LogRocket vs TrackJS: A Hands-On Error Tracking Comparison for 2026

LogRocket and TrackJS agree on one thing most error trackers don't: neither charges you for errors. LogRocket bills by recorded session, TrackJS bills by page view, and a release that throws a thousand exceptions an hour costs the same as a quiet one. Both are built for teams whose hardest bugs happen in the browser, and both price themselves around visits.

From there, they take opposite views of how much a visit should cost to understand. LogRocket is a session replay platform that keeps a full recording of every visit, with each network request and its body, the console and the browser's performance on one timeline. Errors, product analytics and Galileo, its AI assistant, sit on top of those recordings.

TrackJS keeps far less and explains it faster. TrackJS is a lightweight JavaScript error monitor with a browser agent at its core, a smaller Node.js agent, and an AI Debugger that runs on every error. Instead of a recording, each error carries a timeline of the clicks, navigations, requests and console lines that led to it.

In my test, that split showed up everywhere. LogRocket gave me more evidence per failure and handled source maps properly. TrackJS also watched my API, tied a failing checkout in the browser to the exact exception on the server, and its AI read the bad release more clearly than Galileo. Both tools fumbled the one test every team cares about: a fix that didn't hold.

Both ran against my intentionally buggy test bookstore in late October 2026. You'll see them side by side on setup, errors in the browser and on the server, source maps, the error page itself, how each reaches the API, grouping, regressions, slowdowns, alerting, AI and cost.

LogRocket vs TrackJS: Results at a glance

Treat these as results from my own Express and Vite setup. A different framework, or source maps served publicly, would change a few verdicts.

How I tested LogRocket and TrackJS

monitoring-lab, the app I test with, is an online bookshop with bugs planted on purpose. A Vite front end with hash routes talks to an Express API, SQLite holds the catalogue, a fake pricing service fails at random, and a reporting job fires every sixty seconds. Each monitoring tool connects through a browser adapter and a server adapter, so the app's own code stays the same for every tool.

The planted failures:

• In the browser: adding a book to the wishlist crashes with a TypeError, and the order-tracking call hits a route that doesn't exist and leaves its rejection unhandled.
• Failed API calls: invalid book IDs and broken checkouts return 404 or 500, and pricing calls return 502.
• On the server: an unrecognised coupon code crashes checkout with a TypeError, which I'll call the coupon bug. Stock shortages, upstream 503s and an uncaught rejection round it out.
• In the background job: some sales report runs throw.

Scripted shoppers alice and bob browse in headless Chromium, and carol orders through the API only.

The releases

On 1.2.0, the useful answer has two parts: checkout got slower, and a new error appeared. A tool that blends them sends you looking in the wrong place.

What ran, and what to keep in mind

Keep these in mind:

• Only TrackJS watched the server. LogRocket has no Node.js SDK, so my server adapter only printed each request's LogRocket session link to the console.
• Both ran on 14-day trials. LogRocket's was the Pro trial, which includes Galileo, and TrackJS's trial allowed 1 million page views.
• TrackJS was tested on October 28 and LogRocket the next day, so the counts below aren't directly comparable.
• On 1.1.0 I triggered the coupon bug in LogRocket myself, with one order in a real browser tab, since the scripted shoppers didn't reach it that time.
• The AI tests weren't identical. TrackJS's AI Debugger runs on each error automatically, while I asked Galileo about the whole release.
• I didn't test alert delivery or LogRocket's product analytics.

Setup

Both browser agents went in quickly. The differences came from the parts around them: source maps for LogRocket, and the server agent for TrackJS.

LogRocket: Two lines and the first recording

After you pick a use case, you get one npm install and a single init call carrying your app ID:

Once a visitor loads the page, it shows up as a complete recording, even from a headless browser. An AI Setup tab gives the same steps as a prompt for Cursor or Claude. The one hiccup came with releases: logrocket-cli couldn't create one on my fresh project until a session had arrived.

TrackJS: A short wizard, and one handler to fix

TrackJS's onboarding has four steps, and the second creates an application:

The key field fills itself as you type the name, and the snippet below updates to match. I typed over it and ended up with a doubled key. Keys can't be edited afterwards, only names, so I made a new application.

