Sentry vs TrackJS: Hands-On Error Tracking Comparison for 2026

A JavaScript error usually tells you where your application broke. What it doesn't always tell you is what the user did to get there. Was it a button click, a failed API request, or something that happened several seconds earlier?

TrackJS answered those questions better than I expected. When I opened a wishlist error in my test application, its telemetry timeline showed every click, page navigation, network request, and console message leading up to the crash. The entire sequence was visible on one screen, including request durations and masked typing activity.

That's impressive for a tool focused almost entirely on JavaScript errors. But it raises another question: what happens when the problem doesn't begin in the browser?

That's where Sentry has an advantage. While TrackJS specializes in browser error monitoring, Sentry extends into distributed tracing, session replay, application performance, logs, profiling, and AI-assisted debugging.

To understand how much that difference matters, I tested both platforms against the same deliberately unreliable Node.js bookstore. I introduced frontend crashes, backend exceptions, failed API requests, and recurring bugs, then deployed a release that made checkout dramatically slower.

The results weren't entirely one-sided. TrackJS provided better context for individual browser errors, and its browser-to-server correlation worked surprisingly well without distributed tracing. However, its message-based grouping split one payment bug into multiple issues, and a previously fixed error remained marked Fixed when it returned.

Sentry gave me a more complete picture of the application, including the database query responsible for the slowdown. But its grouping wasn't perfect either, and its AI assistant misdiagnosed part of the faulty release.

This comparison examines where each tool genuinely helped, where it introduced extra work, and which approach makes more sense for your application.

Sentry vs TrackJS: Results at a glance

The main distinction is scope. TrackJS concentrates on understanding browser errors, while Sentry connects those errors to a broader application monitoring platform.

Here's how that difference affected my tests.

These results reflect my Node.js and Vite application, the SDK versions I installed, and my specific configuration. Different frameworks and environments may produce different results.

How I tested Sentry and TrackJS

To compare both platforms under the same conditions, I used monitoring-lab, a deliberately unreliable online bookstore.

The application consists of a Vite storefront using hash-based routing, an Express 5 API backed by SQLite, an unreliable price service, and a background reporting job that runs every minute.

Each monitoring platform connects through separate browser and server adapters, allowing the application to generate the same failures without changing its core functionality.

The errors I introduced

The bookstore included several predictable failure scenarios:

• Frontend exceptions: A broken wishlist button that throws a TypeError.
• Unhandled browser rejections: An order-tracking request that fails without a rejection handler.
• Failed API requests: Responses returning 404, 500, or 502 status codes.
• Backend exceptions: Invalid coupon codes and nonexistent books.
• Handled server errors: Out-of-stock requests and failures from an upstream pricing service.
• Background failures: A sales reporting job that occasionally fails.
• Performance problems: Slow database queries, delayed requests, and a missing cover image.

A traffic script simulated three customers, alice, bob, and carol, shopping through headless Chromium and making direct API requests.

This generated enough activity to compare browser context, error grouping, server correlation, and release behavior.

Testing across three releases

I also deployed three versions of the application.

The final release introduced two separate problems.

One caused payment exceptions on every fifth attempt. The other made a database query dramatically slower without throwing an error.

This distinction helped establish what each platform could explain through error reports alone and what required additional performance monitoring.

SDKs and test limitations

I used the following versions:

A few conditions are worth keeping in mind.

TrackJS ran on a 14-day trial that included one million monthly page views.

My first TrackJS traffic run was unusable because of an Express integration issue described below. I corrected the adapter, cleared the original results, and repeated the test. All TrackJS results in this article come from the corrected run.

Sentry's AI assistant had access to my GitHub repository, while TrackJS's AI Debugger only received the error report.

The two platforms also received separate traffic runs, six days apart, so their event totals shouldn't be compared directly.

Finally, I didn't test alert delivery or frameworks other than Express.

With those conditions established, here's how the platforms performed.

Setup and onboarding

Both platforms accepted frontend and backend errors in one project, but the setup experience was quite different.

TrackJS was particularly straightforward to install in the browser. Unfortunately, its Node.js integration introduced a problem that affected the application itself.

Sentry: A working integration from the start

Sentry detected my GitHub repository during project creation and suggested Express and Browser JavaScript as monitoring platforms.

Its setup page presented several monitoring capabilities.

