DEV Community: Rawan

7 Best LogRocket Alternatives in 2026: Which One Should You Choose?

Rawan — Tue, 08 Sep 2026 16:04:03 +0000

LogRocket is one of the better-known tools for understanding what happens inside a web application after it reaches production. It combines session replay with product analytics, frontend performance monitoring, errors, logs, network data, funnels, heatmaps, and AI-assisted analysis.

That makes replacing it less straightforward than simply searching for another session replay tool.

Some teams primarily need to debug frontend errors. Others care more about product analytics and conversion funnels. Some want to watch exactly what a user experienced, while others want production monitoring to lead more directly into an engineering fix.

The right LogRocket alternative therefore depends on what you're actually trying to solve.

In this guide, we'll compare seven options:

HeronSignal
Sentry
PostHog
Fullstory
Microsoft Clarity
Datadog
OpenReplay

We'll look at where each one is strongest, where it differs from LogRocket, and which type of team should consider it.

What should you look for in a LogRocket alternative?

Before comparing products, it's worth defining why you're looking for an alternative.

LogRocket currently combines Session Replay, Product Analytics and Error Monitoring. Its Core offering includes features such as frontend performance monitoring, funnels, path analysis, heatmaps, rage-click maps, alerting, logs and network data. Its Galileo AI layer adds capabilities including AI session summaries, issue detection, funnel insights and MCP/API access. (LogRocket)

So don't choose an alternative based purely on whether it has session replay.

Instead, ask:
Do we primarily need to debug production problems?
If engineers are the main users, error context, network failures, performance information and developer workflows may matter more than sophisticated behavioral analytics.

Do we primarily need to understand product behavior?
If product and growth teams are the main users, funnels, experiments, feature flags and behavioral analytics may matter more.

Do we need detailed session replay?
For some teams, watching exactly what happened during a user's session is central to debugging. For others, continuously recording ordinary sessions creates more data than they actually need.

What should happen after we find a problem?
This is an increasingly important distinction. Finding a broken interaction is useful, but engineering teams still need to understand the technical context, reproduce the problem and eventually change the code.

With that in mind, here are the alternatives.

1. HeronSignal

Best for: engineering teams that want production monitoring to lead toward a fix

HeronSignal approaches the problem slightly differently from traditional session-replay platforms.

Instead of treating replay as the center of the product, it collects signals from the actual production experience: performance metrics, clicks and interactions, sessions and journeys, frontend errors, console logs, network failures and business events.

When visitors visibly struggle, For example through rage clicks or dead clicks, HeronSignal can capture a struggle clip around that experience. Plans also include a small sample of ordinary sessions to provide a baseline for comparison.

The goal is to connect what the user experienced with what happened technically.

Where HeronSignal stands out

The biggest difference is what happens after an issue is discovered.
HeronSignal includes an AI analyst for investigating production data, and coding agents such as Cursor or Claude can access production context through its MCP integration.

HeronAgent goes further: it can clone a connected repository into a sandbox, investigate the problem, make a code change and open a branch for review. Nothing is automatically merged. (HeronSignal)

This creates a workflow closer to:

production signal → affected user experience → technical context → investigation → proposed fix

rather than stopping at the investigation.

Where it may not be the best choice

If your primary requirement is deep, continuous session replay, LogRocket or Fullstory may be a better fit.

Likewise, if you need a large product analytics platform with experimentation, feature flags and extensive analytics infrastructure, PostHog covers considerably more territory.

HeronSignal is more focused on engineering teams trying to understand and resolve problems affecting real users.

Pricing

HeronSignal currently starts at $19/month for its Hobby plan, with a 14-day free trial. Its Starter plan is $49/month and Growth is $149/month at current founding pricing. Core monitoring, funnels, AI and coding-agent access are available across the plans, with limits increasing based on usage. (HeronSignal)

Choose HeronSignal if: you want user-experience monitoring, technical signals and AI-assisted engineering workflows in the same investigation.

HeronSignal connects production signals with the users affected by them.

2. Sentry

Best for: Engineering teams primarily concerned with application errors and performance

Sentry is a natural LogRocket alternative when your problem is less “How are people using our product?” and more “Why is our application failing?”
Its roots are in error monitoring, and that engineering focus remains its biggest strength.

For developers investigating exceptions, stack traces, releases and application performance, Sentry can provide a more error-centric workflow than a behavioral analytics platform.

Session Replay adds visual context around problems, allowing developers to see what happened around an error rather than relying exclusively on logs and stack traces. (Sentry)

Where Sentry stands out

Sentry makes the most sense when errors are the starting point of your investigation.

It is particularly useful for teams that want monitoring integrated closely into the software-development lifecycle rather than primarily into product analytics.

Where it may not be the best choice

If your biggest questions are about conversion funnels, behavioral patterns, heatmaps or product usage, platforms designed around behavioral analytics will generally be a more natural fit.

Choose Sentry if: application errors and developer debugging are your highest priority.

3. PostHog

Best for: Teams that want product analytics and engineering tools in one platform

PostHog is much broader than a session replay product.

It combines product analytics and session replay with feature flags, experimentation and other product-development tooling.

That makes it particularly attractive to startups that would otherwise assemble several different products.

Where PostHog stands out

Product analytics is one of its strongest differentiators.

If you want to understand which features people use, analyze funnels, replay sessions, run experiments and manage feature flags, PostHog can cover a significant portion of the product stack.

Its pricing is also unusually transparent and usage based.

At the time of writing, PostHog includes a free tier of 1 million product analytics events per month and 5,000 session recordings per month, after which usage is charged incrementally. (PostHog)

Where it may not be the best choice

Breadth can also be unnecessary.

