Operations | Monitoring | ITSM | DevOps | Cloud

Westminster is starting to recognise the importance of technology sovereignty. The recent Westminster Hall debate on technology sovereignty was encouraging to see. For those of us working in the UK technology sector, it felt like an important moment. Conversations about cloud infrastructure, data control and platform dependency have been happening inside the industry for years.

In this series, we’ve explored Karpenter’s architecture, the key metrics that reflect its health and performance, and the vendor-agnostic tools for collecting and analyzing its telemetry data. In this final post, we’ll show you how Datadog helps you monitor and alert on Karpenter alongside your Kubernetes cluster and the infrastructure that runs it.

Enterprise private cloud procurement tends to generate impressive security documentation. SOC 2 reports, penetration test summaries, ISO 27001 certificates, detailed descriptions of network segmentation and encryption standards. What it doesn't always generate is clarity on the question that actually matters: does this infrastructure make it possible to operate securely at the level your organization requires, given your specific workloads, your regulatory context, and your threat model?

Karpenter is a fast, flexible Kubernetes autoscaler designed to improve cluster performance and cost efficiency. When the cluster doesn’t have capacity to schedule a pod, Karpenter requests additional compute from the cloud provider, specifying a right-sized instance that matches the preferences you’ve set (for example, instance family).

In Part 1 of this series, we explored how Karpenter’s architecture enables just-in-time provisioning and active node consolidation. Because Karpenter is constantly making infrastructure decisions based on real-time scheduling pressure, its metrics can give you early warning of provisioning slowdowns, cloud API throttling, and misconfigurations that prevent it from scaling the way you expect.

In the first two parts of this series, we explored how Karpenter’s architecture enables just-in-time provisioning and active node consolidation, and we identified the key Karpenter metrics you should track to keep your cluster performant and cost-efficient. In this post, we’ll look at vendor-agnostic tools you can use to capture these signals.

Accessing cloud infrastructure shouldn’t slow down product innovation. Yet for many engineering teams building AI-driven platforms, traditional hyperscalers often introduce unnecessary complexity, high costs, and slow provisioning cycles. At Civo, we’ve seen a different approach emerge. Our cloud platform enables teams to move faster with Kubernetes, compute, and networking designed for simplicity and speed.

Since the rise of Large Language Models (LLMs) like GPT-3 and GPT-4, organizations have been rapidly adopting Agentic AI to automate and enhance their workflows. Agentic AI refers to AI systems that act autonomously, perceiving their environment, making decisions, and taking actions based on that information rather than just reacting to direct human input.