Operations | Monitoring | ITSM | DevOps | Cloud

Modern IT operations rely heavily on monitoring solutions like System Center Operations Manager (SCOM) to detect issues across servers, applications, and services. While SCOM excels at generating alerts, organizations often struggle to ensure these alerts translate into actionable incidents in their IT Service Management (ITSM) platforms. Without proper integration, critical alerts may be missed, tickets may be created manually, and incident resolution can be delayed.

Many cloud providers offer services that scale with usage. However, unanticipated overutilization of compute instances, serverless functions, or managed databases can quickly drive up costs. Managing these resources effectively is crucial for keeping cloud spending predictable.

Everybody knows working with Kubernetes is really hard. It's highly complicated. You have to know how to work with YAMLs, there's lots of stuff to deal with. The classic developer experience with YAML. But what if you could get complete visibility into your Kubernetes workloads and run realistic load tests without touching a single YAML file or running kubectl commands? In this walkthrough, I'll show you how Speedscale makes Kubernetes observability and performance testing as simple as point-and-click.

If you're reading this blog, you likely already know the importance of quality software. But with the overwhelming number of metrics that can be monitored and improved, development teams are struggling with what metrics they should prioritize to have the most significant impact. The Crash Free Users score in Raygun is a perfect place for development teams who care about software quality to focus their efforts. It tells you what percentage of users didn't encounter a crash or error while using your software and is an ideal north star to gauge the overall quality of your software.

Your monitoring stack catches server failures, network issues, and application crashes. But what happens when Microsoft Teams goes down across half your client base at 3 AM? Your on-call tech gets bombarded with alerts that all trace back to one root cause they can't see. This is the MSP blind spot: third-party SaaS dependencies that sit outside your monitoring perimeter but directly impact your SLAs.

Access to data is critical for SaaS companies to understand the state of their applications, and how that state affects customer experience. However, most companies use multiple applications, all of which generate their own independent data. This leads to data silos, or a group of raw data that is accessible to one stakeholder or department and not another. Data silos also prevent information from different sources from being blended together to gain a more accurate picture of what's happening in your application.

The question "what do you use for AI agent infrastructure?" has become one of the most searched queries in the DevOps and platform engineering space. And for good reason: the global AI agent market is projected to grow from $5.1 billion in 2024 to $47.1 billion by 2030, representing a compound annual growth rate of nearly 45%. With 85% of enterprises expected to implement AI agents by the end of 2025, getting the infrastructure right has never been more critical.

Organizations transitioning from traditional on-premises SAP systems to Cloud ERP (formerly known as "RISE" and "GROW") have a new set of monitoring challenges. Unlike the familiar on-prem landscape, where IT teams enjoyed full visibility and control, cloud environments can feel like a "black box," with limited direct access to the underlying infrastructure and reliance on service tickets to understand system status.