LogRocket alternative: for teams who want the replays read for them

LogRocket is a good product and it is not the product most teams need. It is built for the front-end debugging workflow: reproduce the bug, read the Redux actions, read the network waterfall, find the exception. If that is your job, it is excellent at it and you should probably keep it.

Filmroom is built for a different problem, which is that you have thousands of recordings and nobody watches them. It records every visit, has an AI read the ones worth reading, pins what you should watch, archives what you should not, and once a week writes up what it found. It masks every input in the browser by default, and you can run the whole thing yourself as one container against your own Postgres without an enterprise contract.

Here is the honest comparison, including the places LogRocket wins.

At a glance

Your inputs are being recorded unless somebody turned it off

This is the difference that matters most and it is the one nobody checks before signing.

LogRocket's own documentation is clear about it. inputSanitizer is listed as optional with a default of false, and textSanitizer is listed the same way. Their privacy docs describe the philosophy directly: they designed the SDK to let developers determine what information to hide from LogRocket reports, with data-private as the opt-in mechanism. The one automatic protection is that password fields are never recorded.

That is a defensible design choice for a debugging tool, because a debugging tool wants maximum fidelity. It is also a choice with consequences, and the consequences do not land on the engineer who installed it. They land on whoever has to answer for what is in your session archive: the email address, the postal address, the phone number, the order reference, the free-text support message. Unless somebody explicitly configured the sanitizers, all of it is captured and transmitted.

Filmroom masks every input by default, in the visitor's browser, before any network request is made. The typed value never leaves the device. To redact more than inputs you mark elements: sr-mask or data-mask hides text while keeping the layout, sr-block or data-block replaces an element with a placeholder so nothing inside it is recorded at all, and both cover every descendant. data-deny globs keep entire routes from loading the recorder in the first place, so a checkout page can be genuinely not recorded rather than recorded and filtered. The GDPR guide goes through why that ordering is the whole argument.

And because every masking configuration eventually goes stale, Filmroom scans rendered text for what you forgot: email addresses, Luhn-valid card numbers, dashed SSNs, prefixed secret tokens. Each finding becomes a redaction flag with a selector and a masked sample, deduplicated across sessions, so you learn that one component leaks once rather than once per visitor. Phone numbers are deliberately excluded, because a loose digit-sequence detector produces so many false positives that people learn to ignore it.

If your answer to all of this is "we configured the sanitizers on day one", then good, this section does not apply to you and it is a genuine credit to your team. Most teams did not.

Per-session pricing taxes the users you most want

LogRocket publishes Core starting at $176 per month for 25,000 sessions, which is more transparency than FullStory or Quantum Metric offer and deserves credit. The structural problem is not the number, it is the unit.

Sessions are not users. A power user who opens your app five times a day is five sessions, not one customer. So the bill rises with engagement, which means the metric you are trying to improve is the metric that increases your cost. The recurring complaint in public reviews is exactly this shape: teams budget from daily active users, then discover the session count is a multiple of it, and a plan that fit in January does not fit in June because the product got stickier.

There is also the conditional recording tier to understand, because it looks like a saving and is not always one. LogRocket's pricing page shows Conditional Recording at roughly $765 per month for about 25% of sessions captured. Capturing a quarter of your sessions is a reasonable cost control right up to the moment the bug you are chasing is rare, at which point the odds are three to one that the session you need was never recorded. Rare bugs are the ones worth a replay tool. Common ones you can reproduce yourself.

Filmroom's answer is that you decide what gets kept, and nothing samples on your behalf. data-sample is a knob you set if you want it, retention is a number you choose, and the low-value disposal rules (discard sessions under a wall-clock duration, or under an amount of genuinely active time) are yours to configure or leave off. Any recording can be pinned so no cleanup rule touches it, and a session that hits a conversion goal is pinned automatically, because that is the replay most worth keeping. Self-hosted, the ceiling is your disk.

An AI that watches sessions is table stakes. One that does not invent numbers is not

LogRocket's marketing for Galileo is that it is the AI that watches every session for you. We are not going to pretend that is not the same promise we make, because it is. Every serious tool in this category now ships AI session summaries: FullStory has StoryAI, Microsoft Clarity has Copilot, PostHog and OpenReplay both have summaries. The claim is not a differentiator any more.

So here is the part that is, and it is an engineering opinion rather than a feature.

Detection is deterministic. Narration is the model's job. Rage clicks, dead clicks, fast scrolls, per-page dwell and scroll depth, idle time, inputs by field name and errors are all computed in code before the model is called. The model reads a reduced transcript plus those signals and writes a summary, an interest score from 1 to 5, and attention and friction notes. It never detects anything. It prioritizes and explains what was already found.

The weekly report cannot contain a number the model made up. It answers the same four questions every week: which engaged visitors have never converted, who has gone quiet and may be churning, where visitors got most of the way to a goal and stopped, and which traffic sources actually produce converting visitors. Every row in those four sections is computed in SQL, against your own data, using your site's own distributionfor its thresholds, before the model is called. The model's entire contribution is a note of at most 240 characters per row, and any note that cites an entity which is not in the data is discarded. Ask a language model to compute a conversion rate and it will produce a plausible one. This architecture makes that structurally impossible.