If you're not looking for experimentation, feature flags or a broader product analytics stack, you may be adopting considerably more platform than you need.

Choose PostHog if: your engineering and product teams want analytics, replay and experimentation under one roof.

4. Fullstory

Best for: Deep behavioral and digital-experience analysis

Fullstory is one of the strongest choices when understanding how people actually interact with an application is the priority.

It combines Session Replay with heatmaps, click maps, scroll maps, developer tools and behavioral analytics.

Its higher-level offering extends much further into behavioral data, activation, warehouse integrations and enterprise use cases. (Fullstory)

Where Fullstory stands out

Fullstory's strength is the depth of behavioral context it can provide.

Product, UX and digital-experience teams can use it to investigate interactions that may not generate conventional application errors at all.

That makes it useful when the problem isn't necessarily broken code. Sometimes the application technically works, but the experience still confuses or frustrates people.

Where it may not be the best choice

If your workflow is heavily engineering-first and you're primarily trying to go from production failure to code-level resolution, a developer-focused monitoring platform may be more appropriate.

Pricing

Fullstory's paid Business, Advanced and Enterprise plans use request-based pricing.

However, Fullstory now also offers FullstoryFree, which includes up to 30,000 monthly sessions, 12 months of analytics retention and core capabilities including Session Replay and basic analytics for up to 10 users. (Fullstory)

Choose Fullstory if: detailed user behavior and digital-experience analysis are more important than an engineering-first workflow.

5. Microsoft Clarity

Best for: Teams that want free session recordings and heatmaps

Microsoft Clarity is difficult to ignore for one simple reason:
It's free.

Clarity provides session recordings and heatmaps alongside AI-generated summaries and chat functionality. Microsoft currently describes the service as free forever with no traffic limits. (Microsoft Clarity)

For a small website that simply wants to understand where people click, how far they scroll and where visitors appear to struggle, that can be enough.

Where Microsoft Clarity stands out

There is almost no financial barrier to trying it.

Heatmaps provide a quick visual representation of clicks and scrolling, while recordings let teams inspect individual journeys. Clarity also surfaces behavioral insights and AI-generated summaries. (Microsoft Clarity)

For marketers, designers, founders and smaller websites, the value proposition is extremely compelling.

Where it may not be the best choice

Clarity isn't trying to replace a full engineering observability stack.
If you need detailed frontend errors, logs, network failures, application monitoring or workflows that take an issue into engineering investigation, you'll likely need additional tooling.

Choose Microsoft Clarity if: you primarily want free behavioral analytics, heatmaps and session recordings.

6. Datadog

Best for: Organizations that want frontend monitoring inside a broader observability platform

Datadog sits at the other end of the spectrum.

Rather than beginning with session replay or product analytics, Datadog is a broad observability platform.

Its Real User Monitoring can collect user journeys alongside telemetry such as resources, frontend errors, crashes and long tasks. Session Replay can then provide visual context around those sessions. (Datadog)

Where Datadog stands out

The advantage becomes clearer when your organization already uses Datadog.
Frontend behavior can be investigated alongside backend infrastructure, logs, traces and the rest of the application stack.

For complex production systems, that breadth can be much more valuable than adopting a standalone frontend monitoring product.

Where it may not be the best choice

That same breadth can be excessive for a small team that simply needs to understand problems affecting a web application.

Pricing can also become more complex because Datadog offers many independently priced products and add-ons. Its current public pricing, for example, prices RUM and Session Replay based on session volume. (Datadog)

Choose Datadog if: frontend monitoring needs to be part of a much larger observability strategy.

7. OpenReplay

Best for: Teams that want open-source or self-hosted session replay

OpenReplay offers something particularly important for teams with strict infrastructure or data-control requirements: an open-source, self-hosted option.

Its platform combines Session Replay with developer tools and product analytics, while its deployment options range from self-hosting to managed dedicated infrastructure. (OpenReplay)

Where OpenReplay stands out

Control.

Teams can host the open-source version on their own infrastructure rather than sending session data to a standard SaaS environment.

OpenReplay also offers dedicated managed deployments for organizations that want isolation without operating everything themselves.

Where it may not be the best choice

Self-hosting creates its own operational burden.

If you don't actually have regulatory, privacy or infrastructure reasons to manage the deployment yourself, a hosted SaaS product may be substantially simpler.

Its managed Dedicated offering currently starts at $199/month, while the open-source version is free to self-host. (OpenReplay)

Choose OpenReplay if: deployment control, data residency or self-hosting are important requirements.

So, which LogRocket alternative should you choose?

There isn't a universally better LogRocket alternative.

If errors are your starting point, look at Sentry.

If product analytics and experimentation matter most, PostHog is difficult to overlook.

If you want deep behavioral analysis, consider Fullstory.

If you primarily want free recordings and heatmaps, start with Microsoft Clarity.

If your frontend monitoring needs to connect with infrastructure-wide observability, Datadog makes more sense.

If you require self-hosting, OpenReplay deserves serious consideration.

And if you're an engineering team looking for a workflow that connects real-user problems, production telemetry, AI investigation and the eventual code change, that's the problem we're building HeronSignal around.
The important question isn't which platform has the longest feature list.
It's which one gets your team from “something went wrong” to understanding what happened — and then helps you do something about it.

Try HeronSignal

HeronSignal monitors what real users experience in production and connects performance, errors, network failures, journeys and struggle signals so engineering teams can investigate problems with context.

When you're ready to act, you can bring that production context into your coding agent or use HeronAgent to prepare a fix for review.

Try HeronSignal

I Scanned 100 Websites. Here's What I Found.

