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Critical metrics for monitoring Kubernetes

Web application monitoring refers to the practice of observing, tracking, and managing the performance, availability, and reliability of web applications. It ensures users have a seamless experience and minimizes disruptions by identifying issues in real-time. All that’s required for something to be a ‘web application’ is that it’s more than a static site, and that it takes requests via the internet. Even an API mainly accessed via TCP is a web application!

Web application monitoring is a highly generalized term, and distinctions between, for example, ‘metrics’ and ‘monitoring;’ and ‘error tracking’ versus ‘performance monitoring’ are often distinctions without a difference. The question we’re trying to answer with Web Application Monitoring is: “how well is our application performing for users?” Observations that don’t relate to this question, for example the popularity of a single post on our social media site, or how well our live-generated site follows brand standards, are outside the scope of web application monitoring.

Monitoring tools collect data from your web application, servers, and user interactions. They process this data to generate insights about application health, performance, and user behavior. Alerts, dashboards, and reports provide actionable insights for resolving issues and optimizing performance.

Monitoring a web application requires understanding various dimensions of performance, usability, and security. Each type of monitoring addresses specific aspects of the application to ensure seamless operation. Let’s explore these types in greater detail.

Synthetic monitoring uses pre-recorded scripts to simulate user interactions with your web application. By performing these synthetic transactions, you can test key functionalities such as page load times, form submissions, or API calls without relying on real user activity.

• Benefits: It is proactive, enabling teams to detect and resolve issues before users are impacted. Synthetic monitoring is particularly effective for testing uptime, availability during off-hours, and the impact of new deployments.
• Example Use Case: Running tests on an e-commerce checkout page to ensure it processes payments correctly after a new update.
• Challenges: It doesn’t capture real-world user behaviors, so it should complement, not replace, real user monitoring.

See our complete guide to Synthetic Monitoring for a deeper dive.

Real User Monitoring (RUM) captures real-time data from actual users as they interact with your application. By embedding a lightweight tracking code into the application, RUM collects metrics like page load times, errors, and user interactions.

• Benefits: It provides insights into actual user experiences, helping identify regional performance variations, device-specific issues, or areas of improvement in UX design.
• Example Use Case: Tracking how mobile users from Europe experience an application compared to desktop users in North America.
• Challenges: RUM may require substantial data processing infrastructure to analyze the vast volume of user interaction data in real time.

APM focuses on monitoring and optimizing application-level metrics such as response times, throughput, memory consumption, and database query performance. APM tools provide deep visibility into application behavior, often by instrumenting code to measure key metrics.

• Benefits: APM is essential for identifying performance bottlenecks, such as slow database queries, inefficient APIs, or memory leaks.
• Example Use Case: Diagnosing why a specific API endpoint is causing latency spikes under heavy load.
• Challenges: Implementing APM requires careful planning, as excessive instrumentation can add overhead to the application.

Error tracking focuses on detecting, logging, and analyzing application errors to help developers diagnose and fix issues effectively. Logs capture details about errors and events, providing valuable context for debugging.

• Pitfalls of Defining Errors:
  • What is an error? The definition varies widely. For some, an error might include user-facing error messages or unhandled exceptions. For others, it might extend to slow-loading assets or deprecated API warnings.
  • Signaling Theory: Effective error tracking relies on understanding what each sensor (or monitoring system) is intended to capture. Without clear definitions, teams risk alert fatigue or missing critical issues.
• Benefits: Centralized logging reduces the time to identify the root cause of an issue, while structured error tracking can prioritize issues affecting user experience.
• Example Use Case: Monitoring uncaught exceptions in a JavaScript application and prioritizing fixes for errors impacting 10% of users.
• Challenges: Over-logging can lead to excessive noise, making it harder to find actionable insights.

Uptime monitoring ensures that your web application is available and responsive by periodically sending requests to check its status. Most uptime checks verify that a service responds with a 200 OK HTTP status.

• Benefits: Uptime monitoring offers a straightforward way to track availability, often serving as the first line of defense against outages.
• Example Use Case: Monitoring whether an online banking platform’s login page is accessible to users.
• Challenges: Simple uptime checks don’t account for partial outages or degraded performance (e.g., slow response times). Or for responses that look okay to a script, but not the users (e.g., the page loads but the only text says ‘server error’).

