Operations | Monitoring | ITSM | DevOps | Cloud

Regular monitoring practices can emphasize application response time, but queue time is also often an early and important warning sign. If it rises, you’ll quickly see downstream effects: tail latency, timeouts, and error spikes. This means that this metric can give you a head start tackling app issues before they become user problems. In this post, we’ll discuss queue time, how things can go off track, and practical steps to turn it around.

If you're using TrueFoundry to manage your LLM traffic, you can now send those traces directly to Last9 and view them alongside your existing infrastructure telemetry.

Highlights of another laudable year of customer-centric collaboration The integration of Elastic’s capabilities, including vector databases and context engineering, with AWS services helps customers build intelligent, scalable, and secure applications faster and with greater flexibility. Our ongoing collaboration has resulted in another year of notable innovation with AWS. This blog highlights our continued collaboration with AWS throughout 2025 to help you capitalize on the power of AI.

This year’s Gartner IT Infrastructure, Operations and Cloud Strategies Conference made one thing abundantly clear: the industry is moving beyond reactive monitoring and isolated dashboards toward autonomous, outcome-driven IT operations. While AI and agentic automation dominated keynotes and vendor messaging, conversations on the show floor reflected a more grounded reality.

It’s 5:14am, and I wake up to the squawking geese sound of my PagerDuty alert (anyone else have this sound? No?). I’m four months into working for my new team as a junior software engineer, and this is my first time being paged in the middle of the night. Most software engineers probably dread this moment, but I kind of love it. Agile ceremonies and Jira tickets suddenly don’t matter, and you’re fully focussed on stopping a customer-impacting fire.

Setting up and scaling observability across large, distributed environments often requires platform and SRE teams to coordinate access to infrastructure hosts and switch between configuration management tools and product-specific documentation. These tasks increase setup time and create delays in establishing visibility of critical services in Datadog. As teams expand their infrastructure, they need to coordinate Datadog configuration changes in a consistent and auditable way.

Continuous profiling has established itself as core observability practice, so much so that we’ve referred to it as the fourth pillar of observability. But despite the capabilities and growing adoption of continuous profiling, it can still be confusing to approach profiling as a newcomer and correctly apply it to different troubleshooting scenarios.

Operating Cilium at a small scale is straightforward. You install the Helm chart, choose a routing mode, and apply a few network policies. Day 1 is about getting packets to flow. Day 2 is about keeping them boring. At Datadog, we run Cilium across hundreds of Kubernetes clusters, tens of thousands of nodes, and hundreds of thousands of pods in multiple clouds. When operating at this scale, small configuration choices stop being minor details and start becoming risk multipliers.

Netdata has an incredibly powerful alerting engine. But this can sometimes be a double-edged sword: the flexibility to build incredibly specific, intelligent alerts is immense, but mastering its syntax can feel like learning a new language. We’ve heard this from so many of you. You tell us that configuring alerts is often the steepest part of the learning curve, a task that falls to the one “Netdata expert” on the team who has spent the time digging through the documentation.