Operations | Monitoring | ITSM | DevOps | Cloud

We’ve enhanced our API for custom monitors with more powerful filtering options – giving you better control over how incidents are tracked and surfaced.

APIs power nearly every modern digital experience. From mobile apps and SaaS platforms to payment gateways and internal microservices, APIs handle authentication, transactions, content delivery, and system-to-system communication. When an API fails, users often experience broken features, slow responses, or complete service outages. In many cases, they leave before your team even realizes something is wrong. The business impact of API failures is significant.

APIs are no longer just integration layers. They power customer logins, payment processing, SaaS workflows, partner ecosystems, and mobile applications. When an API becomes unavailable, revenue stops, user trust declines, and service level agreements are immediately at risk. Yet many teams still define API availability in the simplest possible way. If an endpoint responds with a 200 OK, the API is considered available. Monitoring dashboards stay green. Alerts remain silent. Everything appears healthy.

Ever had a load balancer become the bottleneck in an on-prem Kubernetes cluster? You are not alone. Traditional hardware load balancers add cost, create coordination overhead, and can make scaling painful. A Kubernetes-native approach can overcome many of those challenges by pushing load balancing into the cluster data plane.

When an incident triggers, the question is not just what broke but also how urgent it is and who on your team needs to respond. Alert Routing rules answer those questions automatically. You define the conditions once and the right response follows every time an incident triggers. Every Alert Routing rule does one or more of these three things: Three conditions drive all of it: incident payload, time of occurrence, and frequency.

The disconnect between enterprise confidence and AI capability is real. MIT reports fewer than 5% of enterprises have achieved measurable ROI from AI, yet Cisco claims 13% feel ready. The gap isn’t about AI technology—it’s about organizational rigidity and change management. More importantly, most studies focus on business intelligence rather than operational use cases, which are far less risky and more measurable.

As organizations shift away from traditional container engines to embrace Podman’s rootless and daemon-less design, visibility often becomes a challenge. Because Podman doesn't rely on a central background service, traditional monitoring tools can leave you in the dark. Applications Manager's new Podman monitoring feature bridges that gap, giving you total visibility into your Podman workloads without compromising the security model you worked so hard to build.

Most teams know they are spending money on AI in production. Far fewer realize how much they are spending outside production. It’s easy to get lost as you evaluate which model has the best responses, is fast enough, and cheap enough to run in production. That is because the AI bill usually shows up as a giant blob. It is easy to see the total.