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

Both LogRocket and Rollbar will show you a video of a user hitting a bug, which surprised me, because the two products start from opposite ends. In LogRocket, the recording is the product and the error is one event inside it. In Rollbar, the error is the product, and a short replay is attached to it when the error fires.

LogRocket records every visit to your web app with its network requests and bodies, console output and browser performance, then layers errors, product analytics and an AI assistant called Galileo over those recordings. There's no Node.js SDK, so the server only appears through the browser's eyes or through a connected backend tool.

Rollbar is an error tracker for the browser and the server in one project. It groups exceptions into items, ties them to versions and deploys, reopens them when they come back, and records a replay for browser errors. It has no tracing, real user monitoring or log product.

The two overlap enough to be real alternatives for a small team, and differ enough that the choice matters. In my test, LogRocket explained what a user saw better than Rollbar's short clips could, while Rollbar caught the server's crashes, told me which deploy brought a bug back, and cost a fraction as much to start.

This comparison walks through setup, capture on both tiers, source maps, the error page, session replay, grouping, releases and regressions, speed, logs and querying, alerts, AI and pricing.

LogRocket vs Rollbar: Results at a glance

Read these as results from one Express and Vite app with each tool set up from its own docs. Another framework, a different plan or custom grouping rules could flip a verdict.

How I tested LogRocket and Rollbar

monitoring-lab, my test app, is a bookstore that breaks in planned ways. Its Vite storefront uses hash routes and calls an Express API on SQLite, a mock pricing service is built to fail, and a sales report is generated every sixty seconds. Every tool connects through the same browser and server adapters, so the app itself is identical across tests.

What breaks:

• In the storefront: the wishlist button crashes with a TypeError, and an order-tracking call fails with its rejection left unhandled.
• In API calls: unknown books return 404 or 500, a crashed checkout returns 500, and price lookups return 502.
• In the API itself: a bad coupon crashes checkout, out-of-stock orders throw a handled error, an upstream service answers 503, and a background promise rejects.
• In the job: some sales report runs fail.

Headless Chromium plays two shoppers, alice and bob, and a third shopper, carol, only uses the API.

The releases

1.2.0 is the hard one. You want a tool to tell you that two things went wrong at once: a slower checkout and a new error.

SDKs and test limitations

Things to keep in mind:

• Runs were separate: LogRocket ran in late October and Rollbar a week earlier, with the same traffic script. Don't compare counts directly.
• LogRocket saw no server data. Its server-side results come from responses in the browser.
• Environments differ by side in Rollbar: the storefront reported "production" and the API "development", which is how my app is built.
• Both tools recorded headless Chromium without any bot setting.

Terms in each tool

Setup and onboarding

Both tools install from npm. What differs is how much each covers once installed, and how smoothly a brand-new account gets to its first event.

LogRocket: A project, a use case and a snippet

LogRocket starts by creating a project and suggests one per environment. Then it asks what you're there for:

"Record sessions and find bugs" is the path for engineers, while "Analyze feedback" aims Galileo at support conversations. Next comes the install screen, with AI Setup, NPM and Script Tag, with the npm version being one install and one LogRocket.init call.

import LogRocket from 'logrocket';

LogRocket.init('<your_app_id>', {
  release: '<your_release>',
});

The highlighted line masks text and inputs, which LogRocket records unmasked unless you ask. Releases created with the CLI failed on my fresh project until the first sessions arrived.

Rollbar: Four steps per SDK, and a token to replace

Rollbar's setup page estimates five minutes and has four steps: install, add ROLLBAR_ACCESS_TOKEN to .env, create the client with captureUncaught and captureUnhandledRejections, and send a test message. The picker shows one SDK at a time:

One project accepts both the Node.js and browser SDKs, so you come back through "Select different SDK" for the second snippet. On my freshly created project, both setup pages printed tokens from a project I'd deleted, and no data arrived. New tokens from Project access tokens fixed it.

