Operations | Monitoring | ITSM | DevOps | Cloud

Finance and engineering both thrive on efficiency. So when companies realize they’re wasting cloud spend, but aren’t sure where or how, both teams become frustrated. It’s remarkable how common this scenario has become, particularly over the past five years. Before the pandemic, Gartner reported that companies wasted over $14 billion on cloud services. During the cloud adoption surge in 2020, IDG found that 70% of US businesses overspent by as much as 62%.

Every millisecond matters when users interact with your PHP application. If a page lags or a request takes too long, most people will leave without a second thought. For DevOps teams, these slowdowns are frustrating because the root cause is rarely obvious. Developers are left combing through logs and traces, often realizing too late that poor response times are already hurting user trust and business outcomes. The pain point: slow PHP response times frustrate users and create hidden costs for teams.

Serverless computing has revolutionized the way applications are built and deployed by eliminating infrastructure management and enabling automatic scaling. However, the dynamic and distributed nature of serverless architectures presents unique monitoring challenges that can impact application performance and user experience.

In our digitally transforming world, the pace of change is relentless. Businesses are tasked with managing increasingly complex hybrid environments, from core mainframes to dynamic cloud services. The pressure is on, not only to keep the lights on, but to innovate faster, deliver flawless services, and fuel business growth. In this high-stakes environment, service orchestration and automation platforms are no longer just a tool—they are the central nervous system of the modern enterprise.

Organizations are under immense pressure to do more with less – streamline operations, reduce costs, all whilst improving both the outcomes of the business and their employees. For IT and end-user computing (EUC) professionals, this challenge is especially prevalent. Systems are becoming increasingly complex, the digital employee experience is now directly tied to customer satisfaction, and the role of technology teams extends much further than solely keeping the lights on.

When you first start coding in Go, you quickly learn how error handling in the language differs from error handling in languages such as Java, Python, JavaScript, or Ruby. In those languages, throwing an exception automatically generates a stack trace. Go, by contrast, provides no built-in error tracing to reveal an error’s origin.

When multiple teams use Grafana, it can start to feel a bit messy. Dashboards pile up, permissions become unclear, and teams accidentally overwrite each other’s work. To help you and your organization stay clear, collaborative, and secure, we recommend putting all users in a single Grafana Cloud stack and managing access with teams, roles, and folders. To illustrate this, I’ll share a hypothetical example of how you can put this into practice across three teams. Let’s dive in!

If you are leading technology innovation in your organization, you know the relentless surge of machine data is rewriting the rules of the digital enterprise. The upside? Incredible opportunities for AI-driven transformation. The challenge? Unprecedented complexity. Today’s leaders are under enormous pressure to unify, analyze, and act on a deluge of data streams across multiple environments.