Rawan — Wed, 02 Sep 2026 13:43:30 +0000

A website can look completely fine and still have problems hiding underneath. The homepage loads, the navigation works, and nothing obviously seems broken, but that doesn't tell you whether search engines can properly understand the site, whether important technical information is missing, or whether performance problems are quietly affecting users.

So we decided to test it. We ran 100 websites through HeronSignal's Scan to see what a normal website looks like when you inspect more than what appears in the browser.

Here's what we found.

How We Ran the Test

We selected 100 publicly accessible websites and ran each one through HeronSignal's Scan. The scanner checks different parts of a website, including SEO, performance, AI readiness, structured data, sitemap and robots.txt configuration, security, SSL, and other technical issues.

The idea wasn't to find deliberately bad websites. We wanted a mix of real websites that people are actively using and maintaining, then see what problems were hiding underneath them.

Most Websites Weren't Actually Broken

The first thing we noticed was that most of the websites worked perfectly well from a visitor's perspective. You could open them, navigate between pages, click buttons, and probably leave without thinking anything was wrong.

The scans told a different story. Across the websites we tested, we found missing metadata, structured data problems, performance warnings, sitemap and robots.txt issues, broken links, and other configuration problems.

These aren't necessarily the kind of issues that make a website completely unusable. That's exactly why they're easy to miss. A website can continue working for months while smaller technical problems accumulate underneath it.

Technical SEO Problems Are Easy to Miss

SEO is often treated as a content problem: keywords, articles, backlinks, and rankings. But there is another layer underneath all of that.

The Scan checks things like page titles, meta descriptions, canonical URLs, sitemaps, robots.txt, structured data, and whether pages are configured in a way that makes sense to search engines. We found issues in these areas even on websites that looked completely normal from the outside.

That's what makes technical SEO problems particularly easy to overlook. A missing canonical tag or incorrectly configured sitemap doesn't necessarily change what a visitor sees, but it can change how a crawler understands the website.

Performance Is Hard to Judge by Opening the Website Yourself

Another common mistake is testing website performance by simply opening the site and seeing whether it feels fast.

That experience depends on your connection, device, location, browser cache, and several other factors. A homepage can also perform well while another important page has completely different problems.

The performance checks gave us a more useful view because they exposed issues that weren't necessarily obvious during normal browsing. Instead of relying on whether a page "feels fast," you can look at what is actually happening and which parts of the site need attention.

AI Readiness Is Still Inconsistent

We also looked at how websites are prepared for AI systems. This is still a developing area, and the scans showed how inconsistent the web currently is.

HeronSignal checks signals such as llms.txt, structured data, page structure, and other machine-readable information. None of these checks individually determines whether an AI system can understand or discover a website, but together they give us an interesting picture of how much thought has gone into making a site's information accessible beyond traditional human visitors.

This matters because websites are increasingly being consumed by more than browsers and search engines. AI assistants, agents, and other automated systems are becoming another way people discover and interact with information on the web.

The Bigger Problem Wasn't Any Individual Issue

After going through the scans, the most interesting finding wasn't one particular SEO problem or performance warning. It was how easily all of these problems can exist without anyone noticing.

Most teams know whether their website is online. They don't necessarily know whether an important page has become slower, structured data has broken, a sitemap is incorrectly configured, or something changed during the latest deployment.

Usually, somebody notices when the problem becomes large enough to have an obvious effect. A customer complains, traffic drops, a page stops appearing where expected, or a developer happens to discover an error.

That means the problem may have already existed for days or weeks before anyone starts investigating it.

A Scan Is a Snapshot

This is also an important limitation of any website scanner. A scan tells you what is happening at a particular point in time.

A website can score well today and have a problem tomorrow. A new deployment can introduce an error, an external API can fail, a payment provider can have problems, or a seemingly harmless code change can affect performance.

That's why we treat the Scan as an entry point rather than the entire monitoring process. It helps you understand the current state of a website without installing anything, while continuous monitoring answers the harder question: what changed after you checked?

From Finding a Problem to Actually Fixing It

Finding problems is useful, but it isn't where we want HeronSignal to stop.
The broader workflow we're building is simple: detect an issue, understand what it affects, help fix it, and then continue watching production to verify that the problem actually went away.

That last part matters. A merged pull request doesn't necessarily mean a production issue has been solved. The real confirmation comes from seeing the original error disappear or the affected metric recover after the fix is deployed.

The Scan is the simplest way to experience the first part of that workflow. You enter a website, wait a couple of minutes, and get a report showing what HeronSignal can see without installing an SDK or creating an account.

What We Learned

Scanning 100 websites changed the question for us. Instead of asking whether a website is working, it makes more sense to ask whether anyone actually knows how healthy it is.

Most of the websites we tested weren't catastrophically broken. They had smaller issues that were easy to overlook because everything still appeared to work. That's probably the more realistic state of the web: not thousands of completely broken websites, but millions of working websites with problems nobody has noticed yet.

That's the reason we built the HeronSignal Scan. It gives you a quick way to look underneath your website and see what might be worth paying attention to before one of those small problems becomes a much bigger one.

See What HeronSignal Finds on Your Website

You don't need to install anything or create an account to run your first scan. Enter your website URL and HeronSignal will analyze it across performance, SEO, security, AI readiness, and other technical checks, then show you what needs attention.

Run a free HeronSignal Scan and see what your website looks like underneath the surface.

Scan your website with HeronSignal →

What Are Rage Clicks? How to Identify and Fix User Frustration

Rawan — Wed, 26 Aug 2026 16:42:55 +0000

What Are Rage Clicks? How to Identify and Fix User Frustration

A user clicks a button.

Nothing appears to happen.

They click again. Then again.

This behavior is known as a rage click: repeated clicks or taps in the same area within a short period, often indicating that the experience is not responding as the user expects.

