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The latest News and Information on Containers, Kubernetes, Docker and related technologies.

Improve Kubernetes reliability faster with Gremlin and Dynatrace

It’s now easier than ever to start testing Kubernetes with Dynatrace and Gremlin. With a new strategic integration, Kubernetes services set up in Dynatrace are automatically discovered in Gremlin to make testing set up simple and fast. At a time when AI is driving massive expansions in infrastructure and dramatically increasing deployment speed, being able to set up and test new services quickly is more important than ever. ‍

What Happens When You Mix AI With Docker?

Discover how Docker is empowering developers in the GenAI era with tools that simplify AI application development. Docker VP of Product Michael Donovan shares how containers are critical for building, testing, and scaling GenAI applications, plus real solutions for the biggest challenges developers face today.

CloudZero: Making Kubernetes Costs Transparent And Actionable

Kubernetes is now the backbone of modern software infrastructure, helping teams deploy, scale, and manage applications efficiently across clouds. But when it comes to understanding costs, Kubernetes remains opaque. Teams often can’t answer basic questions like: How do you solve the gap between engineering usage and financial visibility? CloudZero’s new Kubernetes capabilities are built to address this challenge.

Cloud Credits: The Hidden Lock-In Strategy Hyperscalers Use

In this 5-minute clip from our recent webinar, Canopy's James Marks exposes the most dangerous side-effect of the cloud credit model: the migration loop. Instead of building their product, companies spend months hopping between vendors to chase new credits, falling into a cycle of constant, costly re-architecting. Simon Hansford provides clear advice for the best companies: build your architecture for portability on day one. Restrict proprietary features to maintain optionality and avoid the "entrenched phase.".

What's New in Calico - Fall 2025 Release

As organizations scale Kubernetes and hybrid infrastructures, many are realizing that more tools don’t mean better security. A recent Microsoft report found that organizations with 16+ point solutions see 2.8x more data security incidents than those with fewer tools. Yet platform teams are still expected to deliver resilience and performance across containers, VMs, and bare metal, often while juggling fragmented tools that introduce risk, downtime, and complexity.

Autonomous Self-Healing Capabilities for Cloud-Native Infrastructure and Operations

Modern cloud-native infrastructure was adopted to increase agility and scale, but as it grows in scale and complexity, engineering teams are now drowning in operational noise. Industry research (The State of Observability for 2024) reveals that 88% of technology leaders report rising stack complexity, while 81% say manual troubleshooting actively detracts from innovation.

Deploying Dgraph Clusters to Cycle

One of the best parts of my job is helping Cycle users explore self-hosting options on the platform. This time, I had the pleasure of working with Dgraph (now a part of Hypermode). If you haven't heard of it, Dgraph is a distributed, horizontally scalable graph database that gives you a native graph storage/compute engine with distributed ACID transactions (via Raft and snapshot isolation) and first-class GraphQL.

Building smarter with AI: Why legacy infrastructure is the biggest bottleneck

Josh Mesout (Chief Innovation Officer at Civo) took the main stage at Civo Navigate London 2025 to deliver a critical message: The AI revolution isn't just coming, it's here, and the way companies are built is changing faster than ever before. His session cut through the hype, delivering hard data on what separates the companies that scale AI from the ones that sink money into failed prototypes. The takeaway is blunt: The biggest threat to your AI ambition isn't the model; it’s your infrastructure.

Catch and remediate ECS issues faster with default monitors and the ECS Explorer

Organizations that run applications on Amazon Elastic Container Service (Amazon ECS) often juggle signals across container and task metrics, logs, and events while they hunt for the change or condition that broke a deployment. This work adds operational overhead and extends incident timelines as teams switch between tools and manually correlate symptoms.