Operations | Monitoring | ITSM | DevOps | Cloud

As the unsung hero of modern endpoint management, Microsoft Intune quietly ensures security, compliance, and seamless user experiences across a range of devices and platforms. Where many organisations go wrong, however, is not having the right tool to monitor and leverage Intune’s full potential. But for an organization relying on Intune, what tool should you use?

Setting up monitoring for your SaaS application is crucial for maintaining reliability and keeping customers happy. Without proper monitoring, you're essentially flying blind – unable to detect issues before they impact users or understand how your system performs under different conditions. Here are 10 essential tips to help you build a comprehensive monitoring strategy for your SaaS application.

Engineering excellence is more than code quality or tool choice. It’s about aligning engineering with business goals, improving systems systematically, and building a culture of continuous progress. In a recent episode of the Tech Lead Journal podcast, Cortex CTO and co-founder Ganesh Datta shared lessons from his engineering career.

When I spoke at AWS London earlier this year, I had the chance to discuss something that more and more teams are starting to feel: traditional observability doesn’t cut it for AI systems. In AI, “Is it running?” is no longer enough. We have to ask, “Is it right?” When I delivered that line, I saw the heads nodding. Everyone’s excited to build with LLMs, but when it comes to actually monitoring them in production? That’s where things fall apart.

ScienceLogic’s vision for IT operations isn’t just about improving tools—it’s about changing the entire paradigm, flipping your day-to-day upside down. We’re moving beyond dashboards and alerts, beyond human-only workflows and rules-based systems. The future is autonomous. It’s intelligent. It’s agentic. And it’s already being realized through the power of Skylar AI.

As organizations rely more on complex IT systems and cloud-based services, keeping everything running smoothly — and reliably — has become a top priority. That’s where IT service performance monitoring comes in, giving teams the visibility they need to make sure systems stay healthy and responsive. By tracking a range of technical and user-focused metrics, businesses can quickly identify and address issues before they impact operations or end users.

Cloud-native tools promise speed, scalability, and resilience. The catch is you have to pick the right ones and use them well. Without the right foundation, they can mean more complexity, hidden costs, and a false sense of control. In this guide, we’ll help you avoid that trap. From infrastructure to observability and CI/CD tools, we’ll cover the solutions shaping modern cloud stacks.

Many organizations don’t realize how important it is to monitor how their applications run in production. Without Application Performance Monitoring (APM), it becomes difficult to detect and resolve issues quickly, leading to increased downtime, wasted developer effort, and poor user experience. These hidden costs, though not always visible at first, can impact customer satisfaction, reduce team efficiency, and result in lost revenue.

The recent outage on June 12 proved yet again that service disruptions are inevitable, it’s not a matter of if, but when? And the next question is: how ready are you when that disruption strikes? What sets successful leaders apart is how quickly they are able to recover. Digital businesses are more complex than ever. Teams are managing sprawling cloud environments, microservices architectures, and a dizzying array of third-party integrations.

Some people can get an AI assistant to write a day’s worth of useful code in ten minutes. Others among us can only watch it crank out hundreds of lines of crap that never works. What’s the difference? There are some skills specific to AI development. There are also properties of the codebase we’re working in that make it amenable to AI assistance. Most AI demos use projects created from scratch with AI in mind—cute.