# Sentry vs LogRocket: Hands-On Error Tracking Comparison for 2026
When users report bugs you can't reproduce, the hardest part is often understanding what actually happened. An error message might point to the failing code, but it doesn't necessarily tell you what the user clicked, what the application displayed, or which request caused the problem.
Sentry and LogRocket approach that challenge from different directions. **Sentry focuses on investigating errors across the application stack, connecting frontend exceptions** to backend traces, logs, and performance data. **LogRocket focuses on reconstructing the user's experience through session recordings, network activity, console messages**, and browser performance information.
Both platforms now offer error tracking, session replay, and AI-assisted debugging. But does that overlap make them interchangeable? Can a detailed recording provide enough information to resolve a failure, or do you need access to the server as well?
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I tested both tools using the same deliberately unreliable Node.js bookstore, introducing browser crashes, failed API requests, recurring bugs, and a faulty deployment that made checkout dramatically slower.
**LogRocket provided the richer browser debugging experience.** Its recordings brought together user interactions, request and response bodies, console activity, and performance information. Sentry offered a broader view, connecting browser failures to backend exceptions and identifying the database query responsible for the slowdown.
The differences became more noticeable when I tested error grouping, release tracking, and AI-assisted debugging. Both tools had limitations, but those limitations affected investigations in different ways.
This comparison examines how each platform performed in practice, what worked well, where I encountered problems, and which approach makes more sense for your application.
## Sentry vs LogRocket: Results at a glance
The main difference is how much of the application each platform can observe.
LogRocket specializes in browser sessions, while Sentry follows errors across frontend and backend services. That distinction explains many of the results below.
| Area | Better in my test | Why |
|---|---|---|
| Setup | **Sentry** | LogRocket's browser installation was simple, but release creation initially failed |
| Browser error capture | Tie | Both captured the browser failures generated during testing |
| Source maps | Tie | Both resolved the wishlist error to the original source code |
| Session replay | **LogRocket** | Detailed network waterfall, request and response bodies, console activity, and custom events |
| Replay privacy | **Sentry** | Page text was masked by default |
| Browser-to-server debugging | **Sentry** | One trace connecting browser activity to backend operations |
| Error grouping | **Sentry** | LogRocket grouped a new payment failure under an existing coupon error |
| Release tracking | **Sentry** | Marked returning errors as regressions |
| Performance monitoring | **Sentry** | Identified the database query responsible for slow checkout |
| Alerts | Not compared | Alert delivery wasn't tested |
| AI-assisted debugging | Not directly comparable | Seer had repository access; Galileo analyzed recorded sessions |
| Pricing | **Sentry** | Lower starting price and broader monitoring capabilities |
These findings reflect my Express and Vite application, the SDK versions I used, and my October 2026 testing configuration.
## How I tested Sentry and LogRocket
I used `monitoring-lab`, a deliberately unreliable online bookstore built with a Vite storefront, an Express 5 API, and SQLite.
The application also includes a price service that occasionally fails and a background reporting job that runs every minute.
Both platforms connect through separate monitoring adapters, allowing the bookstore to generate the same failures without changing its core application logic.
### The errors I introduced
The application included several predictable failure scenarios:
- **Frontend crashes:** A wishlist button that throws a `TypeError` and an order-tracking request that produces an unhandled promise rejection.
- **Failed API requests:** HTTP 404, 500, and 502 responses.
- **Backend exceptions:** Invalid coupons, nonexistent books, out-of-stock requests, upstream service failures, and an unhandled recommendation rejection.
- **Background failures:** A reporting job that occasionally throws an exception.
- **Performance problems:** Slow database queries, delayed requests, and a missing cover image.
Two simulated customers, alice and bob, interacted with the storefront through headless Chromium. A third customer, carol, generated API traffic without using a browser.
That distinction matters because LogRocket only received browser telemetry. Errors occurring exclusively in API requests or background jobs weren't directly visible to it.
### Testing across three releases
I deployed three versions of the bookstore.
| Release | What I tested |
|---|---|
| 1.0.0 | Error capture, debugging context, source maps, and session replay |
| 1.1.0 | A previously resolved coupon error returning without a fix |
| 1.2.0 | A faulty deployment introducing a slow database query and intermittent payment failures |
The final release introduced two independent problems.
First, a database query that normally completed in milliseconds began taking seconds.
Second, every fifth payment attempt threw an exception.
**Could each platform distinguish the new payment bug from the performance regression?** That became one of the most useful tests because the two failures affected the same checkout workflow but had different causes.
### SDKs and test limitations
| Platform | Packages |
|---|---|
| Sentry | `@sentry/browser`, `@sentry/node`, and `@sentry/profiling-node` 11.8.0 |
| LogRocket | `logrocket` 12.4.0 and `logrocket-cli` 0.16.0 |
Several conditions are worth keeping in mind:
- **LogRocket ran on a 14-day Pro trial**, which included Galileo AI.
