Operations | Monitoring | ITSM | DevOps | Cloud

Is the "Heroku Tax" draining your budget? Compare Heroku vs. Kubernetes in 2026. Learn how to solve complex orchestration challenges, like queue-based autoscaling and microservice sprawl, without the DevOps toil.

Is the EKS operational burden outweighing its benefits? Learn how to migrate from EKS to ECS, the technical trade-offs of AWS-native orchestration, and how to get ECS-level simplicity without losing Kubernetes power.

Welcome to January’s Cloud Economics Pulse, CloudZero’s monthly look at cloud spend as AI moves from vibe to prod. And this related news flash — AI spend keeps hitting new highs. pilots to production. In last month’s Pulse, we explored the compounding effect of AI becoming part of everyday cloud operations. This month, we see that pattern harden into year-end results.

API metrics have long been a core part of building and operating reliable SaaS products. Teams track the likes of request volume, latency, and uptime to ensure APIs perform as expected under load. First: API cost intelligence metrics measure how API usage translates into cloud, AI, and third-party spend — and attribute that cost to customers, features, workflows, and teams so SaaS businesses can protect margins as usage scales. But today, the API metrics that matter most go beyond performance.

We’re excited to announce the simultaneous releases of HAProxy Kubernetes Ingress Controller 3.2 and HAProxy Enterprise Kubernetes Ingress Controller 3.2! All new features described here apply to both products. These releases introduce user-defined annotations, a new frontend CRD, and other minor improvements, and we’ll cover these in detail below. Visit our documentation to view the full release notes.

In a previous article, we covered how easy it is to create Spring Boot containers with Rockcraft. So the next logical step is to deploy and operate your application in a production environment. The Juju ecosystem is the key to making this process straightforward. In this article we walk through the steps required to deploy a Spring Boot application to production using Juju and Kubernetes.

As a baseline, engineering leaders need their teams to adopt AI tools to speed up velocity and ship faster. Most organizations have already rolled out AI coding assistants or are evaluating them, but there's a really big difference between buying a tool and successfully scaling it across an engineering organization. If you layer AI on top of a chaotic codebase or a disorganized service catalog, you accelerate the creation of legacy code.

Tier 1 ticket automation is one of the most tempting (and, to be brutally honest, most mishandled) initiatives in IT service management. On paper, it seems simple: automate the high-volume requests, reduce handle time, and give your service desk some breathing room. In practice, though, many teams end up with brittle scripts and automations that quietly drift outside ITSM guardrails.

Integrating feature flag context into OpenTelemetry traces enhances observability by recording flag states as span attributes, making it easier to analyze how specific flags influence application behavior. When you toggle a feature flag, you're changing the behavior of your application; sometimes, in subtle ways that are hard to detect through logs or metrics alone. By adding feature flag attributes directly to spans, you can make these changes observable at the trace level.

Redis is a widely used in-memory data store, commonly deployed as a cache, session store, message broker, or fast key-value database. Because Redis often sits on the critical path of an application, having visibility into its behavior (memory usage, client connections, command throughput, cache efficiency) is essential for troubleshooting and performance tuning.