A rage click does not automatically tell you what is broken. It tells you where to investigate.

What Is a Rage Click?

A rage click happens when a user repeatedly clicks or taps the same element because the expected result does not appear.

This can happen when:

A button does not respond
A request takes too long
There is no visible loading state
An action fails silently
An overlay blocks the interaction
An element looks clickable but is not

The repeated clicks are a behavioral signal, not a diagnosis. The underlying problem may be technical, performance-related, or caused by the interface itself.

Why Rage Clicks Matter

A rage click can reveal problems that traditional analytics or error monitoring may miss.

An interaction might technically work, but respond slowly enough that users think it failed. A browser-specific error may affect only a small group of users. Or a broken layout may prevent interaction on a particular screen size.

For example, several users might repeatedly click the same checkout button. The button itself may not be broken. A slow API request, missing loading feedback, or JavaScript error after the click could be the actual cause.

The signal identifies the symptom. The investigation finds the cause.

How to Investigate Rage Clicks

1. Find Where the Pattern Is Happening
One repeated interaction does not necessarily indicate a widespread issue.

The stronger signal appears when the same behavior occurs across multiple sessions or around the same interaction.

Look at:

Which pages generate rage clicks
Which elements users repeatedly interact with
Whether the pattern affects a specific browser or device
Whether the behavior appeared after a deployment or change

HeronSignal captures struggle signals through Clips, including rage clicks and dead clicks, giving you a starting point for investigation.

2. Watch the User Journey
The repeated clicks are only one part of the session.

What happened before and after them provides the context.

A user may fill out a form, click Continue, wait, click several more times, and eventually leave. Watching that sequence is more useful than assuming the button itself is broken.

Session replay shows the path the user took and whether the interaction eventually worked or failed.

3. Check the Production Context
Once you know where users are struggling, investigate what else was happening.

Look for related:

JavaScript errors
Failed requests
Slow page loads
Performance regressions
Browser-specific issues
Device-specific problems

For example, the investigation might move from:

Users are repeatedly clicking the checkout button.

To:

Mobile Safari users are repeatedly clicking the checkout button because payment initialization fails after the interaction begins.

That gives engineering a concrete starting point.

Rage Clicks vs. Dead Clicks

These signals are related, but they describe different behavior.

A dead click occurs when a user clicks something and there is no apparent response.

A rage click occurs when the user repeatedly clicks or taps the same area.

A dead click can become a rage click when the user keeps trying after receiving no visible feedback.

Neither signal proves that a specific element is broken. Both identify moments where user expectations may not match what the application is doing.

Common Causes of Rage Clicks

Slow responses
The action works, but the user receives no immediate feedback and assumes nothing happened.

Broken interactions
A click handler, request, form submission, or navigation flow fails.

Missing feedback
The action fails or takes time, but the interface does not explain what is happening.

Clickable-looking elements

Something looks like a button or link but does not perform an action.

Layout problems
An overlay or another element blocks the intended interaction.

Browser or device-specific issues
The feature works during testing but fails for users on a particular browser, operating system, or screen size.

From a Rage Click to a Reproducible Bug

A useful investigation can follow a simple path:

Rage click detected
↓
Find affected sessions
↓
Watch what happened before and after
↓
Check errors and performance data
↓
Identify the affected environment
↓
Reproduce and fix the underlying problem

HeronSignal connects these stages by treating rage clicks as part of a broader production picture. A struggle signal identifies where to look, while sessions, Clips, errors, and performance data provide the context needed to understand why it happened.

Rage Clicks Are an Investigation Starting Point

A rage click does not necessarily tell you what is broken.

It tells you that something deserves investigation.

The useful process is to identify the repeated interaction, inspect the affected sessions, understand what happened around it, and connect that behavior to the surrounding production context.

That turns a vague signal of user frustration into a specific problem that can be reproduced and fixed.

Why Users Leave After One Page: How to Find Out What Went Wrong

Rawan — Sat, 22 Aug 2026 13:30:04 +0000

A visitor lands on your website, looks around for a few seconds, and leaves.
That pattern is easy to see in analytics. The harder question is figuring out why it happened.
Maybe the page loaded too slowly. Maybe the content didn't match what they expected. Maybe they tried to click something that wasn't working. Maybe they encountered an error and left without reporting it.
A metric like “Left after one page” tells you that users are leaving, but it doesn't explain what happened before they did.
That is where **HeronSignal **helps connect the number to the actual user experience.

Start With the Number, But Don't Stop There

Imagine opening your dashboard and seeing something like this:

A few numbers immediately stand out:
3 visitors today
0 errors
2.1s typical load time
100% left after one page
0 users currently active
The obvious problem is the 100% left after one page.
But the other numbers make the situation more interesting.
There are no recorded errors, and the typical load time doesn't immediately suggest that the site is unusually slow. If you only looked at traditional analytics, you might conclude that the page simply isn't converting.
That could be true.
But it could also be completely wrong.
The next step is finding out what those visitors actually did before they left.

Go From the Metric to the Sessions

A high one-page exit rate tells you that users are leaving. It doesn't tell you whether they looked around, got confused, encountered a broken interaction, or immediately decided the page wasn't relevant.
HeronSignal's Sessions view gives you the journey behind the number.
You can inspect an individual visitor and see the events captured during their session, including the pages they visited and the actions they took.

Instead of looking at an anonymous percentage, you can start asking more specific questions.
Did the visitor interact with the page?
What did they click?
How far did they get?
Did they leave immediately, or did something happen before they left?
A visitor who lands on a page and leaves after two seconds is very different from someone who spends several minutes trying to interact with the product and then gives up.
Both may appear in the same "left after one page" metric.
The sessions tell you the difference.