- **LogRocket doesn't provide a Node.js error monitoring SDK.** My API adapter only wrote session links to the server console; it didn't send backend exceptions to LogRocket.
- **Seer had repository access**, while Galileo worked from browser recordings and telemetry.
- **The traffic runs were separate**, with Sentry tested on October 22 and LogRocket on October 29, 2026.
- **I placed one order manually** during release 1.1.0 because the automated browser traffic hadn't triggered the returning coupon bug.
- I didn't test alert delivery, LogRocket's product analytics, or its external backend monitoring integrations.
The separate traffic runs mean event totals shouldn't be compared directly.
## Setup and onboarding
Both platforms were straightforward to install in the browser. The differences appeared when I configured source maps and tried to monitor the server.
### Sentry: Browser and backend monitoring from one project
Sentry detected my GitHub repository during project creation and suggested Express and Browser JavaScript as monitoring platforms.
Its onboarding page displayed the available monitoring capabilities.
Error monitoring and tracing were selected by default, and the interface displayed their free allowances after the trial, including 5,000 errors and five million spans per month.
The generated configuration already contained the project's DSN and enabled errors, logs, metrics, tracing, and profiling.
I also uploaded browser source maps using `sentry-cli` after each build.
The browser and server sent data to the same Sentry project, while distributed tracing worked through Sentry's SDKs.
### LogRocket: A simpler browser installation
LogRocket began by asking what I wanted to accomplish.
I selected "Record sessions and find bugs", which focused the setup on session recordings, console activity, and error monitoring.
The alternative, "Analyze feedback", directed Galileo toward support-related workflows.
The installation itself was simple.
LogRocket provided an npm installation command and a `LogRocket.init` call containing the application ID.
An AI Setup tab also offered installation instructions that could be used with coding assistants such as Cursor and Claude.
Once the browser sent its first session, the dashboard began displaying activity.
LogRocket also recorded my headless Chromium sessions without additional configuration, which was convenient for automated testing.
### Why did source-map setup initially fail?
The browser integration worked, but uploading source maps introduced an unexpected problem.
LogRocket's CLI creates a release before uploading the relevant files.
When I attempted to create the first release on a new project, the command returned HTTP 500 with an error referring to a missing Sentry project associated with the LogRocket application ID.
That was surprising because I wasn't configuring a Sentry integration.
**The same command succeeded after LogRocket received its first session.**
I couldn't establish whether the issue affects all newly created LogRocket projects, so this result is specific to my setup.
Two other requirements mattered:
- LogRocket required production bundles to retain their `sourceMappingURL` references and didn't support hidden source maps in my test.
- Source maps needed to be uploaded before an error occurred because they weren't applied retroactively to previously captured errors.
Those restrictions made source-map setup more sensitive to the order of deployment steps.
### Setup comparison
| Capability | Sentry | LogRocket |
|---|---|---|
| Browser installation | npm SDK | npm SDK or script tag |
| Backend monitoring | Node.js SDK | No backend error SDK |
| Headless Chromium | Recorded | Recorded without extra configuration |
| Source-map upload | Worked immediately | Initially failed on fresh project |
| Hidden source maps | Not evaluated | Not supported |
| AI-assisted installation | Not offered in my test | Prompt for coding assistants |
**Verdict: Sentry.** LogRocket's browser installation was just as straightforward, but source-map release creation initially failed. Sentry also provided the backend instrumentation needed for full-stack monitoring.
## Browser error capture
Both platforms captured the browser failures generated during their respective traffic runs.
However, LogRocket also detected problems that didn't necessarily produce a JavaScript exception.
### Sentry: Frontend and backend errors together
After the first release, Sentry displayed 11 issues covering browser crashes, failed API requests, backend exceptions, and background job failures.
The browser and server issues appeared in the same list.
Issues with associated recordings also displayed replay counts, making it easier to identify failures with session context.
The deliberately broken cover image didn't become an issue, although its failed request appeared inside a replay.
### LogRocket: Automatic network errors
LogRocket handled browser errors differently.
When I first opened its Issues page, it was empty.
Where had the errors gone?
They appeared under Signals instead, which contained eight errors after the first traffic run.
The list included JavaScript exceptions and automatically detected Network Errors.
The boxed row showed a failed request that LogRocket had recorded without requiring my application to report it manually.
However, my bookstore already reported failed API calls as `ApiError`.
LogRocket captured those same failures automatically, creating duplicate entries.
The platform also normalized some URLs, replacing changing book IDs with patterns such as `GET /books/*/`.
Errors recorded before the source-map upload still displayed their minified bundle locations.
### Can LogRocket detect an error that's only visible on the page?
Yes, and this was one of its more interesting capabilities.
During the faulty release, LogRocket recorded an Error State associated with visible error text in the storefront.
The error text was "Something went wrong".
That appeared separately from the JavaScript exception responsible for the failure.
