mid-market SaaS provider observed the following sequence:

1. Two global synthetic checks (EU & US) failed for static asset loading within 45 seconds.
2. RUM showed a 300% increase in resource timing failures and a jump in 5xx for the CDN-hosted assets.
3. Provider status API for their CDN reported a partial outage in the affected POPs.

Pipeline reaction:

• Correlation engine scored confidence 8/10 and declared P1.
• Automated mitigation: traffic steering to secondary CDN via CDN provider API and temporary origin-serving for critical assets.
• PagerDuty page to network SRE and cloud-provider escalation with provider incident ID included.
• Outcome: user impact decreased within 4 minutes and public social chatter was reduced; company published advisory before mainstream media coverage.

Advanced strategies and 2026 trends

Modern outages are multi-domain. In 2026, expect these developments—and plan accordingly:

• Edge observability: increase in edge-first apps means synthetic checks must include edge function invocation and edge-specific RUM hooks.
• Federated telemetry: privacy-preserving aggregation (differential privacy) for RUM is gaining traction; integrate libraries that support it.
• AI-assisted triage: use ML to suggest root causes by matching incident signatures to historical incidents (BERT-like embeddings for incident fingerprints).
• Provider open telemetry APIs: more providers now expose structured status events—ingest them to reduce time-to-confirmation.

Operational checklist (30/60/90 days)

30 days

• Deploy 3 synthetic checks for critical paths in 3 regions.
• Enable basic RUM with 1% sampling and error capture.
• Subscribe to provider status webhooks for your top 5 vendors.

60 days

• Implement correlation engine and confidence scoring.
• Create alert routing matrix and automate at least one mitigation (traffic failover).
• Run tabletop incident drills using recorded synthetic failures.

90 days

• Enable adaptive RUM sampling and ML-assisted anomaly detection.
• Integrate postmortem automation to populate incident timelines.
• Validate compliance controls for telemetry retention and PII handling.

Common pitfalls and how to avoid them

• Relying solely on social signals — noisy and delayed. Use them as secondary confirmation, not the leading signal.
• Over-alerting on synthetic check flaps — use confidence scoring and backoff policies.
• Blind trust in provider status pages — they may lag; combine provider signals with synthetic+RUM evidence.
• Complex, untestable automation — prefer small, reversible actions and automated rollbacks.

“The fastest teams don’t just detect—they validate and act with confidence.”

Final recommendations

To detect outages faster than social media in 2026, build a lightweight but robust observability pipeline that combines synthetic checks (deterministic lead signals), RUM (ground truth of impact), and provider status APIs (authoritative confirmation). Correlate within tight windows, score confidence, and map to precise alert routing and mitigations. Start small, iterate, and continuously test your playbooks.

Next steps (start this week)

1. Deploy one synthetic check for your primary flow and schedule it at 60s intervals.
2. Enable RUM at 1% sampling and create an error spike dashboard.
3. Subscribe to status webhooks for your CDN and cloud provider and wire them into a Pub/Sub topic.

Operational excellence in 2026 is about speed and precision. With a combined pipeline you’ll detect, triage, and notify on outages before the hashtag even trends—keeping your customers happy and your on-call teams sane.

Call to action

Ready to build this pipeline? Start with our observability starter kit: a synthetic Puppeteer template, an OpenTelemetry RUM config, and a sample correlation ruleset tuned for rapid triage. Download the kit, run the 30-day checklist, and join our weekly SRE workshop to review real incident playbooks.

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