Operations | Monitoring | ITSM | DevOps | Cloud

Enterprise IT networks power business operations across the world. As businesses scale to catch up with an increasingly-demanding user base, networks also grow more complex. IT teams managing these networks have to monitor more data than before, under more stringent SLA terms, with little room for failure. Trying to do this manually across thousands of devices can take a lot of time and effort, and are prone to errors.

On June 24, 2026, Shopify experienced a widespread service disruption that affected storefronts, admin dashboards, and merchant access across multiple regions. While the outage did not impact every user, reports quickly surfaced from merchants around the world who were unable to access stores, log in to administrative tools, or complete routine operations.

Monitor metrics are now available through the StatusGator v3 API for both Website Monitors and Ping Monitors. These endpoints provide the same latency and performance data available in the Monitor Metrics tab, making it accessible through the API and MCP server. You can find the endpoints in the API documentation.

Join us for a practical session on SCOM Reporting and reporting alternatives.

Whether you're using SolarWinds, PRTG, Datadog, or another long-standing monitoring solution, chances are your environment has evolved significantly since the platform was first deployed. New applications have been added. Infrastructure has expanded into cloud environments. Teams have developed custom dashboards, reports, alerts, and workflows. Over time, monitoring becomes deeply woven into daily operations. That's why many organizations continue using tools that no longer meet their needs.

Today, we’re launching Kubex support for the KAI Scheduler and automated GPU sharing for inference workloads. As AI inference moves into production, platform teams are being asked to serve more models, support more teams, and control GPU costs at the same time. But many inference workloads do not need an entire GPU all the time. When teams reserve full GPUs or oversized GPU fractions to stay safe, expensive capacity can sit idle across the cluster.

Eight buyer questions for SharePoint archiving 2026. Compares Microsoft 365 Archive, AvePoint Opus, Archive360 and Squirrel on data, Copilot, labels.

Machine learning models are not like other software artifacts. A single fine-tuned LLM can weigh 70 GB. A model family may share 95% of its weights across dozens of variants. When hundreds of developers, training jobs, and GPU clusters all need the same model at the same time, the infrastructure underneath needs to be built for it.

In November 2025, we introduced new triggers and workflows to Bitbucket Pipelines to help teams manage and scale complex CI/CD workflows. We later extended that foundation with additional event-based triggers for pipeline, deployment, and pull request events. We’re now extending that model with a new package-artifact-created trigger.

When we introduced native Pipelines authentication for Bitbucket Packages, we made it easier to publish artifacts from CI/CD without relying on personal credentials. Now we’re extending that integration further: package artifacts published through the Pipelines integration can display a Source Pipeline, making it easy to trace an artifact back to the pipeline run that created it.