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

Neither LogRocket nor PostHog started life as an error tracker. LogRocket began by recording what people do in the browser, and PostHog began by counting what they do in your product. Both have since grown error tracking, AI assistants and alerting on top of that user data, so if you want errors next to real user behavior, either one can now be the tool you reach for.

LogRocket is a session replay platform for web and mobile apps. Each recording keeps the clicks, the console and every network request with its body, and LogRocket builds errors, product analytics and Galileo, its AI assistant, on top of those recordings.

PostHog took the wider road. PostHog is a product analytics platform that bundles error tracking, session replay, logs, traces and feature flags in one account, and unlike LogRocket it ships a server SDK, so your API's exceptions land in the same list as the browser's.

That server SDK decided most of my results. LogRocket's recordings were the better place to watch a failure unfold, and its replay worked from the first session. PostHog saw further: server stack traces with code, a slow query on the bad release, and a bug that came back after I'd marked it fixed. Each tool also stumbled where the other didn't, as the grouping and AI sections show.

I ran both against the same deliberately broken bookstore at the end of October 2026. The sections below cover setup, browser and server errors, source maps, replay, how each follows a failure into the API, grouping, releases, performance, logs, alerts, AI and pricing.

LogRocket vs PostHog: Results at a glance

These verdicts come from one Express and Vite app as I set it up. A different stack, or the parts of PostHog I didn't test, could move a few of them.

How I tested LogRocket and PostHog

The app behind this test, monitoring-lab, sells books. Its Vite storefront talks to an Express API backed by SQLite, the API calls a deliberately flaky price service, and a background job builds a sales report once a minute. Each tool plugs in through two adapter files, one for the browser and one for the server, so the app's own code never changes between tools.

Here's what it breaks on purpose:

• Browser crashes: the wishlist button throws a TypeError, and the orders page calls a route that doesn't exist and never handles the rejection.
• Failed requests: 404s and 500s for bad book IDs and checkout, and 502s when the price service fails.
• Server bugs: unknown coupon codes throw a TypeError (the coupon bug from here on), and the API also raises out-of-stock errors, 503s from upstream and one rejection nobody catches.
• Job failures: some sales report runs throw.

Scripted shoppers drive the traffic: alice and bob use headless Chromium, while carol only calls the API.

Releases I shipped

Release 1.2.0 asks the hardest question. The tool you want here separates the slowdown from the new error instead of blending them into one story.

SDKs and test limitations

Keep these conditions in mind:

• LogRocket has no server SDK. My server adapter only wrote each request's LogRocket session link into the console, so the API sent LogRocket nothing.
• LogRocket ran on its 14-day Pro trial, which includes Galileo. PostHog ran on its Free plan.
• PostHog's traces, logs and metrics came through OpenTelemetry, which I wired up myself and sent to PostHog's endpoints.
• The runs were a day apart, PostHog on October 28 and LogRocket on October 29, so compare behavior and leave the counts aside.
• I placed one LogRocket order by hand on 1.1.0, because the browser traffic never hit the coupon bug in that release.
• The two AI tests used different prompts, and neither assistant could read my code.
• PostHog's analytics, flags and experiments, and LogRocket's product analytics, were outside this test.

Getting started

Both tools were recording within minutes. What differed was how much of each product I had to switch on by hand.

LogRocket: Two lines, and every visit recorded

The first onboarding question is about your goal:

I picked "Record sessions and find bugs", and the install step that followed was an npm package plus LogRocket.init with the app ID:

From there, every visit was recorded, my headless shoppers included, with no extra option. Source maps took more care. logrocket-cli creates a release before it uploads, and on my brand-new project that call failed with a 500 until the first session arrived. Bundles also need their sourceMappingURL comment, and maps only apply to errors that happen after the upload.

PostHog: A broad product, switched on in pieces

PostHog's onboarding starts with a goal. I picked "Find and fix issues", and the products it preselected left out error tracking:

I added Error Tracking, Logs and Metrics from the full list. The install page then led with an AI wizard whose framework list had no Express, so I followed the manual Node.js steps, which call for setupExpressErrorHandler because Express catches errors itself.

Four more things needed attention before PostHog showed everything:

• Headless browsers are filtered out by default. My test shoppers sent nothing until I set opt_out_useragent_filter: true. Real browsers aren't affected.
• Session replay started off at the project level, even though Session Replay was one of the preselected products.
• Releases follow git commits by default. The CLI picks the release when it injects source maps, so the version flags have to go on inject as well as upload.
• The version property needs a $ prefix, $app_version, before an issue's Releases panel picks it up.

