Application Error Monitoring

Easily fix errors and crashes during development and production

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Monitor in Real Time

Troubleshoot Issues

Set Alert Thresholds

Use Distributed Tracing

View Full Stack Context

Detect and fix application crashes before your application goes live

When you monitor performance during application development, you can help avoid crashes once your application goes live. SolarWinds^® Observability SaaS is built to help you monitor performance metrics during the test period for your application, ultimately helping you avoid end-user downtime during production. Catch and diagnose in-development application performance issues easily with SolarWinds Observability SaaS crash monitoring and root cause analysis.

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Set up application crash monitoring thresholds and alerts to detect errors before they affect performance

SolarWinds Observability SaaS allows you to customize thresholds for your application performance alerts, so you can catch crashes, errors, and other availability issues as soon as possible. Additionally, with real-time alerts, SolarWinds Observability SaaS can communicate the severity and the location of bottlenecks straight to your inbox. It’s built to offer business-friendly wait-time analytics, so you can quickly understand exactly which performance issues are affecting the end-user experience. The platform is also designed to send alerts to notification services like Slack and Teams.

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Troubleshoot application performance issues once applications are deployed

With SolarWinds Observability SaaS, troubleshooting web applications is possible in the development stage and after deployment. Once your application is up and running, you can deep dive into crashes and other slow performance indicators using troubleshooting assistance features. With code profiling, you can pinpoint the location of application errors and crashes to address bottlenecks efficiently. With unified log, digital experience, and database monitoring, you can expand your application troubleshooting capabilities across the entire application stack.

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Understand application performance in the context of your full-stack infrastructure

When your on-premises IT infrastructure exhibits performance issues, your web-based application performance can take a hit. Understanding the availability of your full-stack infrastructure is the key to a bird’s-eye view of your application performance. To troubleshoot application performance issues completely and effectively, SolarWinds Observability SaaS is built to monitor your full-stack IT infrastructure, in addition to application-layer monitoring—all from a single pane of glass

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Use distributed tracing to follow requests through the application cycle

To enhance the efficiency and accuracy of your application crash troubleshooting, SolarWinds Observability SaaS uses automated distributed tracing. With distributed tracing, you can follow each request in your application server and pinpoint where your request has slowed down. Once you’ve identified where a problem is, you can tune at the code level to ensure optimal performance. Distributed tracing with SolarWinds Observability SaaS means you don’t have to manually sift through your server to determine the context of a slow request

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Application Error Monitoring

Do you find yourself asking…

What is application error monitoring?
What are the three common types of application error detection?
How can application error monitoring improve software performance?
What are the best practices and common mistakes in application error monitoring?
Why is an error monitoring tool an absolute must-have?
How do error monitoring tools detect and report errors?
What are the essential criteria for selecting application error monitoring tools?

You know how it goes: your application works perfectly one moment, and the next, a user reports an issue. Error monitoring involves continuously watching your software to automatically detect, track, and report code exceptions and application failures as they occur in real time, on both the front end and back end.

This crucial process captures the complete stack trace and relevant metadata, allowing engineers to avoid guesswork. Instead of searching through massive error logs or waiting for user feedback, an effective error reporting system handles the hard work, giving your team the information needed to begin debugging right away. It shifts you from reacting to problems to proactively tracking errors.

We’ve all experienced it—setting up a system and quickly feeling overwhelmed. Error monitoring is a valuable tool, but it’s only effective if used properly.

Best Practices: Take Proactive Action

Define a Clear Triaging Workflow: Avoid treating every alert equally. Set up severity levels (e.g., P1, P2, and P3) and assign responsibility immediately. Leverage integrations with Jira or GitHub to ensure critical errors are automatically assigned to an engineer for debugging before they leave the dashboards.
Create Actionable Alerts (Service Level Objectives): Instead of triggering alerts on every 500 error , focus on incidents that significantly affect users, such as a "sustained 5% increase in 5XX errors over five minutes." This reduces unnecessary alerts, prevents alert fatigue, and helps your team address critical business issues.
Add Detailed Metadata: As more context leads to faster resolutions, ensure your SDKs are set up to record information such as the user’s ID, transaction/session ID, and deployment markers. This metadata transforms an unclear stack trace into a bug that can be quickly reproduced.
Connect Errors With Performance Monitoring: Fast applications are healthy applications. Use your APM to view the end-to-end trace behind the error. Often, an error is a sign of a larger performance monitoring problem, such as a database deadlock or a slow external API call. Addressing the performance bottleneck often resolves the error itself.

