Operations | Monitoring | ITSM | DevOps | Cloud

The Digital Operational Resilience Act (DORA) requires financial institutions to maintain a comprehensive register of their critical ICT third-party providers. This register isn't just a compliance checkbox, it's a strategic tool for managing operational risk and ensuring business continuity. Building an effective DORA register requires systematic identification, classification, and continuous monitoring of your SaaS dependencies.

Modern IT teams rely on dozens, or even hundreds, of third-party SaaS tools. From Workday and Cloudflare to Slack, Zoom, and GitHub, your business depends on these services to function. When a vendor outage occurs, the consequences are immediate: internal tools fail, support tickets spike, and leadership demands answers. Recent events, like the widespread Cloudflare outage in November 2025, highlight how quickly things can spiral out of control.

In our previous blog post, we outlined why Citrix VAD/DaaS customers using VMware should consider migrating to XenServer. With Citrix now officially supporting XenServer for non-Citrix workloads and Broadcom’s recent licensing changes, many organizations are reevaluating their virtualization strategy.

As businesses expand their digital ecosystems and physical asset base, maintaining control, performance, and visibility across equipment becomes more challenging. This is why asset lifecycle management is becoming a foundational discipline in 2026. Organizations need consistency across planning, deployment, maintenance, and retirement. Modern platforms like Asset Infinity support this shift by helping teams streamline operations, automate key tasks, and manage asset data with clarity.

How to Detect, Alert, and Resolve Memory Issues Before They Cause Downtime When applications scale on Heroku, memory-related issues are among the most common (and most frustrating... -_- ) sources of instability. Two of the most notorious culprits are the R14 (Memory Quota Exceeded) and R15 (Memory Quota Hard Limit) errors.

In today’s hyper-competitive e-commerce environment, customers expect websites to be fast, responsive, and error-free. Even a few seconds of delay can lead to abandoned carts, lost revenue, and poor user experience. With more shoppers browsing on mobile devices and expecting real-time interaction, performance optimization has become a key driver of conversion growth.

Modern AI agents are now highly advanced, frequently becoming essential components of engineering workflows and deployment pipelines. However, operating these systems often feels like trying to navigate a ship through a dense fog. When an agent errors, slows down, or consumes excessive resources, engineers find themselves adrift, lacking the navigational charts needed to diagnose the problem. The absence of deep insight makes debugging, performance tuning, and cost management unnecessarily difficult.