# Grafana Alloy Published articles for Grafana Alloy. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How to monitor Cypress tests with Grafana Cloud DevFeed: [How to monitor Cypress tests with Grafana Cloud]() Original publisher: [Read original article]() Author: Rajesh Mahalingaswamy Published: 2026-09-12T11:22:06.456390Z Content type: tutorial Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [Grafana](), [Node.js](), [ci]() Tags: [analytics](), [cypress](), [grafana](), [grafana-alloy](), [grafana-cloud](), [grafana-cloud-metrics](), [how-to](), [logs](), [metrics](), [monitoring](), [node-js](), [observability](), [prometheus](), [traces]() ### AI overview A tutorial for monitoring Cypress test results by converting them to Prometheus metrics, pushing them through a Pushgateway, scraping them with Alloy, and forwarding them to Grafana Cloud Metrics. ### Source excerpt If your Cypress suite has tests that fail more often or run slower, you know it can be hard to figure out the pattern from a single job. It could be one spec that slowed down, or a single test that fails, or maybe the entire suite is trending slower. The root cause could be a bug in the app, or a flaky test, or something else. Your terminal output and CI log will tell you what happened on a single run, but that doesn't help you spot any larger trends--especially since you lose that data as soon as the job finishes. Thankfully, Cypress, a front-end automated test framework built for web applications, already exposes everything you need through its plugin hooks. After each spec finishes, Cypress hands you a results object with pass and fail counts, per-test durations, and states. You just need to turn that into metrics and ship it somewhere durable. In this post, you'll learn how to monitor your Cypress tests by converting those results into Prometheus metrics inside a Cypress hook, pushing them to a Prometheus Pushgateway, and letting Alloy scrape the gateway and forward everything to Grafana Cloud Metrics--using nothing but the free tiers. By the end you'll have a pipeline running with the following architecture: What you'll need This walkthrough runs everything alongside your existing Cypress project. Before you start, make sure you have: A Cypress project (this example uses Cypress 14.x) with a cypress.config.js you can edit A Prometheus Pushgateway. Cypress runs are short-lived batch jobs, so they can't be scraped directly--the Pushgateway holds the metrics between runs so a scraper can pick them up. Set up a Prometheus Pushgateway in your infrastructure Alloy, our open source collector we use to scrape the Pushgateway and remote-write to Grafana Cloud A Grafana Cloud account. The free tier includes Grafana Cloud Metrics and a Prometheus remote-write endpoint. If you don't have an account, you can sign up here Your Grafana Cloud remote-write URL, numeric user ID, an ## How to scale Alloy as a central telemetry gateway: capacity planning, load testing, and production lessons DevFeed: [How to scale Alloy as a central telemetry gateway: capacity planning, load testing, and production lessons]() Original publisher: [Read original article]() Author: Fatjon Nebiu Published: 2026-09-12T11:22:06.456390Z Content type: tutorial Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [Grafana]() Tags: [analytics](), [auth](), [best-practices](), [cloud](), [cost](), [data](), [deployment](), [enterprise](), [grafana](), [grafana-alloy](), [grafana-cloud](), [how-to](), [kubernetes](), [logs](), [metrics](), [monitoring](), [observability](), [opentelemetry](), [platform](), [production](), [prometheus](), [scale](), [telemetry](), [testing](), [traces]() ### AI overview A practical guide to scaling Grafana Alloy as a centralized telemetry gateway. It covers capacity planning, load testing, and production considerations for collecting metrics, logs, and traces and forwarding them to Grafana Cloud. ### Source excerpt Running Alloy as a single-instance sidecar is simple. Running it as a centralized gateway that absorbs the full telemetry stream of an enterprise platform--tens of millions of active series, terabytes of logs per day, and tens of thousands of trace spans per second--is a different challenge altogether. To get it right, you need deliberate capacity planning, honest load testing, and a monitoring