**This matters because applications often catch exceptions and display friendly error messages without reporting the underlying failure to an error tracker.**
LogRocket's browser-focused monitoring can provide visibility into those user-facing problems.
I didn't independently verify whether this Error State was created without any prior configuration.
The upstream price service's 502 error also occurred in Sentry's traffic run but not LogRocket's, so I couldn't directly compare that specific scenario.
### Browser error capture comparison
| Test | Sentry | LogRocket |
|---|---|---|
| Wishlist exception | Captured | Captured |
| Unhandled browser rejection | Captured | Captured |
| Application-reported API failures | Captured | Captured |
| Automatic network errors | Visible in replay | Reported as Network Errors |
| Error text displayed on page | Not detected as an issue | Error State |
| Missing cover image | Replay Network tab | Session network panel |
| Server-only failures | Captured | Not directly monitored |
**Verdict: Tie for browser error capture.** Both captured the browser failures that occurred. LogRocket also detected network failures and visible error states, although automatic reporting could create duplicates.
## Source maps
Production JavaScript is often minified, making stack traces difficult to read without source maps.
Both Sentry and LogRocket successfully resolved my wishlist error to its original source code.
### Sentry: Original source code after upload
After uploading the source maps with `sentry-cli`, Sentry displayed the original source location.
The stack trace identified `../../src/pages.js` at line 122, column 13.
The failing line appeared beneath the deliberate-bug comment, and the interface offered an option to configure code mapping.
The source maps remained private to Sentry.
### LogRocket: The same source location with additional context
LogRocket also resolved the wishlist error after the maps were uploaded.
The stack trace identified the same source file and position.
It highlighted the failing statement:
```javascript
wishlist.items.push(book.id);
```
The surrounding source code made the problem understandable without opening an editor.
Two additional frames from LogRocket's own wrapper appeared below the application frame, but those didn't prevent me from finding the relevant code.
### What happens when source maps are uploaded too late?
This is where LogRocket's workflow required more attention.
An error recorded before the source-map upload remained minified even after the maps became available.
That meant uploading source maps later didn't improve the old error report.
LogRocket also required source-map references in the generated bundles, which prevented me from using hidden source maps.
These requirements matter for deployment pipelines where source maps and application assets may be published at different times.
### Source map comparison
| Capability | Sentry | LogRocket |
|---|---|---|
| Private source-map upload | Yes | Yes |
| Original source file | Displayed | Displayed |
| Relevant code lines | Displayed | Displayed |
| Hidden source maps | Not evaluated | Not supported |
| Retroactive source resolution | Not evaluated | Earlier errors remained minified |
| Initial upload | Worked | Failed until project received data |
**Verdict: Tie for source resolution.** Both platforms displayed the correct source code once configured. LogRocket required more attention to upload timing and source-map references.
## Session replay
Session replay is LogRocket's main product, so this is where I expected it to perform particularly well.
It did, providing a more detailed browser debugging experience than Sentry in my test.
### Sentry: Session recordings with privacy-focused defaults
Sentry recorded the browser sessions and generated an AI summary for each replay.
The summary correctly described the shopper's activity and highlighted the wishlist failure.
Page text was masked by default, helping prevent names and email addresses from appearing in recordings.
Sentry also included network information alongside the replay.
The Network tab displayed request methods, status codes, resource types, sizes, and timings.
The broken cover image appeared as an HTTP 404 request.
However, the view didn't include request and response bodies in my configuration.
### LogRocket: Browser developer tools alongside every recording
LogRocket's session player provided a more comprehensive browser debugging environment.
The player included a network waterfall, console messages, navigation changes, and custom application events.
For the wishlist failure, I could see the shopper's interactions and the "add to wishlist clicked" event immediately before the exception.
The broken cover image also appeared in the network waterfall.
This made it possible to reconstruct the browser activity surrounding a failure without leaving the recording.
But how much can a browser recording reveal when the actual problem originates on the server?
The failed checkout provided a useful answer.
### LogRocket displayed the request and response bodies
Opening the failed checkout request revealed both its payload and server response.
The request body contained the invalid coupon code `FREEBOOKS`.
The response body contained the server exception message explaining that the application attempted to read `discount` from an undefined value.
**That was enough to understand the coupon failure without opening a backend monitoring tool.**
Sentry retained comparable information on its backend error page, but LogRocket made the browser's request and response directly accessible from the recording.
For errors that return meaningful information to the browser, this can considerably simplify debugging.
### Browser performance and AI-generated summaries
LogRocket also included a Performance panel with CPU usage, long tasks, memory, layout shifts, and crashes.
These measurements appeared on the same timeline as the session recording.
That made it possible to connect a slow or unresponsive browser interaction with activity happening at the same moment.
The session list also offered AI-generated summaries.
The summaries described individual sessions and linked to relevant moments, such as navigating to an invalid book URL.
Very short sessions sometimes displayed "Summary unavailable".
### Does LogRocket mask sensitive information by default?
This was the main weakness in its replay configuration.
