# From failed check to real user impact: Pairing Synthetic Monitoring and Frontend Observability in Grafana Cloud

DevFeed: [From failed check to real user impact: Pairing Synthetic Monitoring and Frontend Observability in Grafana Cloud](<https://devfeed.tech/articles/from-failed-check-to-real-user-impact-pairing-synthetic-monitoring-and-frontend-observability-in-grafana-cloud-8586.md>)

Original publisher: [Read original article](<https://grafana.com/blog/from-failed-check-to-real-user-impact-pairing-synthetic-monitoring-and-frontend-observability-in-grafana-cloud/>)

Author: Mark Meier

Published: 2026-09-12T11:22:06.456390Z

Content type: tutorial

Language: en

Sources: [Grafana Labs blog on Grafana Labs](<https://devfeed.tech/sources/grafana-labs-blog-on-grafana-labs.md>)

Topics: [synthetic monitoring](<https://devfeed.tech/topics/synthetic-monitoring.md>), [Frontend observability](<https://devfeed.tech/topics/frontend-observability.md>), [Grafana Cloud](<https://devfeed.tech/topics/grafana-cloud.md>), [observability](<https://devfeed.tech/topics/observability.md>)

Tags: [analytics](<https://devfeed.tech/tags/analytics.md>), [frontend-observability](<https://devfeed.tech/tags/frontend-observability.md>), [grafana](<https://devfeed.tech/tags/grafana.md>), [grafana-cloud](<https://devfeed.tech/tags/grafana-cloud.md>), [logs](<https://devfeed.tech/tags/logs.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [observability](<https://devfeed.tech/tags/observability.md>), [synthetic-monitoring](<https://devfeed.tech/tags/synthetic-monitoring.md>), [traces](<https://devfeed.tech/tags/traces.md>), [uptime](<https://devfeed.tech/tags/uptime.md>)

## AI overview

This article explains how to combine Grafana Cloud Synthetic Monitoring with Frontend Observability to connect proactive checks with real-user impact. It describes the blind spots of synthetic-only monitoring and presents a combined workflow for faster triage, impact-aware alerts, and tests that evolve with real traffic.

## Source excerpt

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, 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 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 i