Operations | Monitoring | ITSM | DevOps | Cloud

For most of the history of software engineering, the primary constraint was production. Code was expensive, skilled engineers were scarce, and shipping features required concentrated human effort. Velocity was limited by how fast people could reason, implement, test, and deploy. That constraint shaped everything from team size, architecture, release cadence, through to how we thought about technical debt. When production is expensive, you optimise for output. You remove friction from shipping.

In Part 1, we looked at how AI has reduced the cost of building monitoring tools. Then in Part 2, we explored the operational and economic burden of owning them. Now we need to talk about something deeper. Because the real shift isn’t just economic; it’s structural. AI isn’t just helping engineers write code faster. It’s accelerating the entire software ecosystem; including how monitoring tools are built, maintained, and trusted.

In Part 1, we explored how AI has dramatically reduced the cost of building monitoring tooling. That much is clear. You can scaffold uptime checks quickly, generate alert logic in minutes, and set-up dashboards faster than most teams used to schedule the kickoff meeting. So the barriers to entry have fallen. But there’s a quieter question that rarely gets asked in the excitement of building. Have you ever calculated what it would actually cost to replace your monitoring provider?

A few months ago, I spoke to an engineering manager who proudly told me they had rebuilt their monitoring stack over a long weekend. They’d used AI to scaffold synthetic checks. They’d generated alert logic with dynamic thresholds. They’d then wired everything into Slack and PagerDuty, and built a clean internal dashboard. “It used to take us weeks to prototype something like this,” they said. “Now it’s basically instant.” They weren’t wrong.