Best 8 Error Tracking Tools for Developers

We’ve all been in the situation where it’s midnight or a weekend, and you suddenly get an on-call alert about a simple error. When you expand the details, all you get is: “No response on hitting [X] button”, no screenshots and no details on exactly how the error occurred. Determining the source of the problem is like trying to find a needle in a haystack.

In DevOps, fixing errors requires more than merely detecting failures; it depends on many factors. It’s not just a question of what malfunctioned, but what caused that whole experience, which could be a database lag, browser issue, etc. Although numerous tools on the market today help us get to the problem faster, choosing which one best suits your team can be overwhelming.

Below is a hands-on summary of leading tools, which we’ve put together to show what works best for various scenarios, helping you make an informed decision.

The Importance of Error Tracking tools for developers in 2026

These utilities concentrate specifically on faults within application code. They are typically the tools that an organization sets up.

1. Hud

The Runtime Sensor Built for Developers, Not Dashboards

Hud is more of a “heads-up display.” It takes a unique approach to error tracking by acknowledging something most developers feel but rarely say out loud. Hud isn’t positioned as a traditional error tracker, but its ability to observe real production behavior at runtime puts it in direct conversation with the best tools in this category, and gives it a distinctly different edge.

Areas of excellence:

Hud acts more like a real-time runtime code sensor than a traditional monitoring tool in this space. You don’t have to open a browser or skim through a dozen graphs or charts. It streams live production insights, errors, performance hiccups, and execution flow, straight into your IDE (VS Code or JetBrains). The context shows up exactly where you write, read, and debug code.

But the real win is function-level root cause analysis.

Hud doesn’t just say “something failed.” It goes deeper and shows:

• Which function triggered the issue.
• Which call path led there.
• And why that function behaved differently from its baseline.

For developers, this feels less like “monitoring” and more like having a flashlight inside the runtime.

No-Config by Design

One of Hud’s biggest differentiators is that it requires zero configuration.

There are:

• No alerts to set up.
• No thresholds to tune.
• No SLOs to define.
• No sampling rates.
• No dashboards to wire up.

Hud builds automatic baselines by simply watching your code run. That means early detection without predictive models or custom rules. It’s the closest thing to “monitoring that configures itself.”

The “Gotcha”:

Hud is a mindset shift. If your team is used to big wall-mounted dashboards with graphs and traffic-light charts, the lack of a traditional UI may feel unusual at first. Hud is intentionally designed for developers, not for status reporting.

• Best For: Teams leaning heavily on AI-assisted development and developers who prefer staying inside their editor instead of context-switching to dashboards.

2. Sentry

Sentry is an application error monitoring platform that focuses on capturing runtime exceptions across frontend, backend, and mobile applications.

Key Features:

• Captures unhandled exceptions with full stack traces
• Breadcrumbs record user actions and system events leading up to an error
• Release tracking to correlate errors with specific deployments
• GitHub and GitLab integrations to associate errors with recent code changes
• Alerts and notifications via Slack, email, and webhooks
• Supports a wide range of SDKs (JavaScript, Python, Java, iOS, Android, etc.)
• Sampling and rate limiting controls to manage event volume

3. Rollbar

Rollbar is an error tracking tool that is designed to help teams monitor application errors and link them directly to deployments and releases.

Key Features:

• Captures runtime errors with full stack traces
• Tracks releases and connects errors to specific deployments
• Integrates with GitHub and GitLab for issue tracking
• Live error feed during and after deployments
• Automatic error grouping to reduce duplicates
• Alerts when error rates spike after a release
• Notifications via Slack, email, and webhooks

4. Raygun

Raygun combines error tracking with real user monitoring and it also helps teams understand not just what broke, but who it affected.

Key Features:

• Tracks frontend, backend, and mobile crashes
• Real user monitoring (page load times, sessions)
• Prioritizes errors based on user impact
• Session replay and performance timelines
• Strong mobile crash reporting (iOS & Android)
• Captures device details like OS, memory, and battery
• Alerting and team notifications

5. Bugsnag

Bugsnag focuses on application stability and  a strong emphasis on mobile and client-side monitoring.

Key Features:

• Crash reporting for web, backend, and mobile apps
• Stability Score showing crash-free sessions
• Release health tracking
• Detailed mobile diagnostics (device state, memory, orientation)
• Error grouping and impact analysis
• CI/CD integrations
• Alerts via Slack and email

6. New Relic

New Relic is a full observability platform that combines performance monitoring, tracing, and infrastructure visibility.

