Operations | Monitoring | ITSM | DevOps | Cloud

AI cost per customer is each customer's attributed share of your AI spend: API tokens, inference infrastructure, and per-seat AI tools, measured against that customer's revenue. It matters because AI usage is wildly uneven across customers, so healthy blended margins can hide individual customers you serve at a loss. It's cost to serve, not acquisition cost. Cost per customer is the metric CloudZero was built around.

Insights are how your team tracks interesting observations, analysis and optimization work in CloudZero. Each one records the opportunity and its status, so the work gets sized, assigned, and closed out. Teams now do a lot of this work in an AI assistant. For example, you can ask Claude which instances sat idle last month, and it will tell you. But if you have to switch to CloudZero and fill out a form to log that finding, some findings won’t get logged.

AI-powered apps are hard to debug without observability. From one run to another, AI produces different results, goes down different paths, and you’re none the wiser as to why it’s happening. Agent Tracing bridges the gap between prompt, tool calls and final output, all within the context of your app. If things break (it always does at some point, we know from experience), you get everything in one complete trace.

We’re refreshing the Bitbucket Pipelines experience to make it easier to read logs, find the steps you need, and navigate a pipeline run. The update focuses on the Pipelines list and Pipeline result pages. This is a visual refresh only. Your pipeline configurations, builds, data, runners, variables, deployments, and integrations stay as they are. It’s the same Bitbucket Pipelines underneath, with a cleaner, more focused view.

See how DNS turns a domain name into an IP address step by step, and why fast resolution keeps your pages loading quickly for users all around the world.

See how BGP announcements, replacements, and withdrawals shift reachability across networks you don’t own, and why outside-in monitoring catches them.

A critical application slows overnight. Monitoring identifies an unhealthy component, and an engineer finds a familiar remedy: restart a service. The command takes seconds. Determining whether the restart will restore the application or disrupt another dependency takes far longer. That is the real challenge in enterprise incident remediation. Most IT teams already know how to automate a task.

Open architecture, explainable AI, and full context Elastic was named a Strong Performer in The Forrester Wave: AIOps Platforms, Q4 2026, which evaluated 10 providers across 24 criteria. Forrester gave Elastic the highest possible scores in five criteria: algorithmic trust and explainability, observability as code (OaC) and GitOps alignment, AI stack and semantic observability, sovereign deployment and data isolation, and pricing flexibility and transparency.

A sovereign cloud is a cloud computing environment designed to keep data, infrastructure, and operations under the control of a specific country or jurisdiction. It helps organizations maintain control over who can access their data and how it is managed. Depending on the provider and service, this may include storing data within national borders, using locally governed infrastructure, and ensuring that operations comply with regional regulations.

A back-office server that runs out of disk space can interrupt an important internal workflow. A process that stops on a staging host can delay development. Although workloads like these might not require application-level instrumentation, teams still need to know when the hosts that support them require attention. Without a monitoring solution tailored to these requirements, teams have to apply more monitoring than the workload needs, maintain a separate tool, or accept a gap in visibility.