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We’ve previously written about how deeply embedded data is in people’s day-to-day work at incident.io, and I’d have it no other way — demand for data is undoubtedly a good thing. What risks breaking at scale, however, is everything downstream of that demand: data-team capacity gets stretched thin, dashboard sprawl outpaces anyone's ability to maintain it, and stakeholders can't reach an answer without going through the data team.

Migration projects fail in a predictable sequence. The technical work gets scoped. The timeline gets set. The engineering team starts moving workloads. Somewhere in the middle, dependencies surface that weren't in the original assessment, the double-run period extends beyond the budget allocated for it, and the project either stalls or completes at significantly higher cost than planned.

Bring network provisioning into your workflow with Megaport CLI, built for repeatable changes from the terminal, scripts, and CI/CD pipelines. When everything runs through a pipeline, changes are reviewable, repeatable, and consistent from staging to production. Your application code ships that way. Your cloud resources provision that way. Then you need a Megaport connection. So you open the Portal, and step outside the pipeline.

Every so often we sit down with someone from our support team and turn their week into a blog post. First up: Roberto Vizcarra, on four things generating tickets lately, AI credits, student plans, integrations, and Mac performance. Here’s what changed and what to do about it.

As organizations ship software faster than ever, runtime behavior changes are becoming just as frequent as code releases. Teams need a way to update application behavior without waiting for code deployments while maintaining visibility, governance, and control. ‍ Now available in beta, Config Management provides a governed runtime control plane that separates runtime configuration from application deployments, enabling organizations to deliver configuration changes instantly across environments.

Harness shipped 71 features in July, about one every 10 hours. That's more than June's 62, and the surge lines up with what AI is doing to the rest of the SDLC: coding agents are writing more of the code, test agents are now generating and running more of the tests by default, and every stage downstream: deployment, security, cost, and resilience has to absorb that pace without falling over.

Hundreds of files vanished from SharePoint. How to tell ransomware or a wipe from a user re-filing a folder, using the signals in the audit log that actually separate them.

AI cost reduction means lowering what you spend to run AI (tokens, inference, and compute) without sacrificing quality. The highest-leverage tactics, prompt caching, batching, and routing easy work to smaller models, cut spend 50 to 90% by removing waste, not capability. Somewhere right now, a finance leader is opening an AI bill that has quietly tripled, with no new product to show for it. Nobody approved it. No single decision caused it.

If your team builds with AI, someone is defaulting to the biggest model available (say, Fable) because it feels like the safe pick, and the safe pick is almost always the most expensive one. One over-powered choice looks harmless on its own, but multiplied across every prompt, agent, and workflow, and you get a big number on the P&L. All that, yet nobody chose which model on purpose. As we like to say, using a default is not a decision.