Errors, tracing, session replay, logs, and profiling were enabled in the generated configuration.

The snippet also included my project's DSN, making the initial connection straightforward.

After adding the SDKs, browser and backend errors appeared in the same project.

I uploaded browser source maps using sentry-cli after each build, allowing Sentry to display original JavaScript source code rather than minified stack traces.

The setup involved more components than a basic error tracker, but the generated configuration worked with my application.

TrackJS: A simple browser integration

TrackJS begins with a browser agent that can be installed through a script tag or npm.

Its setup page displays the account token and a basic test call.

The test call is straightforward:

TrackJS.track('Testing TrackJS!');

TrackJS then asks you to create an application, which organizes errors from a particular project or environment.

The application key is generated automatically from its name.

I accidentally duplicated part of that key while creating the application. Since TrackJS doesn't allow application keys to be edited afterward, I had to create another application.

That was a minor inconvenience. The bigger issue appeared when I added server monitoring.

TrackJS: An Express handler that interrupted error responses

TrackJS's Express documentation places expressErrorHandler() at the end of the middleware chain.

I followed that approach using trackjs-node 1.3.0.

However, requests that generated server exceptions never completed.

The handler reported the error to TrackJS but didn't pass control to the next error handler or send a response.

As a result, requests involving invalid coupons, nonexistent books, and failed pricing calls remained open until the client timed out.

This also disrupted browser testing because the storefront waited for API responses that never arrived.

To rule out interference from the bookstore's other middleware, I reproduced the behavior in a minimal Express application.

The result was the same.

I then modified my monitoring adapter to pass the error to the next handler, allowing Express to complete the response normally.

After clearing the original data, I repeated the traffic run with the corrected integration.

This finding applies to the package version I tested. It should be checked against newer trackjs-node releases before assuming the issue still exists.

Setup comparison

Verdict: Sentry. TrackJS was faster to configure in the browser, but its documented Express integration caused failed requests to hang in my test. Sentry required more instrumentation but worked without that problem.

Browser error capture

Browser error monitoring is TrackJS's primary purpose, so this was one of the most important parts of the comparison.

Both platforms captured every deliberately introduced browser failure.

The differences came from what they reported automatically and how those reports appeared in the error list.

Sentry: Browser and backend errors in one list

After release 1.0.0, Sentry displayed 11 issues covering the frontend, backend, and background reporting job.

The browser issues included the wishlist crash, failed API requests, and the unhandled order-tracking rejection.

The list also displayed replay counts, indicating which errors had an associated session recording.

The missing cover image didn't become an issue, although it appeared later as a failed network request in session replay.

TrackJS: Automatic network errors

TrackJS captured the same browser failures.

After the first release, its dashboard contained 99 error events across 29 groups.

Each row identified how the error was captured, using categories such as Caught Error, Network Error, Unhandled Rejection, and Express.

The list also showed browser information, URLs, and application versions.

One particularly useful feature was automatic network error reporting.

TrackJS detected failed browser requests even when my application didn't explicitly report them.

That's useful for discovering API failures that aren't handled by application-specific monitoring code.

However, it also created duplicate reports in my test.

My storefront already reported failed API calls as application errors, and TrackJS recorded those same failures again as Network Errors.

The failed checkout appeared as both an application-generated error and an automatically captured 500 : POST /checkout event.

This isn't necessarily a weakness of automatic network monitoring. It becomes a problem when both the SDK and the application report the same failures.

TrackJS provides a network.error setting to disable automatic network error capture when manual reporting is already in place.

What about console errors?

TrackJS also treated console.error calls as errors by default.

During testing, a server log message about headers already being sent became a separate error group.

That was unexpected because the message originated from logging rather than a newly thrown exception.

Sentry treated comparable console activity as contextual information in my configuration.

For applications with noisy console logging, TrackJS's default behavior may therefore create additional error groups.

Browser error capture comparison

Verdict: Tie. Both platforms captured every browser failure I introduced. TrackJS's automatic network monitoring is useful, but it needs careful configuration when your application already reports failed requests.

Debugging a browser error

Capturing an exception is only the beginning. The more useful question is what happened before the error occurred.

This was TrackJS's strongest area.

Its telemetry timeline presented browser activity in a single chronological view, making it possible to understand a failure without navigating through several monitoring screens.