On the server, Rollbar's error handler goes last in your Express app:

import Rollbar from 'rollbar';

const rollbar = new Rollbar({
  accessToken: process.env.ROLLBAR_ACCESS_TOKEN,
  captureUncaught: true,
  captureUnhandledRejections: true,
  payload: { code_version: '<your_release>' },
});

Worth turning on: in LogRocket, set release in LogRocket.init before your first deploy, because first and last seen per release is all the version data an Issue keeps. In Rollbar, report deploys through its API from your pipeline, since auto-resolve, suspected deploys and deploy notifications all depend on them.

Setup comparison

Verdict: LogRocket. Its snippet ran on the first try. Rollbar covers twice as much of your app, but a new account cost me a token hunt before anything showed up. LogRocket fits a frontend team that wants recordings within minutes, and Rollbar fits a team ready to instrument the API too.

Error capture

A tracker is only as good as what reaches it. LogRocket listens in the browser. Rollbar listens wherever you install it, and in my test that was both tiers.

LogRocket: Everything the browser noticed

After the first run, Signals → Errors listed eight browser errors, and the Issues Feed stayed empty until I promoted one:

The marked rows are LogRocket's own Network Error entries for failed requests, sitting beside the exceptions the app threw. Later it added an Error State row when the page displayed "Something went wrong", a kind of failure most trackers never see.

The server's own problems, from out-of-stock orders to the failing report job, never reached LogRocket.

Rollbar: Both tiers in one list

Rollbar listed 11 items after the same kind of run: six from the API and five from the storefront, side by side:

Browser rows carry a replay icon, and caught browser errors appear too, because rollbar.error reports them. The background promise rejection was captured as its own item.

Two gaps: Rollbar shows a level on every item with no handled or unhandled badge, so a handled out-of-stock error looks as serious as a crash. The broken cover image, a failed resource load, created no item.

Error capture comparison

Verdict: Rollbar. Seeing the API's crashes in the same list as the browser's is what most teams need from an error tracker. LogRocket's network and on-page errors are a strong addition for a browser-only team. Rollbar fits full-stack teams, and LogRocket fits teams whose bugs mostly live in the UI.

Source maps

Both tools need your maps uploaded, since my app ran on a private host neither could fetch from.

LogRocket: Upload before the error

logrocket-cli sent 33 files from the build folder in one command. The wishlist crash then pointed at the right source line:

src/pages.js line 122 sits in red with context above and below. Errors that arrive before the upload stay minified, and the bundle must reference its map with a sourceMappingURL comment.

Rollbar: Upload per version, with the replay alongside

Rollbar can't fetch maps from localhost either, so an upload script sent them per release. The same crash in Rollbar:

Rollbar's top frame names bookDetail in pages.js, line 122, column 13, and the replay panel sits beneath it. Rollbar even recovered the function name, which LogRocket showed as an anonymous click handler.

Source map comparison

Verdict: Tie. Both mapped the browser crash to the right line once the maps were in place. Rollbar's function names are a small bonus, and LogRocket's CLI is a little simpler to script.

Debugging an error

The error page is where you spend most of your time. LogRocket puts the visit at the center, and Rollbar puts the occurrence data there.

LogRocket: The failed request, from both sides

The most useful screen in LogRocket for a failed checkout was the request itself. The waterfall marks the 500 and links it to its Issue:

Opening the request shows what the browser sent and what came back. The request carried the bad coupon, and the response held the API's own exception message, so a server crash was readable without any server SDK:

Around it, the session holds the clicks, console lines and route changes that led there.

Rollbar: Occurrence data, telemetry and a curl command

A Rollbar item shows first and latest occurrence, totals, people, IPs, first and latest version, and four occurrence charts. Tabs cover Detail, Summary, Occurrences, Related and Root cause & fix.

The Detail tab is where Rollbar earns its keep on server errors:

• Params with Typicality: every request field, person and header, with a column showing how common each value is across occurrences.
• Replay as curl: a copyable curl command for sending that exact request again, something no other tool in my tests offered.
• Telemetry: the last 100 console lines and HTTP calls before the error.