Look for Signs That the User Was Struggling

Sometimes the problem isn't visible in the error count.
A button can look functional but fail to respond. A link might not lead where the user expects. An element might take too long to react, causing the user to click it repeatedly.
This is where Clips **become useful.
HeronSignal can capture moments where users appear to be struggling and surface interaction signals such as:
**Rage clicks — repeated clicks on the same area
Dead clicks — clicks where the user expects something to happen, but nothing happens
Other unusual interaction patterns that may indicate friction

Now imagine that your dashboard shows a high percentage of users leaving after one page.
You open the sessions and see that several visitors interacted with the same section.
Then the Clips reveal that they repeatedly clicked a button that didn't respond.
Suddenly, the problem isn't just:
Users are leaving after one page.
It becomes:
Users are trying to use this feature, the interaction isn't responding, and they're leaving shortly afterward.
That is a completely different investigation.

A Low Error Count Doesn't Mean Everything Is Working

One of the easiest mistakes in production monitoring is treating the absence of errors as evidence that the user experience is fine.
A JavaScript error is only one type of failure.
Users can still struggle when:
A click does nothing
A request takes too long
The interface gives no feedback
A feature behaves differently on a specific device
A page technically works but doesn't match what the user expected
Navigation leads users into a dead end
None of those problems necessarily produce an obvious error spike.
This is why the first dashboard view matters, but it shouldn't be the end of the investigation.
The dashboard gives you the signal.
Sessions show you the journey.
Clips can show you where the interaction started to break down.

Connect User Behavior With Technical Context

Once you find a suspicious session or clip, the next step is understanding whether there is a technical reason behind the behavior.
For example, you might see a pattern like this:
A visitor lands on the page.
They interact with a key element.
They click it several times.
Nothing visibly changes.
They leave without visiting another page.
At that point, you can investigate the surrounding technical context.
Check whether there were:
Errors during the session
Performance issues
Slow page or resource loading
Problems affecting a particular browser or device
A change that appeared after a deployment
The important part is that you're no longer investigating a generic metric in isolation.
You're moving from the overall signal to the individual user experience, then narrowing down what may have caused it.

Insights Still Matter

The broader **Insights **view is useful for spotting changes over time.
You might notice that page views have dropped, new visitors are leaving more frequently, or a performance metric has changed compared with the previous period.

This gives you the wider context around the issue.

Maybe users have always left after one page. In that case, you're probably dealing with a content, UX, or traffic-quality problem.
Or maybe the exit rate suddenly increased after a new release.
That points to a different type of investigation.
Insights help you notice the pattern.
The dashboard tells you what needs attention now.
Sessions and Clips help explain what individual users experienced.
Together, they give you a path from “the numbers look wrong” to something concrete enough to investigate.

A Simple Example

Suppose your dashboard shows:
0 recorded errors
A normal load time
A sudden increase in users leaving after one page
Nothing immediately explains the problem.
You check **Insights **and notice that the pattern started recently.
Then you open **Sessions **and find several visitors who interacted with the same page before leaving.
Finally, you check **Clips **and discover repeated dead clicks on an important call-to-action.
Now the investigation has changed.
You don't need to guess whether users are interested in the feature.
They are actively trying to use it.
The problem is that something in the experience is preventing them from moving forward.

The Real Problem Behind “Left After One Page”

A high one-page exit rate is not a diagnosis.
It is a signal.
The reason could be poor traffic, unclear messaging, slow performance, broken interactions, confusing navigation, or something happening only for a specific group of users.
The useful workflow is:
Notice the signal → inspect the broader trend → find the affected sessions → watch for signs of user struggle → connect the behavior to errors and performance context → investigate the underlying cause.
Instead of treating “100% left after one page” as the answer, use it as the starting point.
Because the metric tells you that users left.
The investigation tells you why.

From “Something Feels Off” to a Place You Can Actually Investigate: Debugging with HeronSignal

Rawan — Sat, 22 Aug 2026 13:29:35 +0000

A user tells you that something is broken.

You check your error tracker, but there is no obvious spike. The latest deployment looks normal, the server is running, and nothing immediately explains what the user experienced.

At that point, debugging often turns into guesswork. You ask for screenshots, try to reproduce the issue yourself, and start checking different browsers, devices, or user flows without knowing where the problem actually occurred.

HeronSignal is designed around a different workflow. Instead of treating analytics, session data, user struggle, errors, performance, and debugging as separate investigations, it helps you move through the same chain: notice that something is wrong, understand who is affected, inspect what happened, and bring the relevant production context into your development workflow.

Here is what that process looks like.

1. Start With What Needs Your Attention

The first thing you see in HeronSignal is not a collection of charts that you have to interpret manually.

You see what currently needs attention.

HeronSignal surfaces open findings and groups the investigation around areas such as advisor findings, error groups, sessions, clips, logs, metrics, and alarms. This gives you an immediate starting point when something in production needs investigation instead of forcing you to search through multiple dashboards to figure out where to begin.

Below that, you can still see the current state of the application through metrics such as visitors, errors, typical load time, users leaving after one page, and live activity.

The difference is that the dashboard gives you both the overall picture and a path toward the things that may actually require investigation.

For example, you might see a new error group, an advisor finding, or a change in performance that deserves a closer look. From there, you can move into the relevant part of the product instead of starting with a completely open-ended debugging process.

2. Use Insights to Understand What Changed

Once you have an area worth investigating, Insights gives you a broader view of how the application is behaving.

You can look at metrics across different time ranges and explore areas such as visitor breakdown, pages, geography, network activity, events, and clicks. This helps establish whether the problem is isolated or part of a larger change in user behavior.

