Infrastructure monitoring has evolved into a critical component of modern distributed systems, driving organizations to explore robust Zabbix alternatives. While Zabbix has served as a cornerstone of traditional monitoring, today's microservices and cloud-native architectures demand different approaches. The landscape of Zabbix alternatives has matured considerably, offering specialized solutions for various monitoring scenarios.

In the first part of this series, we explored how AppSignal can significantly enhance the robustness of Open edX platforms. We saw the challenges that Open edX faces as it scales and how AppSignal's features — including real-time performance monitoring and automated error tracking — provide essential tools for DevOps teams. Our walkthrough covered the initial setup and integration of AppSignal with Open edX, highlighting the immediate benefits of this powerful observability framework.

Logz.io introduces its AI Agent in Beta, using GenAI to revolutionize observability. The AI Agent simplifies monitoring with automated data analysis and root cause detection, accelerating issue resolution by 3-5x for beta users—marking a critical step toward fully autonomous observability.

In the world of modern IT operations, keeping your systems running smoothly requires measures beyond just basic monitoring. As infrastructures become more complex and dynamic, understanding how telemetry, monitoring, and observability work together is essential. These three concepts may seem similar, but each plays a distinct role in maintaining system health and performance.

Since its inception in 2004, Lansweeper has been at the forefront of helping businesses understand, manage, and protect their IT devices and networks through a powerful IT asset management platform. As the platform grew from an on-premises solution to a cloud-based SaaS offering, Lansweeper expanded its reach to a global, multi-region customer base.

Organizations are seeing measurable benefits from investing in observability, including faster issue resolution, cost reduction, and improved business outcomes. However, challenges still remain, including rising costs, tool fragmentation, and the need for more comprehensive monitoring of internet dependencies and user experience. Let’s explore these challenges and the best practices organizations are adopting to address them.

Observability means you know what’s happening in your software systems, because they tell you. They tell you with telemetry: data emitted just for the people developing and operating the software. You already have telemetry–every log is a data point about something that happened. Structured logs or trace spans are even better, containing many pieces of data correlated in the same record. But you want to start from what you have, then improve it as you improve the software.

Autonomous observability for system monitoring and management aims to use GenAI and machine learning to automatically detect, diagnose and resolve issues. In conversations about cloud observability today, discussions often shift from “what’s possible” to “what’s practical.” Too often, these conversations highlight the shortcomings of current observability processes, tools and financial models.