Kentik announced the general availability of Kentik Kube, designed to reveal how Kubernetes traffic routes through organizations’ data centers, clouds, and the internet.
Kentik Kube gives network, 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, surface anomalies and compliance issues, and identify outliers related to network traffic costs.
Key benefits of Kentik Kube:
- Ensure Kubernetes performance: Discover which services and pods are experiencing network delays in order to troubleshoot and fix problems faster. Configure alert policies to proactively find high latency nodes, pods, workloads, or services.
- Optimize costs: Quickly detect traffic changes tied to new deployments or misconfigurations before egress, inter-region transfer, and gateway charges get out of control.
- Total Infrastructure Visibility: Know which pods were deployed on which nodes — even historically. See which pods and services are communicating with other clusters, non-Kubernetes infrastructure, or the Internet. Quickly detect top talkers. Identify Kubernetes clusters sending traffic to embargoed countries or unapproved external destinations.
“With Kentik Kube, Kubernetes is no longer a black box for network teams,” said Christoph Pfister, Chief Product Officer at Kentik. “Now enterprise infrastructure engineers can quickly understand Kubernetes traffic – from transit costs, to performance problems, to embargoed communications – and see how it flows through the internet and their entire hybrid network infrastructure.”
Kentik Kube collects metadata across Kubernetes pods, clusters, and services – combined with telemetry from a lightweight eBPF agent – for unparalleled breadth and depth of network observability. This dataset, coupled with Kentik’s advanced analytics engine, allows infrastructure and platform teams to move faster, reduce incident resolution times, and answer critical questions about the health and performance of their overall network.
The Latest
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...
When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...