Sentry: Breadcrumbs and contextual information

Sentry's wishlist error included user information, browser details, and breadcrumbs.

The breadcrumb trail recorded a request for book information, the click on the wishlist button, my application's custom message, and the resulting exception.

The click also included a CSS selector identifying the element involved.

That was enough to understand the immediate sequence leading to the failure.

Sentry also linked the error to a session replay, which provided additional context about the shopper's activity.

TrackJS: A timeline of the entire visit

TrackJS displayed a more detailed sequence of events directly on the error page.

The timeline included clicks, route changes, network requests, console messages, and typing activity.

Requests showed their HTTP method, status code, and duration.

Clicks included the corresponding HTML element and its attributes.

Navigation entries recorded the previous and destination routes, while typing activity was masked.

For example, entering a coupon code produced a character count rather than the actual value.

The biggest advantage was that I could read the sequence leading to an error without opening another view.

TrackJS also displayed an "Errors on Page" indicator, allowing me to move between failures associated with the same visit.

The General Information section included the URL, viewport size, and time spent on the page.

For one error, the time on page was just 0.367 seconds.

That provided useful context: the failure happened almost immediately after the relevant page became active.

Is the timeline enough to understand the failure?

For the wishlist error, it was.

The sequence showed the user's navigation, the button click, and the exception that followed.

However, a timeline can't explain every problem.

When the failure originates from backend code or a slow database query, knowing which request failed is only the starting point.

That's where Sentry's connected traces and session replay become more valuable.

Browser debugging comparison

Verdict: TrackJS. Its telemetry timeline offered the clearest standalone explanation of what happened before a browser error. Sentry provided comparable context through multiple connected views, but TrackJS made it easier to read in one place.

Source maps

Browser error reports become much less useful when stack traces point into minified JavaScript bundles.

Source maps solve that problem by connecting generated code back to the original files.

Both platforms supported source maps, but the difference became obvious because my application ran on localhost.

Sentry: Private source-map uploads

Sentry allows source maps to be uploaded using sentry-cli.

I uploaded them after each storefront build.

When I opened the wishlist error, Sentry displayed the original source file.

The stack trace highlighted line 122 of pages.js, where the error occurred.

The maps were stored privately, so they didn't need to be publicly accessible from the application server.

This worked consistently throughout my local testing.

TrackJS: Source maps retrieved from the application

TrackJS takes a different approach.

Its servers attempt to retrieve the relevant JavaScript files, inspect their source-map references, and download the associated maps.

That works when the required files are accessible.

However, my bookstore ran on localhost, so TrackJS couldn't retrieve its scripts.

The error page reported that it couldn't retrieve the script to inspect its source-map directive.

As a result, the stack remained minified.

But what happens when you need to debug an application that isn't publicly accessible?

TrackJS provides a manual alternative.

Applying a source map manually

The stack trace panel includes a drop zone for source-map files.

I dragged the generated map from my build directory onto the wishlist error.

The stack trace immediately changed to the original source location.

TrackJS identified src/pages.js line 122 and displayed the relevant code.

An advantage of this approach is that the map is processed locally in the browser rather than uploaded to TrackJS.

However, the manually applied source map wasn't retained after reloading the error page.

When I reopened the issue, the stack trace returned to its minified form.

For production environments, TrackJS can retrieve maps from accessible URLs or endpoints configured to allow its crawler access.

It also provides options involving allowlisted crawler IP addresses or a secret request header.

However, private development and staging environments may still require manual intervention.

Source map comparison

Verdict: Sentry. Its private source-map uploads worked consistently, including on localhost. TrackJS successfully resolved the original code when I supplied the map manually, but its default retrieval approach wasn't suitable for my environment.

Following a browser error to the server

A browser error doesn't always originate in the browser.

A failed checkout might appear as a rejected API request, while the underlying cause is an exception in the server's application code.

Both platforms provided ways to connect those failures.

TrackJS handled this better than I expected for a browser-focused error tracker, although Sentry offered more information once the connection was made.

Sentry: Distributed tracing across the application

Sentry linked the browser's failed checkout to the backend coupon exception.

The browser issue displayed a related server error in its trace preview.

Opening the trace revealed the request's execution path.

The trace connected the shopper's click to the browser HTTP request, Express handler, and database query.

The server error also included request information and the corresponding session replay.