Server and infrastructure monitoring tracks the health of the hardware, virtual machines, or cloud infrastructure that supports your application. It collects data on CPU usage, memory availability, disk I/O, and network traffic.

• Benefits: Essential for ensuring that the underlying infrastructure can meet application demands, especially during peak loads.
• Example Use Case: Monitoring CPU usage to detect bottlenecks during a holiday sale on an e-commerce site.
• Challenges: Infrastructure monitoring can produce misleading signals in scenarios like network outages that reduce user traffic but increase resource availability, falsely suggesting optimal performance.

• Response Time: Time taken by the application to respond to requests.
• Error Rate: Frequency of errors in the application.
• Throughput: Number of requests processed over a time frame.
• Uptime and Availability: Percentage of time the application is operational.

By monitoring your web application directly, you have a better chance of knowing exactly when your service goes down. By alerting on metrics like response time and volume, you can get early indicators of growing problems.

Often, poor performance doesn’t happen all at once. For every user who gets in touch complaining of slow response times, dozens or hundreds will have simply abandoned your service, and written off the quality of your site. Without web application monitoring, you’ll use users for some time before you’re even aware of a problem.

Monitoring tools may struggle with rapidly changing, personalized content. A robust testing framework like Playwright can be helpful for writing smarter assertions about how interfaces should look.

As mentioned in the introduction, Web Application Monitoring has a specific use: finding how well a service performs for users. Once the use case expands into security monitoring or business analytics, mission creep can kill your efficiency.

Effective monitoring is essential to maintaining the performance, availability, and security of web applications. However, monitoring is more than just collecting data; it’s a strategic process of learning and acting based on insights. By following these best practices, organizations can ensure their monitoring efforts are both meaningful and actionable.

Before implementing monitoring, define what success looks like for your application. Monitoring without clear goals often leads to data overload without actionable insights.

• Why It Matters: Objectives guide what data you collect and how you interpret it. For example, minimizing downtime might focus on uptime monitoring and incident alerts, while improving user experience might emphasize performance metrics like load time and responsiveness.
• How to Do It: Align your objectives with business goals. For instance, if your goal is to increase conversion rates, focus on monitoring checkout processes and page performance.

Selecting the right tools for your web application’s unique needs is critical. Not all monitoring tools are created equal, and mismatched tools can lead to unnecessary complexity or blind spots.

• Why It Matters: A tool tailored to your application architecture provides more relevant data and reduces noise.
• How to Do It: Assess your application stack (e.g., serverless, microservices, or monolithic), the types of metrics you need, and your team’s familiarity with specific tools. For example, use Prometheus for metrics, Loki for logs, and Jaeger for tracing in a Kubernetes-based application.

KPIs translate business objectives into measurable metrics, bridging the gap between technical monitoring and organizational goals.

• Why It Matters: Without KPIs, monitoring efforts can lack focus, leading to wasted resources and misaligned priorities.
• How to Do It: Identify KPIs that directly affect user experience or business outcomes, such as uptime, response time, error rates, or user engagement metrics. For example, define a goal like “99.9% uptime for key services over a month.”

Modern monitoring goes beyond infrastructure to focus on the end-user journey. Users don’t care if your CPU is underutilized—they care if your site loads quickly and works smoothly.

• Why It Matters: User experience (UX) monitoring ensures that technical performance aligns with user satisfaction and retention.
• How to Do It: Combine Real User Monitoring (RUM) and Synthetic Monitoring to capture both actual and simulated user interactions. Focus on load times, time to interact (TTI), and error rates that directly impact UX.

Web applications operate in dynamic environments, where issues can arise at any time. Continuous monitoring ensures constant vigilance.

• Why It Matters: Continuous monitoring helps teams catch problems early, reducing downtime and improving system reliability.
• How to Do It: Automate monitoring across all layers of your stack—servers, APIs, front-end performance, and user interactions. Use tools like CI/CD integrations to monitor deployments for potential issues.

Alert fatigue is a common problem in monitoring, where too many notifications desensitize teams. A proactive approach focuses on actionable alerts.

• Why It Matters: Timely and meaningful alerts enable faster incident resolution while avoiding unnecessary noise.
• How to Do It: Configure alerts for critical thresholds and anomalies. For instance, set alerts for unusual spikes in response time or memory usage, but suppress notifications for predictable auto-scaling events.