Server telemetry has a catch: it isn't scoped to the request. One shopper's checkout error showed other shoppers' orders in its timeline, because the SDK keeps one queue for the whole process. On a busy API, read it as "what the server was doing", not "what this request did".

The Related tab only lists items with similar titles, and both it and Summary are paid features after the trial:

Nothing here links the browser's failed checkout to the server's coupon crash. Rollbar has no tracing to connect them.

Debugging comparison

Verdict: LogRocket. For anything a user can trigger, the recording plus both bodies answered my questions fastest. Rollbar is the better page for a server crash, with Typicality and a curl command ready to reproduce it. LogRocket fits teams debugging from the user's side, and Rollbar fits teams debugging from the API's side.

Session replay

Both tools record what users did. LogRocket records whole visits. Rollbar records a clip when an error fires.

LogRocket: Full visits with devtools beside them

Every visit is recorded, and the session list shows user, event count, duration and platform:

Headless Chromium showed up with no bot setting. Inside a session, the Performance panel lines up browser CPU, memory, long tasks, network throughput and layout shift with the video:

These are numbers from one browser, which helps when a page janks on a particular device. On Pro, Summarize writes a one-line description of each session in the list.

Rollbar: A clip attached to each browser error

Rollbar's replay starts from an occurrence trigger, so it records around errors. The player sits inline on the item, next to an event list:

Each event row is a request with its status and duration, a click identified by CSS selector, or a route change. It also links other Rollbar items from the same session:

The marked row jumps to the order-tracking error from the same visit. A Replays page lists every recording with its trigger and duration, and my headless shoppers' clips ran from 0 to 15 seconds. Request bodies weren't part of the event list.

Worth turning on: in LogRocket, mark sensitive elements with data-private before recording real customers. In Rollbar, keep replay sampling at 1.0 on the error trigger while you're small, since every account includes 1,000 free replays a month.

Replay comparison

Verdict: LogRocket. Full sessions with request bodies and performance data answer far more than a short clip. Rollbar's replay is a solid add-on that costs nothing extra at small volumes. LogRocket fits teams for whom the visit is the evidence, and Rollbar fits teams that want a quick look at the moments around an error.

Error grouping

Grouping turns events into a to-do list. A good list gives a new bug a new row and keeps a known bug in one row.

LogRocket: One stack, one row, one title

LogRocket groups by where the browser threw, and keeps the title of the first error it saw. In 1.2.0, payment failures arrived through the same browser code as the old checkout error, so they landed in its row:

The row kept its coupon message while the newest session inside it held a payment rejection. A new bug on a shared code path was invisible from the list.

Rollbar: Framework frames in the fingerprint

At 1.0.0, Rollbar grouped correctly, with one item per bug. In 1.2.0 it started splitting:

• The new payment error became two items, thrown from the same line, one with framework frames below it and one without.
• The old coupon crash spread across three items, for the same reason.
• Two other old errors opened fresh items, leaving their originals resolved.

The 1.2.0 version page shows the effect:

The New tab counts five, though only one error was new. In 1.2.0, checkout waits on a slow query before the crash, so how much of the Express stack survives varies by request, and each variant gets its own fingerprint. Rollbar offers Custom fingerprinting in project settings and Merge on items to fix this.

At the other extreme, Rollbar grouped every out-of-stock error under the first book's title, so the item name didn't match most of its occurrences.

Grouping comparison

Verdict: Neither. LogRocket buried the bug that mattered, and Rollbar reported four new items that weren't new. Rollbar at least gives you tools to fix it, which LogRocket teams may have to work around. Rollbar fits teams willing to write fingerprint rules, and LogRocket fits apps whose error sources rarely change.

LogRocket hid the payment failures under an old title, and Rollbar's 1.2.0 page counted five new items for one new bug. The same release in Better Stack produced exactly one new group, holding all 10 payment exceptions:

Charge amounts and stack depth didn't split it, and the code that threw is right on the page. One new bug showed up as one new row, with no fingerprint rules to write.