This made it possible to follow the failure across application boundaries without manually matching timestamps or identifiers.

TrackJS: Browser-to-server correlation without tracing

TrackJS's Node.js agent adds a correlation identifier to HTTP responses.

The browser agent records that identifier and uses it to associate requests with server errors.

On a failed checkout request, TrackJS displayed a link indicating that a server error occurred during the request.

Clicking the link opened the backend coupon error.

The error belonged to the same request and user.

This was a meaningful capability because TrackJS connected frontend and backend errors without a distributed tracing product.

However, the backend error contained less debugging context.

It displayed the stack location but didn't show the surrounding source code or request body.

The correlation link also appeared on successful requests and requests without an associated server error.

In those cases, opening it could lead to an empty error page.

What happens after you follow the link?

This is where the tools differ most.

TrackJS takes you from the browser request to the associated backend exception.

Sentry goes further by showing what happened inside the request, including the operations and durations recorded by its tracing instrumentation.

That additional context becomes valuable when the failure depends on a database query, external service, or another backend operation.

Browser-to-server comparison

Verdict: Sentry. TrackJS's correlation links worked well and added useful backend context to browser errors. Sentry provided a deeper investigation path through distributed tracing, request details, and source code.

TrackJS connected the browser failure to its server error, while Sentry provided a full distributed trace.

I also investigated the same checkout failure in Better Stack.

The server exception included direct links to related logs, spans, releases, and session replays.

Opening the associated session displayed browser and backend activity together.

The timeline contained 30 frontend spans and seven backend spans, connecting the shopper's interaction to the server work behind it.

The browser integration was still in beta during my test, and I couldn't resolve the storefront's minified stack frames.

Follow a click to the query behind it

Error grouping

A useful error tracker should turn hundreds of occurrences into a manageable list of actual problems. That means keeping repeated failures together without combining unrelated bugs.

Both Sentry and TrackJS managed that reasonably well at first. The differences became clear when I introduced errors with changing values and deployed code that moved existing stack traces.

Sentry: Better grouping when error messages change

Sentry uses stack trace information as an important part of its grouping logic. This worked particularly well with the payment failures I introduced in release 1.2.0.

Each failed payment included a different amount in its error message, but Sentry kept all 13 occurrences in one issue.

However, changing the checkout code introduced another problem.

The updated code shifted the stack traces for the existing coupon and out-of-stock errors.

Sentry interpreted those changes as new issues, creating duplicate groups for bugs that were already being tracked.

So while Sentry handled changing messages well, its grouping wasn't completely stable across code changes.

TrackJS: One bug can become several issues

TrackJS takes a different approach, grouping errors primarily by their messages.

That sounds reasonable until the message includes something that changes with every occurrence.

What happens when the same payment failure includes a different amount each time?

In my test, TrackJS created a separate server error group for each amount.

Three failed payments became three separate server groups, with another error reported by the browser.

Although they originated from the same underlying bug, each appeared as an independent problem.

The issue wasn't limited to payments.

TrackJS also separated out-of-stock errors by book title and quantity, order-tracking failures by changing IDs, and background job failures by their run numbers.

That makes the error list harder to manage because multiple rows can represent the same problem.

TrackJS normalized some network URLs

TrackJS did handle some automatically captured network errors differently.

It normalized certain request URLs so failures with changing identifiers could appear together.

The difference is visible here.

TrackJS grouped some automatically detected network failures under normalized URL patterns, while errors reported by my application remained separate.

This distinction matters when deciding whether to rely on automatic network monitoring or manually report failed API requests.

TrackJS also offers a Create Group action to combine issues manually, although I didn't test that workflow.

Where message-based grouping helped

There was one advantage to TrackJS's approach.

The coupon error's message remained the same across all three releases.

When release 1.2.0 changed the checkout code, TrackJS kept the error in its original group.

Sentry created a duplicate because the stack location had changed.

So neither approach was perfect.

Sentry was more resistant to changing message values, while TrackJS kept groups stable when stack locations changed but messages remained identical.

Error grouping comparison

Verdict: Sentry. Its default grouping handled changing error messages more reliably, reducing duplicate issues during normal operation. TrackJS kept groups stable when code moved, but repeatedly split the same underlying bugs whenever their messages contained changing values.

Session replay and real user monitoring

TrackJS's telemetry timeline is excellent at explaining the sequence of actions before an error.

