Operations | Monitoring | ITSM | DevOps | Cloud

Everybody knows working with Kubernetes is really hard. It's highly complicated. You have to know how to work with YAMLs, there's lots of stuff to deal with. The classic developer experience with YAML. But what if you could get complete visibility into your Kubernetes workloads and run realistic load tests without touching a single YAML file or running kubectl commands? In this walkthrough, I'll show you how Speedscale makes Kubernetes observability and performance testing as simple as point-and-click.

You're two weeks from launch. Development says they're done. QA signed off. Then you hand the system to actual users and watch everything fall apart. Buttons nobody clicks. Workflows nobody understands. Features that technically work but make zero sense in real life. That's what happens when you skip proper User Acceptance Testing planning. UAT isn't just the final testing phase. It's your last chance to catch the gap between what you built and what users actually need. Miss this step and you're fixing production issues while angry customers flood your support inbox.

The question "what do you use for AI agent infrastructure?" has become one of the most searched queries in the DevOps and platform engineering space. And for good reason: the global AI agent market is projected to grow from $5.1 billion in 2024 to $47.1 billion by 2030, representing a compound annual growth rate of nearly 45%. With 85% of enterprises expected to implement AI agents by the end of 2025, getting the infrastructure right has never been more critical.

Every time a new feature goes live, teams hold their breath. Web apps are updated faster than ever, and expectations keep rising. In fact, software bugs cost teams up to 25 times more to fix after release than during development, reinforcing how expensive missed issues can be for small teams trying to grow. Many teams push updates weekly or daily. At first, manual testing feels manageable.