Operations | Monitoring | ITSM | DevOps | Cloud

How to install Kubernetes using OpenShift's CLI | Site24x7

Running Kubernetes on Red Hat OpenShift adds powerful enterprise capabilities—but also introduces operator-driven workloads, stricter RBAC and SCC policies, and platform-specific complexity. In this video, learn how Site24x7 enables platform-aware monitoring for OpenShift environments, helping DevOps and platform teams gain complete visibility without blind spots.

Site24x7 Free Training Day 2: Infrastructure monitoring, custom plugins, cloud cost management

This session covers everything you need to know about infrastructure monitoring, starting from agent-based server monitoring to database monitoring, container monitoring (Docker & Kubernetes), multi-cloud monitoring (AWS, Azure, GCP, OCI), IT automation, custom plugins, and ManageEngine CloudSpend for cloud cost management. If you're looking to master full-stack infrastructure observability, this hands-on walkthrough shows you exactly how to set up and use each Site24x7 module inside the live console.

Product leaders talk safer, faster releases and deeper analysis with Bits | This Month in Datadog

In July’s This Month in Datadog, Jeremy is joined by Datadog product leaders for in-depth conversations about how Bits enables you to confidently evaluate and release features containing AI-generated code, and use natural language to ask, understand, and act across Datadog.

Shipped: Put every AI task on the cheapest model that can actually do 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.

AI cost reduction: tactics that preserve performance

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.