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

If your users keep reporting bugs you can't reproduce, you're likely choosing between two kinds of tool. One replays the visit so you can watch what went wrong. The other follows the failure out of the browser and into your API. Sentry and LogRocket lead those two camps, and their features now overlap enough to make the choice harder than it looks.

LogRocket is a session replay platform built around the browser. Each recording keeps the clicks, the console and every request with its body, and LogRocket adds error tracking, product analytics and Galileo, its AI assistant, on top. Everything it learns about your backend arrives through the browser.

Sentry is an error tracking platform that follows a failure across the stack, from a browser click to the server code and database query behind it. It also records replays, collects logs and profiles, and runs Seer, its AI agent.

That difference shaped almost every result. LogRocket gave me the most detailed view of a broken visit I've seen, down to the server's error message read from a response body. It struggled when the answer lived on the server or in the issue workflow: telling a new bug apart from an old one, flagging a fix that didn't hold, and explaining a slow request.

So the comparison is uneven by design. Where a section tests the server, you'll see what LogRocket could work out from the browser, and I don't score it on work it doesn't claim to do.

I ran both tools against the same deliberately buggy bookstore in October 2026. Below, you'll find how they handled setup, error capture, source maps, session replay, the browser-to-server link, grouping, releases, performance, alerts, AI and pricing.

Sentry vs LogRocket: Results at a glance

The verdicts reflect one Express and Vite app as I configured it. Another stack, or different traffic, could change a few of these results.

How I tested Sentry and LogRocket

The test app, monitoring-lab, is an online bookstore. A Vite storefront talks to an Express 5 API over hash routes, the API keeps its data in SQLite and depends on a price service that fails on purpose, and a report job runs every minute in the background. Two adapter files, one per side, connect each monitoring tool, so the app's own code never changes.

These are the problems it creates:

• Front-end crashes: a TypeError when you add a book to the wishlist, and an order-tracking request to a route that doesn't exist, with its promise rejection left unhandled.
• Failed requests: 404s, 500s for unknown book IDs and for checkout, and 502s from the price service.
• Server failures: a TypeError for unknown coupon codes, called the coupon bug below, another for missing books, out-of-stock errors, upstream 503s and a rejection no code catches.
• Job failures: some report runs throw.

Signed-in shoppers alice and bob browse through headless Chromium, and carol shops through the API only.

Three releases

That last release is the real test. A useful tool should tell you that checkout got slower, that a new error appeared, and that the two have different causes.

SDKs and test limitations

These conditions affect the results:

• LogRocket ran on a 14-day Pro trial, which includes Galileo AI.
• LogRocket has no Node.js SDK. On the API, my adapter just wrote each request's LogRocket session link into the console beside any server error, and sent LogRocket nothing.
• Seer could read my GitHub repository, and Galileo couldn't.
• Each tool got its own traffic run, Sentry's on October 22 and LogRocket's a week later on October 29, so compare behavior and leave the totals aside.
• I placed one LogRocket order by hand. The 1.1.0 traffic never hit the coupon bug in the browser, so I triggered it in a real browser tab.
• I didn't test alert delivery in either tool, LogRocket's product analytics, or any backend integration.

Setting up both tools

Both tools had the storefront reporting within a few minutes. The differences showed up once I wanted readable stack traces.

Sentry: Five signals in the snippet

Sentry found my GitHub repository while I created the project and proposed Express plus browser JavaScript. The next screen listed everything it would collect:

Error monitoring and tracing start selected, and each card shows its free allowance after the trial, such as 5,000 errors and 5M spans a month. The install snippet that followed carried the DSN and turned on errors, logs, metrics, tracing and profiling, and a short sentry-cli script uploaded source maps after each build.

LogRocket: Two lines, then a release that wouldn't create

LogRocket's onboarding opens by asking what you want to do:

I chose "Record sessions and find bugs", which covers sessions, logs and errors. The alternative, "Analyze feedback", points Galileo at support channels.

The install step is two lines, an npm install and LogRocket.init with the app ID:

The page waits for data until the first session arrives. An AI Setup tab offers the same install as a prompt for Cursor or Claude.