The bigger problem was on the server. In trackjs-node 1.3.0, the Express error handler ignores the options you pass it, so it reports a 5xx error and never hands the request on. Every failing request hung with no response, and the browser shoppers waiting on them timed out. I reproduced it in a minimal Express 5 app, then worked around it in my adapter by passing the error on myself.

Loading the settings page from a typed URL also returned TrackJS's own error screen:

The sidebar link works, so it's a minor bug. It does sit oddly on an error tracker.

Setup comparison

Verdict: LogRocket. Its browser install worked first time, and nothing it did broke the app. TrackJS's server agent did, until I patched around it.

Catching browser errors

Both tools caught every browser failure the traffic produced, and both added error reports of their own for failed requests.

LogRocket: Automatic network errors and text on the page

Errors live under Signals until you promote one to an Issue. The errors list after the first run:

Each failed request gets a Network Error row of its own, beside the ApiError my code reports, and book IDs in the URL collapse into GET /books/*/. Rows count sessions rather than events. On the bad release, LogRocket also added a row for a failure no exception announced:

That Error State row tracks "Something went wrong" text shown on the page, so a failure your code catches and turns into a friendly message still gets counted.

TrackJS: Every failure, twice, plus console errors

TrackJS also reports each failed fetch on its own, as a Network Error next to the app's report:

The checkout failure appears as the app's own custom error and as TrackJS's 500 : POST /checkout. TrackJS goes one step further and turns console.error calls into error reports by default. That caught a server side effect other tools only logged, and it can also fill the list with noise from libraries that log loudly.

Browser capture comparison

Verdict: Tie. Both caught everything and doubled up API failures. LogRocket notices error text on the page, and TrackJS notices console errors.

Catching server errors

Only TrackJS takes part here. Its Node agent reported the server failures, and LogRocket can't receive them.

Once my adapter worked around the handler bug, TrackJS caught every server failure: the coupon and missing-book TypeErrors, the out-of-stock errors, the 503s, the sales report and the unhandled rejection. Each server error showed server.js with a line number, though without the code around it, and the user who hit it, because the adapter sets the user per request.

Two details are worth knowing before you rely on it:

• No request data. The error page shows no body, headers or params, only the server, OS and uptime.
• The telemetry leaks across requests. A server error's timeline included this request's breadcrumbs and, below them, console lines from minutes earlier, such as startup messages and other jobs.

The agent also adds the hostname, the OS user name and the working directory to every server error as metadata. Check whether you're comfortable sending those before you deploy.

Verdict: TrackJS. It's the only one of the two that reports what your API throws.

Source maps

Readable stack traces depend on source maps, and the two tools handle them in very different ways.

LogRocket takes private uploads from its CLI. With the maps uploaded, the wishlist crash pointed at my code:

The frame reads ../../src/pages.js at line 122:13, with wishlist.items.push(book.id); marked and the code around it. Errors stored before the upload stay minified, and hidden source maps aren't supported.

TrackJS fetches maps from the script's public URL. On my localhost build, it couldn't:

The page offers a drop zone instead. Dragging my .map file onto the stack trace worked:

Sourcemap Support switched on, and the frame became ../../src/pages.js 122:13. The map is applied in your browser and never sent to TrackJS, which is good for privacy, but it doesn't persist. After a reload, the same error was minified again.

Verdict: LogRocket. If your bundles aren't public, TrackJS leaves you mapping errors by hand, one page load at a time.

Debugging an error

Once you open an error, the question is how quickly you can see what led to it. LogRocket answers with a recording, TrackJS with a timeline.

LogRocket: A recording with the browser's devtools beside it

LogRocket's error page opens the recording just before the failure, and the full player adds the network and console:

Every request is on the waterfall, including a broken cover image's 404 that no error report mentioned, and the console mixes log lines, hash navigations and the app's custom events. A Performance panel and AI summaries of each session sit alongside.

TrackJS: A transcript of the page

TrackJS has no recording. Its error page leads with a telemetry timeline:

Clicks show the element that was clicked, fetches show their status and duration, and my breadcrumbs appear as console entries, all in order with the time since the page loaded. "Errors on Page" steps through every error from the same load, and the header adds Time On Page, viewport and browser. It reads quickly, though you can't watch what the shopper saw.

The two also treat privacy differently. TrackJS masks input values in its telemetry, while LogRocket recorded page text as written until you set its sanitizers or mark elements data-private.

