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Zenoss Launches Advanced Monitoring Capabilities for Kubernetes

Zenoss released expanded monitoring capabilities for Kubernetes, the most widely deployed open-source orchestration platform used to manage container technology across cloud environments.

Initially released in 2014, the platform evolved from Google’s code used to manage its data centers and was later donated to the Cloud Native Computing Foundation.

Kubernetes, also known as K8s, has been supported by a community of professional programmers and coders from around the world. Along with containers more generally, Kubernetes has emerged as a primary technology for modern cloud-native workloads. Accordingly, almost 50% of organizations have adopted Kubernetes.

Zenoss monitoring for Kubernetes now provides:

- Overall cluster health monitoring

- Health monitoring for nodes, services and pods

- Dashboards for Kubernetes clusters, nodes, pods and containers

- Service impact and root-cause analysis

- Monitoring of StatefulSet component, enhancing management of stateful applications

- Enhanced filtering for pods and containers

- Enhanced templates for clusters, containers and nodes

- Enhanced dynamic modeling of pods and containers

- Enhanced visibility for controlling cloud expenses

Zenoss initially released monitoring and analytics capabilities for Kubernetes in 2018 and has continuously expanded those capabilities to become a leading monitoring platform for container-based environments. Zenoss provides full-stack monitoring and AIOps for public and private clouds, as well as for all on-prem IT infrastructure.

"Cloud-native environments create new challenges for monitoring highly distributed applications due to the unprecedented complexity and scale," said Ani Gujrathi, CTO for Zenoss. “The solution requires modernizing the approach to monitoring, and that’s exactly what we’ve done.”

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For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

Zenoss Launches Advanced Monitoring Capabilities for Kubernetes

Zenoss released expanded monitoring capabilities for Kubernetes, the most widely deployed open-source orchestration platform used to manage container technology across cloud environments.

Initially released in 2014, the platform evolved from Google’s code used to manage its data centers and was later donated to the Cloud Native Computing Foundation.

Kubernetes, also known as K8s, has been supported by a community of professional programmers and coders from around the world. Along with containers more generally, Kubernetes has emerged as a primary technology for modern cloud-native workloads. Accordingly, almost 50% of organizations have adopted Kubernetes.

Zenoss monitoring for Kubernetes now provides:

- Overall cluster health monitoring

- Health monitoring for nodes, services and pods

- Dashboards for Kubernetes clusters, nodes, pods and containers

- Service impact and root-cause analysis

- Monitoring of StatefulSet component, enhancing management of stateful applications

- Enhanced filtering for pods and containers

- Enhanced templates for clusters, containers and nodes

- Enhanced dynamic modeling of pods and containers

- Enhanced visibility for controlling cloud expenses

Zenoss initially released monitoring and analytics capabilities for Kubernetes in 2018 and has continuously expanded those capabilities to become a leading monitoring platform for container-based environments. Zenoss provides full-stack monitoring and AIOps for public and private clouds, as well as for all on-prem IT infrastructure.

"Cloud-native environments create new challenges for monitoring highly distributed applications due to the unprecedented complexity and scale," said Ani Gujrathi, CTO for Zenoss. “The solution requires modernizing the approach to monitoring, and that’s exactly what we’ve done.”

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...