Imagine that page views are stable, but returning visitors suddenly drop. Or perhaps sessions are declining while traffic remains relatively unchanged. A change like that does not automatically tell you what is broken, but it gives you something concrete to investigate.

You can then narrow your attention to specific pages, visitor segments, events, clicks, browsers, locations, or other available context.

This matters because debugging an entire production application is usually inefficient. The goal is to reduce the surface area of the investigation before you start looking for a technical cause.

Insights helps answer the first question:

What changed?

3. Find the Sessions Behind the Data

Metrics can tell you that something changed, but they cannot always explain what an individual user experienced.

That is where Sessions becomes useful.

A session gives you the actual journey of a visitor through your application. You can see information such as where they came from, their country and city, and the sequence of events that occurred during their visit.

For example, instead of knowing only that users are visiting your website and leaving, you can inspect a session and see a journey such as:

Visited the homepage
        ↓
Clicked Teams
        ↓
Navigated to another page
        ↓
Performed additional actions
        ↓
Left the site

The exact journey gives you context that aggregate analytics cannot provide on its own.

If a particular page has an unusual exit rate or a group of users appears to encounter the same problem, you can move from the metric to the actual sessions involved.

Instead of asking, "Why are users leaving this page?" you can start looking at what they actually did before they left.

4. Use Clips to Find User Struggle

Not every production problem appears as a JavaScript error.

Sometimes the application technically works, but users clearly struggle to use it.

A button may look clickable when it is not. A request may take too long to respond. An element may fail to react to user input. A form may appear stuck.

HeronSignal records Clips when it detects signs that a user may be struggling, including interactions such as rage clicks and dead clicks.

A rage click happens when a user repeatedly clicks the same area because they expect something to happen. A dead click can indicate that a user clicked an element that did not produce the expected response.

This can be one of the fastest ways to discover problems that would otherwise be invisible.

Imagine several users clicking a checkout button repeatedly before leaving the page. Your error count may still look normal, but the interaction itself tells you that something is wrong.

The button could be broken. The request could be slow. The loading state could be unclear. The click target might not be responding on a specific device.

The point is that you no longer have to start by guessing.

You have evidence that users are struggling with a specific interaction.

That changes the investigation from:

"Someone said checkout feels broken."

to something much more concrete:

"Multiple users repeatedly clicked the checkout button without getting the expected response."

5. Connect User Behavior With Technical Context

Finding a struggling user is only part of the debugging process.

The next step is understanding why the problem happened.

A user repeatedly clicking a button is the visible symptom. The underlying cause could be a failed request, a JavaScript error, a browser-specific issue, a performance problem, or something else entirely.

HeronSignal brings together multiple types of production context so the investigation does not stop at the user behavior.

You can move between areas such as:

Sessions
Clips
Error groups
Logs
Metrics
Alarms
Advisor findings

This allows you to investigate the same issue from more than one angle.

For example, you might start with an advisor finding that points to a problem worth investigating. You then check the relevant metrics and notice a change in behavior, inspect the affected sessions, find clips showing repeated interactions, and finally discover an error or log entry connected to the same flow.

The investigation gradually becomes narrower.

A vague production problem turns into a specific chain of evidence.

6. Give Your Coding Agent the Production Context

Even after you understand the issue, there is usually one more problem: transferring everything you found into your development environment.

Traditionally, this means manually copying error messages, session details, screenshots, reproduction steps, logs, and your own interpretation into another tool.

Important details can easily get lost during that process.

With HeronSignal's MCP integration, your coding agent can access HeronSignal's production context directly as part of the investigation.

Instead of giving your agent a vague description such as:

"Checkout seems broken on Safari. Some users clicked the button multiple times. I found an error, but I'm not sure if it's related."

you can investigate with the relevant production context available to the agent.

The workflow becomes:

Advisor finding or production signal
                ↓
        Insights and metrics
                ↓
         Affected sessions
                ↓
      Clips showing user struggle
                ↓
     Errors, logs, and performance
                ↓
      Coding agent investigation

The goal is not to replace debugging with AI.

The goal is to reduce the amount of context you have to manually reconstruct before debugging can begin.

Your coding agent starts with a clearer picture of what happened in production instead of relying only on a short description of the problem.

7. Move From the Symptom to a Reproducible Bug

By this point, the investigation should be much more specific.

Instead of saying that "checkout is broken," you may have something like this:

Problem:
Users are struggling to complete checkout.

Observed behavior:
Multiple affected users repeatedly click the checkout button.

Session context:
The behavior appears during the payment flow.

Technical context:
A related error occurs during payment initialization.

Scope:
The issue appears to affect a specific browser or device group.

Investigation starting point:
Check the payment initialization flow and
browser-specific handling.

This is a much stronger starting point for debugging.

You have a visible symptom, evidence of user behavior, affected sessions, and technical context that can help narrow down the cause.

The bug may still require investigation, but you are no longer starting from an unstructured report.

The Full Production Debugging Workflow

A production issue does not always begin with an error alert.

Sometimes the first sign is an advisor finding. Sometimes it is a change in metrics. Sometimes users begin leaving a page more often. Sometimes HeronSignal detects repeated clicks or interactions that are not producing the expected result.

A practical debugging workflow looks like this:

1. Notice what needs attention

Start with the findings, error groups, metrics, alarms, or other production signals that indicate something may require investigation.

2. Understand what changed

Use Insights to examine changes in traffic, sessions, pages, visitor behavior, events, clicks, or other available data.

3. Narrow down the affected users

Move from the aggregate data to the sessions behind the behavior and inspect the journey of users who may have encountered the issue.

4. Look for signs of struggle

Use Clips to identify interactions such as rage clicks and dead clicks that may reveal where users are having trouble.

5. Connect the technical evidence

Investigate related errors, logs, performance data, and other production signals to move from the visible symptom toward the underlying cause.