Setup comparison

Verdict: LogRocket. It was useful from the first recorded visit. PostHog covers far more, and it takes several settings before all of it is on.

Catching browser errors

Both tools caught the storefront's crashes and failed API calls. They differed in what else they flagged without being told.

LogRocket: Errors it finds by itself

LogRocket's Issues feed starts empty, because errors live under Signals until you promote one. After the first run, the errors list held eight rows:

LogRocket adds its own Network Error row for every failed request, next to the ApiError my code reports, so each API failure shows up twice. It also replaced the book IDs in URLs with a wildcard, GET /books/*/, which keeps one bad route in one row. The list counts sessions per error, which reads well when you care about how many people were affected.

On the bad release, a different kind of row appeared:

That Error State row means LogRocket flagged "Something went wrong" text on the page, separately from any exception. If your app catches errors and shows a friendly message, this is how those failures still surface.

PostHog: Browser and server in one issue list

PostHog's issues list mixed browser and server errors:

Each row shows occurrences, sessions and users, with status, severity and assignee editable inline. One quirk inflated the numbers: requests without a signed-in user got a new random ID each time, so the price service's 503 showed 24 users for 24 events.

Browser error comparison

Verdict: LogRocket. It caught failures my code never reported. The extra rows double up your own reports, so expect a busier list.

Catching server errors

This is the one area where only one tool takes part. PostHog's Node SDK caught the server bugs, and LogRocket can't receive them.

PostHog: Server code read from disk

The coupon bug arrived with its source:

posthog-node reads the code from disk, so you see the lines around src/server.js@150 with no repository connected. "Show 1 vendor frame" hides the Node internals. The event's properties held the user's email, the URL and my version tag, though not the request body.

PostHog missed one server failure, the unhandled recommendation rejection. The SDK only listens for unhandled rejections in some Node modes, and in the default mode it relies on Node turning them into crashes.

LogRocket: The server's answer, from the browser

LogRocket never sees a server exception. It does keep the server's reply to every request, which you'll see in the browser-to-server section, and for many bugs that's enough to read the message.

Verdict: PostHog. It's the only one of the two that monitors your API.

Source maps

Both tools turned the minified wishlist crash back into my source, and each had a catch you should plan for.

LogRocket mapped the frame to the exact line:

Line 122, wishlist.items.push(book.id);, sits in red with the code around it, and two frames from LogRocket's own wrapper sit underneath. One error from before my upload stayed minified, because maps don't apply to stored errors.

PostHog reached the same line:

Its catch came in the issue list. After the upload, the wishlist crash had two rows:

PostHog builds its fingerprint from the resolved frames, so minified and mapped events never group. Upload your maps before launch, or you'll be merging issues by hand.

Verdict: Tie. Both mapped the crash correctly once the maps were in place.

Session replay

Replay is where these two meet head-on, and LogRocket's whole product is built around it.

LogRocket: The browser's devtools for every visit

LogRocket's session player puts the recording beside a network waterfall and the console:

Every request is there, including a broken cover image's 404 that my error reports never mentioned, and the console interleaves console output, hash-route navigations and the events my adapter tracked. A Performance panel adds CPU, memory, long tasks and layout shift on the same timeline. The session list can also summarize every recording in one line:

The summaries matched the sessions I'd watched and link to the moments they describe. Short visits get "Summary unavailable".

PostHog: A Recording tab on each error, once replay is on

Replay was disabled for my project until I turned it on:

After that, I also enabled network header and body capture and Web Vitals. Each error then gained a Recording tab that starts at the moment of the failure:

You can see the shopper's cursor on "Add to wishlist", with an "Open in session replay" link to the full player. I didn't test PostHog's full player tabs or its AI summaries.

Neither tool masks page text by default. LogRocket recorded "Ordering as alice@example.com" as written, and PostHog's Normal setting masked inputs only. Both offer stricter options you should set before real users arrive.

Replay comparison

Verdict: LogRocket. Its player gave me more evidence per failure, and it worked from the first visit. PostHog's free allowance is five times larger, which matters if you record everyone.

Following a failure to the server

A failed checkout starts with a click and ends in your API. This is where the two designs pull furthest apart.

LogRocket: The server's reply in the request

Clicking the failed checkout request in a LogRocket session showed both bodies:

The request carries the invalid coupon, and the response carries the server's exception message. For a bug whose message names the problem, that's often enough. Past it, LogRocket points you to integrations:

Each joins a separate backend product's data to the requests in a recording. I left them untested, and all of them mean a second subscription.