So where did it go wrong? Source maps were the hard part. logrocket-cli first creates a release, then uploads the files. On my new project, creating the release returned a 500 with this message: "Sentry project not found for appID uyuwnw/monitoring-lab. Ensure local Sentry has matching org/project slugs." The same command worked once the project had received a session.

Two more rules apply. LogRocket doesn't support hidden source maps, so your bundles need the sourceMappingURL comment. Maps also have to be uploaded before an error happens, because they aren't applied to errors already stored.

One step went smoother than in other tools: LogRocket recorded my headless test browser with no extra setting.

Setup comparison

Verdict: Sentry. LogRocket's browser install was as quick as Sentry's. Its release step failed on a fresh project, and Sentry also gave me the server side.

Catching browser errors

Both tools caught every browser failure their traffic runs produced. One type, the price service's 502, showed up in Sentry's run and never occurred in LogRocket's, so I can't say how LogRocket would have handled it.

Sentry: 11 issues from both sides

After release 1.0.0, Sentry had 11 issues:

The browser crashes, the failed API calls and the server errors share one list. Rows with a recording carry a replay count, and the broken cover image appears only inside a replay.

LogRocket: Every browser failure, twice

My first look at LogRocket's Issues feed was confusing, because it was empty. Where were the errors? They live under Signals, where 8 errors appeared after the run:

The boxed row is a Network Error that LogRocket created by itself from a failed request. My app reports the same failure as an ApiError, so each one appears twice. LogRocket did tidy URLs, turning book IDs into GET /books/*/. The arrow points at an error from before the source map upload, still showing the bundle name.

The list counts sessions per error. Server-only failures, such as the out-of-stock errors and the report job, never reached LogRocket.

On the bad release, LogRocket added a kind of row no other tool in my tests has produced:

The first boxed row is an Error State: LogRocket noticed "Something went wrong" text visible on the page itself, separately from the exception behind it. Think about the failures your code shows to shoppers but never reports, such as a caught error turned into a friendly message. This is how you'd find them.

Browser capture comparison

Verdict: Tie. Neither tool missed a browser failure. LogRocket's extra rows catch more on their own, and they also double the list when your app already reports its API failures.

Source maps

Both tools turned the minified wishlist frame into my source code once the maps were uploaded.

Here's Sentry's version of the wishlist crash after the upload:

The frame reads ../../src/pages.js:122:13, with the failing line highlighted under the deliberate-bug comment. The maps stay private to Sentry, and an offer to set up code mapping sits on the frame.

LogRocket's version was the same line with more surrounding code:

The frame header names ../../src/pages.js at line 122:13, and the code view highlights wishlist.items.push(book.id);. Two frames from LogRocket's own wrapper sit underneath, which you learn to skip.

Verdict: Tie. Both mapped the frame correctly. LogRocket asks for more care about timing and map comments.

Session replay

Replay is LogRocket's main product, so this is where you'd expect it to pull ahead. It did, by a wider margin than I expected.

Sentry: A summary and masked text

Sentry recorded every test shopper and opened each replay on an AI summary:

The steps match the session, with the wishlist failure in red. All page text shows as asterisks by default, which keeps names and emails out of recordings.

Sentry's replay has a network view too:

You get each request's method, status, type, size and timing, and the missing cover image shows up there with its 404. What you don't get in this view, in my setup, is the request and response body.

LogRocket: Browser devtools for every visit

LogRocket's error page opens the recording a few seconds before the failure. The full session player looks like Chrome's devtools:

The network panel lists every request on a waterfall, including the 404 for the broken cover image at the top. The console below mixes log lines, route changes and my app's custom events, such as "add to wishlist clicked", right before the uncaught TypeError.

How much can a browser recording tell you about a bug that lives on the server? Clicking the failed checkout request answered that:

The request body shows the invalid coupon code the shopper sent, and the response body holds the server's exception message. For server bugs that return their message, you can often find the cause without opening a server tool.

The session player also has a Performance panel with CPU, long tasks, memory, layout shift and crashes on the same timeline, and the session list has a Summarize button that writes a one-line summary for every session:

The boxed summaries describe each visit and link to its moments, such as "navigates to an invalid book URL resulting in a book not found error". Very short sessions get "Summary unavailable".