Debugging comparison

Verdict: LogRocket. A recording with request bodies answered more of my questions. TrackJS's timeline is faster to scan when you don't need the picture.

Following a failure to the server

When checkout fails, the cause is in the API. LogRocket shows what the API sent back, and TrackJS links to the error the API reported.

LogRocket: The response the browser received

Clicking the failed checkout in a LogRocket recording shows both bodies:

The request holds the invalid coupon, and the response holds the server's exception message, which is often enough to name the bug. For the server's side, LogRocket offers integrations with tools such as Datadog and Splunk, which I didn't test.

TrackJS: A link to the server's own error

Every fetch in a TrackJS timeline carries a link to any server errors from that request:

The Node agent sets a correlation header on the response, and the browser agent records it. Following the link from the failed checkout opened the coupon TypeError for that exact request:

It worked again on the bad release, from a slow checkout to the payment error behind it. One annoyance: the link shows on every fetch, successful ones included, so you can't tell in advance which ones lead anywhere.

Verdict: TrackJS. It joins the browser's failure to the server's, with no tracing product involved.

LogRocket stopped at the response body, and TrackJS's link reached the server error without saying what the server did before it failed. Better Stack recorded the same storefront visit as one timeline of browser and server spans:

Navigations, clicks and fetches line up with the server work each one caused, so you can find the slow or failing step on one screen. The same view also has session replays, which TrackJS doesn't offer.

Follow a visit from click to database

Error grouping

How a tool groups errors decides whether its counts mean anything. LogRocket and TrackJS do it in opposite ways, and the 1.2.0 release exposed both.

LogRocket: Grouped by stack, so a new bug hid

LogRocket groups browser errors by stack and keeps the first message as the row's title. The 1.2.0 payment failures came through the same checkout code as the coupon bug:

The row still names the coupon bug, while its newest session holds a payment gateway message. Nothing in the list hinted that a new kind of failure had started.

TrackJS: Grouped by message, so one bug split

TrackJS groups by message text. The payment error puts the charge amount in its message:

Three failed charges made three server groups, one per amount, plus a browser copy. The same habit split other bugs earlier:

Out-of-stock errors split by book and quantity, and each distinct list of order IDs in the tracking rejection got its own row. TrackJS's automatic URL grouping only rewrites network error URLs, so the app's own messages stay split. You can merge groups by hand or write grouping rules, which I didn't test.

Grouping comparison

Verdict: Neither. LogRocket hid the new bug, and TrackJS turned it into a stream of one-off errors. On either tool, plan on tuning grouping before you trust the list after a deploy.

One tool hid the payment bug under an older title, and the other filed every failed charge separately. Better Stack received the same release and put the payment bug in a single group of its own:

Every amount landed in that one group, with the throwing lines shown under the stack, and the coupon bug stayed in its own group through all three releases. You'd see one new row after the deploy, which is the signal you want.

See each new bug once

Releases and regressions

A fix that doesn't hold should get your attention. Ahead of 1.1.0, the coupon bug was set to fixed in both tools, and the next build kept the bug in place.

LogRocket: Closed, through two more releases

LogRocket's Close is a single click on an Issue, with no release attached. When the bug returned, the Issue counted the new sessions and moved Last Seen forward, and that was all:

By the end of 1.2.0 it counted five sessions, the badge still read Closed, and the history had no entry for the comeback.

TrackJS: Fixed is a label

TrackJS's Set Status menu offers Fixed, Investigating, Prioritized, Unable to Recreate, Won't Fix and a custom status, with no version attached. After 1.1.0, the coupon error had grown and kept its label:

It went from 7 events to 12 and still says Fixed. The only sign was inside the group, in its version chart:

1.1.0 appears in red next to 1.0.0, with "Versions Impacted 2". You'd only see it if you opened the group to check.

Release comparison

Verdict: Neither. Both tools recorded the return and left the fix looking done.

LogRocket kept the coupon bug Closed and TrackJS kept it Fixed, so in both lists the returning bug looked finished. In Better Stack, the same bug changed status the moment it came back:

A red "Reoccurred" replaced "Unresolved" on that row alone, and its activity feed kept the order of events:

The comeback is listed right above my resolve, which is the record neither tool here kept.

Know when a fix doesn't hold

Slowdowns on 1.2.0

In 1.2.0, checkout got slow, and the slow part threw nothing. Neither tool measures server timing, so the question is what each one lets you notice.