PostHog: A server trace, joined to the session by ID

PostHog's coupon issue had a "View trace" link:

The POST /checkout trace has eight spans, from Express's middleware down to my custom SELECT book for checkout span, plus the user and a tab with the request's logs. The trace starts at the server, though. posthog-js doesn't create browser spans, so the click itself isn't part of it, and the link back to the browser runs through the session ID.

Verdict: PostHog. It shows what happened inside the API. LogRocket shows what the API sent back.

LogRocket stopped at the response the browser received, and PostHog's trace started at the server with no click in it. When I sent the same storefront to Better Stack, both halves of a visit shared a single timeline:

The browser's navigation, clicks and API calls sit in sequence with the server spans they triggered, so you can read a slow click and its database work as one story. Both halves came from one setup, with no second product to buy.

See the browser and your API on one timeline

Error grouping

After a deploy, you scan the error list for anything new, so it matters whether a fresh bug earns a row of its own. Release 1.2.0 put a payment error on the coupon bug's checkout path to test exactly that.

LogRocket: A new bug under an old title

LogRocket groups browser errors by their stack and titles each row with the first message it saw. After 1.2.0, the checkout row looked like this:

The title still names the coupon bug, and Last Seen says 1.2.0. The payment failures were inside that row, and their message only appeared once I opened the latest session. Nothing in the list told me a new bug had shipped.

PostHog: A new issue, twice

PostHog gave the payment failure its own issue, then a second one:

Both issues have the same two lines of my code. The first also includes two Node timer frames, hidden behind "Show 2 vendor frames", and they still count toward the fingerprint:

The amounts didn't cause the split, since each issue held several. The slow query in 1.2.0 changed the async path through Node, and that alone made new fingerprints, for the coupon and out-of-stock bugs too. Grouping rules can merge them:

The page starts empty. I didn't create a rule, so I can't say how well one would hold.

Grouping comparison

Verdict: PostHog. Its duplicates are noisy, but you can see them. LogRocket's grouping hid the new bug entirely.

LogRocket buried the payment bug under the coupon title, and PostHog filed it twice because of Node's timer frames. Better Stack received the same 1.2.0 traffic and kept the payment bug in one group:

Every failed charge landed in that group, with the lines that throw shown underneath, and the coupon bug stayed one group through all three releases. You get one new row for one new bug, with no grouping rules to write.

Spot new bugs after every deploy

Releases and regressions

After shipping a fix, you want to know whether it held. To find out, I resolved the coupon bug in both tools ahead of 1.1.0 and left the code as it was.

LogRocket: Closed, and still closed two releases later

LogRocket's errors get a status once you open an Issue. Closing one is a single click, with no release to pick:

When the bug returned on 1.1.0, the Issue counted the new session and moved Last Seen to the new release. It never reopened:

Twenty-four minutes and one more release later, it showed five sessions and still read Closed. Its history recorded nothing, and I found no setting for reopening.

PostHog: Reopened, and split by version

PostHog's Resolve is a single click too, and the toast that follows offers something unusual:

"Tell them it's fixed" emails the signed-in users who hit the bug, two in my case. I didn't send it. Once 1.1.0 traffic arrived, the issue reopened by itself:

The status went back to Active with 20 new occurrences, though nothing labels it as a regression in the list. The Breakdown panel answers which release brought it back:

1.0.0 holds 68.8% of the occurrences and 1.1.0 holds 31.3%. The same panel works on any property, such as the user's email or the URL.

Proper releases took more work. Until I passed the version at the inject step, every build landed on one release named after the git commit:

Releases comparison

Verdict: PostHog. It noticed that the fix didn't hold. LogRocket recorded the new release on the Issue and left it Closed.

PostHog reopened the coupon bug with no label, and LogRocket didn't reopen it at all. In Better Stack, the returning bug changed its status in the list:

A red "Reoccurred" took the place of "Unresolved" on that one row, so a broken fix stands out while you scan. Its activity feed keeps the order of events, with the comeback listed above my resolve.

Get told when a fix doesn't hold

Performance on the bad release

Release 1.2.0 made one checkout query slow. Users feel that as a long wait after placing an order, and a good tool should tell you where the time went.

LogRocket: One slow request, no breakdown

In a 1.2.0 session, the failed checkout request stood out in the waterfall:

It took 2.899 s, where the same failure took 15 ms on 1.0.0. The Timing tab said timing data was unavailable, and with no server data, LogRocket couldn't say what made it slow. The Performance panel measures the browser instead:

That's the right view for heavy scripts and layout shifts. During the slow checkout, it showed the browser waiting. I didn't find a latency view by release.

