What is server monitoring?

It consists of monitoring systems that collect and analyze metrics (for example, CPU usage, memory consumption, disk space) across physical, virtual and cloud-based environments.

According to ITIC’s Hourly Cost of Downtime Survey, 97% of large enterprises report that, on average, a single hour of server downtime per year costs their company over USD 100,000. 41% of respondents reported costs between USD one million and over five million per hour.¹ This makes server monitoring essential for achieving optimal user experiences (UX) and overall business outcomes.

Organizations rely on server monitoring to catch problems early, optimize resources and maintain high availability. As IT infrastructure becomes increasingly complex, involving hybrid cloud environments and distributed architectures, effective monitoring helps IT teams maintain reliable operations and avoid endless troubleshooting.

Server monitoring operates through a multilayered system that collects and analyzes server data and alerts users to changes in performance. Monitoring software or remote protocols (for example, SNMP) gather metrics from servers and send the data to a central system for processing and visualization.

Today’s monitoring architecture consists of four primary functions:

• Data collection from servers involves collecting capacity, configuration and performance metadata and sending it to a central monitoring service.
• Processing and storing metrics encompasses organizing and storing data in real-time for analysis and historical tracking.
• Artificial intelligence (AI) driven analysis and alerts use machine learning (ML) to detect anomalies, predict failures and automate root analysis.
• Visualization and automated response include graphical user interfaces (GUI) and dashboards that display system status and automated responses.

There are three main types of server monitoring, each designed for different infrastructure environments:

1. Dedicated server monitoring: Physical server (or dedicated server) monitoring tracks hardware features, including temperature, power and memory errors.
2. VM monitoring: Virtual server monitoring is more complex. Multiple virtual machines (VMs) share the same physical hardware through platforms like VMware, which involves monitoring the host system and individual VMs.
3. Container monitoring: Container technology takes this virtualization concept further, with apps running in lightweight, portable containers. Kubernetes has made container monitoring essential, requiring specialized tools that can track containers as they move across different servers and automatically scale based on demand.

Today’s monitoring strategies typically combine all three approaches. On-premises dedicated servers, cloud servers and virtual servers each handle different workloads based on specific requirements, while containers enable rapid deployment and scaling. Modern monitoring platforms use AI and automation to handle this complexity, automatically discovering new resources and adjusting monitoring as infrastructure changes.

It’s worth noting that the distinction between server infrastructure monitoring, server performance monitoring and application monitoring has largely disappeared. Comprehensive server environment monitoring now covers both server health and application performance in unified platforms.

Server monitoring relies on five essential components working together to provide comprehensive infrastructure visibility:

1. Data collectors
2. Storage systems
3. Analysis engines
4. Alert systems
5. Dashboards and integrations

Automated agents or remote protocols gather performance metrics including server health indicators, resource utilization data and application status information. Modern collectors use minimal system resources while providing real-time data transmission.

Time-series databases optimized for monitoring data compress information significantly while maintaining fast query performance. These systems include automated retention policies that balance historical analysis needs with storage costs.

Modern analysis engines combine traditional rule-based monitoring with AI pattern recognition. They evaluate metrics against both static thresholds and dynamic baselines learned from historical data, while tracking dependencies between systems. This approach enables more accurate anomaly detection across interconnected infrastructure.

Intelligent alerting systems use predefined rules to prevent overwhelming IT teams with multiple related alerts and AI to reduce false positives. They include integration with communication platforms and DNS monitoring for rapid response time.

Visualization platforms convert raw metrics into actionable insights through real-time dashboards and automated reporting. Integration capabilities connect monitoring with existing IT infrastructure and automation platforms.

Server monitoring solutions range from open source solutions to commercial platforms and cloud provider services. Organizations typically combine multiple tools to create comprehensive monitoring strategies across infrastructure, apps and SaaS platforms.

These tools include:

• Data collection tools: Software tools gather metrics, including network monitoring, real-time data analysis and performance data from servers and applications. Proprietary solutions like PRTG and open source options like Nagios and Zabbix offer customization for notifications and ecosystem plug-ins to meet specific business needs.
• Alert and incident management systems: Alert and incident management systems detect problems, notify teams and coordinate response workflows. Advanced platforms use AI-powered alerting to reduce false positives and provide frameworks for rapid incident resolution.
• Performance monitoring platforms: These comprehensive platforms track server and application performance in real-time. Leading platforms incorporate AI for automatic issue detection and root cause analysis for optimal performance.
• Cloud provider services: Cloud service providers offer native monitoring built into their platforms, including AWS CloudWatch, Microsoft Azure Monitor, Google Cloud Operations and IBM Cloud® Monitoring. These tools provide deep cloud integration with various pricing tiers and feature levels. Analytics and visualization platforms process monitoring metrics into actionable insights through real-time dashboards and automated reporting.

Organizations use server monitoring to deliver measurable business value across multiple operational contexts:

• High availability
• Proactive issue resolution
• Resource optimization
• Security and compliance
• Operational efficiency

Server monitoring prevents costly outages by detecting problems with web servers, databases, operating systems (for example, Linux) and other critical infrastructure before they impact users. This allows organizations to maintain high availability.

According to ITIC research conducted with the 2023 Global Server Hardware Server OS Reliability Survey, 90% of organizations now require a minimum of 99.99% availability. This percentage equals 52 minutes of unplanned downtime per server per year for crucial systems and applications.¹

Modern monitoring helps teams identify bottlenecks, capacity constraints and potential failures before they impact users. This approach optimizes IT operations by addressing performance issues during planned maintenance windows rather than emergency response situations. It also reduces stress on both systems and teams while improving overall service reliability.

Server monitoring provides historical analysis for accurate capacity planning and identifies underutilized resources for reallocation.

Organizations can prevent overprovisioning while ensuring adequate resources for peak demand periods.

Comprehensive monitoring enables rapid threat detection across servers, firewalls and network infrastructure. It maintains audit trails for regulatory compliance and provides the visibility required for frameworks such as HIPAA and GDPR.

Automated server monitoring frees technical resources from manual system checks and provides data-driven insights for infrastructure decisions. Application programming interfaces (APIs) enable integration with existing business systems, allowing teams to scale monitoring capabilities to support business growth.

Monitoring technologies are evolving rapidly to address three significant shifts in modern IT operations:

• AI-driven monitoring
• Edge computing
• Serverless architectures

AI integration is becoming standard across monitoring platforms, with adoption accelerating in mission-critical environments. IBM’s Institute for Business Value research shows that 78% of IT executives are piloting or operationalizing AI capabilities in mainframe applications.  

AI enables pattern recognition that analyzes system behavior and provides context-aware alerting. Machine learning (ML) reduces false positives by considering historical patterns, while modern monitoring capabilities include predictive analytics and automated root cause analysis baselines.

Edge monitoring addresses the growth in connected devices and distributed computing. These monitoring technologies process data locally for reduced latency while using AI to create adaptive performance.

Serverless monitoring handles architectures where code runs on-demand without visible servers, making traditional infrastructure monitoring ineffective. These architectures require distributed tracing to follow requests across multiple functions and specialized observability tools that combine server metrics, logs and traces.