How to Monitor and Fix Application Errors in Production: Complete Guide

Why Production Monitoring Is Non-Negotiable

The moment your application goes live, you lose direct visibility into how it behaves. Users encounter errors they don't report—research shows only 1 in 26 unhappy customers actually complain. The rest simply leave. Without production monitoring, you're flying blind, learning about critical issues from angry tweets or, worse, from churned customers.

Error monitoring isn't just about catching bugs—it's about understanding your application's health in the real world. Production environments expose edge cases that development and staging can't replicate: slow network connections, browser extensions that interfere with JavaScript, race conditions under load, and the infinite creativity of users who use your product in unexpected ways.

Error Monitoring Tools Compared

Sentry: The Error Tracking Standard

Sentry has become the default choice for application error monitoring, used by over 4 million developers:

Core Capabilities:

Error Aggregation: Groups similar errors together, preventing alert fatigue while surfacing unique issues. A single release that breaks authentication will show as one issue with thousands of occurrences, not thousands of separate alerts.

Rich Context: Captures everything needed to reproduce issues:

• Full stack traces with source maps
• User information and session data
• Request details (headers, body, URL)
• Browser/device information
• Custom context you add

Release Tracking: Associates errors with specific deployments, enabling quick identification of which release introduced a bug.

Performance Monitoring: Transaction tracing, slow endpoint detection, and frontend vitals tracking.

Pricing:

• Free: 5,000 errors/month
• Team: $26/month for 50K errors
• Business: Custom pricing with advanced features

LogRocket: Session Replay Focus

LogRocket approaches monitoring differently—by recording user sessions:

Primary Value Proposition:

Session Replay: Watch exactly what users experienced, including:

• Mouse movements and clicks
• DOM changes and network requests
• Console logs and JavaScript errors
• Rage clicks and frustration signals

Use Case: When stack traces aren't enough. Seeing the user's actual interaction often reveals issues that error logs miss—confusing UI that leads to errors, broken layouts that make elements unclickable, or sequences of actions that create race conditions.

Pricing:

• Free: 1,000 sessions/month
• Team: $99/month for 10K sessions
• Professional: Custom pricing

Datadog: Full Observability Platform

Datadog provides comprehensive observability for larger organizations:

Suite of Products:

• Application Performance Monitoring (APM)
• Log Management
• Real User Monitoring (RUM)
• Infrastructure Monitoring
• Security Monitoring

When Datadog Makes Sense:

• Large engineering teams needing unified observability
• Complex distributed systems
• Strong infrastructure monitoring requirements
• Budget for enterprise tooling

Pricing: Consumption-based, typically starting at several hundred dollars/month

Implementation Guide: Sentry with Next.js

Step 1: Installation

npx @sentry/wizard@latest -i nextjs

The wizard automatically:

• Installs necessary packages
• Creates configuration files
• Sets up source maps upload
• Configures error boundaries

Step 2: Manual Configuration (if needed)

// sentry.client.config.js
import * as Sentry from "@sentry/nextjs";

Sentry.init({
  dsn: process.env.NEXT_PUBLIC_SENTRY_DSN,

  // Performance Monitoring
  tracesSampleRate: 0.1, // 10% of transactions

  // Release tracking
  release: process.env.NEXT_PUBLIC_VERCEL_GIT_COMMIT_SHA,
  environment: process.env.NODE_ENV,

  // Replay configuration
  replaysSessionSampleRate: 0.1, // 10% of sessions
  replaysOnErrorSampleRate: 1.0, // 100% of errors

  integrations: [
    new Sentry.Replay({
      maskAllText: false,
      blockAllMedia: false,
    }),
  ],

  // Ignore known non-errors
  ignoreErrors: [
    'ResizeObserver loop limit exceeded',
    'Network request failed',
    'Load failed',
  ],

  beforeSend(event, hint) {
    // Filter or modify events before sending
    if (event.exception?.values?.[0]?.value?.includes('Canceled')) {
      return null; // Don't send user-cancelled requests
    }
    return event;
  },
});

Step 3: Add User Context

// When user logs in
import * as Sentry from "@sentry/nextjs";

function setUserContext(user) {
  Sentry.setUser({
    id: user.id,
    email: user.email,
    username: user.name,
    subscription: user.plan,
  });
}

// When user logs out
function clearUserContext() {
  Sentry.setUser(null);
}

Step 4: Add Custom Context

// Add breadcrumbs for debugging
Sentry.addBreadcrumb({
  category: 'user-action',
  message: 'Clicked export button',
  level: 'info',
  data: {
    format: 'csv',
    recordCount: 150,
  },
});

// Add custom tags for filtering
Sentry.setTag('feature', 'export');
Sentry.setTag('team', user.team.id);

// Add extra context
Sentry.setContext('export-details', {
  fileSize: 1024000,
  duration: 3500,
});

Step 5: Capture Custom Events

// Capture handled exceptions
try {
  await riskyOperation();
} catch (error) {
  Sentry.captureException(error, {
    tags: { operation: 'risky-operation' },
    extra: { attemptNumber: attempt },
  });
  // Handle gracefully
}

// Capture messages for non-exception issues
if (response.data.items.length === 0 && shouldHaveItems) {
  Sentry.captureMessage('Unexpected empty response', {
    level: 'warning',
    contexts: {
      response: {
        status: response.status,
        endpoint: '/api/items',
      },
    },
  });
}

Alerting Best Practices

Effective alerting balances awareness with noise:

Alert Hierarchy

Level	Trigger	Action	Notification
Critical	Core flow broken	Immediate	PagerDuty/Phone
High	New error spike	Within 1 hour	Slack + Email
Medium	Known issue increase	Same day	Slack
Low	First occurrence	Weekly review	Dashboard only

Alert Configuration in Sentry

Alert when:
- New issue is seen
- Issue frequency exceeds threshold (e.g., 10x normal)
- Error rate exceeds percentage (e.g., >5% of sessions)
- Specific error types (e.g., PaymentError, AuthError)

Avoiding Alert Fatigue

1. Group similar errors: Don't alert on each occurrence
2. Set meaningful thresholds: Not every error needs immediate attention
3. Time-based alerts: Only alert during business hours for non-critical issues
4. Owner assignment: Route to the right team automatically
5. Snooze capability: Temporarily silence known issues being worked on

Debugging Workflow: From Alert to Fix

Step 1: Triage

• Check error frequency and trend
• Identify affected users (volume, type)
• Determine if it's new or recurring

Step 2: Investigate

• Review stack trace and source context
• Check user context and session data
• Watch session replay (if available)
• Review related breadcrumbs

Step 3: Reproduce

• Create minimal reproduction case
• Use captured data to set up conditions
• Verify the exact failure path

Step 4: Fix

• Implement fix with regression test
• Deploy to staging for verification
• Deploy to production

Step 5: Verify

• Monitor error frequency post-deploy
• Confirm fix resolves the specific issue
• Mark issue as resolved in Sentry

Metrics Dashboard

Track these key metrics:

Metric	Target	Action if Exceeded
Error rate	< 0.5%	Investigate top errors
Crash-free sessions	> 99.5%	Priority bug fixes
P95 latency	< 500ms	Performance investigation
Error trend	Decreasing	Review release quality

Effective error monitoring transforms reactive firefighting into proactive quality improvement, catching issues before they impact business metrics.