Track each bug as one error

Releases and regressions

The real test of release tracking is a fix that doesn't hold. In each tool I marked the checkout bug fixed, then shipped it unchanged in 1.1.0.

The red arrow shows Rollbar's reopen rule, which LogRocket doesn't have. Neither tool labels the reopened row in its main list.

LogRocket: A Closed Issue stays Closed

LogRocket's Close Issue takes one click and has no version option. The bug came back on 1.1.0, and the Issue counted it while keeping its badge:

Sessions went from one to two and Last Seen moved to 1.1.0, with no change to the status or the history. The Issue was still Closed after 1.2.0, with five sessions.

Rollbar: Resolve in a version, reopen with a suspect

Rollbar's Resolve dropdown takes a version and a comment:

A false start proved the version field does real work. For a few minutes the app was still on 1.0.0, and the item stayed resolved through a further 11 events from that old build, since a fix recorded against 1.1.0 doesn't cover earlier versions.

Once 1.1.0 really ran, the item reopened, and its page explained why:

The marked fields read "Reactivated version: 1.1.0" and "Suspected deploy", linking to the deploy I'd reported. In the items list the row simply went back to active, so the version page is the better place to look:

Reactivated (1) held exactly the bug I'd marked fixed. Versions are per environment, so the browser's errors sit on a separate 1.1.0 page.

Auto-resolve on deploy

For 1.2.0, I turned on Auto-resolve on deploy for the API's environment. The deploy resolved all six backend items at once, including bugs nobody had fixed:

Three of them came back during 1.2.0 traffic, and the version page listed them as reactivated. It gives you a clean slate per release, at the cost of a list that briefly looks healthier than your code is.

Release comparison

Verdict: Rollbar. It reopened the bug, named the deploy that brought it back, and listed it on the version page. LogRocket never changed the status. Rollbar fits teams that review every release, and LogRocket fits teams that triage from what users experienced.

LogRocket kept the returning bug Closed, and Rollbar's reopen showed on the item and version page while its list row just turned active. When I shipped 1.1.0 to Better Stack, its error list flagged the bug directly:

Only that row changed, to a red "Reoccurred". Better Stack doesn't name a suspected deploy as Rollbar does, but you see the regression from the list without opening anything.

See regressions where you triage

Performance monitoring

Every checkout in 1.2.0 took seconds. Neither product sells tracing, so what matters is whether you can see that slowdown at all.

LogRocket: The slow request, and nothing behind it

One recorded 1.2.0 session shows the failing checkout running for 2.899 s, against 15 ms for the identical failure in 1.0.0. Its Timing tab came up empty:

LogRocket had no timing breakdown and no server spans, so the slow query stayed hidden. I didn't find a chart of request latency across sessions or releases.

Rollbar: No timing anywhere

Rollbar has no performance, tracing or RUM product. The 1.2.0 checkouts took 3 to 5 seconds, and no Rollbar page showed it. The new payment error was caught and tied to the 1.2.0 deploy, but its slowness was invisible.

Performance comparison

Verdict: Neither. LogRocket at least shows a slow request when you find the right session. Neither can tell you why checkout got slower. Plan on a tracing tool alongside either.

LogRocket showed one slow request without a breakdown, and Rollbar had no timing at all. In my Better Stack test, the same 1.2.0 checkout came with server spans:

The book lookup accounted for 1.53 s and the rejected charge for 660 ms, out of 2.19 s in total. Both of the release's problems were visible in a single request.

Break a slow request into its spans

Logs and querying

Neither tool is a log platform, but each has a way to dig through what it collected.

LogRocket: Console per session, and backend connectors

Each session keeps the browser console with navigations and custom events. For the server, a request's Backend Logs tab offers connectors:

The tab's prompt to connect a backend service opens a list of integrations, Datadog, Splunk and AWS CloudWatch among them, none of which I tried. LogRocket also has Analytics for building metrics from sessions, which Galileo used but I didn't explore myself.

