--- title: From failed check to real user impact: Pairing Synthetic Monitoring and Frontend Observability in Grafana Cloud source: Grafana Blog source_slug: grafana url: https://grafana.com/blog/from-failed-check-to-real-user-impact-pairing-synthetic-monitoring-and-frontend-observability-in-grafana-cloud/ published: 2026-08-24 fetched: 2026-08-24T14:13:28+00:00 ingested: true --- Say you get a support escalation about a page in the app that won’t load. But when you pull up your synthetic checks, they're all green: 100% uptime, probes are passing. Something's not adding up, but which one do you trust? If you’ve run _[Grafana Cloud Synthetic Monitoring](https://grafana.com/products/cloud/synthetic-monitoring/?pg=from-failed-check-to-real-user-impact-pairing-synthetic-monitoring-and-frontend-observability-in-grafana-cloud&plcmt=in-text)_ , you’ve been on both sides of this. Sometimes it's the ticket: real users hit a wall on the path but your checks pass cleanly. Other times, it’s the inverse: a check is failing, you're in a panic, and you start trying to reproduce things for 30 minutes—only to find it was a blip from a single region, with minimal impact to real users. Neither the green dashboard nor the red alert were lying, they just weren’t answering the correct question. This ends up being the root problem. Synthetic Monitoring is exceptionally good at telling you if something broke. It can not, however, tell you _who it happened to, how bad it was, or why it matters_. This is **not** a flaw in Synthetic Monitoring; it's the boundary of what a controlled, scheduled test can know. _[Grafana Cloud Frontend Observability](https://grafana.com/products/cloud/frontend-observability/?src=ggl-s&mdm=cpc&camp=nb-frontend-monitoring&cnt=193617327783&trm=frontend%20observability&device=c&gad_campaignid=23700538005&gbraid=0AAAAADkOfqvALvG4--E34mVLy71Sy3qAz&pg=from-failed-check-to-real-user-impact-pairing-synthetic-monitoring-and-frontend-observability-in-grafana-cloud&plcmt=in-text)_ helps to close this gap. Synthetic Monitoring gives you a proactive, outside-in signal; Frontend Observability gives you the real-user, inside-out signal. Together they form a closed loop: synthetic alerts end up getting some real user context, and real user data can make your synthetic tests smart. In this post, we’ll look at why a synthetic-only strategy can leave blind spots, what Frontend Observability adds, and walk through practical workflows for running them together in Grafana Cloud. Along the way, you'll learn that the payoff is concrete: faster triage, alerts that carry blast-radius context, and a check suite that evolves with real traffic instead of aging against it. ## Green checks don't mean happy users **Synthetic Monitoring is an active signal.** You script a journey or declare a target, run it on a schedule from known probe locations, and in return get clean consistent results. This precise control of variables is its value: when a check fails or metrics from a check deviate, it’s easy to know the exact locations, the exact steps, and the exact assertions causing it. It’s how you detect issues before your users do. But, a synthetic-only strategy has fundamental gaps: **Synthetic tests only cover what you think to test.** Your scripted paths only reflect the journeys you predict. Real users can take emergent paths, arrive from long-tail browser and device combinations, and hit regional edge cases that probes might not be able to reproduce. The error that enters your escalation chain could be a path nobody thought about. **No visibility of the blast radius.** A failing check tells you that a user will fail. It cannot tell you how many real users have already failed. Is this failure impacting three users in a single region? Or is it silently degrading the experience for thousands? A synthetic check cannot tell you this. **Alert fatigue without signal enrichment.** When every synthetic alert carries the same (lack of) user-impact context, every alert can feel equally urgent. Many users combat this with proper labeling strategies and tweaking alert sensitivities; but sometimes you get caught in the rut where there’s so many alerts they stop feeling urgent and suddenly monitoring has been eroded. I always think of Synthetic Monitoring as a canary: it’s trying to warn you early, reliably, and before too many users are affected. But the canary can only tell you that the air has gone bad; it can’t tell you how many people might be impacted by it. ## What’s actually happening in your users’ browsers Frontend Observability is real user monitoring (RUM), powered