LogRocket recorded ordinary page text as written during my test.
For example, the recording included a shopper's email address and coupon information.
Sentry masked page text automatically.
LogRocket provides privacy controls through `LogRocket.init`, sanitization options, and `data-private` attributes on individual elements.
However, those protections required configuration.
**LogRocket provided richer session evidence, while Sentry's defaults prevented more potentially sensitive page content from appearing in recordings.**
Teams using LogRocket should review text masking and network body sanitization before recording real customer sessions.
### Session replay comparison
| Capability | Sentry | LogRocket |
|---|---|---|
| Session recording | Available | Available |
| AI summaries | Per replay | Per session |
| Network waterfall | Available | Detailed |
| Request and response bodies | Not captured in my replay setup | Available |
| Console and custom events | Available | Available |
| Browser performance | Limited in tested replay | CPU, memory, long tasks |
| Broken image request | Visible | Visible |
| Default page-text masking | Yes | Required configuration |
**Verdict: LogRocket for debugging, Sentry for privacy.** LogRocket provided more useful evidence inside individual recordings. Sentry masked more page text automatically.
## Following a browser failure to the server
LogRocket's replay revealed enough information to understand the coupon error, but not every server problem can be diagnosed from an HTTP response.
What happens when the server doesn't return a useful error message, or the failure depends on an internal database operation?
That's where Sentry's broader monitoring becomes valuable.
### Sentry: A browser error linked to the server exception
Sentry connected the browser's failed checkout to the matching backend error.
The browser issue displayed a message indicating that another issue appeared in the same trace.
Opening the trace revealed the full request path.
The trace contained 12 spans over approximately 64.90 milliseconds.
It connected the shopper's checkout interaction to the Express handler and database query.
Sentry also retained additional context on the backend issue page.
The original source code appeared with the failing line highlighted.
The request body included the invalid coupon, while the shopper's replay appeared below the stack trace.
This connected the server error to the browser session that triggered it.
### LogRocket: Browser responses and external backend integrations
LogRocket doesn't directly collect backend exceptions.
Its browser telemetry can show error messages returned by the API, but it can't inspect the server's internal stack traces, logs, or database operations.
For deeper backend investigations, LogRocket offers integrations with external monitoring services.
The Backend Logs tab provided integration options for services including Datadog, AWS CloudWatch, Google Cloud, Azure, Splunk, Dynatrace, and others.
These integrations can connect browser requests to backend monitoring information stored elsewhere.
I didn't test them.
The important distinction is that **LogRocket can provide excellent browser evidence, but investigating internal server behavior still requires another monitoring system**.
### Browser-to-server comparison
| Capability | Sentry | LogRocket |
|---|---|---|
| Browser exceptions | Captured | Captured |
| Backend exceptions | Captured | No server SDK |
| Server error message | Backend issue | HTTP response body |
| Server source code | Displayed | Not available |
| Distributed tracing | Browser to database | Requires external integration |
| Request body | Backend issue | Replay |
| Backend logs | Integrated | External integration required |
**Verdict: Sentry.** LogRocket's response bodies were enough to explain some failures, but Sentry provided the backend information needed for deeper investigations.
[summary]
LogRocket exposed the backend response through the browser, while Sentry connected the browser failure to its server trace.
I also investigated the same checkout failure in Better Stack.
The session contained 30 frontend spans and seven backend spans, displaying browser navigation, interactions, and server operations together.
From an exception, the View related menu also provided links to logs, spans, releases, and recordings.
The browser integration was still in beta during my test, and the storefront's stack traces remained minified.
**[Trace a failed click into your API](https://betterstack.com/tracing)**
[/summary]
## Error grouping
Error grouping determines whether repeated failures appear under one issue or create separate entries.
It also determines whether a genuinely new bug receives its own issue or disappears inside an existing group.
The faulty release exposed the second problem.
### Sentry: A separate issue for the new backend failure
Release 1.2.0 introduced a `PaymentGatewayError`.
Sentry captured 13 occurrences affecting three users.
Although the individual messages contained different payment amounts, Sentry grouped the backend failures together.
The issue list made the new error visible.
The payment error appeared as a new issue near the top.
However, Sentry's browser grouping was less precise.
The storefront already reported failed checkout requests using a generic message. When payment failures started occurring, those browser errors joined the existing checkout group.
Sentry also created duplicate backend issues for the coupon and out-of-stock errors because release 1.2.0 shifted their stack traces.
### LogRocket: A new bug inside an old issue
LogRocket groups browser errors primarily by stack information, and the issue row retains the first error message as its title.
That became a problem when the payment failure started occurring on the same checkout code path.
The title still referred to the older coupon error involving `discount`.
However, opening a newer session revealed a completely different payment gateway failure.
**The new payment bug had been grouped under the old coupon error.**
Why is that particularly concerning?
A developer scanning the issue list after deployment might see an older checkout error with additional occurrences and assume the same bug was continuing.
