Operations | Monitoring | ITSM | DevOps | Cloud

"There are only two hard things in Computer Science: cache invalidation and naming things."—Phil Karlton In the age of increasingly frequent deploys, edge caching, and Jamstack adoption, caching plays a key role across the software delivery life cycle. In build and CI pipelines, caching compiled assets or dependencies helps reduce compute costs, speed up job runtimes, and lower the environmental impact (regarding energy usage) of repeated builds.

From optimizing supply chains to personalizing customer experiences, artificial intelligence and machine learning models are no longer statistics-based revenue initiatives; they’re foundational to modern business strategy. Organizations are pouring resources into developing and deploying AI, driven by the promise of unprecedented efficiency, insight, and competitive advantage. Yet, beneath this surging wave of innovation lies a growing tension: the Innovation vs. Control Syndrome.

Many, many years ago, I was a computer science major at the University of Illinois, hoping someday I’d be able to write code for a living. I started my career in QA hoping to learn the ins and outs of software development. But it turns out I wasn’t very good at coding. I was just good enough to get a role as a sales engineer, where all I had to do was write code that could hold together for 30 minutes in a demo.

Cloud services have become an indispensable resource for businesses seeking agility, scalability, and innovation. However, with this increased reliance on the cloud comes the challenge of managing and optimizing costs effectively. For Chief Financial Officers (CFOs), understanding and tracking cloud cost metrics is crucial to maintaining financial health and ensuring strategic investments yield the desired returns.

Self-hosted or fully managed? Learn how mid-size engineering teams can pick the right hosting strategy for control, cost, and scalability.