Modern IT infrastructure isn’t what it used to be. We’ve moved far beyond a handful of on-prem servers tucked away in a data center. Today’s setups include a mix of public cloud, private cloud, on-prem systems, containers, microservices, and SaaS tools, often spread across multiple regions and providers. This mix of environments introduces new layers of complexity, more moving parts, and more ways for things to break.
As infrastructure grows in scale and diversity, monitoring becomes a core part of running reliable systems. You need visibility across the stack: your servers, networks, containers, databases, load balancers, and third-party APIs.
This guide walks you through the main categories of metrics you should track for end-to-end monitoring and visibility. It breaks them down into practical sections, so you can build or refine a monitoring setup that actually works in production.
A Deep Dive into Performance Metrics
Performance metrics help you understand how well your systems are running. They can tell you whether your infrastructure is healthy or under pressure.
Here are the key performance metrics you should measure:
CPU Utilization: Tracks how much of the CPU is being used. High CPU for long periods can cause slow response times or service outages. Sudden spikes may indicate inefficient code or unbalanced load.
Load Average: A measure of the number of processes waiting to run on the CPU. Useful on Unix/Linux systems. A load higher than the number of cores for extended periods usually signals a bottleneck.
Memory Usage: Shows how much memory is used versus available. Low available memory can lead to swapping, which slows everything down. Useful for spotting memory leaks or inefficient caching.
Swap Usage: Indicates whether the system is using disk space as virtual RAM. If you’re hitting swap often, performance takes a big hit and it usually means your memory is under-provisioned.
Disk I/O (Input/Output): Measures how fast data is being read or written to disk. High I/O wait time means your app is stuck waiting for the disk. Can impact database performance or file-heavy applications.
Disk Usage (Capacity): Tracks how much disk space is used and how much is left. If you run out of disk space, you will start encountering app crashes, I/O latency, logging failures, or broken backups.
Network Throughput: Measures the amount of data sent and received over the network. Useful for spotting overloaded connections or services struggling with data transfer.
Network Errors and Dropped Packets: These point to unstable network conditions, misconfigured firewalls, DNS failures, or overloaded interfaces. Can explain intermittent failures or degraded service quality.
Thread Count / Process Count: A sudden rise in the number of running threads or processes could mean a runaway process or poor resource management.
System Uptime: Helps track restarts and unexpected reboots. Frequent reboots could mean underlying OS or hardware issues.
Context Switches: Too many context switches can be a sign of over-scheduling and can hurt performance, especially on systems handling high concurrency.
Application-Specific Metrics (e.g., request rate, latency, error rate): Often exposed via instrumentation, these sit on top of raw system metrics and give insight into how performance changes affect the end-user experience.
How to Monitor Performance Metrics Effectively
Here are some tips on how to monitor performance metrics:
Set Baselines for Each Metric: Understand what “normal” looks like for your systems. A CPU usage of 80% may be fine for one app but unacceptable for another.
Use Time-Series Monitoring Tools: Monitoring tools like Site24x7 let you collect and graph these metrics over time. Time-series data helps with trend analysis and capacity planning.
Set Alerts with Thresholds and Trends: Don’t just alert on fixed thresholds. Combine static limits (e.g., CPU > 90%) with trend-based alerts (e.g., CPU rising steadily over 15 minutes).
Monitor at Multiple Levels: Collect metrics from different layers, such as system, service, application, and log.
Tag and Aggregate by Context: Use tags like region, environment, instance type, or service name. This makes it easier to zoom in or compare between different parts of your infrastructure.
Correlate Metrics with Logs and Traces: When something goes wrong, raw metrics don’t always give you the full picture. Correlate them with logs or distributed traces to pinpoint the root cause.
A Deep Dive into Security Metrics
Security metrics help you measure the safety of your infrastructure and how well you’re protecting services, apps, data, and users.
Here are the key security metrics you should measure:
Failed LoginAttempts: Tracks how often users or systems fail to authenticate. A high volume could point to brute-force attacks or misconfigurations.
Unauthorized Access Attempts: Shows how often access to resources is attempted without the right permissions. Helps detect internal misuse or lateral movement in an ongoing attack.
Firewall Rule Hits / Denied Connections: Measures how many connections are blocked or allowed by firewall rules. Spikes in denied connections could indicate scanning or attack activity.
Antivirus or EDR Alerts: Counts how often your antivirus or endpoint detection systems trigger alerts. A sudden rise may be because of malicious activity or insider threats.
Open Ports: Tracks how many services are publicly exposed. Helps prevent accidental exposure of sensitive services like admin panels and databases.
Patch and Vulnerability Status: Measures how many systems are missing patches or have known vulnerabilities. A high number increases your attack surface.
User Privilege Changes: Monitors when users are granted or lose admin rights. Unexpected privilege changes can be an early sign of compromise.
Security Group and IAM Policy Changes: Tracks changes to cloud access rules. Useful for spotting risky modifications or privilege escalations.