Common Mistakes: Avoid These Pitfalls

Excessive Alerting (The "Boy Who Cried Wolf" Scenario): Excessive alerting is the most significant issue. Triggering alerts for every minor, noncritical, or self-resolving error (such as trivial client-side warnings) leads your team to ignore all notifications. Adjust your alert thresholds and filter out known, harmless noise.
Insufficient Logging Context: Depending on basic, generic error logs without comprehensive stack traces and relevant metadata can result in slow and complex debugging. To get the most from your tool, capture the application's state at the time of failure.
A Focus Only on the Back End: Issues on the front end (e.g., JavaScript errors, slow asset loading, and mobile application crashes) directly affect users. If your monitoring stops at the server's API, you're missing insights into the user experience. For modern applications, comprehensive full-stack monitoring is essential.
Neglect for Price and Scalability: Whether you use a paid tool or maintain an open-source solution, failing to manage the volume of incoming data can lead to high costs, either through monthly charges or increased maintenance. Regularly optimize your monitoring scope and archive outdated data to keep costs affordable.

As an IT professional, you understand that complexity slows you down. The most effective error monitoring tools are those that streamline and speed up your team's workflow. Ignore the marketing hype and focus on these three essential features:

1. Unified Context (End-to-End Visibility)

You need a single, comprehensive view—not multiple dashboards.

Correlation Matters: The tool should automatically connect an exception’s stack trace to the entire distributed transaction trace, related logs, and underlying infrastructure performance. This means you don’t only see "Error 500," but understand it was caused by a four-second database query on service X within container Y.
Complete Stack Monitoring: It should monitor your full application seamlessly—from back-end services (e.g., Java or Python) running in Docker to front-end platforms (e.g., JavaScript and mobile applications). Using separate tools creates unnecessary friction and gaps in context.

2. Intelligent Prioritization (Focus on Impact)

As too many alerts can reduce productivity, your tool should intelligently filter out the noise.

Smart Grouping: It should automatically consolidate thousands of occurrences of the same bug into a single issue.
Impact-Based Alerts: Notifications should be triggered by impact or anomaly detection instead of the number of errors. This ensures you prioritize the issues that truly affect customer experience.

3. Rapid Debugging Context

Reduce the time spent trying to reproduce bugs.

Detailed Metadata: Beyond basic error logs, the tool should capture critical context such as user IDs, session information, and deployment specifics. This metadata is crucial for efficient debugging.
Seamless Workflow Integration: It should offer a robust API to automatically send prioritized errors to Jira or GitHub, streamlining your error tracking and aligning with your engineers’ existing workflows.

Your objective is to choose monitoring software that empowers your team to optimize their time by providing instant, actionable insights.

You know how it goes: your application works perfectly one moment, and the next, a user reports an issue. Error monitoring involves continuously watching your software to automatically detect, track, and report code exceptions and application failures as they occur in real time, on both the front end and back end.

This crucial process captures the complete stack trace and relevant metadata, allowing engineers to avoid guesswork. Instead of searching through massive error logs or waiting for user feedback, an effective error reporting system handles the hard work, giving your team the information needed to begin debugging right away. It shifts you from reacting to problems to proactively tracking errors.
When discussing error detection, there are generally three primary methods our monitoring software uses to accomplish this:

Code-Level Exception Detection: This is the classic approach, where the application itself reports exceptions or crashes. We add instrumentation by using software development kits (SDKs) to capture uncaught exceptions, which are usually logged in error logs. This method provides a detailed, technical perspective on what occurred in the code.