But is that enough when you need to understand what a user actually experienced?

Session replay adds a visual recording of the interaction, while real user monitoring measures browser performance.

Sentry provides both capabilities. TrackJS doesn't attempt to offer them.

Sentry: Session recordings with AI summaries

Sentry recorded the storefront sessions during my test.

Page text was masked by default, and each replay opened with an AI-generated summary.

The summary described the shopper's activity and highlighted the wishlist failure.

The replay also included network requests and related browser events.

That provided a visual record of what happened rather than only a list of actions.

Sentry also collected Web Vitals.

Its dashboard gave the storefront an overall score of 90.

However, my hash-based routing caused every page to appear under /, making it difficult to identify the slow book page from the default breakdown.

TrackJS: A timeline instead of a recording

TrackJS doesn't provide session replay.

Its telemetry timeline records clicks, navigation, network requests, and console activity, but doesn't show the visual state of the page.

For many JavaScript exceptions, that may be enough.

For problems involving confusing UI behavior, missing content, or layout changes, a replay can provide context that an event timeline doesn't capture.

TrackJS also doesn't provide general page-speed monitoring or Web Vitals.

Its Time On Page value measures how long the visitor had been on the page before the error, not how quickly the page loaded.

Session replay comparison

Verdict: Sentry. TrackJS provided a detailed timeline of browser activity, but Sentry's session replay and Web Vitals offered more complete visibility into the user's experience.

TrackJS offered a detailed event timeline without a visual recording, while Sentry connected browser errors to session replay.

I also recorded the storefront sessions in Better Stack.

The replay displayed the browser session alongside an event list containing API requests, handled errors, console output, clicks, and custom application events.

The sessions list also made it possible to identify recordings with errors.

Each session showed browser information, visited pages, clicks, and error counts.

Headless Chromium sessions were initially classified as bots, so I disabled that filtering during testing.

Watch the session behind the error

Release tracking and regressions

A monitoring platform should help you distinguish new bugs from problems that return after being marked as fixed.

To test this, I marked the coupon error as fixed during release 1.0.0.

I then deployed release 1.1.0 without correcting the bug.

Both platforms recorded the new occurrences, but their handling of the issue's status was very different.

Sentry: Returning errors clearly identified

Sentry allows you to resolve issues for a future release or commit.

I selected the next-release option.

When release 1.1.0 generated the same coupon error, Sentry reopened it and marked it as Regressed.

The regression label appeared directly in the issue list.

Sentry also recorded the event in the issue's activity history.

The release page contained a dedicated Regressed tab showing the returning error.

This made it clear that the bug had previously been considered fixed.

TrackJS: The error remained Fixed

TrackJS offers several issue statuses, including Fixed, Investigating, Prioritized, Unable to Recreate, and Won't Fix.

These statuses describe how a team is handling an issue.

However, they aren't tied to particular releases.

I marked the coupon error as Fixed and then deployed release 1.1.0 with the bug still present.

The issue received additional events, but its status didn't change.

The event count increased from seven to 12, while the issue continued displaying Fixed.

There was no automatic reopening or regression label.

TrackJS did record which application versions contained the error.

The Show Versions chart separated occurrences from releases 1.0.0 and 1.1.0.

That made it possible to see the bug had returned, but only after opening the issue.

Nothing in the main error list highlighted the regression.

Release tracking comparison

Verdict: Sentry. It automatically marked the returning error as a regression, while TrackJS continued displaying it as Fixed despite receiving new occurrences.

TrackJS left the returning coupon error marked Fixed, while Sentry reopened it with a Regressed label.

I also tested the same scenario in Better Stack.

The error appeared with a red Reoccurred status directly in the list.

The activity history recorded the resolution and subsequent return.

This made the sequence easy to understand without manually comparing versions.

Sentry also provided a dedicated Regressed tab per release, which Better Stack didn't offer during my test.

Know when a fixed bug comes back

Alerts and notifications

TrackJS and Sentry both provide notifications for application errors, although I didn't test alert delivery in either platform.

The difference is in the available conditions.

TrackJS: Error notifications and summaries

TrackJS configures notifications per application.

Daily and weekly summaries were enabled by default.

Additional notification options included new error messages, trending errors, and error-rate increases.

Notifications could be delivered by email, Slack, or Microsoft Teams.