PostHog: Latency by version in the Operations table

PostHog's Tracing has an Operations view with p50, p95 and error rate for each span name. Filtered to 1.1.0, checkout looked healthy:

On 1.1.0, the checkout endpoint's p95 sat at 4.21 ms. Including 1.2.0 pushed it to 4.76 s:

Sorting by p95 brought SELECT book for checkout to the top, at 2.93 s, so the slow query took two clicks to find. One caution: GET /search looked just as slow, but its time sat outside any span and was the same on every version.

Performance comparison

Verdict: PostHog. It named the query that made checkout slow. LogRocket could only time the wait.

LogRocket saw a 2.9-second request with no reason attached, and PostHog found the query in a table apart from the error. In Better Stack, the failing 1.2.0 checkout opened straight into its own spans:

The book lookup ate 1.53 s of a 2.19-second request, and the rejected charge added 660 ms, which puts the slow query and the payment bug side by side. The request's logs were a click away:

The app's own slow-query warning sits just above the checkout failure, filtered by the request's trace ID.

Find what slowed down your last release

Logs

PostHog has a logs product. LogRocket keeps the browser console inside each session and leaves server logs to integrations.

PostHog's viewer took my OpenTelemetry logs from the server:

It showed 1,068 logs in an hour, with facets for level, service, environment and host, plus Patterns, Group and Live tail views and an SQL tab. I didn't test the pattern or alert views.

In LogRocket, each request in a session has a Backend Logs tab that asks you to connect Datadog, Splunk or another service. The browser's console lines, on the other hand, are right there in every recording.

Verdict: PostHog. It keeps server logs next to traces and errors. LogRocket's logs stop at the browser.

Alerts

Both tools can tell you about new issues, though I configured delivery in neither.

LogRocket's issue alert rule reads like this:

It fires on a severe issue first seen within 24 hours, sent to Slack or a webhook, and severity comes from Galileo. Galileo rated both of my Issues Low, so in my test that rule would have stayed silent. Metric alerts and a Monday error digest are available too.

PostHog's issues list carries a reminder:

Its alerts can fire on new, reopened or spiking issues, sent to Slack, Teams, PagerDuty or another destination, and its configuration adds spike detection, rate limits and severity rules.

Verdict: Not compared. On paper, PostHog's triggers cover more cases, including reopened issues.

AI-assisted debugging

Both assistants worked without my code, which makes this a fairer match than most AI comparisons. The prompts differed, though: Galileo got a question about the whole release, and PostHog AI got its own prompt for one error.

LogRocket: Galileo, careful with what it can't see

Galileo writes a summary when you open an Issue. On the wishlist crash, it was candid about its limits:

It said the evidence didn't establish which action triggered the error, though the recording shows the wishlist click right before it. Ask Galileo, its chat, did better on the release question. When my first release name matched no sessions, it said so instead of guessing, and with the right names it rebuilt its answer:

It corrected its own earlier claim, agreeing that the coupon error predated the release, and named the payment timeout as the only new checkout failure. For the cause, it pointed to an upstream dependency and said it couldn't confirm anything at the code level.

PostHog: PostHog AI, fluent past its evidence

Every PostHog issue has a menu that builds a fix or explain prompt and opens it in PostHog AI, Claude Code, Cursor or Codex. On the coupon bug, PostHog AI got the mechanism right:

It saw that the code checks whether a coupon was sent, not whether it exists, and suggested optional chaining with a 400 response. Then it filled gaps with guesses:

Those coupon codes don't exist in my app. On the payment error, it blamed the duplicate issues on the amount in the message, which the data contradicts, and reconstructed a retry loop that isn't in the code.

AI comparison

Verdict: LogRocket. Neither found a root cause without code. Galileo told me what it couldn't know, and PostHog AI presented guesses as findings.

Galileo and PostHog AI both pinned the payment failures on the gateway, and neither looked at the slow query. Better Stack's AI SRE got the release question with no repository either:

It noticed that checkout's own database calls had become multi-second while the other statements stayed fast. Then it took the payment failures on their own:

It tied all 10 failed charges, out of 48, to the new error on 1.2.0, and kept them apart from the slowdown. The run cost $0.49 and took about four minutes, and it was the only assistant in this comparison to get both causes right.

Ask AI SRE what broke in your last deploy

Pricing

LogRocket charges for recorded sessions, with errors, logs and analytics events left out of the bill. PostHog has no base fee and charges each product per unit above a large monthly free tier.

I took these numbers from the two pricing pages on October 28 and 29, 2026. When I checked again on November 3, LogRocket's page no longer listed its free tier and quoted "starting at $176" a month on an annual plan, so confirm current prices before you buy.

