Operations | Monitoring | ITSM | DevOps | Cloud

For more than two decades, monitoring has been the foundation of IT operations. Organizations invested heavily in tools designed to collect metrics, visualize performance, and trigger alerts when thresholds were breached. This model was effective in an era when infrastructure was largely static, workloads were predictable, and system dependencies were relatively easy to trace. That environment no longer exists.

Why digital experience is now a regulatory priority In regulated industries like financial services, even minor technology friction can quickly become a regulatory risk. Gaps in visibility, slow systems, and inconsistent performance can trigger audit findings, SLA breaches, and increased compliance scrutiny.

LogicMonitor delivers AI cost optimization by unifying infrastructure telemetry, AI-specific signals, and cloud financial data into a single workflow, so teams can move from visibility to continuous, operationalized cost control. In Cost Optimization for AI Workloads: From Visibility to Control, we explored why AI workloads introduce new layers of cost complexity—from GPU-heavy compute and token-based pricing to distributed infrastructure that obscures true spend.

ITOps teams can achieve cost management of AI workloads with an observability platform that connects AI usage and performance with cloud spend for clear visibility and predictability. Behind the buzz around artificial intelligence, or AI, many companies are discovering the hidden and compounding costs of AI adoption.

Platform engineering has emerged as a response to the growing complexity of modern software delivery. As organizations adopt Kubernetes, microservices, CI/CD pipelines, and infrastructure as code, they are creating dedicated teams responsible for building and operating the internal platforms that power developer workflows.

As organizations scale their services and adopt AI-assisted coding, code changes are landing faster and in greater volume than ever before. While this powerful new practice is accelerating the pace of development, it is also increasing the likelihood that untested code may slip into repositories without detection. What makes this problem even worse is that most teams have no reliable way to know which code is covered by tests.

Struggling with Kubernetes configuration sprawl and long deployment queues? Discover how to identify technical vs. workflow bottlenecks and why shifting to a self-service Kubernetes management platform like Qovery is the key to scaling your engineering velocity.

Using Kubernetes to manage containerized applications has its fair share of challenges. One of those challenges is managing complexity. Using namespaces can help minimize that complexity. Yet, a common misconception is that using multiple namespaces in a single Kubernetes cluster can degrade performance. Another issue: Kubernetes namespaces can reduce visibility into costs. There’s more to it than that.