Key Features:

• Distributed tracing across microservices
• Application performance monitoring (APM)
• Infrastructure and host monitoring
• Error tracking with log correlation
• End-to-end transaction tracing
• Custom dashboards and alerting
• Supports multiple programming languages

7. GlitchTip

GlitchTip is an open-source error tracking tool that is built to be compatible with Sentry’s SDKs.

Key Features:

• Works with existing Sentry SDKs
• Self-hosted deployment option
• Captures exceptions and stack traces
• Issue grouping and tagging
• Email and webhook notifications
• User data controls for compliance
• Open-source and community maintained

8. Honeycomb

Honeycomb is an observability tool that is built for teams that need deep visibility into complex production systems.

Key Features:

• Supports high-cardinality queries
• Distributed tracing
• Custom event instrumentation
• Ad-hoc querying on production data
• Breakdowns by user, region, version, and more
• Service dependency mapping
• Alerting and SLO monitoring

Critical Strategy: Moving Beyond the Tools

Acquiring a gym membership won’t get you in shape. Likewise, getting Sentry won’t fix your bugs. Here are three approaches that distinguish performing engineering teams from others.

Strategy 1: The “Session Replay” Revolution

The biggest trend in 2025-26 is something called “Session Replay”.

Tools like LogRocket, Highlight.io, and now Sentry (Replay) record a video-like reproduction of the user’s session, allowing you to see exactly how the issue occurred.

• Why it matters: A stack trace points to the location of the code failure. A session replay shows the user pressed the “Submit” button 14 times because the spinner was not visible.
• The Strategy: Activate Session Replay for error incidents to conserve bandwidth. When an error happens, avoid checking the logs and view the video instead. It immediately resolves “Could not reproduce” cases.

Strategy 2: Fighting “Alert Fatigue”

The number one reason error tracking fails is alert fatigue.

If all errors are posted to the production-error Slack channel, developers will silence it within two days. After being muted, the tool becomes essentially ineffective.

The Fix:

1. Stop sending “Warning” logs to Slack. Only send “Critical” or “Error.”
2. Use Dynamic Thresholds. Don’t alert on every single error. Alert if “Error rate > or equal 1% of traffic” or if there are, let’s say, more than 50 errors in 5 minutes. Otherwise, it’d be too noisy.
3. Ownership: Route errors to the specific team responsible for the code (Checkout errors -> Checkout Team Slack) instead of a general channel.

Strategy 3: Scrubbing PII (Protect Your Users)

Error logs can expose sensitive data, making them a target for hackers.

• “Sign-in attempt unsuccessful, for the email: bob@gmail.com using password: hunter2”
• Transaction unsuccessful, for card: 4242-4242.
  If this information reaches Sentry or Datadog, it results in a security and compliance violation.
• The Fix: Set up your SDKs to “clean” data before it is transmitted from the browser or server. Every leading tool offers beforeSend hooks. Employ regex to automatically substitute credit card information and passwords with [REDACTED].

Summary: The Final Verdict

The ecosystem is crowded, but the choices are actually quite clear when you look at your team structure.

FAQs

Do we need more than one error tracking tool?

Sometimes, because different error-tracking tools focus on different stages of the problem, traditional error trackers are very good at detecting that an error occurred and surfacing stack traces and breadcrumbs. Hud focuses on what error trackers typically struggle with: understanding why the error happened in real production conditions, at the function level, without additional logging or configuration. Teams often use Hud alongside existing tools to shorten the gap between detection and root cause.

Is Sentry enough for a startup?

For many teams, yes, Sentry is often the most common way to get visibility into crashes and exceptions. Teams start to feel its limits when errors are hard to reproduce, occur only under specific conditions, or require adding more logs and alerts to understand. Hud is designed for exactly those cases, providing function-level runtime context automatically, so teams can get to the root cause without extra instrumentation.

How do we avoid alert fatigue?

Alert fatigue usually happens when error tracking relies on predefined rules, thresholds, and event volume. Traditional tools manage this with sampling and careful alert configuration, which helps but adds overhead. Hud takes a different approach by learning normal behavior automatically and surfacing errors and performance deviations when they actually matter, reducing the need for manual alert tuning as systems evolve.

What is the biggest mistake teams make with error tracking?

Installing a tool and assuming it magically fixes issues. Tools surface data; you still need processes, ownership, and clean instrumentation.

Where does Hud fit in this ecosystem?

Hud fits alongside traditional error tracking tools as a runtime-first approach to error diagnosis. Instead of focusing on collecting and aggregating error events, Hud observes how code behaves in production at the function level and captures deep context only when errors or degradations occur. The result is faster root-cause understanding, without additional logging, sampling trade-offs, or alert configuration.

About the author

Omer Grinboim

Founding Engineer & Head of Customer Operations @ Hud