Performance and Transaction Monitoring: This is where application performance monitoring (APM) comes into play. These tools monitor each request as it moves through the back-end services. An error in this context might be a crash, a transaction that returns a 5XX HTTP status code, or a trace that reveals high latency before failure. This approach offers end-to-end insight into failures across distributed systems.

Digital Experience Monitoring (DEM): As errors can occur directly at the user interface, DEM focuses on the front end and client-side experience. We leverage real user monitoring to observe errors users experience and synthetic monitoring to run automated, scripted transactions that verify the application’s uptime and functionality. Advanced error monitoring solutions may include session replay, which lets you see what a user did before encountering a bug in their mobile application.
Although many people mistakenly believe that error and performance monitoring are entirely separate, they are closely connected. An error, in fact, is a performance issue—often resulting in a crash or a failed transaction.

When you choose a robust error monitoring solution, you gain two main advantages:

Identification of the Slowest Failure Points: Each time an application encounters an error, it consumes valuable resources, including CPU, memory, and database time. With tools such as our APM solution, which offer end-to-end visibility and trace waterfalls, not only can you identify that an error occurred, but also where time was spent before the failure. This allows you to detect and improve the most inefficient or failure-prone code sections, ultimately enhancing the stability and speed of your entire service.

Preservation of Operational Efficiency: High error rates lead to repeated processing, retries, and manual fixes. By systematically resolving the most critical errors, you lower background resource usage, freeing up infrastructure capacity and ensuring your application spends less time handling exceptions and more time serving customers efficiently. This is the essence of effective APM.
We’ve all experienced it—setting up a system and quickly feeling overwhelmed. Error monitoring is a valuable tool, but it’s only effective if used properly.

Best Practices: Take Proactive Action

Define a Clear Triaging Workflow: Avoid treating every alert equally. Set up severity levels (e.g., P1, P2, and P3) and assign responsibility immediately. Leverage integrations with Jira or GitHub to ensure critical errors are automatically assigned to an engineer for debugging before they leave the dashboards.

Create Actionable Alerts (Service Level Objectives): Instead of triggering alerts on every 500 error , focus on incidents that significantly affect users, such as a "sustained 5% increase in 5XX errors over five minutes." This reduces unnecessary alerts, prevents alert fatigue, and helps your team address critical business issues.

Add Detailed Metadata: As more context leads to faster resolutions, ensure your SDKs are set up to record information such as the user’s ID, transaction/session ID, and deployment markers. This metadata transforms an unclear stack trace into a bug that can be quickly reproduced.

Connect Errors With Performance Monitoring: Fast applications are healthy applications. Use your APM to view the end-to-end trace behind the error. Often, an error is a sign of a larger performance monitoring problem, such as a database deadlock or a slow external API call. Addressing the performance bottleneck often resolves the error itself.

Common Mistakes: Avoid These Pitfalls

Excessive Alerting (The "Boy Who Cried Wolf" Scenario): Excessive alerting is the most significant issue. Triggering alerts for every minor, noncritical, or self-resolving error (such as trivial client-side warnings) leads your team to ignore all notifications. Adjust your alert thresholds and filter out known, harmless noise.

Insufficient Logging Context: Depending on basic, generic error logs without comprehensive stack traces and relevant metadata can result in slow and complex debugging. To get the most from your tool, capture the application's state at the time of failure.

A Focus Only on the Back End: Issues on the front end (e.g., JavaScript errors, slow asset loading, and mobile application crashes) directly affect users. If your monitoring stops at the server's API, you're missing insights into the user experience. For modern applications, comprehensive full-stack monitoring is essential.

Neglect for Price and Scalability: Whether you use a paid tool or maintain an open-source solution, failing to manage the volume of incoming data can lead to high costs, either through monthly charges or increased maintenance. Regularly optimize your monitoring scope and archive outdated data to keep costs affordable.
If you're an IT leader or engineer, your time is extremely valuable, and maintaining your peace of mind is essential. Depending only on log files or tickets puts you at a disadvantage. Here’s why having a specialized tool is essential:

Save Time on the Most Frustrating Debugging Tasks: Many tools automatically record the complete context of an issue—including the stack trace, environment, and important metadata—the moment it occurs. This means your team spends less effort reproducing bugs and more time fixing them.