Data alone is not valuable unless it leads to action. Effective monitoring transforms raw data into insights that drive meaningful improvements.

• Why It Matters: Many organizations collect vast amounts of monitoring data but fail to act on it, leaving potential optimizations on the table.
• How to Do It: Establish regular review processes to analyze trends, identify recurring issues, and implement fixes. For example, a monthly review of error logs can reveal patterns like frequently failing endpoints.

Synthetic monitoring simulates user activity to proactively identify potential issues.

• Why It Matters: This type of monitoring allows teams to test functionality and performance before users are affected.
• How to Do It: Use scripts to mimic common user actions, such as navigating pages, submitting forms, or using APIs. Test critical user paths regularly, especially after updates or deployments.

RUM provides insights based on actual user interactions, capturing the diversity of real-world experiences.

• Why It Matters: Real user data reflects the performance users experience, including regional differences, device-specific issues, and varying network conditions.
• How to Do It: Deploy lightweight tracking scripts to collect metrics such as page load time, interaction speed, and error rates. Segment data by user demographics or device type for targeted improvements.

Periodic audits ensure that your monitoring strategy remains effective and that your application continues to meet performance expectations.

• Why It Matters: Web applications evolve over time, and so do the challenges they face. Regular audits help identify outdated metrics, unnecessary alerts, and new performance bottlenecks.
• How to Do It: Schedule audits to review KPIs, monitoring coverage, and tool configurations. For instance, ensure your monitoring setup includes new microservices added to your architecture.

Effective monitoring isn’t just about data collection—it’s about using data to improve your application and achieve your organization’s goals. By integrating these best practices into your strategy, you ensure that monitoring becomes a driver of growth, user satisfaction, and operational excellence.

Datadog provides a unified platform that integrates metrics, logs, and traces for comprehensive monitoring. It includes features like Real User Monitoring (RUM), Synthetic Monitoring, and Application Performance Monitoring (APM).

Personal Experience: When I worked on a large e-commerce platform, Datadog was indispensable for tracking the performance of our microservices. However, the pricing became a pain point as our usage scaled. Integrating some of our custom Prometheus metrics required additional work, and once we were deep into the Datadog ecosystem, migration seemed daunting. It felt like we were trapped in their ecosystem because they handled everything, but that also meant learning to live with their constraints.

New Relic is known for its robust APM, RUM, and distributed tracing capabilities. It offers strong support for OpenTelemetry, allowing integration of custom telemetry data.

Personal Experience: I worked at New Relic for a number of years, and won’t go into what I learned behind the scenes. As a part of the observability industry now outside of New Relic, I’d say its users have very high standards for how integrated their performance data can be. Small teams and open source projects will never have the slick features of a mature dashboard and monitoring system with hundreds of engineers working to expand and maintain it.

Logz.io combines the ELK Stack (Elasticsearch, Logstash, and Kibana) with Grafana, providing log management and visualization. It targets engineers familiar with open source tooling but who want the convenience of a managed service.

Personal Experience: At a startup, we adopted Logz.io because it fit well with our existing ELK stack workflows. The integration was smooth, and their alerting system helped us catch critical API failures. However, as our log volume grew, the costs escalated, forcing us to revisit whether we should return to managing ELK ourselves. Overall the DIY solution had cheaper infrastructure costs, but way higher team overhead.

Sentry specializes in error tracking, offering detailed stack traces and insights into application crashes. It focuses on developer workflows, making it easy to identify and fix bugs.

Personal Experience: We used Sentry extensively during a high-stakes product launch. It was a lifesaver for catching JavaScript errors on our front end and exceptions in our Node.js backend. One memorable instance was tracking down a browser-specific bug affecting a subset of users—Sentry pinpointed the exact issue within minutes. However, the volume of alerts sometimes led to fatigue, requiring us to fine-tune what constituted an actionable error.

Originally a fork of Nagios, Icinga offers monitoring for servers, networks, and applications. It is an open-core product, with its core technology available on GitHub.

Personal Experience: This is the one tool on here I’ve never installed or explored, it has a loyal Reddit following and clearly has some knowledge transfer benefits from Nagios.

Site24x7 offers application, server, and website monitoring, positioning itself as an all-in-one solution for small-to-medium-sized organizations.