setup that doesn't rely on the very thing you're testing. As part of the Professional Services team here at Grafana Labs, we've seen this firsthand working with customers. In this post, we'll walk you through the best practices we follow to help them find success, and we'll do so using real, anonymized data from a recent engagement. We'll cover how we sized and load tested a production Alloy central collector deployment on Kubernetes, what the numbers looked like under real stress, and how the cluster behaves today handling the full production telemetry workload for a large enterprise platform. By the end, you should have a better sense for how you can create your own central gateway for collecting telemetry in Grafana Cloud. Why a central gateway? Before diving into numbers, it's worth explaining the pattern. In a central gateway setup, all telemetry from application teams--metrics, logs, and traces--flows to a shared Alloy fleet via OTLP or native Prometheus/Loki write protocols. Alloy buffers, processes, batches, and forwards everything to Grafana Cloud. This gives you several things that per-team sidecar deployments struggle to provide: A single control plane: Auth, rate limiting, and routing in one place so application teams don't need to manage Grafana Cloud credentials Centralized buffering: Ensure a transient Grafana Cloud slowdown doesn't immediately cause data loss at the source Cost visibility: Configure the gateway to only accept telemetry data containing the label or attribute that is mandatory for cost-attribution Protocol normalization: Send OTLP, Prometheus Remote ## nixos-telemetry: A NixOS flake for opt-in observability pipelines DevFeed: [nixos-telemetry: A NixOS flake for opt-in observability pipelines]() Original publisher: [Read original article]() Author: palo Published: 2026-08-23T08:27:36Z Content type: article Language: en Sources: [Announcements - NixOS Discourse]() Topics: [telemetry](), [OpenTelemetry](), [Tooling](), [log management](), [Prometheus](), [Grafana](), [Grafana Alloy]() Tags: [announcements](), [collector](), [config](), [grafana](), [grafana-alloy](), [loki](), [opentelemetry](), [prometheus](), [telemetry]() ### AI overview A discussion introduces nixos-telemetry, a NixOS flake that uses an OpenTelemetry collector to connect opt-in scrapers, storage, and visualization components. It supports local stacks, remote forwarding, and fan-out to multiple sinks through a unified option tree. ### Source excerpt Hi everyone, I'd like to share nixos-telemetry, a NixOS flake that makes setting up observability in your infrastructure easy. An OpenTelemetry collector sits at the center of each machine. Scrapers, storage, and visualization are all opt-in. You enable what you need; the collector wires the pipeline together automatically. Why? Wiring up telemetry in NixOS today means gluing together Telegraf, Prometheus, Loki, Grafana, Alloy, each with its own config format, ports, and inter-service dependencies. nixos-telemetry puts all of that behind a single telemetry.* option tree: Turn on the system with telemetry.enable = true. Every app is opt-in. Nothing starts that you didn't ask for. The collector starts automatically once a complete pipeline exists, a matching source and sink for the same signal type. No sink? It waits. Forward to a remote collector, run a full local stack, or both. Fan-out to multiple sinks is supported. What it looks like Full local stack on one machine: { telemetry.enable = true; telemetry.telegraf.enable = true; # host metrics telemetry.alloy.enable = true; # journald logs telemetry.prometheus.enable = true; # metrics storage telemetry.loki.enable = true; # logs storage telemetry.grafana.enable = true; # visualization (datasources auto-provisioned) } Forward to a remote collector: # machine 1 { telemetry.enable = true; telemetry.telegraf.enable = true; telemetry.opentelemetry.exporter.endpoints.remote = "100.64.0.1:4317"; } # machine 2 { telemetry.enable = true; telemetry.opentelemetry.receiver.endpoint = "0.0.0.0:4317"; } Full option reference: OPTIONS.md Thanks! Regarding Discourse LLM Policy : I must disclose that substantial parts of this project are llm generated. And because I coppied parts of the README in this post, substantial parts of this announcmement too. 27 posts - 7 participants Read full topic