Operations | Monitoring | ITSM | DevOps | Cloud

In collecting and analyzing trillions of events each day, Datadog ingests a massive amount of data. We spend substantially to process and store this data in the cloud, and teams across the organization are committed to optimizing the return on this investment. To this end, our FinOps analysts have always tracked the costs of delivering our services and identified opportunities for savings.

As your organization scales, managing observability resources and usage becomes increasingly important. More users and teams mean more dashboards, tags, API keys, and costs to manage. The job of keeping track of these resources and ensuring that they’re compliant can quickly grow in complexity.

Okay, picture this: if you drew a Venn diagram of folks in tech right now, it'd probably look something like this: You'll probably find yourself in one of those circles, right? I’m guilty of falling in the intersection! Because let's be real, the 'will AI replace developers by 20xx?' debate is everywhere – Reddit, Hacker News, team Slack and even your local cafe. Well, we decided to go straight to the source.

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.

The promise of large language models (LLMs) brings new performance gains to development teams, sharing information and context to your team. How is this information stored?

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.