Operations | Monitoring | ITSM | DevOps | Cloud

There usually isn’t a hard and fast rule about who should be on-call. Teams often look for criteria like seniority, experience, or expertise. While those factors certainly help, they might matter less than you think. It is often more useful to look at whether your processes are ready. When incident responses rely on memory and intuition rather than documentation, even experienced engineers can struggle. They might handle things through internal knowledge that isn’t available to everyone else.

Megaport On-ramp as a Service is the fastest way for service providers to offer secure, private connectivity to mission-critical applications, and it’s now available. Considered the gold standard, private connectivity is the superior way to reach enterprise applications, storage services, or security services. But for most service providers, offering a private on-ramp for customers to connect to isn’t straightforward.

In 2025, Oracle shocked the market. Its cloud growth was so aggressive that Oracle’s stock surged, briefly making founder Larry Ellison the world’s richest person. That didn’t happen by accident. Oracle closed fiscal 2025 with $57.4 billion in revenue, mainly driven by cloud services. Oracle Cloud Infrastructure (OCI) grew roughly 50% year over year, driven by enterprise databases, AI workloads, and network-intensive applications migrating from more expensive platforms.

Right now, 48% of organizations say they’re being asked to measure or report on AI-related costs. The problem is that they’re still figuring out how to do it. That was a very telling stat from a recent CloudZero webinar on AI and profitability, and speaks loudly to the reality that many organizations are still struggling to get a grasp on AI spend which our data shows to be rising sharply as a part of total spend in recent months.

Test coverage is one of those metrics everyone agrees matters until it’s time to actually write the tests. Between shipping features, fixing bugs, and handling production issues, writing comprehensive tests for edge cases and error paths often falls to the bottom of the backlog. The result is coverage gaps that accumulate technical debt and leave your codebase vulnerable to regressions. As AI-powered development tools reshape how we write code, the volume and velocity of changes is accelerating.

Bugs hide in plain sight. A date validator that rejects February 29th on leap years. An edge case that slips through code review. A flaky test that passes locally but fails in CI. These issues erode trust in your codebase and waste hours of debugging time. In the era of AI-assisted development, code is being written faster than ever. But speed creates risk.