Operations | Monitoring | ITSM | DevOps | Cloud

Managed Service Providers (MSPs) are under intense pressure: 24/7 uptime, seamless client experiences, and proactive service models are table stakes. But many are still operating with fragmented, manual-heavy toolsets designed for a simpler era.

It’s been a year since we launched Grafana Alloy, our OpenTelemetry Collector distribution with built-in Prometheus pipelines and support for metrics, logs, traces, and profiles. OpenTelemetry is quickly becoming an industry standard for telemetry collection, processing, and delivery, and we’re committed to making Alloy the best possible collector for telemetry data, whether you’re using it with Grafana Cloud or not.

Moving your existing Grafana instance to Grafana Cloud just got dramatically simpler. Today, we’re excited to announce the general availability of the Grafana Cloud Migration Assistant, a powerful yet intuitive tool designed to streamline your migration journey. Traditionally, migrating from Grafana OSS or Grafana Enterprise to Grafana Cloud required technical expertise with Grafana’s HTTP API or command-line tools like Grizzly.

When your MongoDB database slows down, it affects your entire application stack. Performance issues can range from minor inconveniences to major outages, making a solid understanding of MongoDB metrics essential for any DevOps engineer. This guide covers the key performance metrics you need to monitor in MongoDB, how to interpret what you're seeing, and practical steps to resolve common issues.

Message queues quietly power a lot of what happens behind the scenes in distributed systems. RabbitMQ is no exception—when it’s working, you don’t notice it. But when it’s not, things break in ways that are hard to trace. This guide walks through what you need to monitor in RabbitMQ, how to set it up, and how to troubleshoot when things go wrong—so you’re not stuck guessing when messages go missing.

Fat bills from observability vendors and tons of not-so-insightful telemetry data have turned out to be a very common issue today. This often leaves teams having to explain the lack of clear ROI, despite the growing costs. If you’re using OpenTelemetry to record your observability data, there are some practical methods you can apply to keep those costs from piling up.

Docker containers isolate application environments, making performance monitoring essential for visibility and stability — especially at scale. To manage production effectively, teams need clear insights into resource usage, bottlenecks, and failure points. This guide covers key Docker metrics, how to collect them, and how to use that data to keep your containerized systems running smoothly.