There's a catch on privacy. LogRocket recorded page text as written, including "Ordering as alice@example.com with coupon SAVE10". Masking all text, inputs or network bodies takes options in LogRocket.init or a data-private attribute on elements, so it's worth setting up before real users arrive.

Replay comparison

Verdict: LogRocket for debugging, Sentry for privacy. LogRocket's player gave me more evidence per failure. Sentry's defaults keep more personal data out of recordings without extra work.

Following a failure to the server

When the storefront's checkout fails, the cause is on the server. LogRocket already showed the server's response, so how far does that get you compared with a tool that monitors the server itself?

Sentry: The server error inside the same trace

On the storefront's failed checkout, Sentry pointed straight at the matching server error:

"1 other issue appears in this trace" takes you across. The trace itself covers both sides:

It runs from the "place order" click through Express to the database query, 12 spans in 64.90 ms. On the server issue, Sentry kept what LogRocket showed from the browser, and more:

Line 150 sits highlighted under the deliberate-bug comment, and alice's replay is attached below the stack, linked from the browser through a header. The same issue page also kept the request body with the bad coupon code, so the server error and the visit that caused it share one page.

LogRocket: The server's answer, and integrations for the rest

LogRocket can't open a server error, because it never receives one. What you get is the server's response inside the session, as shown above. For the server's own logs and traces, each request has a Backend Logs tab:

"Connect a backend logging service to get started" leads to integrations for Datadog, Google Cloud, Azure, AWS CloudWatch, Splunk, AppDynamics, Dynatrace, Kibana, Sumo Logic or a custom link. Those connect your backend tool's traces to LogRocket's network requests. I didn't test them, and each one means running a second monitoring product.

Browser to server comparison

Verdict: Sentry. LogRocket's response bodies go surprisingly far when the server returns its error message. Past that, you need another product to see the server.

LogRocket stops at the response the browser received, and Sentry shows the server side in a preview and a separate trace. When I ran the same checkout through Better Stack, one session timeline held the browser and the server:

The visit had 30 browser spans and 7 server spans in sequence, so the click and the work it triggered on the API read together. Each exception also has a "View related" menu with that request's logs, spans, release and recordings, so you never need a second product for the server half. In my test, Better Stack's beta browser tag left the storefront frames minified, which LogRocket and Sentry both handled better.

Trace a failed click into your API

Error grouping

Grouping decides whether a new bug gets its own row or hides in an old one. It matters most right after a deploy, when you scan the list for anything new. Release 1.2.0 tested it with a new payment error on the same checkout path as an existing bug.

Sentry: A new issue for the new bug

Sentry opened a new server issue for the payment failure, PaymentGatewayError, with 13 events across 3 users. Each message carried a different amount, and Sentry still kept them in one issue.

Here's Sentry's issue list after the bad release:

PaymentGatewayError is the new row near the top, three minutes old. Its browser side behaved like LogRocket, though: the storefront's "POST /checkout failed with 500" group already existed from the coupon bug, so the payment failures joined it. Sentry also created second copies of the coupon and out-of-stock server issues, because 1.2.0 shifted the checkout code's stack.

LogRocket: Grouped by stack, titled by the first message

LogRocket groups browser errors by their stack, and each row keeps the first message it saw as its title. That works until two bugs share a code path. After 1.2.0, the checkout error looked like this:

The title still names the older coupon bug, and Last Seen says release 1.2.0. Where was the new bug? Only inside the latest session, where the exception named the payment gateway. In LogRocket's list, a new bug on a familiar code path looks like an old one, and nothing else pointed to it.

Sentry's browser group did the same thing. The difference is that Sentry also monitors the server, where the payment error became its own issue.

Grouping comparison

Verdict: Sentry. Both tools hid the new bug in an existing browser group. Only Sentry had a second place where it surfaced.

LogRocket filed the 1.2.0 payment bug under the coupon error, and Sentry split its coupon issue in two when the stack moved. Better Stack received the same release and gave the payment bug its own group:

Every charge amount landed in that one group, and the source view shows the line that throws. The coupon TypeError stayed a single group through all three releases, with no duplicates after the code change. A new bug got a new row, and an old one stayed in its existing row.