Personal Experience: We briefly evaluated Site24x7 for a mid-sized SaaS platform. While the promise of a unified monitoring solution was appealing, the results fell short in every area—synthetic checks, uptime reports, and server monitoring all felt basic compared to more specialized tools. It seemed like Site24x7 was spread too thin trying to cover every use case, and it struggled to meet the expectations of a team with even modest experience in monitoring tools.

Raygun focuses on real user monitoring, crash reporting, and performance tracking. It’s tailored for front-end and mobile developers looking to improve user experience.

Personal Experience: We relied on Raygun for monitoring a mobile app with a global user base. It excelled in surfacing crash reports and slow performance patterns tied to specific device types. One particular win was discovering a memory leak issue affecting older Android devices—it gave our team the data we needed to patch the issue quickly. However, integrating Raygun into our CI/CD pipeline required extra effort, as it wasn’t as seamless as some other tools.

AppDynamics, now part of Cisco, provides APM capabilities with a focus on deep-dive analytics and business transaction monitoring.

Personal Experience: At an enterprise client, we implemented AppDynamics to monitor a sprawling microservices architecture. Its transaction-based insights helped us identify bottlenecks in our checkout flow, translating directly into improved conversion rates. However, navigating its interface felt cumbersome, and despite its advanced features, team adoption was slower than anticipated due to the tool’s complexity. It became a love-hate relationship—powerful but not always user-friendly.

Instana promises AI-driven insights and automatic monitoring for dynamic applications. Its focus is on reducing manual configuration by detecting and mapping dependencies in real time.

Personal Experience: During an initiative to modernize a legacy application, Instana was pitched to us as the “next-generation AI observability solution.” While it delivered on automated dependency mapping, the AI insights often felt like little more than a repackaging of existing metrics with an added buzzword. It was helpful for identifying sudden resource spikes, but I often found myself relying on other tools for detailed root-cause analysis. The promises of “AI-driven” observability didn’t live up to the hype.

Open source monitoring plays a pivotal role in modern cloud-native web application monitoring by leveraging community-driven tools to provide robust, scalable, and accessible monitoring solutions. These tools empower organizations to manage the complexity of distributed systems without being locked into proprietary solutions.

Cloud-native applications, by design, introduce new complexities:

• Obfuscation: Dependencies across microservices, Kubernetes orchestration, and cloud-managed services obscure system behavior.
• Dynamic Dependencies: The interactions among thousands of microservices, infrastructure layers, and APIs shift dynamically with scaling and updates.
• Data Volume: High data granularity across logs, metrics, traces, and flows creates immense operational overhead to derive actionable insights.

1. Metrics Collection with Prometheus

   Prometheus is the cornerstone of open source monitoring, providing time-series data collection and querying capabilities. With exporters like Node Exporter and Kubernetes State Metrics (KSM), it gathers metrics at both the system and application levels.

2. Log Aggregation with Loki

   Loki collects and queries logs efficiently, ensuring contextual insights alongside metrics. Integrated with Prometheus and Kubernetes, Loki enables rapid troubleshooting.

3. Distributed Tracing with Jaeger

   Jaeger offers a standardized approach to tracing requests across microservices, enabling visibility into service-to-service interactions and latency.

4. Kernel-Level Observability with eBPF

   The extended Berkeley Packet Filter (eBPF) collects real-time data on network flows and application behavior, bypassing traditional agents and minimizing performance overhead.

Open source monitoring is not just a viable alternative to proprietary solutions—it is the backbone of modern observability. With tools like Prometheus, Loki, Jaeger, and eBPF, organizations can effectively monitor cloud-native applications, dynamically adjust to changing workloads, and achieve operational excellence. By embracing these technologies, teams can focus on delivering exceptional user experiences while keeping operational costs in check.

Checkly offers an integrated platform for synthetic monitoring and API testing, ensuring applications are functional and performant. For simulating user requests to perform synthetic monitoring, Checkly harnesses the power of the open source automation tool Playwright. With Checkly and Playwright, there’s no user behavior that you can’t test, and no critical path you can’t monitor continually.

Web application monitoring is essential for ensuring high performance, user satisfaction, and business success. With various tools and techniques available, businesses can proactively manage their applications and deliver exceptional user experiences.