Operations | Monitoring | ITSM | DevOps | Cloud

Most production incidents in Google Cloud don't announce themselves as infrastructure problems. A checkout service on GKE starts timing out, a Cloud Function cold-starts under load, a Cloud SQL replica falls behind, and a Pub/Sub subscription quietly backs up until messages start expiring. None of that shows up as a red node in a compute dashboard. It shows up as slow requests, failed webhooks, and a support queue filling up faster than anyone can triage it.

A production service degrades, and one question decides the next hour: is it the server, the network, or a cloud dependency? Each layer usually reports into a separate console, so pinning down the answer can absorb an hour the business would rather not lose. The right infrastructure monitoring tool is what turns that hour into minutes. On paper, most monitoring platforms look identical. Each one promises full-stack visibility and shows a polished dashboard.

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.

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.

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.

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.

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.