See new bugs as new errors

Releases and regressions

Release tracking answers the question every team asks after shipping a fix: did it hold? To test it, I marked the coupon bug fixed in each tool before 1.1.0, then shipped a release that still had it.

Sentry: A fix tied to a release, and a label when it failed

Sentry lets you resolve an issue for the coming release, one you pick, or a specific commit:

I chose the coming release, so the fix was tied to whatever shipped after 1.0.0. Once 1.1.0 traffic arrived, the coupon row changed:

"Regressed" appears under the row's trend line. The release page tells the same story from the release's side:

The Regressed tab for 1.1.0 holds exactly one issue, the coupon bug, so you can check a release for broken fixes in one click.

LogRocket: Releases on every session, but Closed stays Closed

In LogRocket, an error gets a status once you turn it into an Issue. Closing one takes a click, with no release to choose. I closed the checkout Issue, then triggered the bug again on 1.1.0, and LogRocket recorded the new session against it:

The session count rose from 1 to 2 and Last Seen moved to release 1.1.0, but the Issue stayed Closed. Maybe reopening just takes a while? Twenty-four minutes and one more release later, it still looked like this:

Five sessions, last seen on 1.2.0, and the badge still says Closed. Nothing in the Issue's history recorded the return, and I found no setting for reopening closed Issues.

Release comparison

Verdict: Sentry. LogRocket recorded which release each session came from, but it left a bug I'd closed looking fixed while it kept happening.

LogRocket kept the coupon bug Closed through two releases, and Sentry's warning was a label plus a tab on the release page. Better Stack turned the same return into the row's status:

"Reoccurred" in red replaced "Unresolved" on the one error that came back, so the list answers the question without any filtering. The error's history keeps the order of events:

The reoccurrence sits above my resolve, which is the record LogRocket's Issue never wrote. Sentry still has one thing Better Stack lacks, a Regressed tab per release.

Get told when a fix doesn't hold

Measuring the slowdown in 1.2.0

Release 1.2.0 makes one database query at checkout slow. A shopper feels that as a long wait after clicking "Place order", so the question is which tool can tell you why the wait got longer.

Sentry: The query in the waterfall

A slow 1.2.0 checkout in Sentry:

The book query held the request for 1.79 s of its 2.26 s, and the failed charge added 459 ms. Grouping checkout's latency by release in Trace Explorer showed when it started:

The p95 sits at 5.29 ms on 1.0.0 and 3.63 ms on 1.1.0, then jumps to 5.93 s on 1.2.0, so the release that caused the slowdown is obvious.

LogRocket: The request's duration, without a reason

The same kind of checkout in a LogRocket session:

The boxed request took 2.899 s, where the 1.0.0 checkout failure took 15 ms. Its Timing tab said "Timing data is unavailable for this request", and with no server spans, LogRocket couldn't say where the time went. The Performance panel showed the browser's CPU during the wait, which tells you about the browser.

I didn't find a LogRocket view of request latency by release. Its analytics can build charts from session data, which I didn't test.

Performance comparison

Verdict: Sentry. LogRocket showed that checkout got slower for this shopper. Sentry showed why.

LogRocket timed the slow checkout at 2.9 s and stopped there, and Sentry's waterfall lived apart from the error that came with it. Better Stack's payment error opened straight into its request's spans:

Of the request's 2.19 s, the book query took 1.53 s and the failed charge 660 ms, so the slowdown and the error separate at a glance. One more click filtered the logs to that same request:

Just above the checkout failure is the app's own slow-query warning, so the number and its explanation share one screen. The Service column was blank, because the SDK I used there set no service name.

Find what slowed down a release

Alerts

LogRocket's Issue alert rule reads: "Send alert when a severe issue, first detected within the last 24 hours, impacts more than [0] users", with Slack or a webhook as destinations:

So LogRocket alerts on new, severe issues, and severity comes from Galileo. Would that rule have caught anything in my test? Probably not. Galileo rated both of my Issues Low, and the 1.2.0 bug joined an existing Issue instead of opening a new one. LogRocket also has metric alerts and a weekly error digest.