Free plans and trials

PostHog's plan step lists the same free tier on both its plans:

Free is limited to one project and capped usage. Pay-as-you-go starts at $0, allows six projects and bills only what you use past the free tier.

Entry-level paid plans

Seats, retention and add-ons can raise LogRocket's quote, and past 100,000 sessions a month only Pro is offered.

Usage beyond the free allowance

Comparing the actual cost

Take a storefront that records 10,000 visits a month. On PostHog, 5,000 of those recordings are free and the other 5,000 cost about $25, with errors, logs and traces covered by their own free tiers. On LogRocket, the same volume costs $83 a month on Core, or $292 on Pro if you want Galileo, and you'd still need a separate product for the server.

LogRocket's pricing has one real advantage. Errors never count toward the bill, so a broken release that throws a million exceptions costs the same as a quiet week.

Teams that also want uptime checks billed alongside errors and logs can see that math in our Sentry pricing breakdown against a combined platform.

Verdict: PostHog. Its free tier covers a small team's replay and errors outright, and paid usage grows a few dollars at a time.

Full feature comparison

Which should you choose?

It depends on whether your hardest bugs live in the browser or behind it.

Choose LogRocket if you need to see exactly what users experienced

LogRocket fits front-end and product teams who debug by watching. Every recording comes with its requests and their bodies, the console and browser performance, and that worked from the first visit. It flagged failures my code never reported, Galileo was honest about its limits, and errors never add to the bill.

Plan around the gaps. You'll need another product for server errors, traces and logs, closed Issues stay closed when a bug comes back, and new failures can slip into an older row. Our Sentry vs LogRocket comparison shows how it pairs with a tool that covers the server.

Choose PostHog if you want errors, replay and your API in one account

PostHog fits teams who want their error tracker to share data with the rest of their product stack. It caught server errors with code, traced the failed request, reopened the returning bug, broke it down by version, and found the slow query by p95. Its free tier covers far more than LogRocket's, and its analytics and flags come in the same account.

Expect setup work. Replay, error tracking and headless traffic each needed a setting, releases follow git commits unless you flag them at inject, and Node's async frames can split one bug into several issues. For a closer look at PostHog against a dedicated error tracker, see our Sentry vs PostHog test.

FAQ

Does LogRocket capture server-side errors?

No. LogRocket only ships web and mobile SDKs, so your API's exceptions stay invisible to it. What it does keep is each response body, which often carries the server's message, and it offers links to tools such as Datadog or Splunk for deeper backend data.

Is PostHog's session replay as detailed as LogRocket's?

In my test, LogRocket's player showed more without any setup: request and response bodies, console output and browser performance on one timeline. PostHog captured network headers and bodies once I turned them on, and its Recording tab opened at the moment of each error. I didn't test PostHog's full player tabs, so I can't compare them line by line.

Which one is cheaper for a small team?

PostHog, in most cases. Its free tier includes 5,000 recordings and 100,000 exceptions a month, and paid usage is billed per unit with no base fee. LogRocket's free tier stops at 1,000 sessions, and its paid plans started at $83 a month for 10,000 sessions on an annual plan when I checked.

Do I need to change any PostHog settings for automated or headless tests?

Yes. posthog-js drops headless browsers by default, so test traffic sends nothing until you set opt_out_useragent_filter: true. LogRocket recorded my headless sessions with no change, though every one of them counted toward its session bill.

Final thoughts

LogRocket and PostHog both put errors next to what users did, and both do it well enough that you can stop guessing at reproduction steps. The difference is how far each one looks past the browser.

LogRocket's recordings were the richest evidence I got for a front-end failure. They also left it blind on the bad release: a new bug filed under an old title, a fixed bug that came back without reopening, and a slow request with no explanation. PostHog caught all three, at the cost of more switches to flip and a grouping habit you'll want to tune.

If your team already monitors its API somewhere else, LogRocket's player is worth its session bill. If one tool has to cover the whole app, PostHog sees the parts LogRocket can't.

Got an article suggestion? Let us know

Explore more

Better Stack vs LogRocket: A Complete Comparison for 2026

LogRocket excels at frontend session replay and UX analytics. Better Stack covers the full stack: logs, metrics, traces, incidents, and more. Here's how they compare on features, AI, and pricing.

Top 13 LogRocket Alternatives 2026

Find the top 13 LogRocket alternatives for 2026. Compare features and pricing to choose the best tool for session replay, user analytics, and error tracking.

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

I tested Sentry and LogRocket on the same buggy bookstore across three releases, comparing browser error capture, session replay, the link to the server, grouping, regressions, slowdowns, AI and pricing

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.