Nothing in the list clearly identified the new payment problem.
Sentry's browser grouping behaved similarly, but its backend monitoring created a separate issue that exposed the new failure.
LogRocket had no equivalent backend issue.
### Error grouping comparison
| Scenario | Sentry | LogRocket |
|---|---|---|
| New payment error on server | Separate issue | Server not monitored |
| New payment error in browser | Joined existing checkout group | Joined existing checkout group |
| Changing payment amounts | Grouped | Grouped |
| URLs with changing IDs | Stack-based grouping | Normalized patterns |
| Duplicate issues after code changes | Two backend duplicates | None observed in browser |
| New backend bug visible independently | Yes | No |
**Verdict: Sentry.** Both platforms grouped the payment failure under an older browser error, but Sentry's server monitoring also created a separate issue for the new bug.
[summary]
LogRocket grouped the payment failure under an older coupon error, while Sentry identified the backend exception but duplicated some existing issues.
I also sent the same faulty release to Better Stack.
Better Stack kept the payment failures together in one group despite changing payment amounts.
The coupon error also remained in one group across all three releases, avoiding the duplicates Sentry created after the code changed.
**[See new bugs as new errors](https://betterstack.com/error-tracking)**
[/summary]
## Release tracking and regressions
Release tracking should help answer an important question after every fix: did the problem actually go away?
To test this, I marked the coupon error as fixed during release 1.0.0 and deployed release 1.1.0 without correcting it.
Both platforms recorded the returning failure, but they handled its status differently.
### Sentry: A returning bug clearly identified
Sentry allows issues to be resolved for a specific release, the next release, or a commit.
I selected the next-release option.
When the coupon error returned in release 1.1.0, Sentry reopened it and displayed a Regressed label.
The label appeared directly in the issue list, making the returning bug easy to recognize.
Sentry also included a dedicated Regressed tab on the release page.
The tab contained exactly one issue, the returning coupon bug.
This made it possible to review regressions associated with a deployment without manually inspecting every error.
### LogRocket: A closed issue that continued receiving events
LogRocket uses an Issues workflow for tracking errors.
I created an Issue for the checkout error and marked it Closed.
After deploying release 1.1.0, I manually triggered the coupon failure because the automated browser traffic hadn't encountered it.
LogRocket recorded the new session against the existing issue.
The session count increased from one to two, and Last Seen updated to release 1.1.0.
However, the issue remained Closed.
Would it reopen after additional occurrences?
I checked again after another release.
The issue now showed five sessions, with the most recent occurrence coming from release 1.2.0.
It was still Closed.
I also didn't find an activity entry recording the return or a setting for automatically reopening closed issues.
**LogRocket continued collecting evidence of the bug while leaving its status unchanged.**
That makes it harder to distinguish genuinely resolved problems from errors that are still occurring.
### Release tracking comparison
| Capability | Sentry | LogRocket |
|---|---|---|
| Release identification | SDK release value | SDK release option |
| Resolve for a future release | Supported | Not observed |
| Automatic reopening | Yes | Issue remained Closed |
| Regression label | Regressed | Not observed |
| First and last seen | Available | Available |
| Release-specific regression view | Regressed tab | Not observed |
| Activity history | Recorded regression | No reopening recorded |
**Verdict: Sentry.** It clearly identified the returning bug as a regression. LogRocket recorded new sessions but continued displaying the issue as Closed.
[summary]
LogRocket continued displaying the returning coupon bug as Closed, while Sentry marked it Regressed.
I also tested the same scenario in Better Stack.
The issue displayed a red Reoccurred status directly in the error list.
Its activity history recorded the original resolution and subsequent return.
The history made it clear that the bug had been considered fixed before occurring again.
Sentry still offered an additional dedicated Regressed tab per release, which Better Stack didn't provide in my test.
**[Get told when a fix doesn't hold](https://betterstack.com/error-tracking)**
[/summary]
## Performance monitoring
Release 1.2.0 made the book lookup query dramatically slower.
The user experienced this as a delay after clicking Place order.
Both platforms recorded evidence of the slowdown, but only Sentry could identify its backend cause.
### Sentry: The database query identified in a trace
Sentry's distributed tracing displayed the checkout request and its internal operations.
The request took approximately 2.26 seconds.
The book lookup query accounted for 1.79 seconds, while the payment operation failed after another 459 milliseconds.
This made the two problems visible within one request.
Sentry also allowed me to compare checkout latency across releases.
The p95 duration was 5.29 milliseconds in release 1.0.0 and 3.63 milliseconds in release 1.1.0.
In release 1.2.0, it increased to 5.93 seconds.
That made the performance regression easy to associate with the faulty deployment.
### LogRocket: A slow request without the backend explanation
LogRocket recorded the failed checkout request inside the shopper's session.
The request took approximately 2.9 seconds.
By comparison, a failed checkout request from release 1.0.0 took around 15 milliseconds.