On Sentry's side, alert rules can fire on new issues, on regressions or on metric thresholds. I configured no alert destination in either product.

Verdict: Not compared.

Seer and Galileo

Both tools ship an AI assistant, built for different jobs. Seer is a debugging agent that reads your code to find root causes. Galileo reads sessions, so its strength is describing what users went through. In my test, Seer could read my repository and Galileo had no code at all, so read the two halves separately.

Sentry: Seer reads the repository

Within about 15 seconds of starting, Seer had the coupon bug's root cause:

Its answer named the failing line and the input that triggers it. The 1.2.0 payment error took about 20 seconds:

It found the code that makes the charges fail and the flag that switches it on for 1.2.0, which Galileo had no way to see. Asked about the whole release, its chat agent did worse:

It quoted correct latencies but put the slowdown down to the payment retries, while the trace gives most of the time to the book query.

LogRocket: Galileo, careful with sessions

Galileo works in two places. On an error, "Analyze and open an Issue" writes a summary and sets a priority. On the wishlist crash, it wrote this:

The boxed sentence says the evidence "does not establish which action triggers the null access", although the recording shows the wishlist click right before the error. Galileo judged the bug by its effect on shoppers, found none, and set it to Low. On the checkout Issue, it called the trigger "unclear", although the request body in the same session showed the bad input.

Ask Galileo is a chat that queries your data, and it did better with a broader question. I asked it the same release question I gave Seer. When my first release name didn't match any sessions, it said so instead of guessing, and once I gave the right name, it rebuilt the answer:

It corrected itself, saying the coupon error it had first called new "isn't new in 1.2.0", and named the payment gateway timeout as the only new checkout failure. It built a side-by-side table of sessions and failures per release, and it noticed that my manual order was the only non-headless session. For the cause, it pointed to "an upstream payment dependency timing out" and said it couldn't confirm a code-level cause.

AI comparison

Verdict: Not a head-to-head. Seer found both code causes because it could read the code. Galileo couldn't, and it was candid about that, corrected its own mistake, and kept the two failures apart.

Galileo blamed the payment gateway, and Seer's agent blamed payments for a slowdown the trace pinned on a query. Better Stack's AI SRE got the same release question, also without my repository:

It found that only the book lookup had slowed while every other statement stayed fast, which cleared the database server itself. Then it handled the payment failures separately:

Of 48 card charges, 10 failed, every one on 1.2.0 and every one tied to the new error. The run took roughly four minutes at a cost of $0.49, where Seer's agent answered in 51 seconds. Getting both causes right put it ahead of every other assistant I've tested.

Ask what changed in your last deploy

Pricing

Sentry charges for errors, spans, replays and logs in plans with included quotas. LogRocket charges for sessions, and errors, logs and analytics events don't count toward the bill.

The numbers come from each vendor's pricing page, read on October 28 and 29, 2026. Prices change, so check them yourself.

Free plans and trials

Entry-level paid plans

LogRocket's final price also depends on seats, retention and add-ons, and Pro is required above 100,000 sessions a month. Sentry's next tier, Business, runs $89 a month, dropping to $80 on annual billing.

What changes the bill

Comparing the actual cost

For error tracking alone, Sentry is far cheaper. Its free plan covers 5,000 errors a month, and $26 a month adds tracing, logs and server monitoring that LogRocket doesn't offer.

When does LogRocket's model come out ahead? When replay is the reason you're buying. Each recorded session includes its console, network bodies and errors, and an error flood costs nothing extra. The AI features I tested all need Pro, though, which starts at $292 a month for 10,000 sessions, and you'd still need a separate tool for the server.

If uptime checks and on-call belong in the same bill as errors and logs, this pricing breakdown for a platform that bundles them with Sentry's features runs the numbers.

Verdict: Sentry. It costs less to start and covers more of the stack per dollar. LogRocket's pricing is easier to predict for a busy front end with noisy errors.

Full feature comparison

Here's every result from the test in one place.

Which should you choose?