TLS Certificate Expiry: Checks how soon SSL/TLS certificates are set to expire. Expired certs can lead to broken services and loss of secure communication.
How to Monitor Security Metrics Effectively
Here are some tips on how to monitor security metrics:
Use a Centralized SIEM Tool: Set up SIEM to collect and analyze logs, events, alerts, and metrics in one place. This makes it easier to spot coordinated activity.
Enable Real-Time Alerting on Critical Events: Don’t wait for daily summaries. Get immediate alerts on high-severity events like privilege escalations or repeated failed logins.
Baseline Normal Behavior for Security Events: Know what normal looks like in terms of access attempts, and rule changes. This helps catch unusual patterns like login attempts during off-hours or from new geolocations.
Segment Metrics by Environment and Role: Track production, pre-production, staging, and development separately. Also group metrics by user roles (admin vs. standard users) for more accurate threat detection.
Cross-Reference with Threat Intelligence Feeds: Combine internal security metrics with threat intel to flag IPs or domains already known to be malicious.
Encrypt and Monitor Audit Logs: Make sure logs can’t be modified silently. Monitor not just the content of logs but also for any gaps or suspicious changes to them.
Track Long-Term Trends for Compliance: Many regulations require you to keep an eye on patch status, login attempts, vulnerabilities, and access control changes. Trend data helps show you're staying compliant.
A Deep Dive into Capacity Metrics
Capacity metrics help you track how much of your infrastructure resources are being used versus what’s available.
Here are the key capacity metrics you should measure:
CPU Capacity: Measures the total processing power available versus what’s currently in use. Helps you plan scaling needs and avoid CPU saturation during high traffic.
Memory Capacity: Tracks total RAM versus used RAM over time. Useful for spotting services that are close to memory limits and for deciding when to scale vertically or horizontally.
Storage Growth Rate: Shows how quickly disk usage is increasing. Important for forecasting future needs and avoiding last-minute upgrades.
Database Storage Usage: Tracks how much space your databases are using. Helps ensure views, indexes, logs, and backups aren’t eating up unexpected storage.
Pod or Container Limits (CPU/Memory): For Kubernetes or container environments, tracks usage against resource limits. Helps identify containers that are consistently under or over-provisioned.
Virtual Machine Quotas: Measures how close you are to VM limits set by cloud providers or internal policies. Useful when managing large-scale deployments across environments.
Network Bandwidth Capacity: Tracks actual throughput against max available bandwidth. Helps avoid saturated links and degraded application performance.
Concurrent User or Session Limits: Useful for services that support a limited number of users, threads, processes, or sessions at once. Helps avoid service denial due to resource caps.
Autoscaling Threshold Usage: Monitors how often your systems are hitting the thresholds that trigger autoscaling. Useful for tuning autoscaling policies and avoiding resource waste.
How to Monitor Capacity Metrics Effectively
Here are some tips on how to monitor capacity metrics:
Set Warning Thresholds Before Critical Limits: Trigger alerts when you reach 70–80% of a resource. Don’t wait until you’re fully out of space or CPU.
Use Forecasting Based on Historical Trends: Use historical data to predict when you’ll run out of resources. Most monitoring tools offer basic trend forecasting.
Track Usage by Service, Environment, Region, and More: Avoid looking at raw global metrics. Break them down to catch hotspots or imbalanced usage across teams or zones.
Include Cloud-Specific Quotas: Cloud providers have limits on instances, IPs, serverless functions, and other resources. Monitor these quotas alongside regular usage.
Use Dashboards for Growth Monitoring: Create visuals showing disk, memory, CPU, and GPU growth over time. Helps with capacity reviews and stakeholder reporting.
Monitor Container and Pod Limits Closely: In dynamic environments like Kubernetes, tracking capacity at the pod level helps avoid resource starvation or waste.
Correlate with Deployment Events: Link capacity spikes with releases, feature releases, configuration changes, or traffic increases to understand what’s causing demand.
A Deep Dive into User Experience Metrics
User experience metrics help you understand how your infrastructure and applications feel from the user's point of view.
Here are the key user experience metrics you should measure:
Page Load Time: Measures how long it takes for a page to become usable in the browser. Slow pages lead to high bounce rates and poor engagement.
Time to First Byte (TTFB): Tracks how long it takes for the first response from the server. A high TTFB usually points to backend or network latency.
Application Latency: Measures how long it takes for your app to process and return a response. Helps identify slow endpoints or overloaded components.
Error Rate: Tracks the percentage of failed requests versus total requests. A rising error rate directly impacts usability and signals underlying issues.
Apdex Score: Combines latency and error data into a single score showing overall user satisfaction. Helps prioritize performance fixes based on impact.
Availability / Uptime from User Perspective: Measures whether the app or service is reachable and usable from different user locations. Useful for tracking the real-world impact of outages.
Mobile vs. Desktop Performance: Tracks differences in speed and errors across platforms. Helps optimize for the actual usage pattern of your users.
User Session Duration and Drop-Off Points: Shows how long users stay engaged and where they leave. Can reveal slow pages or confusing UX.
