Operations | Monitoring | ITSM | DevOps | Cloud

Imaginary Test Data. Real Token Bill.

Ask an AI for K-pop concert advice without saying the group, city, date, or budget. It may confidently send you to a BLACKPINK tribute night in Cleveland with a $400 resale ticket. The AI was plenty confident. It just had nothing real to go on. That is exactly what happens when developers test AI applications with invented traffic. The test may look reasonable. The result may even pass.

Migrating Workloads and Performance Issues in Public Cloud

When on-premises capacity runs short, public cloud tends to be the first option infrastructure teams reach for. It is quick to provision, removes the hardware procurement problem, and sidesteps the question of what to do with an ageing estate. What it does not settle is whether migrated workloads will perform as the business requires once they are live in production, or whether the recovery design has kept pace with where services now sit.

How To Build An MSP Team That Truly Relies On Data-Driven Decision-Making

Managed Service Providers (MSPs) aren’t short on data. Most of them have dashboards, KPIs, and utilization reports running across multiple screens. But having data and using it to make better decisions are two different things, and the gap between them is wider than you might think, with one study finding that only 32% of companies effectively use data to drive business value. For the other 68%, the numbers exist, they just… don’t do much.

Why AI Governance Is Now the Biggest Challenge for Enterprise AI

For the past couple of years, most conversations about AI in business have centered on adoption. Which tools should organizations deploy? Where can AI improve productivity? How quickly can it deliver value? Those questions still matter, but they are no longer the most difficult ones to answer. AI is now appearing across business applications, employee workflows, development environments, and customer-facing services.

The Near-Term Wins in AI for NetOps Rest on the Same Foundation

Walk into a network operations center this year and the useful AI is not running the place. It is doing three specific jobs, and doing them well: cutting an alert storm down to the one incident that matters, pointing at the likely cause, and deciding what deserves a human’s attention first. That is where AI in NetOps pays for itself right now. The part worth noticing is that all three jobs lean on the same thing.

Power BI vs. SquaredUp: Which is right for IT teams?

At first glance, Power BI and SquaredUp both look like dashboarding tools. In practice, they solve different problems. SquaredUp serves engineering teams and IT teams, including SREs, DevOps engineers, engineering managers, and technology leaders. It connects to monitoring, cloud, DevOps, and ITSM platforms. This gives teams a real-time insights into service health, performance, and key metrics in one place. Power BI is Microsoft's business intelligence platform, designed for analysts and business teams.

Why Model Routing Backfires and How to Build Agents That Don't Burn Your Budget

Model routing promises to cut your AI agent spend by offloading routine tasks to cheaper models like Claude Haiku while reserving frontier models like Claude Sonnet for complex reasoning. In the right configuration, routing strategies can reduce inference costs by 40–85%. But if you implement routing incorrectly in a multi-turn agent, you can end up paying more than if you’d never routed at all. Here’s why and how to fix it.

Send DNS Spy Alerts to Your SIEM: Introducing Webhook Alerts

Enterprise teams can now add a Webhook alert channel that POSTs every DNS Spy alert — DNS record changes, domain outages, security check failures, WHOIS updates, phishing look-alike detections — to any HTTPS endpoint as structured JSON. Requests are optionally HMAC-signed, every delivery is logged and retried, and a Send Test button verifies your integration end to end.

Don't Trust the Diff: Making AI-Generated Code Reviewable And Maintainable

Coding agents changed implementation economics faster than they changed confidence. They let us produce more code, more quickly, but they did not make reviewers any better at understanding system-wide consequences. In our Kubernetes automation stack, that gap became impossible to ignore once AI started generating meaningful amounts of controller code.