The choice comes down to where your failures start and how much of the visit you need to see.

Choose Sentry if you need one tool across the stack

Sentry fits teams whose failures can start anywhere: a browser click, an API handler or a database query. It links a browser error to the server issue and trace behind it, opens new issues for new server bugs, marks a fixed bug Regressed when it returns, and breaks latency down by release. Seer adds code-level root causes when it can read your repository, and the free plan and $26 entry price make Sentry cheaper to start with.

Its replays show less than LogRocket's, without request bodies or browser performance. If you're comparing it with a front-end tool that has no replay at all, our Sentry vs Rollbar test covers one that does include it.

Choose LogRocket if your hardest bugs live in the browser

LogRocket fits product and front-end teams who need to see what a user saw and what their browser sent and received. Its player puts the network waterfall, request and response bodies, console and custom events beside the recording, it flags error text shown on the page, and Galileo's session summaries were accurate. Because errors don't count toward the bill, a noisy front end won't surprise you either.

Plan around the gaps. You'll need a backend tool for server errors and traces, a closed Issue won't tell you when its bug returns, and a new bug on a familiar code path can land inside an older error. Turn on text masking before real users arrive. For another browser-first option, our Sentry vs Raygun comparison tests a tool with its own real user monitoring.

FAQ

Does LogRocket track backend or server errors?

No. LogRocket has SDKs for web and mobile apps only, so errors thrown in a Node.js API never reach it. You can still read a server's error message when the API returns it in the response body, because LogRocket records response bodies by default. For anything deeper, LogRocket links to backend tools such as Datadog, Dynatrace or Splunk, and its docs suggest sending the session link to your backend in an X-LogRocket-URL header so your server logs can point back to the recording.

Can you use LogRocket and Sentry together?

Yes. One split that works is Sentry for errors and tracing across the stack, and LogRocket for detailed session recordings. LogRocket's getSessionURL() call returns a link to the current recording, which you can attach to Sentry events or any other error report. Its docs also list a Sentry integration, which I didn't test. Running both means paying for two products, so it makes most sense when replay detail matters more than the extra bill.

Is LogRocket free?

There's a free tier of 1,000 sessions a month, and a 14-day trial of every feature without a card. Paid pricing depends on how many sessions you record. On the day I checked, 10,000 sessions a month cost $83 on the Core plan and $292 on Pro, which adds Galileo AI, both billed annually. Errors, logs and analytics events don't add to the bill.

Does LogRocket record sensitive data like emails and form input?

It can, so check your settings before launch. In my test, page text was recorded as written, including a shopper's email address. LogRocket offers textSanitizer and inputSanitizer options to hide all text or all form input, a data-private attribute to hide individual elements, and sanitizer functions to scrub request and response bodies or URLs. Sentry's replay, by comparison, masked all page text by default.

Does LogRocket work with automated or headless browser traffic?

In my test, yes. It recorded sessions from headless Chromium with no extra configuration, which some other tools block as bot traffic by default. Galileo even pointed out that the sessions looked like synthetic test traffic and warned against reading the counts as real user impact. That's handy for testing your setup in CI. Keep in mind that LogRocket bills by session, so heavy synthetic traffic has a cost.

Which is better for debugging front-end performance?

It depends on where the time goes. LogRocket's per-session Performance panel shows the browser's CPU, memory, long tasks and layout shifts on the same timeline as the recording, which suits slow rendering and heavy scripts. Sentry's browser tracing and Web Vitals cover page loads, and its traces continue into the server, which suits slowdowns caused by the API. In my test, the slowdown came from a database query, so only Sentry could explain it.

Final thoughts

LogRocket gave me the most complete record of a broken visit in any test I've run. The recording, the requests with their bodies and the console share one player, and when the server returns a clear error message, that's often all you need.

Its limits come from the same browser-first design. It can't explain a slow request whose time goes into a database query, it can't see server errors that never reach a response, and its closed Issues stay closed when a bug comes back. Sentry covers all three, with replays that show you less.

The real decision is what you already run. If your team has backend monitoring and spends its days on front-end bugs, LogRocket earns its session bill. If it would be your only tool, buy the one that can see past the browser.

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