However, I didn't find a dedicated notification for an error that returned after being marked Fixed.

TrackJS's message-based grouping also creates a potential problem for new-error notifications.

When one payment bug generates a different message for each amount, those occurrences may be treated as separate new errors.

That could generate more notifications than the number of actual underlying problems.

Sentry: More alert conditions

Sentry supports issue-based alerts, metric alerts, and regression conditions.

Its broader monitoring platform also provides additional signals that can be used for alerting.

However, I didn't configure or evaluate notification delivery.

Alerts comparison

Verdict: Not compared. Both platforms offered notification options, but I didn't test their delivery or reliability.

Backend errors and performance monitoring

One of the most important limitations of error-only monitoring appeared in release 1.2.0.

Checkout became dramatically slower, but the database slowdown didn't throw an exception.

Instead, requests continued running while taking seconds instead of milliseconds.

The question was whether each platform could identify the source of the delay.

Sentry: The slow query identified in a trace

Sentry's distributed tracing showed the checkout request and its backend operations.

The request took approximately 2.26 seconds.

The book lookup accounted for 1.79 seconds, while the payment operation failed after approximately 459 milliseconds.

That made the source of the slowdown clear.

Sentry also showed the performance regression by release.

Checkout's p95 latency increased from single-digit milliseconds to 5.93 seconds in release 1.2.0.

This connected the performance change to the faulty deployment.

TrackJS: Timing information inside failed requests

TrackJS doesn't provide general application performance monitoring or distributed tracing.

However, its browser telemetry does record network request durations.

In my test, a failed checkout request took more than four seconds.

Earlier checkout requests had taken approximately 15 milliseconds.

That made the slowdown visible when investigating the browser error.

TrackJS's Node.js agent also captured my application's slow-query warning as console activity.

However, TrackJS couldn't trace the request into the database to identify the expensive operation.

Its timing information was limited to requests appearing in the browser error context.

Why did the broken release have fewer errors?

TrackJS's Versions page revealed another interesting result.

Release 1.2.0 had 68 errors, compared with 102 in release 1.0.0 and 97 in release 1.1.0.

Looking only at error counts, the faulty release could appear healthier.

However, checkout had become dramatically slower.

The traffic script completed fewer operations because requests took longer.

Lower error counts didn't mean the application had improved. The slowdown simply reduced the number of operations completed during the test.

This is an important limitation of judging release health through error counts alone.

A performance regression can make an application less usable without generating more exceptions.

Backend metadata and privacy

TrackJS's Node.js agent also attached machine information to server errors by default.

That included the hostname, operating system username, and working directory.

This can help identify the environment where an error occurred.

However, some of those values may be sensitive, so teams should review the agent's defaultMetadata configuration before using it in production.

Backend monitoring comparison

Verdict: Not scored. Sentry identified the slow database query because it includes performance monitoring. TrackJS doesn't attempt to provide those capabilities, so I wouldn't treat their absence as a failure of its browser error tracking focus.

TrackJS showed that a failed checkout took several seconds, while Sentry identified the slow database operation within the request.

I also investigated the same checkout failure in Better Stack.

The request took 2.19 seconds, including 1.53 seconds in the book query and 660 milliseconds in the payment operation.

The corresponding application logs were available from the same request.

The slow-query warning appeared immediately before the checkout failure, connecting the measured delay with the application's own diagnostic information.

My SDK didn't set a service name, so the Service column remained empty.

Open the request behind a slow error

AI-assisted debugging

Both Sentry and TrackJS include AI-assisted debugging, but their assistants work with different information.

Sentry's Seer can inspect a connected source repository.

TrackJS's AI Debugger analyzes the contents of an error report without requiring repository access.

That difference shaped the results.

Sentry: Exact explanations with repository access

Seer analyzed the coupon error in approximately 15 seconds.

It correctly identified the invalid coupon lookup and the code responsible for the exception.

It also recognized the accepted coupon codes and proposed a reasonable fix.

On the payment error from release 1.2.0, Seer identified the counter responsible for failing every fifth payment.

Those results matched the application code.

However, Seer Agent struggled when I asked it to explain the checkout performance regression.

It identified the increase in latency but blamed payment timeouts rather than the slow database query.

A follow-up question didn't correct the explanation.

So while Seer performed well on individual code defects, its broader performance diagnosis wasn't reliable in this test.

