Operations | Monitoring | ITSM | DevOps | Cloud

Sources: G2 reviews, vendor documentation, 2026 market data. Docker's release in 2013 made Linux namespaces and cgroups accessible without deep kernel expertise, and container adoption took off fast. The value was clear: one portable unit with everything the process needs, running consistently across any host. Teams that were previously shipping VMs with bundled OS, runtime, and application code finally had a better option, and they took it.

Platform engineering is rapidly evolving as businesses look for more efficient ways to manage infrastructure, automate workflows, and improve developer productivity. In this edition, we’ll explore the top 10 platform engineering platforms for March 2026, optimized for scalability, automation, and ease of use. These platforms empower developers to focus on building code while platform engineers handle infrastructure with reduced complexity.

Today, we announce the release of HAProxy Fusion 2.0. This release marks a generational leap for the authoritative control plane that orchestrates HAProxy Enterprise’s high-performance application delivery and security. With a combination of new headliner features, structural changes, and improvements to the performance of the underlying API, HAProxy Fusion has jumped from version 1.3 to version 2.0.

Back in May of 2021, containers had already won. Kubernetes adoption was surging. ECS and EKS were powerful. But for many teams, deploying a simple containerized web service still meant stitching together clusters, networking rules, scaling policies, load balancers, IAM roles, and CI/CD pipelines. It felt heavier than it should. Developers no longer wanted more orchestration power. They wanted less operational drag.

This is part 1 of a five-part series on building production-grade AI engineering systems. Across this series, we will cover: Most teams experimenting with AI coding agents focus on prompts. That is the wrong starting point. Before you optimize how an agent thinks, you must standardize what it sees. AI agents do not primarily fail because of reasoning limits. They fail because of environmental ambiguity.