LogRocket: One slow request, and the browser's view

In a 1.2.0 recording, the failed checkout stood out on the waterfall:

It took 2.899 s, where a 1.0.0 checkout failure took 15 ms, and the Timing tab had no breakdown. LogRocket's Performance panel measures the browser itself:

CPU, long tasks, memory and layout shift line up with the recording, which helps with heavy scripts and janky pages. For my slow query, it only showed the browser waiting.

TrackJS: A duration in one timeline, and a clue from the server

TrackJS has no timing view either. The slowdown turned up in a failing checkout's timeline:

The fetch took 4,073 ms, against about 15 ms on 1.0.0. On the linked server error, the timeline also held my app's own slow-query warning, because the Node agent records console.warn lines. Successful slow checkouts left no trace, since nothing is reported without an error. The Versions page made it worse:

1.2.0 has 68 errors, against 102 on 1.0.0, because fewer checkouts got through. Judged from this page alone, the bad release looks like an improvement.

Performance comparison

Verdict: LogRocket, narrowly. It shows slow requests whether or not they fail, and it measures the browser. Neither tool can tell you why your API got slower.

LogRocket measured one slow checkout in a recording, and TrackJS saw a 4-second one only because it failed. Better Stack timed the server side of the same 1.2.0 checkout:

In that request, the book lookup accounted for 1.53 s out of 2.19 s, and the rejected charge for 660 ms, so you can see two separate problems. The request's logs were one filter away:

The same slow-query warning TrackJS stumbled on sits here by design, filtered to the request's trace ID.

Trace a slow release to the query

Alerts

Both tools can notify you about new problems, and I set up delivery in neither.

Notifications in TrackJS live in each application's settings:

A daily and a weekly summary email start switched on. Beyond those, it can warn you about a never-seen message, a fast-climbing error or a jump in the error rate, through email, Slack or Teams. Because new-error alerts key on message text, each payment amount on 1.2.0 would have counted as a new error.

LogRocket's issue alert fires on a severe issue first seen in the last 24 hours, sent to Slack or a webhook:

Severity comes from Galileo, which rated both of my Issues Low, so that rule would have stayed quiet. LogRocket also has metric alerts and a weekly digest.

Verdict: Not compared. Neither tool alerts on a fixed bug that returns.

AI-assisted debugging

Neither assistant could read my code. TrackJS's AI Debugger works on one error at a time, and I asked Galileo about the whole release.

LogRocket: Galileo, careful and correctable

Galileo writes a summary when you open an Issue, and Ask Galileo is a chat over your sessions. I asked it what changed in 1.2.0. My first release name found no sessions, and Galileo told me so plainly. Given the correct names, it redid the analysis:

It took back its earlier claim about the coupon error, and singled out the payment timeout as what 1.2.0 actually added. It blamed an upstream dependency and said it couldn't confirm a code-level cause. Without server data, it never saw the slow query.

TrackJS: An AI Debugger on every error

TrackJS runs its AI Debugger as soon as you open the tab, in about 20 seconds, with sanitized error data sent to OpenAI. On the 1.2.0 payment error, it read the server timeline:

It placed the failure at server.js:173 in an async call at line 163, ruled out a declined card or the coupon, and tied the change to 1.2.0. Its fix list kept the two problems apart:

Its last item treats the slow book lookup as a separate latency problem, and that's the line that matters. It took the "upstream timeout" message at face value, though, and didn't find the code that fails every fifth charge.

AI comparison

Verdict: TrackJS. With server telemetry to read, its Debugger split the slowdown from the payment failure. Galileo was just as careful but had less to work with.

TrackJS's Debugger kept the slow query apart from the payment error, but neither assistant could say how big each problem was. Better Stack's AI SRE took the release question with no repository either:

It found that checkout's own database calls had turned multi-second while other statements stayed fast. Then it measured the payment side:

Of 48 card charges, 10 failed, all on 1.2.0 and all with the new error. The run took about four minutes and $0.49, and it quantified both problems instead of only naming them.

Ask what broke in the last release

Pricing

Neither tool bills you for errors. LogRocket charges by recorded session, and TrackJS charges by page view.

I read LogRocket's pricing on October 29 and TrackJS's on October 28, 2026, and rechecked both on November 3. LogRocket's page no longer listed a free tier on the recheck, so confirm both before you buy.

