# synthetic monitoring Published articles for synthetic monitoring. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Digital Experience Monitoring with Grafana Cloud: Session Replay, synthetic checks, and faster investigations DevFeed: [Digital Experience Monitoring with Grafana Cloud: Session Replay, synthetic checks, and faster investigations]() Original publisher: [Read original article]() Author: Bukola Ayodele Published: 2026-09-15T01:35:30.954353Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [digital experience monitoring](), [Grafana Cloud](), [Frontend observability](), [synthetic monitoring](), [real user monitoring](), [session replay](), [Instrumentation](), [web applications](), [JavaScript](), [Core Web Vitals](), [Traces]() Tags: [analytics](), [core-web-vitals](), [digital-experience-monitoring](), [frontend-observability](), [grafana](), [grafana-cloud](), [grafana-cloud-frontend-observability](), [instrumentation](), [javascript](), [logs](), [metrics](), [monitoring](), [observability](), [real-user-monitoring](), [session-replay](), [synthetic](), [synthetic-monitoring](), [traces](), [web-applications]() ### AI overview This article explains how Digital Experience Monitoring in Grafana Cloud combines Frontend Observability, Synthetic Monitoring, real user monitoring, and session replay. It shows how these capabilities help engineering teams detect issues proactively, understand their scope and root causes, correlate frontend signals with backend traces, and resolve production problems faster. ### Source excerpt When something breaks in production, the questions that matter most are also the toughest to answer from metrics alone: who was affected, what did they actually see, and is this worth waking someone up for? Answering those questions requires a fuller picture of the issue and its impact on your users. That's where Digital Experience Monitoring (DEM) in Grafana Cloud comes in. By combining Frontend Observability and Synthetic Monitoring, DEM connects real user experiences with proactive testing, helping engineering teams understand the scope of an issue, investigate its cause, and resolve it faster, all within Grafana Cloud. In this blog post, we'll walk through some of the latest DEM updates in Grafana Cloud, and how to get started. You can also learn more by watching the video below. First, what is Digital Experience Monitoring? Digital Experience Monitoring in Grafana Cloud gives you a complete picture of how users experience your web applications, from real user data to proactive synthetic checks. DEM helps your team achieve: Real user visibility: know how users truly experience your web application, not just what your backend metrics suggest. Proactive detection: catch problems before your users do, using automated checks against your critical user journeys. End-to-end correlation: connect a frontend signal to the backend trace behind it. Faster resolution: cut your mean time to recovery from hours to minutes. Session Replay: see exactly what your users saw Session Replay in Grafana Cloud Frontend Observability lets you visually replay what a user saw and did inside your web application. Your team can watch exactly what users experienced and correlate it with real user monitoring signals like Core Web Vitals, user actions, and traces, which makes it a powerful tool for investigating bugs and running root cause analysis. Session Replay is powered by Faro, Grafana's open source JavaScript instrumentation library for collecting real user monitoring data. Let's walk ## 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]() Original publisher: [Read original article]() Author: Mark Meier Published: 2026-09-12T11:22:06.456390Z Content type: tutorial Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [synthetic monitoring](), [Frontend observability](), [Grafana Cloud](), [observability]() Tags: [analytics](), [frontend-observability](), [grafana](), [grafana-cloud](), [logs](), [metrics](), [monitoring](), [observability](), [synthetic-monitoring](), [traces](), [uptime]() ### 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 ## Custom labels in Grafana Cloud Synthetic Monitoring: New updates for consistency and ease-of-use DevFeed: [Custom labels in Grafana Cloud Synthetic Monitoring: New updates for consistency and ease-of-use]() Original publisher: [Read original article]() Author: Anant Sharma Published: 2026-09-12T11:22:06.456390Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [Grafana Cloud](), [synthetic