6. Investigate with your coding agent

Bring the production context into the development workflow so the investigation can start from actual evidence rather than a manually reconstructed description.

7. Fix and verify

Once the cause is identified, make the change, deploy it, and continue monitoring the relevant signals to verify that the issue has actually been resolved.

Why This Changes the Debugging Process

Most production tools are useful in isolation.

Analytics can show that behavior changed.

Session data can show what a visitor did.

Clips can reveal where users struggled.

Error tracking can show where something failed.

Logs can provide additional technical detail.

Performance metrics can show where the application became slower.

The difficult part is usually connecting those pieces.

When they are separated across different tools, debugging often becomes an exercise in manually reconstructing the story. You notice a problem in one place, search for related evidence somewhere else, collect screenshots and logs, then try to explain the whole situation to the person or agent working on the fix.

HeronSignal is built around reducing that gap.

The investigation starts with the things that need attention, moves into Insights to understand what changed, uses Sessions to inspect affected user journeys, uses Clips to identify real signs of user struggle, and connects those signals with errors, logs, performance, and the rest of the available production context.

The result is a shorter path from:

Something feels wrong.

to:

This is what changed, this is how users were affected, this is where they struggled, and this is the context needed to investigate the cause.

Your Funnel Should Start With a Question, Not a Form

Rawan — Tue, 18 Aug 2026 11:36:33 +0000

Funnels are supposed to help you answer simple questions. Where are users dropping off? Which step loses the most people? Did something change after a deployment?

But getting to those answers often starts with a completely different problem: building the funnel. You have to choose the events, define the steps, configure the journey, and make sure everything matches the way your application is already tracking users. By the time you've finished setting everything up, you haven't actually investigated the problem yet.

That's why we changed how funnel creation works in HeronSignal.

Instead of starting with a configuration form, you can start with the question you're trying to answer. HeronSignal looks at the events your workspace is already recording and uses them to build the funnel for you.

Start with what you want to understand

When you're investigating a product problem, you usually don't start by thinking about events. You think about what went wrong.

Maybe checkout conversion dropped. Maybe users are getting stuck during signup. Maybe people are starting a process but never completing it. The problem is usually clear before the exact events you need are.

So the new funnel experience starts with a simple question:

What do you want to understand?

You can describe the journey in your own words. For example:

  Why do people who start checkout never finish it?

HeronSignal then looks at the events already being recorded in your workspace and uses them to build the funnel.

Your events are already there

One of the unnecessary parts of building a funnel is having to think about the tracking implementation before you can analyze it. Your application is already generating events, whether that's checkout_started, checkout_completed, payment_failed, signup_started, or something specific to your product.

The new funnel workflow surfaces the events currently arriving in your workspace, so you can use what's already being recorded instead of starting from an empty configuration screen. This also makes it easier to discover what your application is actually tracking. Sometimes the problem isn't that you don't have the data. You just don't know which data you need yet.

Let HeronSignal build the funnel

Once you've described what you're trying to understand, HeronSignal can turn that question into a funnel using the events available in your workspace.

You don't have to start by deciding which event should be step one. You start with the journey you're trying to understand, and HeronSignal handles the initial configuration.

For example, if you're investigating checkout, you can ask:

  Why do people who start checkout never finish it?

Instead of spending time figuring out how to configure the analysis, you can get to the funnel and start looking at where the journey breaks down.

You still have control

AI shouldn't mean giving up control. If you already know exactly which events you want to use, you can still build the funnel yourself through Set the events myself.
The difference is that manual configuration is no longer the only way into the workflow. You can let HeronSignal build the initial funnel and adjust it, or skip the AI path entirely when you already know exactly what you want.
That gives you two ways to work: describe the problem and let HeronSignal handle the configuration, or configure everything yourself when you need more precision.

The point isn't to add another AI button

It's easy to put an AI button into an existing workflow and call it an AI feature. That's not really what we're trying to do.

The goal is to remove work that shouldn't have been necessary in the first place.

If your application is already recording the events, you shouldn't have to spend the first few minutes translating a business question into technical configuration before you can start investigating it. You should be able to describe what you're trying to understand and let the product help you get there.

From building funnels to answering questions

This change is part of a bigger direction for HeronSignal.

Traditional analytics tools often organize the experience around the tools themselves: Sessions, Funnels, Events, Metrics, Logs. But when something goes wrong, that's usually not how you think about the problem.

You don't think, "I need to use the funnel feature." You think, "Why did signups drop?" or "Where are users abandoning checkout?" or "Did yesterday's deployment affect conversion?"

Those questions might require several different pieces of production data, and the user shouldn't always have to figure out which feature to open first. The product should help connect the question to the right data.

A funnel tells you where. The investigation tells you why.

Finding the drop off is only the beginning. If a funnel shows that users are leaving between two steps, the next question is obvious: why are they leaving?
That's where the rest of HeronSignal becomes useful. You can move from the funnel into sessions, events, logs, and other production context to understand what happened to the users who dropped off. Maybe a request failed, a button didn't respond, users encountered an unexpected state, or something changed after a deployment.
The funnel tells you where the problem is. The rest of the investigation helps you understand why.

The workflow should follow the problem

This is the direction we're taking with HeronSignal. Instead of making users learn the platform before they can investigate a problem, we're trying to make the platform adapt to the problem they're already trying to solve.
The workflow becomes:
Question → Relevant data → Investigation → Root cause → Fix
rather than:
Choose a feature → Configure it → Find the information → Figure out what to do next
The change to funnel creation is one example of that approach. The goal is to make the product work more like the way engineers actually investigate problems.
Try it with your own data
If you're already recording events in HeronSignal, you can create a funnel by describing the journey you're trying to understand. Start with the question, let HeronSignal handle the initial configuration, and switch to manual configuration whenever you need more control.
What do you want to understand?
Try HeronSignal

Your Website Is Live. But Is It Actually Ready?