The difference was obvious.
However, LogRocket's Timing tab reported that detailed timing data was unavailable for that request.
Without backend spans, it couldn't identify the database query responsible.
The session's Performance panel showed browser CPU activity during the delay, but that explained what happened in the browser rather than inside the API.
### Can LogRocket compare performance across releases?
LogRocket provides analytics capabilities built around session data, but I didn't test its ability to chart request latency by release.
I also didn't find an equivalent to Sentry's release-filtered Trace Explorer during this investigation.
Consequently, I can confirm that LogRocket exposed the slow request in an individual recording, but not that it could identify the regression's backend cause.
### Performance comparison
| Capability | Sentry | LogRocket |
|---|---|---|
| Slow request visible | Yes | Yes |
| Database query duration | Available | Not available |
| Cause of slowdown | Identified in trace | Not identifiable from browser telemetry |
| Latency by release | Trace Explorer | Not tested |
| Browser CPU and memory | Limited in tested replay | Per-session performance panel |
| Backend tracing | Supported | External integration required |
**Verdict: Sentry.** LogRocket showed that checkout had become slower for the shopper. Sentry identified why it was slow and connected the change to the faulty release.
[summary]
LogRocket recorded the slow checkout but couldn't explain the server-side delay.
I also investigated the same request in Better Stack.
The request took 2.19 seconds.
The book lookup accounted for 1.53 seconds, while the failing payment operation took another 660 milliseconds.
The related logs were available from the same trace.
The slow-query warning appeared immediately before the checkout failure, connecting the delay to the application's own diagnostic information.
The Service column remained empty because my instrumentation hadn't configured a service name.
**[Find what slowed down a release](https://betterstack.com/log-management)**
[/summary]
## Alerts and notifications
Both platforms support notifications, but I didn't test alert delivery.
LogRocket provides issue alerts based on severity and affected users.
The rule shown in my test targeted severe issues detected within the previous 24 hours that affected more than a configured number of users.
Available destinations included Slack and webhooks.
Would that rule have detected the failures I introduced?
Probably not under the tested conditions.
Galileo rated both Issues I created as Low priority, so neither would have met the severe-issue threshold.
The new payment failure also appeared under an existing checkout issue instead of becoming a new one.
LogRocket additionally offered metric alerts and a weekly error digest.
Sentry supported alerts for new issues, regressions, and metric thresholds.
However, I didn't configure notification delivery in either platform.
**Verdict: Not compared.** Both provided alerting options, but I didn't evaluate their delivery or reliability.
## AI-assisted debugging
Both platforms provide AI-assisted debugging, but their assistants work with different information.
Sentry's Seer could inspect my connected GitHub repository.
LogRocket's Galileo worked from session recordings, error reports, and browser telemetry.
That distinction mattered because the test application contained deliberately introduced code defects.
### Sentry: Repository-aware error analysis
Seer analyzed the coupon error in approximately 15 seconds.
It correctly identified the invalid coupon lookup, the source line responsible, and the input that triggered the failure.
I also asked Seer to investigate the payment error from release 1.2.0.
The analysis identified the code responsible for failing payment attempts and the condition enabling that behavior in the faulty release.
These explanations matched the application code.
However, Seer struggled when I asked about the broader performance regression.
Its chat assistant recognized the increase in checkout latency but attributed the slowdown to payment retries.
The trace showed that the database query accounted for most of the delay.
**Seer correctly identified individual code defects but misinterpreted the overall performance problem.**
### LogRocket: Galileo focused on recorded sessions
Galileo provides error analysis and a conversational assistant called Ask Galileo.
When I asked it to analyze the wishlist crash, it created an Issue with a summary and priority.
Galileo concluded that the evidence didn't establish which action triggered the failure.
That was cautious, although the recording showed the wishlist click immediately before the exception.
It also assigned Low priority because the error had limited observed impact on shoppers.
On the checkout issue, Galileo described the trigger as unclear even though the invalid coupon appeared in the request body.
### Could Galileo explain what changed between releases?
I asked Galileo to compare release 1.2.0 with the previous version.
Initially, I used the release identifier `monitoring-lab@1.2.0`.
Galileo couldn't find sessions under that name and explained that the release tagging might be incorrect.
My LogRocket release was actually named `1.2.0`.
After correcting the identifier, Galileo produced a more useful comparison.
It compared sessions and failures across releases.
More importantly, **Galileo corrected an earlier conclusion**, recognizing that the coupon error wasn't new in release 1.2.0.
It identified the payment failure as the new checkout problem.
It also noticed that my manually placed order was the only session that didn't use a headless browser.
However, Galileo interpreted the payment error as an upstream dependency timeout.
Without repository access, it couldn't identify the deliberately introduced fifth-payment condition.
It also couldn't explain the database slowdown because the relevant server telemetry wasn't available.