Free plans and trials

My TrackJS trial ran on these terms:

After the trial, a paid plan is required. TrackJS lists discounts for bootstrapped businesses and charities.

Entry-level paid plans

LogRocket's Core plan was $176 a month at 25,000 sessions on an annual plan, and Pro $407. Pro is required above 100,000 sessions a month. One tier up from Professional, TrackJS Business costs $199 a month with 5 million page views and 30-day retention.

The pricing page I could fetch on November 3 didn't show which TrackJS plans include the AI Debugger. My trial had it on every error.

LogRocket's calculator at 25,000 sessions:

Comparing the actual cost

The two billing units are close for a single-page app, because TrackJS counts a page view each time its agent loads, not on every route change. Take a storefront with 100,000 visits a month. TrackJS Starter covers that for $45 a month on a yearly plan. LogRocket's Core plan cost $176 at a quarter of that volume, and above 100,000 sessions you'd need Pro.

What LogRocket's extra money buys is the recording: every visit replayed with its network bodies and browser performance. TrackJS keeps a timeline, saves only so many errors a minute, and holds them for 10 days on Starter.

LogRocket's free tier is the only way to start without paying, and it covers a small site's first 1,000 sessions a month.

Verdict: TrackJS. Per visit, it costs a fraction of LogRocket, and its paid plans start low. Pay LogRocket's premium if you need to watch the visits.

Full feature comparison

Which should you choose?

Both are browser-first, so the decision comes down to how much of each visit you need to keep.

Choose LogRocket if you need to watch what happened

LogRocket fits teams that debug by replaying the visit. Its recordings carry the network waterfall, request and response bodies, console and browser performance, its source maps work with private builds, and it spots error text on the page that no exception reports. Its free tier lets you try it on real traffic.

Plan around what it doesn't see. There's no server agent, closed Issues stay closed when bugs return, and grouping by stack can hide a new failure in an old row. For LogRocket next to a tool that also watches the API, read our Sentry vs LogRocket comparison.

Choose TrackJS if you want lean, cheap browser error tracking

TrackJS fits teams that want every JavaScript error explained quickly without paying for recordings. Its timeline shows the steps before each error, its Node agent links a failed fetch to the server error behind it, and its AI Debugger handled the bad release well. At $45 a month for 100,000 page views, it's the cheaper tool by a wide margin.

Plan around the gaps. Patch the Express handler in trackjs-node 1.3.0 before you rely on it, keep source maps public or drop them in by hand, and expect message grouping to split errors that carry IDs or amounts. For how it holds up against a broader platform, see our Sentry vs TrackJS test.

FAQ

Does TrackJS record sessions like LogRocket?

No. TrackJS keeps a telemetry timeline for each error, with clicks, navigations, network requests and console lines in order, but no video and no request bodies. LogRocket records the whole visit and shows the network bodies, console and browser performance beside the recording.

Can LogRocket monitor my Node.js server?

No. LogRocket's SDKs run in web and mobile apps only, so it never receives your API's exceptions. A recorded request's response body will often show the server's message, though. TrackJS offers a Node.js agent that reports server errors and links them to the browser request.

Do I need public source maps for TrackJS?

For automatic mapping, yes. TrackJS fetches source maps from the script's public URL, optionally limited to its IP addresses or a secret header. On localhost or a private staging site, you can drop a map file onto a stack trace by hand, but the mapping lasts only for that page load.

Which is cheaper for a busy front end?

TrackJS, in most cases. It charges by page view and never bills errors, starting at $45 a month for 100,000 page views on a yearly plan. LogRocket also leaves errors off the bill, but its session pricing was $176 a month at 25,000 sessions on Core when I checked.

Final thoughts

LogRocket and TrackJS both leave errors off the bill, and both are built around the browser. They differ in how much of a visit each one keeps.

LogRocket keeps all of it, and that made it the better debugger whenever I could watch the problem happen. TrackJS keeps a transcript, and with its Node agent alongside, it reached places LogRocket never could. It linked a failed checkout to the exact server error, and it pulled a slow-query warning into the AI's answer. Both tools let a fixed bug come back without telling me.

If you need the recording, LogRocket is worth its session bill. If you need to know what broke and where, TrackJS gets you there for a fraction of the price.

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