monitoring](), [Grafana](), [Monitoring](), [telemetry](), [dashboards]() Tags: [analytics](), [dashboards](), [grafana](), [grafana-cloud](), [how-to](), [logs](), [metrics](), [monitoring](), [observability](), [synthetic-monitoring](), [telemetry](), [traces](), [update]() ### AI overview Grafana Cloud Synthetic Monitoring is updating custom labels so they attach directly to every check metric and log, rather than only sm_check_info. The label_ prefix will be removed, and labels will appear exactly as defined. Existing users must migrate dashboards, SLOs, alerts, and queries that reference these labels by March 1, 2027. ### Source excerpt Labels are a powerful way to organize telemetry and define policies across Grafana Cloud, helping to streamline alerting, attribution, access control, and more. But traditionally, custom labels in Synthetic Monitoring have worked a little differently: they only lived on a single sm_check_info metric, and Grafana Cloud prefixed each one with label_. To make custom labels in Synthetic Monitoring work consistently with the rest of Grafana Cloud--without extra joins, naming conventions, or workarounds--we're rolling out an update that lets your custom labels attach directly to every check metric, not just sm_check_info, and removes the label_ prefix. Starting today, labels appear exactly as you write them, making Synthetic Monitoring data easier to navigate and use with label-based policies across Grafana Cloud. If you currently use custom labels in Synthetic Monitoring, read on to learn how to migrate to the new labels. We are asking users to migrate by March 1, 2027 to ensure their custom dashboards, SLOs, alerts, and queries that reference Synthetic Monitoring metrics do not break, and continue to work as expected. If you do not use custom labels in Synthetic Monitoring, you don't need to do anything to prepare for this update. How custom labels work in Synthetic Monitoring Until now, if you wanted to filter a dashboard, scope an alert, or attribute cost by team or service within Synthetic Monitoring, you had to join sm_check_info against the check metric you actually want to query. You also had to remember that team is really label_team in this context. That approach worked to ensure your custom labels were never at odds with system-defined labels. However, it broke down as usage scaled up and dozens of teams started running hundreds of checks across services, environments, and regions. Teams rely on consistent schemas to direct label-based workflows, and this update brings Synthetic Monitoring further into the fold of your existing policies. With the update, labels i ## Monitor critical user journeys with Datadog Journey Monitoring DevFeed: [Monitor critical user journeys with Datadog Journey Monitoring]() Original publisher: [Read original article]() Author: Maël Lilensten; Arti Arutiunov; Younes Berradia; Lauren Zuniga Published: 2026-06-09T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [real user monitoring](), [dashboards]() Tags: [availability](), [dashboards](), [digital-experience-monitoring](), [digital-experiences](), [metrics](), [monitoring](), [performance](), [product-analytics](), [real-user-monitoring](), [synthetic-monitoring]() ### AI overview Datadog Journey Monitoring centralizes user behavior, technical performance, availability SLOs, and synthetic test results to help teams assess critical digital journeys and investigate issues in context. ### Source excerpt See how teams can monitor critical digital experiences from a single hub. ## Introducing Upright: An Open Source Synthetic Monitoring System DevFeed: [Introducing Upright: An Open Source Synthetic Monitoring System]() Original publisher: [Read original article]() Author: Lewis Buckley Published: 2026-02-16T18:00:00Z Content type: release Language: en Sources: [37signals Dev]() Topics: [synthetic monitoring](), [Monitoring](), [Open Source](), [Playwright](), [Rails](), [Prometheus](), [Grafana](), [Deployment](), [SQLite]() Tags: [deployment](), [grafana](), [monitoring](), [open-source](), [playwright](), [prometheus](), [rails](), [sqlite](), [synthetic-monitoring]() ### AI overview 37signals is open-sourcing Upright, a synthetic monitoring system built to monitor Basecamp, HEY, Fizzy, and other services. Upright runs health checks from multiple geographic locations and reports results through Prometheus metrics for alerting and Grafana visualization. ### Source excerpt We're open-sourcing Upright, the synthetic monitoring system we built to watch over Basecamp, HEY, Fizzy and our many other services.