Transaction Success Rate: Measures how often users complete key actions like logins, targeted searches, checkouts, or form submissions. Drops here often point to backend or API issues.
Third-Party Dependency Impact: Tracks how external services (like CDNs, analytics, security, and payment processors) affect performance. Helps reduce reliance on slow or unstable vendors.
How to Monitor User Experience Metrics Effectively
Here are some tips on how to monitor user experience metrics:
Use Real User Monitoring (RUM):RUM tools collect data from actual users in real time. This gives the most accurate picture of performance as experienced by users.
Set Performance Budgets: Define limits for key metrics like page load time or latency, and alert when those budgets are exceeded. Helps enforce consistent experience.
Segment by Device, Browser, OS, and Location: Break down metrics to find issues affecting only certain user groups. One browser or region may be dragging down the overall numbers.
Use Synthetic Monitoring for Critical Flows: Simulate user interactions like logins or purchases on a schedule. Helps catch problems before users report them.
Correlate Frontend and Backend Metrics: Poor frontend performance isn’t always a frontend problem. Link metrics to trace issues back to root causes.
Monitor CDN and Third-Party Load Times: External scripts can slow down pages. Track how these dependencies perform and set alerts for significant slowdowns.
Tie Metrics to Business Outcomes: Don’t just track latency; connect it to conversion rates or revenue loss to make performance a business priority.
A Deep Dive into Network Metrics
Network metrics help you understand how data flows between your services, applications, external systems, and users.
Here are the key network metrics you should measure:
Network Latency: Measures how long it takes for a packet to travel from source to destination. High latency can impact app responsiveness and service coordination across regions.
Packet Loss Rate: Tracks the percentage of packets that are lost in transit. Packet loss causes slowdowns and retransmissions.
Connection Establishment Time: Measures how long it takes to complete a TCP handshake or establish a connection. Spikes may signal DNS issues or misconfigurations.
Network Interface Utilization: Shows how much bandwidth is being used on each network interface. Helps detect overuse or unused capacity.
DNS Resolution Time: Tracks how long it takes to resolve domain names to IPs. Slow resolution times can delay connections and break service dependencies.
Inter-Zone or Inter-Region Transfer Delays: Measures the time it takes for data to move between zones or regions. Important for services with components spread across geographies.
Firewall Throughput and Load: Tracks how many packets or sessions the firewall is handling. Helps detect if the firewall is a bottleneck during peak loads.
Bandwidth Usage by Protocol or Service: Breaks down bandwidth usage by type (HTTP, HTTPS, SSH, TCP, etc.). Helps identify unusual activity or bandwidth-heavy services.
How to Monitor Network Metrics Effectively
Here are some tips on how to monitor network metrics:
Set Baseline Performance Levels: Record normal latency, packet loss, throughput, and other metrics during typical load. This makes it easier to detect unusual patterns.
Enable Continuous Network Monitoring: Use tools that provide real-time monitoring for key metrics across all network segments.
Segment by Zones, Regions, and Critical Links: Monitor separately for internal networks, cloud environments, and external connections to pinpoint where issues occur.
Correlate with Application Metrics: Network slowdowns often impact application performance. Link network data with app response times to confirm root causes.
Use Synthetic Traffic Tests: Simulate user traffic across regions to measure latency and packet delivery even when no active users are reporting issues.
Log and Store Historical Data: Keep a record of long-term trends to plan capacity upgrades and meet compliance requirements for network performance monitoring.
Common Challenges and Best Practices to Overcome Them
Finally, here are some common challenges you may face while monitoring your infrastructure, along with some tips on how to resolve them.
Inconsistent Data Across Sources
Different monitoring tools may report conflicting values for the same metric, especially when data is pulled at different intervals or processed differently.
How to overcome:
Use a unified monitoring platform like Site24x7 that collects and correlates data from all layers.
Set consistent polling intervals and data retention settings.
Normalize metrics using tags or labels to reduce confusion.
Alert Fatigue
When thresholds are too sensitive or there are too many noisy alerts, teams stop paying attention to them.
How to overcome:
Group related alerts into a single notification.
Use dynamic thresholds or anomaly detection instead of fixed values.
Route alerts to the right team using tags and severity levels.
Lack of Context in Dashboards
Dashboards often show metrics but no clear connection to incidents or user impact.
How to overcome:
Include visual annotations for deployments, outages, config changes, or other key events in your monitoring dashboards.
Combine infra metrics, app logs, traces, and user experience data in the same views.
Create dashboards per service or team to focus on what matters.
Data Gaps and Missed Metrics
Metrics can drop out due to network issues or overloaded agents.
How to overcome:
Monitor the health of your monitoring system just like any other service.
Set up health checks and availability tracking for your monitoring agents.
Use buffering or local caching so agents can store data temporarily during network outages.
Conclusion
End-to-end infrastructure monitoring is key to keeping your systems healthy, spotting problems early, resolving bottlenecks, and making smarter decisions. We hope the insights shared in this guide help you build a more reliable and effective monitoring setup.