Operations | Monitoring | ITSM | DevOps | Cloud

If you’ve ever tried to debug a request that touched five services, a queue, and a database you don’t own, you already know why monitoring distributed systems is hard. Logs live in different places, requests disappear halfway through a flow, and when something breaks in production, you’re reconstructing what happened from fragments. Microservices make this worse by design. A single request fans out across small, independently deployed services, often communicating asynchronously.

Your hero image takes 5 seconds to show up. Your headline sits invisible while JavaScript churns away. Your users? They’ve already hit the back button. That’s the cost of a slow Largest Contentful Paint, and it’s killing your conversions and search rankings. LCP is one of Google’s Core Web Vitals, which means it directly impacts how Google ranks your website. A slow LCP doesn’t just frustrate users, it actively hurts your SEO.

Teams can use Datadog Real User Monitoring (RUM) and RUM without Limits to get full visibility into the frontend health of their applications while retaining only the sessions that contain critical problems that affect the end-user experience. But application errors or slowness often result from backend issues, such as database bottlenecks. To diagnose these issues, you need to correlate the frontend data from RUM with the backend data from Datadog Application Performance Monitoring (APM).

In cloud-native environments, infrastructure is in constant flux. Teams move fast, leveraging Infrastructure-as-Code (IaC), ephemeral resources, and automation to iterate quickly. But speed brings a cost: configuration drift. A single manual change in the cloud console, an untracked automation script, or an out-of-band fix can cause your infrastructure to fall out of sync with code. Over time, this erodes trust, breaks pipelines, and introduces silent risk.

Efficient monitoring delivers fast results: alerts fire within seconds, dashboards refresh continuously, and teams know the moment something changes. Understanding arrives later. An alert may show that a value shifted, but it does not explain why it shifted, how far the impact will spread, or which components truly matter. Teams see the signal, not the system behavior behind it. This gap defines the limit of traditional monitoring. Detection has improved, but explanation has not kept pace.

Consider this: traditional cloud cost monitoring was like checking your fuel gauge once a month — after the trip was already over. That model worked when infrastructure scaled slowly. You provisioned resources predictably and paid for stable, linear usage. AI breaks that model. Today, AI costs behave like a high-performance engine with a hypersensitive throttle. A small input, like a prompt change or a single power user, can dramatically increase your fuel burn in seconds.

Last month we shared that a new navigation system is coming to Bitbucket, and we know many of you have been eager to see what it looks like. Today, we’re happy to share that the new navigation is available for to all Bitbucket users. This article covers what’s changing in Bitbucket, when it’s happening, and how you can share feedback with us.

This is the eighth edition of the SRE Report. Eight years of tracing reliability's arc, from uptime obsession to experience, from toil to intelligence, from systems to people. This year's report is also the first since Catchpoint joined LogicMonitor. We want to acknowledge their support in keeping this work going. They get what this report means to the reliability community, and that matters. We made a deliberate choice this year to say less.

You shouldn’t have to understand the care behind this report, unless it’s missing. For the past eight years, this research has focused on all things related to reliability and resilience. How systems behave under stress. How teams respond when things break. And how the practices continue to evolve. Reaching the eighth edition of The SRE Report attests to that and gives me pause. You can read the full report here and you can find a summary of the key findings here.

GPUs are no longer a niche component. Gamers know them for immersive graphics, workstation users rely on them for balanced performance, and in the age of AI, GPUs have become one of the most in-demand resources in modern infrastructure. They are also expensive. That reality creates two immediate constraints, for individuals and enterprises alike: GPU-backed instances should be provisioned deliberately, and once provisioned, they should be used efficiently.