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Kentik Kube Beta Released

Kentik announced the availability of Kentik Kube beta, a solution to reveal how K8s traffic routes through organizations’ data centers, cloud, and the internet.

Kentik Kube gives cloud and infrastructure engineers detailed network traffic and performance visibility both inside and among their Kubernetes clusters — so they can quickly detect and solve network problems, and surface traffic costs from pods to external services.

This allows them to:

- Discover which services and pods are experiencing network delays

- Ensure pod, node and namespace communication patterns adhere to policy

- Know exactly who was talking to which pod, and when

- Identify service misconfigurations without the need to capture packets

- Identify all clients and requesters consuming your Kubernetes services

“Enterprise applications have ridiculously complex and hybrid infrastructures,” says Avi Freedman, Co-founder and CEO of Kentik. “As network and platform engineers run critical applications and infrastructure, Kentik Kube shows performance and connectivity within the context of their entire network and internet infrastructure.”

Pods and services often experience network delays or errors that degrade the digital experience, and it’s difficult to identify them quickly. With the inherent complexity of microservices; network, cloud and infrastructure teams are left wondering if the network reality matches their design, who are the top requesters consuming Kubernetes services or which microservices are oversubscribed, and how the infrastructure is communicating both with itself and across the internet.

Kentik Kube relies on data generated from a lightweight eBPF agent installed on Kubernetes clusters. It sends data back to the Kentik SaaS platform, allowing teams to query, graph, and alert on their infrastructure. With this new data, coupled with Kentik’s advanced analytics engine, these teams can move faster, reduce MTTI and MTTR and answer critical questions about the health and performance of their network.

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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 ...

Kentik Kube Beta Released

Kentik announced the availability of Kentik Kube beta, a solution to reveal how K8s traffic routes through organizations’ data centers, cloud, and the internet.

Kentik Kube gives cloud and infrastructure engineers detailed network traffic and performance visibility both inside and among their Kubernetes clusters — so they can quickly detect and solve network problems, and surface traffic costs from pods to external services.

This allows them to:

- Discover which services and pods are experiencing network delays

- Ensure pod, node and namespace communication patterns adhere to policy

- Know exactly who was talking to which pod, and when

- Identify service misconfigurations without the need to capture packets

- Identify all clients and requesters consuming your Kubernetes services

“Enterprise applications have ridiculously complex and hybrid infrastructures,” says Avi Freedman, Co-founder and CEO of Kentik. “As network and platform engineers run critical applications and infrastructure, Kentik Kube shows performance and connectivity within the context of their entire network and internet infrastructure.”

Pods and services often experience network delays or errors that degrade the digital experience, and it’s difficult to identify them quickly. With the inherent complexity of microservices; network, cloud and infrastructure teams are left wondering if the network reality matches their design, who are the top requesters consuming Kubernetes services or which microservices are oversubscribed, and how the infrastructure is communicating both with itself and across the internet.

Kentik Kube relies on data generated from a lightweight eBPF agent installed on Kubernetes clusters. It sends data back to the Kentik SaaS platform, allowing teams to query, graph, and alert on their infrastructure. With this new data, coupled with Kentik’s advanced analytics engine, these teams can move faster, reduce MTTI and MTTR and answer critical questions about the health and performance of their network.

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 ...