by the open source Grafana Faro SDK. Once your app is instrumented with the lightweight JavaScript snippet, it captures what’s actually happening in your users’ browsers. With Frontend Observability, you have access to metrics like: * **Core Web Vitals and page performance** from real sessions: loading, interactivity, and visual stability as users actually experience them, across real devices, networks, and geographies * **JavaScript errors** with stack traces and the user context around them—including the errors your checks were never scripted to catch * **[Session Replay](https://grafana.com/blog/visual-playback-of-the-user-journey-introducing-session-replay-in-grafana-cloud-frontend-observability/?pg=from-failed-check-to-real-user-impact-pairing-synthetic-monitoring-and-frontend-observability-in-grafana-cloud&plcmt=in-text)** so you can follow a real user's journey from entry to exit and see exactly what they did before something broke This data isn’t redundant with your synthetic data, even though they both can produce Web Vitals. The reason why is the entire logic behind running them together. **A synthetic check is a controlled experiment**. Same script, same target, same probe locations, same runtime. Over and over and over; the only input changing is the time it ran. So when a result of a check starts to deviate from its baseline, that deviation means something; your system changed, not the test. This determinism is what makes synthetic data trustworthy enough to alert on. Real user data is quite the opposite. Every session is effectively unique. Different users on different devices, browsers, operating systems, networks, geography, and much more. No single session will tell you whether a problem is your code, a spotty connection, or an aggressive browser extension. But if you aggregate enough sessions, commonalities begin to surface: every affected user is on Chrome, or in a single region, or hitting the same JS error. The signal isn't in any one sample, it’s the broad pattern across them. This is why neither replaces the other. Synthetics checks give you a number of samples you can trust individually; RUM gives you thousands to trust in aggregate. One detects something has changed; the other tells you who it's affecting and what the affected have in common. ## The reliability loop in practice Here’s where it gets practical. The loop runs in both directions: reactive (synthetic alert fires -> real user data to scope it) and proactive (real-user data reveals a gap-> codify it as a check). ### Reactive: Understand a blast radius after a synthetic alert Every on-call engineer knows the scramble that follows a failed check: Is it real? Does it actually matter? Can I reproduce it myself or through a proxy of someone else? Real-user data turns that struggle into a look up. The scenario: your browser check on `/checkout` fails from the Frankfurt probe. Before Frontend Observability, the next step was guesswork, but with both signals in Grafana Cloud, the workflow looks like this: 1. **The check fails.** Synthetic Monitoring alerts you that the `/checkout` browser check failed from Frankfurt at 09:13. 2. **Pivot to real sessions.** Follow the direct link in Synthetic Monitoring straight into the captured session in Frontend Observability. We built this connection specifically to skip manual timestamp-matching and url-hunting. From here, you can see what page may be experiencing the problem, a session replay from your browser session, and click the **Page** button to see real user sessions that visit the same page. 3. **Quantify the impact and look for the pattern.** You can now see how many real users hit an error in the same window, and just as importantly, what the affected sessions have in common: one browser? One region? The same JavaScript error? Those commonalities narrow root cause before you've opened a single trace. Something no individual session, and no synthetic check, could tell you alone. 4. **Confirm the root cause.** Compare the error real users encountered against the failed assertion in your check. 5. **Act with data, not gut-feel.** You now have: 1. _**Scope**_ : How many users were affected 2. _**Duration**_ : When the issue actually started in the real world, not just when your check cadence caught it 3. _**Severity**_ : What users experienced, a hard failure or a slow degradation The outcome: incident priority and customer comms are data-driven. "The checkout flow has degraded for approximately 8% of EU users since 09:07" is a very different first Slack message than "a check failed, investigating."