TrackJS: AI debugging without connecting a repository

TrackJS's AI Debugger was available directly on error pages.

Opening the tab started the analysis, without requiring a repository connection.

According to the product information available during testing, TrackJS sends a sanitized version of the error report to OpenAI, excluding user identifiers and IP addresses.

Each response took approximately 20 seconds.

On the wishlist crash, the assistant examined the telemetry timeline and identified two possible explanations.

Rather than choosing one without evidence, it acknowledged that the report didn't contain enough source code to determine the exact cause.

On the coupon error, it suggested a problem with the coupon lookup but didn't identify the precise code defect.

That caution was appropriate given the information available.

TrackJS's strongest result came from the faulty release

The AI Debugger performed particularly well when examining the payment failure introduced in release 1.2.0.

It identified the relevant area of server.js and recognized that the failure belonged to the new release.

It also ruled out several unrelated explanations.

Its fix suggestions included investigating the slow book lookup separately.

That was significant because the database slowdown and payment failures were independent problems.

TrackJS's AI Debugger recognized that distinction, while Seer Agent incorrectly attributed the slowdown to the payment operation.

However, TrackJS couldn't identify the deliberate fifth-payment failure condition because it didn't have access to the source code.

It treated the error message as an upstream timeout rather than identifying the counter inside the application.

AI comparison

Verdict: Sentry. Repository access allowed Seer to identify the exact causes of the coupon and payment errors. TrackJS's assistant required less setup and provided a more cautious explanation of the faulty release, but couldn't identify every code-level cause.

Seer correctly explained individual exceptions but misidentified the cause of the slowdown. TrackJS's AI Debugger recognized that the slow query was separate from the payment failure, but couldn't inspect the code responsible.

I also asked Better Stack's AI SRE to investigate the same release without connecting my repository.

AI SRE identified the book query as the operation responsible for the slowdown.

It then investigated the payment failures separately.

The analysis found that 10 of 48 payment calls failed in release 1.2.0.

The investigation took approximately four minutes and cost $0.49.

It correctly separated the two problems, although it couldn't identify the deliberate fifth-payment condition without repository access.

Ask AI SRE what your last release changed

Pricing

Sentry and TrackJS use different pricing models.

Sentry charges according to the telemetry collected, including errors, spans, replays, and logs.

TrackJS primarily charges by page views, with per-minute limits on the number of errors retained.

The following figures reflect the vendor pricing information collected on October 27 and 28, 2026. Confirm current prices before purchasing.

Free plans and trials

Sentry offers a permanent free plan.

TrackJS provides a 14-day trial but doesn't offer an equivalent ongoing free tier.

TrackJS also advertises discounts for bootstrapped businesses and sponsorship options for nonprofit and open-source projects.

Entry-level paid plans

Sentry Team starts at $29 per month, or $26 per month with annual billing.

TrackJS Starter costs $49 per month, or $45 per month with annual billing.

TrackJS Professional costs $99 per month, or $91 per month with annual billing.

It includes one million page views, five applications, and higher per-minute error limits.

TrackJS Business starts at $199 per month for five million page views.

Sentry Business costs $89 per month, or $80 per month with annual billing.

The availability of TrackJS's AI Debugger across individual paid plans should be verified before purchasing.

How the pricing models differ

The biggest difference isn't the starting price. It's what happens when traffic or error volume increases.

TrackJS charges primarily by page views rather than individual errors.

That means an increase in JavaScript exceptions doesn't automatically increase the subscription cost.

However, each plan limits how many errors can be saved per minute.

For example, Starter retains up to 20 errors per minute.

Additional occurrences may be dropped when that limit is exceeded.

TrackJS offers predictable costs during noisy incidents, but you may lose some error occurrences when volume spikes.

Its lower plans also have shorter retention periods.

Starter keeps errors for 10 days, while higher plans provide longer history.

Sentry's pricing works differently.

Errors, spans, replays, and logs each contribute to separate allowances.

This gives you flexibility over which signals to collect, but high telemetry volume can increase the bill.

The advantage is that Sentry includes capabilities TrackJS doesn't provide, including session replay, distributed tracing, and profiling.

With TrackJS, those capabilities would require separate products.

