Operations | Monitoring | ITSM | DevOps | Cloud

The Kubex UI is a model of analytic depth and flexibility. You can break down your environment’s utilization, risks, and optimization potential in many ways. You can view more than 100 different properties for each container and many different graphs showing historical performance and identified trends.

Kubernetes efficiency metrics now work with infrastructure filters.

For most IT leaders, multicloud didn't arrive as a decision. It arrived as a fait accompli. A team chose AWS for one workload. Azure came in through a Microsoft enterprise agreement. A SaaS acquisition brought its own cloud dependencies. A DR requirement pointed to a second region with a different provider. Nobody declared a multicloud strategy; the organization just became one. Today, 87% of organizations run a multicloud strategy, balancing an average of 2.6 public cloud providers simultaneously.

General cloud storage and photo storage for photographers are similar in the sense that photos are stored in the cloud, but professional photographers have different requirements when choosing the best cloud storage for photographers.

Application autoscaling has existed for years. Database autoscaling has lagged behind. Today, that changes on Upsun. Managed PostgreSQL and MariaDB read replicas now autoscale, joining worker autoscaling in production since March 2026. Apps, workers, and managed databases all autoscalead replicas saturate first, connection pools max, someone getse on one platform - natively, without a third-party database engine or a Kubernetes layer to operate. Three operational headaches just disappeared.

Ford's competitors had the same electric motors he did. Most just swapped out the steam engine and kept the old factory layout, a costly mistake. Ford used the new tech to rebuild the plant around the flow of the car. Knowing how much power each machine drew, he knew his cost to produce a car, and made personal automobiles affordable for all.

Cloud resilience often feels like an uphill battle. When you’re overseeing hundreds of applications across different providers, identifying potential failure points manually is nearly impossible. You’re left trying to find the needle in a haystack—a needle that could take down your entire application at any moment. To truly protect your uptime, you have to break the cycle of reactive troubleshooting.

AI cost observability is the practice of measuring, attributing, and analyzing AI workload costs at the request, model, and workflow level in real time. It connects cloud infrastructure spend, inference and token costs, and business attribution (cost per feature, team, customer, or product) so engineering, finance, and product teams can see where AI spend goes and whether it creates value. On July 14, IBM had its worst trading day since 1987.