Operations | Monitoring | ITSM | DevOps | Cloud

Connecting nearly a billion professionals is no small feat. It requires an infrastructure that puts the user experience above everything else. At LinkedIn, this principle created a massive engineering challenge: delivering a fast, consistent experience across various use cases, from the social feed to real-time messaging and enterprise tools.

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.

Fleet, Rancher’s built-in GitOps engine, is designed to scale up to thousands of clusters. However, “how far” can it scale in a real world scenario, you might ask? Earlier this year, we wrote about the Fleet benchmark tool and we made a few discoveries that were very instructive, especially concerning resource consumption and its impact on deployments’ performances.

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.

Most security stacks only move when told to. They wait for known IOCs, hunt for pre-defined suspicious strings, and trigger automation only after a condition lights up. By then, attackers have already pivoted. Agentic AI rewrites the rules. Instead of signature-based detection, it monitors behavioral baselines and identity signals, watching for violations of expectations formed from observed context.

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.