Rollbar: Telemetry and RQL

Rollbar's server telemetry includes console lines, so my app's slow-query warnings appeared in item timelines. There's no log search, though.

What Rollbar does have is RQL, an SQL-like language over occurrences, with an editor, templates, CSV export and a Metrics API. A query like this gave me per-item counts:

SELECT item.counter, item.title, count(*)
FROM item_occurrence
GROUP BY item.counter, item.title

It enforces SQL's GROUP BY rules and starts each query with "Project: None", so you pick the project first. Search uses a syntax of its own: email:"bob@example.com" worked where person.email: returned nothing. RQL is part of Rollbar Analyze on the Advanced plan.

Logs and querying comparison

Verdict: Rollbar, narrowly. RQL let me count and slice every occurrence, and its telemetry caught a server warning. Neither replaces log management. Rollbar fits teams who like to query their errors, and LogRocket fits teams who'd connect an existing log tool.

LogRocket needs another tool for server logs, and Rollbar's telemetry mixes every request's console lines with no search. Better Stack linked the failing 1.2.0 checkout to its logs in my test:

The filter is that one request's trace, so the slow-query warning sits next to the failure with nothing from other shoppers mixed in. Those logs stay searchable after the incident.

Search logs by the request that failed

Alerts

An alert decides whether a regression reaches you or waits for a user complaint. I reviewed each tool's rules but didn't wire up a delivery channel.

LogRocket: Metric thresholds and severe new issues

LogRocket's alerting has two kinds of rule: Metric Alerts for thresholds like page load spikes, and Issues Alerts for new problems:

The rule form narrows it to severe issues first detected within 24 hours, delivered to Slack or a webhook. Since Galileo rated both of my Issues Low, neither would have triggered.

Rollbar: Eleven email rules out of the box

Rollbar turns on email with 11 default rules, including New item, Item reactivated, Deploy, New version, a daily summary, and a burst rule for ten occurrences inside five minutes. Channels include Slack, Jira, GitHub, PagerDuty, Opsgenie, Datadog and webhooks. Snoozing an item stops its notifications for a set time.

Neither tool has uptime checks or status pages.

Alerts comparison

Verdict: Not compared. No alert from either tool reached me during the test. On paper, Rollbar's defaults cover new and returning bugs from day one, while LogRocket's issue alerts wait for Galileo to call something severe.

AI-assisted debugging

LogRocket's Galileo summarizes issues and sessions and answers questions. Rollbar has Analyze root cause on items, which needs its GitHub app, and an AI assistant in the RQL editor. I tested Galileo and neither Rollbar feature, so this isn't a head-to-head.

LogRocket: Galileo, careful with what it can see

Asked to open an Issue from the checkout failure, Galileo wrote an accurate impact summary but said the trigger was unclear, although the request body held the bad coupon:

Among its suggested next steps was checking a server stack trace, data LogRocket never gathers. On the 1.2.0 release question, Galileo first searched for the release name exactly as I typed it, found nothing, and guessed wrong. After I gave it the right names, it corrected itself:

It moved the old coupon error out of 1.2.0 and kept the payment timeout as the one new failure. It never found the slow query, which lives on the server.

Rollbar: Root cause analysis tied to your repository

Root cause & fix lists related occurrences and then asks you to connect GitHub repositories. It spends credits, of which the trial included 15,000, and the paid plans include from 4,000 a month. I didn't connect a repository, so I can't judge its answers.

AI comparison

Verdict: Not compared. Galileo was cautious and fixable, and blind to the server. Rollbar's analysis works from your code, which I didn't connect. LogRocket fits teams wanting AI over user sessions, and Rollbar fits teams happy to link GitHub for code-level answers.

Galileo couldn't see the server, and Rollbar's analysis waits for a GitHub connection. Better Stack's AI SRE answered the same 1.2.0 question in my test with no repository connected:

It pointed to the slow checkout query and counted 10 failed charges out of 48, all from 1.2.0. That took about four minutes and $0.49, longer than Galileo, with both problems sized from server data.