### AI comparison
| Test | Sentry Seer | LogRocket Galileo |
|---|---|---|
| Repository access | Yes | No |
| Coupon error cause | Identified precisely | Described trigger as unclear |
| Payment error cause | Found deliberate failure condition | Attributed failure to gateway |
| Release comparison | Available | Available through Ask Galileo |
| Old and new errors distinguished | Yes | Yes, after correcting release names |
| Slowdown cause | Misidentified | Couldn't establish backend cause |
| Acknowledged uncertainty | Not evaluated | Yes |
| Corrected earlier conclusion | Not evaluated | Yes |
**Verdict: Not directly comparable.** Seer had repository access and identified the code-level causes. Galileo worked from browser evidence, acknowledged uncertainty, and corrected its release analysis, but couldn't fully explain the server-side problems.
[summary]
Seer identified the individual code defects but misdiagnosed the performance regression. Galileo analyzed browser sessions but lacked the backend evidence needed to identify the root causes.
I also asked Better Stack's AI SRE to investigate the faulty release without repository access.
It identified the book lookup query as the cause of the slowdown.
The payment failures were investigated separately.
The analysis found 10 failed payment calls out of 48, all associated with release 1.2.0.
The investigation took approximately four minutes and cost $0.49.
It correctly distinguished the two problems, although it couldn't identify the deliberate fifth-payment condition without repository access.
**[Ask what changed in your last deploy](https://betterstack.com/ai-sre)**
[/summary]
## Pricing
Sentry and LogRocket charge for different types of usage.
Sentry uses allowances for errors, spans, replays, and logs.
LogRocket primarily charges according to recorded sessions, with errors, console activity, and analytics information included in the session-based model.
These figures reflect vendor pricing collected on October 28 and 29, 2026.
### Free plans and trials
Both platforms provide free plans.
| Feature | Sentry Developer | LogRocket Free |
|---|---|---|
| Monthly price | Free | Free |
| Included usage | 5,000 errors, 5 million spans, 50 replays | 1,000 sessions |
| Logs | 5 GB | Included in recorded sessions |
| Users | 1 | Not evaluated |
| Paid-feature trial | Not evaluated | 14-day trial |
LogRocket's free tier is useful for evaluating session replay, while Sentry includes a wider range of application monitoring capabilities.
### Entry-level paid plans
LogRocket's paid pricing depends on monthly session volume.
| Feature | Sentry Team | LogRocket Core | LogRocket Pro |
|---|---|---|---|
| 10,000 sessions/month | $29 monthly or $26 annually | $83/month annually | $292/month annually |
| 25,000 sessions/month | Same base plan | $176/month annually | $407/month annually |
| Errors | 50,000 included | Unlimited | Unlimited |
| Session replay | 50 included replays | Session-based | Session-based |
| AI assistant | Additional cost | Not included | Galileo |
| Backend monitoring | Supported | Not included | Not included |
The LogRocket prices shown are annual-billing equivalents.
Final costs can also depend on seats, retention, and add-ons.
Sentry Business costs $89 per month, or $80 with annual billing.
### What happens when traffic increases?
LogRocket charges primarily by recorded sessions.
That means an increase in errors doesn't directly increase the subscription bill.
Each session includes its browser activity, console messages, network requests, and errors.
This can make pricing predictable when session volume remains stable.
However, **recording every visitor becomes more expensive as traffic increases**.
Sentry's pricing behaves differently.
Higher traffic only increases usage when it generates additional telemetry, such as errors, spans, logs, or replays.
That provides more control over which signals to collect, but the resulting bill can depend on several different quotas.
### When does LogRocket's pricing make more sense?
LogRocket becomes more attractive when detailed session replay is the main reason you're buying a monitoring product.
The recording includes browser activity, request and response bodies, console messages, and performance information.
An error spike doesn't directly increase session-based charges.
However, the Galileo features I tested required the Pro plan, which cost $292 per month at 10,000 sessions with annual billing.
You'd also need another service for backend error tracking and distributed tracing.
Sentry's entry-level subscription includes those backend capabilities, although recording large numbers of sessions would consume additional replay allowances.
### Pricing comparison
| Consideration | Sentry | LogRocket |
|---|---|---|
| Permanent free plan | Yes | Yes |
| Billing unit | Telemetry usage | Recorded sessions |
| Entry paid price | $29/month | $83/month at 10,000 sessions, billed annually |
| AI assistant | Additional cost | Pro plan |
| Backend monitoring | Supported | Requires another tool |
| Session replay | Quota-based | Core product |
| Error spikes | Consume error allowance | Don't directly increase session charges |
| Best fit | Full-stack error monitoring | Detailed browser session analysis |
**Verdict: Sentry for overall monitoring cost.** It offers a lower starting price and covers more of the application stack. LogRocket's pricing may make sense when comprehensive session replay is your primary requirement.
