# Grafana Cloud Grafana Cloud is an OpenTelemetry-native observability platform that combines observability, incident response, and AI-powered workflows. 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 ## How to measure and improve instrumentation quality for better full-stack observability DevFeed: [How to measure and improve instrumentation quality for better full-stack observability]() Original publisher: [Read original article]() Author: Arpit kumar Published: 2026-09-12T11:22:06.456390Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [Instrumentation](), [observability](), [Grafana Cloud](), [telemetry]() Tags: [analytics](), [grafana](), [grafana-cloud](), [how-to](), [instrumentation](), [knowledge-graph](), [logs](), [metrics](), [monitoring](), [observability](), [traces]() ### AI overview This article explains how to measure and improve instrumentation quality for full-stack observability. It introduces Grafana Cloud's continuously updated instrumentation quality report, which evaluates each service using automated checks for telemetry completeness, correctness, signal coverage, naming, Kubernetes correlation, and connections across the service graph. ### Source excerpt Modern engineering teams instrument everything, with metrics, logs, traces, and profiles flowing from hundreds of services at once. But full-stack observability isn't really about collecting more telemetry; it's about having a single, unified picture of how your services connect to every layer beneath them, including their dependencies, the pods and nodes they run on, and the logs, traces, and profiles that explain their behavior. But there's often a quiet problem hiding underneath all that data: not all instrumentation is created equal, and every gap silently breaks one of those connections. One service, for example, might emit metrics but no logs, so when you pivot from "this is erroring" to "show me why," you hit a dead end. Another might have logs but an invalid service.name or a missing k8s.pod.name that breaks correlation, dropping it out of the graph and away from its pods and nodes. A third service might look perfectly healthy right up until an incident, when you discover its traces were never wired up and the trail goes cold exactly when you need it most. To fix this, Grafana Cloud's Knowledge Graph now includes an instrumentation quality report: an automated, continuously updated assessment of how well each of your services is instrumented--and, in effect, how they plug into the full-stack picture. In this post, we'll walk through how to read the instrumentation quality report, how the scoring works, and how to use it to systematically raise the observability bar, keeping every layer of your stack joined up across every service you run. What is instrumentation quality? Instrumentation quality is a measure of how complete and correct the telemetry for a given service is, judged against a set of automated checks. Each service is evaluated by a server-computed set of quality checks: small, focused rules that validate one specific thing about a service's telemetry. A few examples: Does the service emit logs? Are service graph metrics present? Is the service nam ## Knowledge Graph as context for LLMs: demonstrating decisive RCA and faster production performance DevFeed: [Knowledge Graph as context for LLMs: demonstrating decisive RCA and faster production performance]() Original publisher: [Read original article]() Author: Sarah Constant Published: 2026-09-12T11:22:06.456390Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [Large Language Model](), [Grafana Cloud](), [debugging](), [incident](), [telemetry](), [observability ai agents]() Tags: [ai](), [ai-agents](), [analytics](), [debugging](), [grafana](), [grafana-cloud](), [incident](), [knowledge-graph](), [llms](), [logs](), [metrics](), [monitoring](), [observability](), [telemetry](), [traces]() ### AI overview The article reports Grafana Labs experiments testing whether providing AI agents with Grafana Cloud's Knowledge Graph improves incident debugging compared with raw telemetry alone. In one incident replayed 16 times per approach, Knowledge Graph context led to the correct root cause 15 times, versus once with raw telemetry. The article also describes challenges including misleading signals, unsupported confident answers, and inconsistent investigations, arguing that well-structured context matters more than context-window size alone. ### Source excerpt On the product team here at Grafana Labs, we consider AI agents our users, too. That's why we set out to test how well agents can debug incidents across the full stack, and how much better they perform with Grafana Cloud's Knowledge Graph vs. using raw telemetry alone. Our early results are promising. In one real incident we replayed 16 times each way, an agent with Knowledge Graph context found the correct root cause 15 times, compared with just once using raw telemetry alone. Along