Operations | Monitoring | ITSM | DevOps | Cloud

Cloud environments generate high volumes of security signals every day. With each one, you have to determine if it’s benign, a clear false positive, or something worth investigating. The challenge is needing to make these calls continuously, often without knowing whether any single event is part of a larger attack. Spending too much time investigating benign activity reduces the ability to detect threats elsewhere, and missing a legitimate threat has clear consequences.

Modern organizations often operate from multiple locations. From retail stores to global enterprises, many companies rely on distributed wired and wireless networks to keep business-critical applications online. Aruba Central provides a centralized, cloud-based platform for managing that infrastructure at scale.

Load balancers are the primary entry points to distributed applications. By strategically directing the flow of incoming web traffic to specific endpoints, load balancers help optimize throughput and ensure the horizontal scalability of applications. In modern systems, load balancers often do more than their name suggests: Beyond basic load distribution, they analyze requests and route traffic based on a wide range of variables, such as client identity.

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.

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.

As tools such as AI agents become more integrated with the instrumentation, governance, and centralization of product analytics data, product managers (PMs) still own the meaning of those events and the connected outcomes. Knowing when to trust the data, forming strong hypotheses, and being able to act on the insights requires an expert in the loop.