The Loom Workaround That Every Team Knows
You know the drill. A client records a ten-minute Loom video, narrating their way through your staging site. They click around, mumble something about the header "feeling weird," spend three minutes on an unrelated tangent about their brand guidelines, then circle back to a button that "doesn't seem right." The video lands in your inbox. Now it's your job to watch the whole thing, pause repeatedly, take notes, and manually turn those scattered observations into actual tasks.
Loom is a genuinely useful product for asynchronous communication. It was built so remote teams could replace meetings with short video messages, and Atlassian's acquisition in 2023 expanded its reach further. It does that job well. But somewhere along the way, teams started repurposing it for website feedback, and the cracks in that workflow are hard to ignore.
This article breaks down why a general-purpose screen recorder and a purpose-built website feedback tool solve fundamentally different problems, and what that difference means for your team's efficiency.
What Loom Does Well (And Where It Stops)
Loom lets anyone record their screen, their camera, or both, and share the video with a link. It's fast, the interface is clean, and the free tier is generous enough for casual use. Since Atlassian acquired Loom in 2023, the product has also added AI-powered summaries and transcription features under its Loom AI branding.
For explaining a concept, walking through a slide deck, or giving a teammate a quick status update, Loom is excellent. The problem starts when you try to use it as a feedback tool for a live website.
The core gap: Loom records your screen, not your session
When you record a Loom video on a website, the output is a flat video file. It captures the pixels on your screen at 30 frames per second. That's it. There's no structured data about what was clicked, what was scrolled past, which page was active, or what the DOM looked like at any given moment.
This matters because website feedback is about what happened during the interaction, not just what something looks like. (Our guide to giving good website feedback explains why context is everything.) A developer debugging a layout issue needs to know the viewport width, the scroll position, and the exact element that was clicked. A video doesn't give them any of that. They're left squinting at a recording, trying to reverse-engineer the context.
givefeedback.dev takes a different approach. Instead of recording pixels, it captures a full session replay (every click, scroll, hover, and resize) synced with the reviewer's voice. That gets turned into a plain-language transcript, time-aligned with the actions the reviewer took, so the exact moment behind any sentence is one click away instead of a guess.
The Scrubbing Problem
Here's where the real cost shows up, and it isn't the ten minutes spent recording. It's everything that happens afterward.
To Loom's credit, its AI features are a genuine improvement over scrubbing a raw video blind: transcript search and auto-chapters will get you close to the right moment in a long recording, and Loom can even pull generic action items out of the narration. If a reviewer said "the checkout button doesn't feel right," you can search for it and land near that point instead of guessing.
But none of that reaches what a developer actually needs to act. Loom's transcript captures what was *said*. It was never built to capture what was clicked, what the viewport was, which page was open, or what element the cursor was over when the reviewer said it, because none of that was ever recorded as data. It only exists as pixels in the clip you land on. To get the technical detail, you watch. And whoever needs that same detail next (a developer picking up the bug, a designer weighing in, a project manager checking the fix) watches it again, because Loom's summaries and action items are text written for a person to read, not a record built for a coding agent to query.
According to a frequently cited internal study by Loom, their average video is around 4 minutes long. But website review videos tend to run much longer, because reviewers are working through multiple sections and thinking out loud. Even with search and chapters narrowing the window, a 10-minute review video revisited by two or three people over the life of one task adds up to real time spent watching pixels for information that was never captured as anything more than pixels.
The record that replaces the video
givefeedback.dev captures the moment differently in the first place. Every click, scroll, and page change is logged as data, not just shown as pixels, and it's time-aligned with a plain-language transcript of what the reviewer said. A person can skim the transcript and jump straight to the line that matters, the same way they'd search a Loom transcript. The difference is what's waiting there: the exact element, the viewport, the scroll position, stored as structured, time-stamped data instead of a frame of video. Your AI coding agent can pull that same record over the Model Context Protocol (MCP) and read it directly, no video to watch and no data to reconstruct by hand.
For a deeper look at what makes feedback truly actionable, see our guide on reducing revision cycles. And if you want to understand what makes feedback actionable in the first place, our guide on how to give good website feedback covers the principles that both tools benefit from. What givefeedback.dev changes is what survives the trip from reviewer to editor: structured, time-aligned data instead of a video that only a human can watch.
Reliability Concerns After the Atlassian Acquisition
Since Atlassian completed its acquisition of Loom, a notable number of users have reported reliability issues. On Trustpilot, Loom currently holds a rating well below expectations for a tool in its category, with recurring complaints about login difficulties, video upload failures, and recordings that fail to save after lengthy sessions (Trustpilot, "Loom Reviews"). G2 reviews echo similar frustrations, with users noting that the desktop app occasionally crashes mid-recording and that customer support response times have lengthened since the acquisition (G2, "Loom Reviews 2025-2026").
These issues are particularly painful in a feedback context. If a client spends ten minutes recording detailed website feedback and the upload fails, they're unlikely to re-record with the same level of detail. The feedback is effectively lost.
givefeedback.dev captures feedback through a lightweight widget embedded directly in the site. There's no desktop app, no browser extension, and no separate upload step. You can try the live demo to see the difference firsthand. The session data streams as the reviewer speaks, so there's no single point of failure at the end of a long recording.
The Pricing Reality
Loom's pricing has evolved considerably. As of 2026, Loom Business with AI features costs between $20 and $24 per user per month, depending on the plan and billing cycle (Loom pricing page). That's a per-seat cost, which means a team of five is paying $100-$120 per month. And every person who needs to review the recordings also needs a paid seat to access the full AI features.
givefeedback.dev takes a different approach to pricing. The Pro plan is $19 per month total, not per user. It includes 3 projects and 50 feedback sessions. There's also a Free tier (1 project, 10 sessions) for testing, and an Agency plan at $79 per month for teams managing multiple client sites with up to 200 sessions. You can see the full breakdown on our pricing page.
The cost difference is significant, but the more important distinction is what you're paying for. With Loom, you're paying for a general-purpose video tool, then doing the work of turning a recording into something actionable yourself. With givefeedback.dev, the session replay, the time-aligned transcript, and the MCP handoff to your coding agent are the core product.
Feature Comparison at a Glance
Session replay with interaction metadata
- Loom: No. Records screen pixels only.
- givefeedback.dev: Yes. Captures clicks, scrolls, hovers, and DOM state.
Voice recording synced to session
- Loom: Camera and mic record alongside screen, but not tied to session events.
- givefeedback.dev: Voice is synced to the exact moment in the session replay.
A transcript your agent can read
- Loom: Loom AI transcribes the narration and can pull chapters and action items from it, but it's speech-to-text: the click, the element, and the page state behind a comment were never captured as data, only as video frames.
- givefeedback.dev: The transcript is time-aligned with the actual click, scroll, and page data captured during the session. A person can skim and pinpoint the relevant line; your AI coding agent can pull the same time-aligned transcript over MCP and read it directly, technical detail included.
Setup for reviewers
- Loom: Reviewer needs a Loom account, browser extension or desktop app, and must manage recordings.
- givefeedback.dev: Reviewer clicks a widget on the site. No account, no extension, no app.
Embedding in a live site
- Loom: Not applicable. Loom is a standalone tool.
- givefeedback.dev: One