the way, we also uncovered some of the challenges that still stand in the way of reliable AI-assisted debugging, from chasing the wrong signals to confidently making things up and producing inconsistent answers. We're still early, but our findings point to an important idea. The industry's shorthand right now is that a bigger context window will lead to better outputs. Our findings suggest it's not just about more context; it's about structuring your data well enough to serve the right context. Here's a look at what we've learned so far, as we continue to experiment out in the open and bring you along, the Grafana Labs way. Giving an agent access to telemetry is just the beginning Give a current-generation model like Opus 4.8 access to your raw telemetry during a live incident, and it genuinely starts to figure things out: querying metrics and logs, forming a hypothesis, and checking it. We have watched it work on our own incidents, and it does it affordably. But if you run software at scale, where uptime is business-critical and large teams share the responsibility, a better model alone doesn't get you all the way there for debugging. From analyzing how LLMs do root-cause analysis on our own infrastructure, and from speaking to our customers, we've uncovered three problems that get in the way: The further the cause is from the alert, the more likely the model is to get it wrong. An agent may confidently make things up when it doesn't have the evidence it needs. The same investigation c ## 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 ## K6 Load Testing on Laravel Cloud: 17,000 Requests per Second and Autoscaling Under Pressure DevFeed: [K6 Load Testing on Laravel Cloud: 17,000 Requests per Second and Autoscaling Under Pressure]() Original publisher: [Read original article]() Author: Devon Garbalosa Published: 2026-03-25T11:35:18Z Content type: tutorial Language: en Sources: [Laravel Blog]() Topics: [k6](), [Laravel](), [autoscaling](), [Cloud](), [Grafana Cloud](), [Benchmark](), [benchmarking](), [nginx]() Tags: [autoscaling](), [benchmark](), [benchmarking](), [cloud](), [grafana-cloud](), [k6](), [laravel](), [latency](), [testing]() ### AI overview A practical benchmark of Laravel Cloud using Grafana Cloud k6, a custom endpoint, 20,000 concurrent virtual users, and bimodal response-time jitter. The test sustained 17,000 requests per second while examining how autoscaling handled realistic traffic and long-lived connections. ### Source excerpt Learn how to load test a Laravel app with k6: 20,000 virtual users, bimodal latency jitter, and Laravel Cloud autoscaling under real pressure. Here's what happened. ## Own Your Observability: Supabase Metrics API DevFeed: [Own Your Observability: Supabase Metrics API]() Original publisher: [Read original article]() Author: Steven Eubank Published: 2025-12-16T07:00:00Z Content type: article Language: en Sources: [Supabase Blog]() Topics: [Supabase](), [API](), [telemetry](), [observability](), [Prometheus](), [Grafana](), [Grafana Cloud](), [datadog agent](), [Amazon Web Services](), [Self-hosted]() Tags: [api](), [aws](), [datadog-agent](), [grafana](), [grafana-cloud](), [metrics](), [monitoring](), [observability](), [prometheus](), [replication](), [self-hosted](), [storage](), [telemetry](), [visualization]() ### AI overview Supabase's Metrics API streams roughly 200 Postgres performance and health metrics in Prometheus exposition format, enabling integration with Grafana Cloud, Datadog, AWS Managed Prometheus, self-hosted Grafana, and other Prometheus-compatible backends. The article explains how this unifies database telemetry with existing observability stacks for monitoring, visualization, alerting, and troubleshooting. ### Source excerpt Stream your Supabase database telemetry into any Prometheus-compatible observability stack with the Metrics API. Full control over monitoring, visualization, and alerting. ## Monitoring Temporal Cloud Workflows with Grafana Cloud DevFeed: [Monitoring Temporal Cloud Workflows with Grafana Cloud]() Original publisher: [Read original article]() Author: Ishan Jain Published: 2025-11-21T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Grafana Cloud](), [Monitoring](), [observability](), [dashboards](), [telemetry]() Tags: [agent](), [ai](), [announcements](), [dashboards](), [grafana-cloud](), [latency](), [metrics](), [monitoring](), [observability](), [telemetry]() ### AI overview This post describes a new Temporal Cloud integration for Grafana Cloud. It explains how the integration collects metrics from Temporal Cloud's OpenMetrics endpoint and sends them to Grafana Cloud Metrics, where teams can monitor workflow health, task queue backlogs, latency, capacity usage, dashboards, and alerts. ### Source excerpt In this post, Ishan explores how the new Temporal Cloud integration for Grafana Cloud gives teams a simple way to visualize, monitor, and alert on the health of their Temporal Workflows.