Get a release explained from your telemetry

Pricing

LogRocket meters recorded sessions and leaves errors uncounted. Rollbar meters error occurrences and replays, with AI credits from its first paid tier.

I checked both pricing pages on November 10, 2026. Rollbar's page didn't show paid prices in its text that day, so its paid figures are from my October 23 reading. Confirm before you buy.

Free plans and trials

LogRocket's calculator at the free tier:

"$0, Free forever" covers 1,000 sessions a month, with errors, logs and analytics unlimited inside them.

Entry-level paid plans

What costs extra

Comparing the actual cost

Rollbar is the cheaper way to cover errors on both tiers: its free plan handles 5,000 occurrences and 1,000 replays, and paid tiers started below $15 a month. LogRocket's first paid tier is $83 a month for 10,000 sessions, and the AI features need Pro at $292.

The two meters behave differently under stress. A bad release that throws thousands of errors costs LogRocket nothing extra, since it bills by session. Rollbar stops at your plan limit by default, so a noisy deploy can use up your occurrences early. Our TrackJS vs Rollbar comparison covers the same meter against a page-view model.

Verdict: Rollbar. It's cheaper for a small team and covers the server. Pay for LogRocket when full recordings are the evidence you need.

Full feature comparison

Which should you choose?

Ask which question you answer most often in a bug report: "what was the user doing?" or "is this new, and which deploy caused it?"

Choose LogRocket if your evidence is the visit itself

• You reproduce bugs from user reports. Whole recordings with request and response bodies remove most of the guesswork.
• Most of your failures happen client-side. LogRocket flags failed requests and visible error messages by itself, and measures the browser while it records.
• You want AI over sessions. Galileo summarizes visits and issues, and Summarize labels sessions so you can pick which to watch.

The trade-offs: no server errors, no regression handling, a grouping model that can hide new bugs, and a higher starting price. For LogRocket next to a tool that also covers your API, see our Sentry vs LogRocket comparison.

Choose Rollbar if you want full-stack errors tied to deploys

• You run an API next to your front end. Both tiers land in one project, with server details like Typicality and a curl command.
• You ship often. Resolve in a version, suspected deploys, version pages and auto-resolve make each release reviewable.
• You're on a budget. The free tier and low paid tiers cover a small app, replays included.

The trade-offs: short replays without bodies, process-wide server telemetry, grouping that needs fingerprint rules after code changes, and no performance data. Our Sentry vs Rollbar comparison looks at it against a tool with tracing.

FAQ

Does Rollbar have session replay like LogRocket?

Yes, for browser errors. Rollbar records a clip when an error fires and attaches it to the item, with network calls, clicks and navigation. LogRocket records every visit in full, including request and response bodies.

Can LogRocket catch errors in my Node.js API?

It has no Node.js SDK. You see an API failure only through the browser, for example in the response body of a failed request, or by connecting a backend tool to its Backend Logs tab.

Which tool reopens a bug that comes back after a fix?

Rollbar reopens it, shows the version it returned in, and names a suspected deploy. LogRocket kept my closed Issue closed and only updated its session count and last-seen release.

Will Rollbar's auto-resolve hide real bugs?

Briefly. Auto-resolve on deploy marks every active item resolved, fixed or not. Any bug that still happens comes back as reactivated in the new version, so check the version page after each deploy.

Final thoughts

LogRocket and Rollbar both put a recording next to a browser error, and that's where the overlap ends. LogRocket shows the whole visit and stops at the browser. Rollbar follows the error onto the server and across releases, then shows you a few seconds of what the user did.

The decision is which gap you'll fill with something else. A team that debugs from support tickets will get more from LogRocket and should add server error tracking. A team that ships an API every week will get more from Rollbar and can live with short clips.

Before you trust either list after a deploy, check how many of its "new" errors are really new, because neither tool's count matched reality in my test.

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