Operations | Monitoring | ITSM | DevOps | Cloud

In our previous post, we addressed the most common questions platform teams are asking as they prepare for the retirement of the NGINX Ingress Controller. With the March 2026 deadline fast approaching, this guide provides a hands-on, step-by-step walkthrough for migrating to the Kubernetes Gateway API using Calico Ingress Gateway. You will learn how to translate NGINX annotations into HTTPRoute rules, run both models side by side, and safely cut over live traffic.

You might expect an AI-SRE agent to target 100% reliable services, ones that never fail. It turns out that past a certain point, however, increasing reliability is worse for a service (and its users) rather than better! Extreme reliability comes at a non-linear cost: maximizing stability limits how fast new features can be developed, dramatically increases the operational cost, and reduces the features a team can afford to offer.

As Large Language Models (LLMs) evolve from simple chatbots into agentic workflows, the need for a standardized way to connect them to external data and infrastructure has become critical. In a recent workshop hosted by Nir Adler, Innovation Engineer at Komodor, we explored how to bridge this gap using the Model Context Protocol (MCP).

When I joined Kubex last year, the company was already well aware of the growing power of Large Language Models. As a company focused on intelligent resource optimization for Kubernetes, GPUs, and cloud infrastructure, generative AI didn’t feel like a threat so much as a natural extension of where the industry was heading. Kubex had already invested heavily in machine learning, but it was becoming clear that foundation models could unlock an entirely new class of capabilities for our customers.

The promise of Artificial Intelligence in Site Reliability Engineering (SRE) is seductive: an autonomous system that never sleeps, instantly detects anomalies, and fixes broken infrastructure while humans focus on high-value work. However, the gap between a demo-ready chatbot and a production-grade Autonomous AI SRE is vast. In complex, noisy environments like Kubernetes, a “naive” implementation of Large Language Models (LLMs) is not just ineffective, it can be dangerous.

The BCG recently released a report on the cost of cloud. The findings? Hyperscalers are charging up to 30% more for their sovereign-cloud offerings. It supports an earlier notion that if you want control, compliance, and jurisdictional certainty, you have to pay a premium. At Civo, we think that is broken. As data volumes grow and AI workloads become central to business strategy, the economics of cloud computing are being re-examined.

Kubernetes has become the default foundation for a lot of modern application infrastructure. It’s powerful, flexible, and widely supported, which makes it an obvious starting point for many teams building a cloud-native application platform (a standardized way for teams to deploy, run, secure, and operate applications in production). But there’s a distinction that often gets lost early in the decision process: Kubernetes is a framework. It is not a platform.