The AI only reads sessions worth reading.A session is annotated if it was pinned, captured an error, has an identified visitor, hit a conversion goal, or has active time at or above your site's own 75th percentile. Sites with fewer than twenty recordings get everything annotated. This is hardcoded rather than configurable, on purpose: the median visit is somebody reading one page and leaving, and spending your tokens narrating that buys you a table row nobody reads.

You bring your own key. Supply an Anthropic or OpenAI key, pick the models your key can actually access, set a monthly token budget. No key means the AI features are simply inert. Nobody marks up your inference and the AI cost is a line item you already understand. Compare this with StoryAI, where the interesting half is gated behind a separate paid tier you get by contacting your account team.

Self-hosting without an enterprise contract

LogRocket does offer self-hosting, with a genuinely good line about it: your data never leaves your network. It is Enterprise tier only, and Enterprise is quote only.

That is the normal shape of this in the category, and it means self-hosting is available precisely to the companies that could most afford not to need it. The team that most wants the recorder inside their own network is often the small team in a regulated industry who cannot sign a data processing agreement for behavioural recording, and they are the least likely to clear an enterprise minimum.

Filmroom self-hosts on every paid plan. It is one Docker image plus one Postgres database. Migrations run on boot. There is no ClickHouse, no Redis, no queue, no worker fleet. Two useful comparisons for calibration: PostHog's own documentation says self-hosted deployments are officially unsupported and that all paid-plan features are cloud only, and their own alternatives page concedes that self-hosting PostHog is operationally complex at scale. OpenReplay's stack is Postgres plus ClickHouse plus Redis plus its backend services. Filmroom is deliberately boring by comparison, because the operational surface is the actual product decision when you self-host. The self-hosted options guide counts the systems for each one.

To be clear about what self-hosting costs you: your disk, your backups, and your upgrade timing. Retention limits, storage caps and disposal rules are knobs you set, which means they are also knobs you can misconfigure.

A technical opinion: a replay tool is not an APM

Worth stating because it explains the feature gaps rather than hiding them.

LogRocket instruments your application: Redux actions, network requests, console output, performance traces. That is deep integration and it is why it is good at reproducing bugs. It is also why it is a thing your front-end team owns and configures.

Filmroom instruments the page. It observes the DOM, captures errors from window.onerror, unhandled rejections and console.error, and otherwise does not know or care what framework you use. The install is a script tag with no SDK to keep in sync with your bundler, as the Next.js guide shows.

The consequence is a real trade, in both directions. Filmroom does not log your network requests or your state transitions, so it will not hand you a network waterfall next to the replay. What it does instead is group the errors it does capture into issues, fingerprinted by a normalized message and source, so one bad chunk load hitting four hundred visitors is one row rather than four hundred. Line numbers are deliberately excluded from the fingerprint, since a rebuild shifts every line, and build hashes are normalized away, including bare hashes in framework chunk paths and hashes riding in query strings. Without that, every deploy forks your issue list. Issues also survive their recording being deleted, so a bug report does not vanish when a recording ages out.

Pick based on which failure you have more of. If it is "I cannot reproduce this exception", that is LogRocket's shape. If it is "I have four thousand recordings and no idea which twelve matter", that is Filmroom's.

What LogRocket does better

Not a bullet buried in a paragraph. These are real, and if any of them is load-bearing for you, Filmroom is the wrong choice.

• Network request and response logging. Filmroom does not have it. If your debugging loop is reading the failed request next to the replay, that is not a gap you can work around.
• Redux, NgRx and state instrumentation. Filmroom has nothing equivalent. It observes the DOM, not your store.
• Full console output capture. Filmroom captures console.error (plus window errors and unhandled rejections) and nothing else. LogRocket gives you the whole log.
• Mobile SDKs. Filmroom is web plus Electron. If you need iOS, Android or React Native session replay, this comparison ends here.
• Unlimited analytics, error events and logs on every tier. That is genuinely generous and published on their pricing page.
• Front-end performance monitoring as a first-class product surface.
• Alerting and issue-tracker integrations into an established ecosystem. Filmroom surfaces issues in its own UI.
• A published price at a known session volume, which is more than most of this category offers.

Why is Filmroom not free?

Because we do not sell your visitors' data and there is no other way to pay for this.

The free tier is 50 sessions per month with no AI features. That is deliberately small and it is honest about what it is for: evaluating the product on a real site before you pay for it. It is not a competitor to Microsoft Clarity, which is genuinely free forever with no traffic cap, and if free and unlimited is your requirement you should use Clarity and we mean that. Its ceiling is retention: playback data is kept 30 days, after which Microsoft's own docs say only 1% of your recordings or 10 per day, whichever is higher, survive for up to 9 months.

Paid pricing is published and self-serve. No call, no quote, no annual commitment to see a number. See pricing.

You might want LogRocket instead if

• You need network request logging or state-management instrumentation. This is the big one.
• You need mobile session replay.
• Your primary workflow is exception reproduction rather than session review, and your team is already fluent in it.
• You want unlimited error events and logs bundled at a published price.
• You are already on Core, it fits your budget, and you configured the sanitizers. Switching tools has a real cost and nothing above is worth paying it for on its own.

Is Filmroom right for you?

Probably yes if: you have more recordings than anyone has time to watch, you want the sessions triaged before you open the dashboard, you want a weekly summary you can trust at a glance, you want masking to be the default rather than a configuration task, and you either want to self-host or want to know that you could.

Probably no if: you need network and state logging, you need mobile, or you need a platform rather than a focused tool.