Pricing comparison

Verdict: Sentry. It offers a permanent free plan, lower entry-level pricing, and more monitoring capabilities. TrackJS's page-view model may appeal to teams with unpredictable error volumes, but its collection limits and shorter retention deserve attention.

Full feature comparison

The following table brings together the main findings from my tests.

Which should you choose?

Both platforms captured every browser failure I introduced, so the decision depends on how much additional information you need during debugging.

Choose Sentry if you need full-stack debugging

Sentry is the stronger choice when application failures can originate in the browser, backend, database, or external services.

It connected browser errors to backend exceptions, displayed source code, and traced failed requests into database operations.

Session replay and release tracking were particularly useful, providing visual context for browser failures and clearly identifying returning bugs.

Sentry also detected the slow database query introduced in release 1.2.0.

However, its grouping created duplicate issues after code changes, and Seer misidentified the cause of the performance regression.

Its usage-based pricing also requires attention as telemetry volume grows.

For another browser-focused comparison, our Sentry vs Raygun comparison evaluates a platform with real user monitoring, while Sentry vs Rollbar examines another error tracker offering session replay.

Choose TrackJS if you primarily investigate browser errors

TrackJS makes sense for frontend teams that want detailed JavaScript error information without adopting a broader monitoring platform.

Its telemetry timeline was the clearest browser debugging interface in my test.

It displayed clicks, navigation changes, network requests, console messages, and durations together.

Automatic network error reporting and browser-to-server correlation were also useful.

Its AI Debugger required no repository connection and provided reasonable explanations based on the available error context.

However, there are important limitations.

TrackJS doesn't offer distributed tracing, session replay, or general performance monitoring.

Its message-based grouping can create duplicate issues when messages contain changing values, and its Fixed status doesn't automatically change when an error returns.

Source-map access also needs planning, especially for private environments.

Finally, the Node.js Express integration caused requests to hang in the SDK version I tested, so that behavior should be verified with the version you intend to use.

Frequently asked questions

Is Sentry better than TrackJS for JavaScript error monitoring?

Sentry provides a broader debugging platform, including distributed tracing, session replay, and performance monitoring.

However, TrackJS provided better standalone context for individual browser errors in my test through its detailed telemetry timeline.

The better choice depends on whether your investigations usually stay in the browser or extend into backend services.

Does TrackJS automatically capture browser errors?

Yes. TrackJS captures JavaScript exceptions, unhandled promise rejections, and failed network requests.

It can also report console.error messages.

In my test, automatic network monitoring created duplicate reports because the application was already reporting the same failures manually.

Can TrackJS monitor Node.js applications?

Yes. TrackJS provides a Node.js agent that captures server-side errors and supports correlation with browser requests.

However, it doesn't provide the same distributed tracing, database performance analysis, or profiling capabilities as Sentry.

The Express error handler in the version I tested also required a workaround to prevent failed requests from hanging.

Does TrackJS support session replay?

No. TrackJS provides a telemetry timeline rather than visual session replay.

The timeline records clicks, navigation changes, typing activity, network requests, and console messages.

Sentry provides full session replay with masked text and AI-generated summaries.

Which platform handles recurring errors better?

Sentry performed better in my regression test.

When a previously resolved coupon error returned in a new release, Sentry marked it as Regressed.

TrackJS recorded the new occurrences but continued displaying the issue as Fixed.

Is TrackJS cheaper than Sentry?

Not at the entry level in my October 2026 pricing snapshot.

Sentry offered a permanent free plan and Team pricing starting at $29 per month.

TrackJS provided a 14-day trial, with Starter starting at $49 per month.

However, TrackJS charges primarily by page views rather than individual errors, which may make costs more predictable for applications with noisy error volumes.

Final thoughts

TrackJS proved that a focused browser error tracker can sometimes provide a clearer debugging experience than a much larger monitoring platform. Its telemetry timeline made it easy to understand user actions and network requests leading up to a failure, without searching through several views.

The limitations appeared when problems extended beyond the browser. TrackJS split one payment bug into multiple issues, kept a returning error marked Fixed, and couldn't explain the database slowdown. Sentry connected those failures to backend traces and provided more complete debugging context, although its grouping and AI analysis weren't flawless either.

Choose TrackJS if your priority is investigating browser JavaScript errors quickly. Choose Sentry if you need to follow failures beyond the browser, particularly when debugging involves backend exceptions, slow requests, or regressions after deployment.

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