Address the Most Critical Issues First: All errors aren’t equally important. An effective error reporting tool leverages machine learning or simple logic to group related errors and help you prioritize them by severity or frequency. This enables efficient triaging instead of simply reacting to the latest alerts. For example, you can avoid “sentry fatigue,” as irrelevant noise is filtered out.

Keep Customers Satisfied: Monitoring enables you to detect errors before they result in outages. By being proactive, you enhance your application’s reliability, which leads to a better user experience and improved business outcomes. This is fundamental to a seamless development and operations workflow and effective performance monitoring.
These tools act as your application's "flight recorder," automatically gathering the evidence needed to resolve issues quickly. They offer much more than basic error logs by delivering detailed context.

1. Detection: Failure Identification

Detection depends on agents or SDKs embedded throughout your codebase (front end and back end):
Exception Hooks: The SDK connects with the application's exception handlers and immediately captures an unhandled error or crash
Trace Monitoring: The tool has an end-to-end request tracking system for complex applications; any request that fails or produces a 500 error is marked as a detected failure
User Experience Monitoring: Errors visible to the user (e.g., JavaScript errors and mobile application crashes) are tracked through real user sessions, often recording breadcrumbs (the sequence of actions before the crash) to improve debugging

2. Data Collection: Context Matters

After detecting an error, the tool collects vital details:
Complete Stack Trace: Critical for identifying the precise line of code that failed
Environmental Metadata: Essential for capturing user IDs, session information, OS, browser, and relevant environment variables to aid in reproducing the issue

3. Reporting and Triaging

The last step is making the problem actionable within your workflow:
Intelligent Grouping: Similar errors are grouped to minimize noise, simplifying triaging
Prioritized Alerts: The tool ranks errors based on their impact and frequency, assisting your team in prioritizing fixes
Automated Ticketing: Through an API, the system immediately sends the error and its context to Jira or GitHub, speeding up your error tracking and debugging workflow
As an IT professional, you understand that complexity slows you down. The most effective error monitoring tools are those that streamline and speed up your team's workflow. Ignore the marketing hype and focus on these three essential features:

1. Unified Context (End-to-End Visibility)

You need a single, comprehensive view—not multiple dashboards.
Correlation Matters: The tool should automatically connect an exception’s stack trace to the entire distributed transaction trace, related logs, and underlying infrastructure performance. This means you don’t only see "Error 500," but understand it was caused by a four-second database query on service X within container Y.
Complete Stack Monitoring: It should monitor your full application seamlessly—from back-end services (e.g., Java or Python) running in Docker to front-end platforms (e.g., JavaScript and mobile applications). Using separate tools creates unnecessary friction and gaps in context.

2. Intelligent Prioritization (Focus on Impact)

As too many alerts can reduce productivity, your tool should intelligently filter out the noise.
Smart Grouping: It should automatically consolidate thousands of occurrences of the same bug into a single issue.
Impact-Based Alerts: Notifications should be triggered by impact or anomaly detection instead of the number of errors. This ensures you prioritize the issues that truly affect customer experience.

3. Rapid Debugging Context

Reduce the time spent trying to reproduce bugs.
Detailed Metadata: Beyond basic error logs, the tool should capture critical context such as user IDs, session information, and deployment specifics. This metadata is crucial for efficient debugging.
Seamless Workflow Integration: It should offer a robust API to automatically send prioritized errors to Jira or GitHub, streamlining your error tracking and aligning with your engineers’ existing workflows.

Your objective is to choose monitoring software that empowers your team to optimize their time by providing instant, actionable insights.

Gain Faster Insights With Real-Time Application Monitoring

Prevent crashes by monitoring performance during development and testing
Set instant alerts with custom thresholds to detect issues and notify Slack or Teams
Diagnose issues after launch with distributed tracing and code profiling to find errors and slow requests

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Fully functional for 30 days

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