## Full feature comparison
| Feature | Sentry | LogRocket |
|---|---|---|
| Primary focus | Full-stack error monitoring | Browser session replay |
| Browser exceptions | Captured | Captured |
| Backend error SDK | Available | Not offered |
| Automatic network errors | Network context in replay | Supported |
| Visible error text detection | Not observed | Error State |
| Source maps | Supported | Supported |
| Request and response bodies in replay | Not captured in my setup | Available |
| Console and custom events | Available | Available |
| Browser performance timeline | Limited in tested replay | Available |
| Default page-text masking | Yes | Requires configuration |
| Browser-to-backend tracing | Available | External integration required |
| New payment bug | Separate backend issue | Grouped under old checkout error |
| Returning bug | Marked Regressed | Remained Closed |
| Database query timing | Available | Not available |
| Alerts | Issue and metric rules | Issue severity and metric rules |
| AI assistant | Seer | Galileo |
| Repository-aware AI | Yes | No |
| Free tier | 5,000 errors | 1,000 sessions |
| Billing model | Errors, spans, replays, logs | Sessions |
## Which should you choose?
Sentry and LogRocket overlap across browser error tracking, replay, and AI-assisted investigations, but they serve different primary needs.
The better choice depends on whether you mainly investigate application failures or reconstruct user experiences.
### Choose Sentry if you need monitoring across the stack
Sentry makes more sense when failures can originate in the browser, API, database, or external services.
It connected failed checkout requests to backend exceptions, identified the slow database query, and created a separate issue for the new payment error.
**Its regression tracking was particularly useful**, clearly identifying the returning coupon error after deployment.
Seer also found the code-level causes of both planted exceptions when given repository access.
However, Sentry wasn't perfect.
Its grouping created duplicate issues after code changes, and its AI assistant misidentified the cause of the performance regression.
Its session replay also exposed less browser request information than LogRocket in my setup.
### Choose LogRocket if session replay is your priority
LogRocket is more appealing when your main challenge is understanding what users experienced before something went wrong.
**Its session player was the strongest part of my testing.**
The network waterfall, request and response bodies, console activity, custom events, and browser performance information all appeared alongside the recording.
This made some failures understandable without opening a separate monitoring tool.
LogRocket also detected visible error messages and provided useful AI-generated session summaries.
However, its browser-first design introduces limitations.
You need another platform for backend exceptions, distributed tracing, and database performance analysis.
A new bug can also appear under an older browser error, while closed Issues didn't automatically reopen during my regression test.
Finally, configure text masking and network body sanitization before recording real customer sessions.
## Frequently asked questions
### Does LogRocket track backend or server errors?
LogRocket doesn't provide a Node.js error monitoring SDK.
It can display backend error messages returned in HTTP responses, but investigating server stack traces, internal logs, and database operations requires another monitoring platform.
LogRocket offers integrations with external backend monitoring services and can pass session links to server logs.
### Can you use LogRocket and Sentry together?
Yes.
One approach is to use Sentry for error tracking and distributed tracing across the application, while LogRocket provides detailed browser session recordings.
LogRocket's `getSessionURL()` method returns the current recording's URL, which can be associated with other error monitoring events.
I didn't test the integration directly.
Using both tools also means paying for two monitoring products, so the additional replay detail needs to justify the cost.
### Is LogRocket free?
Yes.
LogRocket offered a free tier with 1,000 monthly sessions and a 14-day trial of paid features during my October 2026 research.
Paid pricing depended on recorded session volume.
At 10,000 sessions per month, the annual-billing equivalents were $83 monthly for Core and $292 monthly for Pro.
### Does LogRocket record sensitive information?
It can, depending on the configuration.
In my test, ordinary page text appeared in recordings, including customer email addresses.
LogRocket offers sanitization controls for text, inputs, network bodies, and individual page elements.
Sentry masked page text by default in my configuration.
### Does LogRocket work with headless browsers?
Yes.
LogRocket recorded my headless Chromium test sessions without additional configuration.
Galileo also recognized that much of the traffic appeared synthetic and warned against treating the session counts as real customer impact.
Because LogRocket charges by recorded sessions, high-volume synthetic traffic can also affect usage.
### Which platform is better for frontend performance monitoring?
It depends on where the slowdown occurs.
LogRocket's session Performance panel provides browser CPU, memory, long-task, and layout-shift information synchronized with the recording.
Sentry offers browser tracing and performance monitoring that can continue into backend services.
In my test, the slowdown originated from a database query, so Sentry provided the information needed to explain it.
## Final thoughts
**LogRocket provided the richer browser debugging experience.** Its recordings combined user activity, network requests, response bodies, console messages, and performance information in one place. For some failures, that was enough to understand the problem without opening another tool.
However, the faulty release exposed the limits of browser-only monitoring. LogRocket grouped a new payment failure under an older issue, left a returning bug marked Closed, and showed a slow request without identifying its backend cause.
Sentry provided the missing server context through distributed tracing, error grouping, and regression detection, although its own grouping and AI-assisted analysis weren't perfect.