Operations | Monitoring | ITSM | DevOps | Cloud

When a web service slows down or errors spike, metrics can tell you what changed (active connections rise, error rate increases), but the root cause can sometimes be found in your logs (which IPs are hammering POST endpoints, 4XX/5XX occurrences). Put the two together and you get the full observability picture. Time-series metric trends to spot incidents, and line-level details to fix them fast.

We consistently roll out helpful updates and fun features in Grafana Cloud, our fully managed observability platform powered by the open source Grafana LGTM Stack (Loki for logs, Grafana for visualization, Tempo for traces, and Mimir for metrics). In case you missed them, here’s our monthly round-up of the latest and greatest Grafana Cloud updates. You can also read about all the features we add to Grafana Cloud in our What’s New in Grafana Cloud documentation.

The more your infrastructure grows, the more likely you are to face a familiar challenge: where to safely store the API keys, passwords, and tokens that power your observability stack. Unfortunately, a common response to this dilemma is to scatter credentials across configurations, making security and management of secrets increasingly complex.

Launch Week Day 3: Introducing Discover Infrastructure Your dashboard looks perfect. APIs responding in 80ms, background jobs processing smoothly, error rates at 0.02%. Everything's green. Then production breaks. "Why is checkout so slow?" "The payment service keeps timing out!" You run kubectl get pods and discover payment-service pods restarting every 3 minutes due to OOM kills. Then you check your database host—CPU at 98% because someone forgot the new ML training job runs there too.

Azure Kubernetes Service (AKS) continuously generates a high volume of telemetry, ranging from node-level CPU and memory usage to request latencies and error rates within individual pods and services. Without a structured monitoring strategy, this flood of metrics can easily become noise, leaving teams blind to early warning signs. Effective monitoring in AKS is about identifying the right signals, correlating them across layers, and acting before they impact application performance or cluster stability.

This is a guest post by Simon Grimm, founder of Galaxies.dev, a platform dedicated to helping developers master React Native through hands-on courses, expert guidance, and personal support. React Native performance matters more in 2025 than ever before. With the New Architecture now stable and apps competing against lightning-fast native experiences, users expect sub-second load times and buttery-smooth 60fps interactions.

Obviously nobody is disagreeing with this. It’s just that during ongoing development and while focusing on features and bug-fixes, testing often falls behind in priority, especially when developers would need to write tests for existing or legacy code, teams can be hesitant to invest the time. C++ applications have to run a diverse set up target environments, varying in OS, compilers, C/C++ standard libraries and dependency versions.

Let's be provocative for a moment. You probably don't know what is actually on your network. You have the CMDB, spreadsheets, diagrams from the last big refresh, and the institutional knowledge of your veteran engineers. But is this information accurate? Is it complete? Answering that question with absolute certainty can be difficult for many who manage complex IT environments.

Predictive Analytics Models and Algorithms are an important component of eG Enterprise’s AIOps engine for proactive observability. eG Enterprise collects and analyses metrics, events, logs and traces and the data including real usage data is used to make intelligent predictions to forecast future system behavior and IT resource metric levels.