Grafana Cloud's Synthetic Monitoring and Frontend Observability (built on the Faro SDK) can be paired to close blind spots that a synthetic-only strategy leaves open. Synthetic checks reliably detect that something broke but can't reveal who was affected, how badly, or why. Frontend Observability adds real-user data (Core Web Vitals, JS errors, session replay) to quantify blast radius, confirm root cause, and set smarter thresholds. The post walks through reactive workflows (using real-user data to scope a failed check) and proactive workflows (using real-user error patterns to fill gaps in synthetic test coverage), plus practical setup steps for correlating both signals in the same Grafana Cloud stack.
Table of contents
Green checks don't mean happy usersWhat’s actually happening in your users’ browsersThe reliability loop in practiceQuestions this post answers
What's the difference between synthetic monitoring and real user monitoring for frontend reliability?
Synthetic monitoring runs scripted, scheduled checks from fixed probe locations, producing deterministic results that reliably detect when something breaks, but it only covers predicted user paths and can't show impact scope. Real user monitoring (RUM) captures actual browser sessions across real devices, networks, and geographies, revealing who is affected and what affected sessions have in common, though no single session is trustworthy alone. Teams weighing synthetic checks versus RUM for frontend reliability can track this comparison on daily.dev.
How can I use real user data to reduce alert fatigue from synthetic monitoring checks?
Pivot from a failed synthetic check directly into captured real-user sessions for the same page to quantify how many users were actually affected and what they have in common, such as browser, region, or a shared JavaScript error. This turns a vague alert into a data-driven statement like a specific percentage of users affected since a specific time, rather than a generic 'a check failed' message. Engineers cutting through alert fatigue can follow ongoing observability practices on daily.dev.
How do I decide which pages need synthetic monitoring checks?
Compare real-user data on top errors and slowest pages against the existing synthetic check inventory to find coverage gaps, then set latency thresholds from real-user p75/p95 baselines instead of arbitrary guesses. Checks on pages with little real traffic can be retired and their execution budget reallocated to high-traffic, high-struggle paths like an onboarding step users actually get stuck on. Anyone tuning test coverage against real traffic patterns can revisit this workflow via daily.dev.