Rawan — Sun, 09 Aug 2026 04:55:24 +0000

Deploying a website is the easy part. Knowing whether it's actually ready for the public is much harder.
The application loads. Your health checks are green. There are no obvious errors in production. Everything looks fine until someone notices your site isn't appearing in search results, a page isn't being indexed, or AI assistants can't understand your documentation.
None of these problems usually trigger an alert. They quietly reduce discoverability, hurt traffic, and make it harder for people, and increasingly AI systems, to find and understand what you've built. By the time someone notices, they've often existed for days or even weeks.
That's why we built HeronSignal Scan.
Scan analyzes your public website from the outside and shows you how ready it is for visitors, search engines, and AI systems in just a few minutes. Instead of waiting for users to discover problems, you can find them before they affect traffic, visibility, or customer experience.

Check your website from the outside

When you work on a website every day, it's easy to assume everything is working as expected. Visitors, search engines, and AI systems don't see your application the way you do. They only see what your public website exposes.
HeronSignal Scan gives you that outside perspective. Simply enter your website URL and start the scan. A few minutes later, you'll have a report showing how your website appears to the systems responsible for discovering, indexing, sharing, and understanding your content.
Whether you're launching a new product, publishing documentation, updating a marketing site, or reviewing changes after a deployment, Scan gives you an objective view of your website without requiring any setup.

What does Scan actually check?

A website can appear to work while still having issues that affect discoverability, performance, and user experience.
Scan organizes its findings into six categories.
Availability: Can visitors reach your website?
SEO & AI Readiness: Is your website exposing the signals modern search engines and AI systems rely on?
Crawler & AI Search: Can search engines and AI crawlers access your public content?
Metadata & Sharing: Does your website expose the metadata needed for search results and rich previews?
Public Speed Estimate: A first look at response speed from an external scan. Measuring real visitor performance requires the HeronSignal tracker.
Monitoring Readiness: Shows what a public scan cannot observe and what becomes available once HeronSignal is installed, such as real user behavior, frontend events, logs, and production investigations.
Rather than producing a long technical report, Scan summarizes the results into clear categories, highlights your weakest area, and prioritizes the most important issue so you immediately know where to begin.

Start with what matters most

One of the biggest frustrations with website auditing tools is information overload. Many generate hundreds of warnings without telling you which ones actually deserve your attention.
Scan takes a different approach. Instead of overwhelming you with raw technical output, it highlights the issue that deserves your attention first.
For example, when we scanned LockerRoomors, the highest priority finding wasn't a generic SEO warning. It was AI friendly public signals need attention.
The report immediately explained why.
AI crawlers are allowed.
Structured data is present.
Readable HTML is available.
The sitemap is configured correctly.
An llms.txt file hasn't been detected.
Within a few minutes, you know what's already configured correctly, what's missing, and what should be addressed first. Instead of spending time interpreting reports, you can focus on solving the right problem.

Every recommendation includes context

A warning is only useful if you understand why it matters.
Every recommendation explains what was checked, what was found, and why the result affects your website.
Take AI readiness as an example. More teams are asking whether AI assistants can understand their documentation, whether AI powered search engines can discover their products, and whether they're exposing the right public signals.
If something is missing, Scan doesn't simply mark it as a failure. It explains exactly what could improve your website, whether that's exposing readable HTML, adding structured data, allowing AI crawlers, or adding an llms.txt file.
Instead of searching documentation to understand a recommendation, the explanation is already part of the report.

Scan is only the beginning

Most website auditing tools stop once they've generated a report. They identify the problem, then leave the implementation to you.
HeronSignal takes the next step.
Every finding includes a Fix button that hands the issue to HeronAgent. HeronAgent investigates what needs to change and, just as importantly, where that change belongs.
Sometimes the solution is in your application's codebase. Other times it's a CDN configuration, a robots.txt rule, a DNS record, or another part of your infrastructure. Rather than assuming every issue belongs in your repository, HeronAgent determines the right place to make the change, generates the change, and brings it back for your review.
That's what makes Scan different from traditional website auditing tools. It doesn't stop at identifying problems. It helps you move toward fixing them.

Run a scan whenever your website changes

Every deployment changes something. Most changes are intentional, but some introduce subtle regressions that nobody notices immediately. A sitemap disappears, metadata is removed, crawler rules change, search visibility drops, or AI readiness regresses.
Running a scan after every meaningful change gives you confidence that your public website is still ready for visitors, search engines, and AI systems before those small issues become larger ones.
You can run a free scan anytime at heronsignal.com/scan.

Build for people and for the systems that help them find you

Today's websites have more than one audience.
You're building for people, but you're also building for search engines, AI assistants, answer engines, and other crawlers that help people discover your content.
Each of these systems relies on public signals to understand your website. Scan helps verify that those signals are present and correctly configured, making it easier for your website to be discovered, indexed, understood, and shared.
It's not just about finding technical issues. It's about making sure your website is ready for everyone who interacts with it.

Try HeronSignal Scan

HeronSignal Scan gives you a fast, actionable view of your public website in just a few minutes. You'll know what's working, what needs attention, and which issue deserves your attention first.
And when you're ready to move beyond identifying problems, every finding can be handed to HeronAgent so you can move from investigation to resolution without leaving the workflow.
Run your free scan at heronsignal.com/scan.

Getting Started with HeronSignal: From Installation to Your First AI-Powered Investigation

Rawan — Sat, 01 Aug 2026 00:54:10 +0000

Every production issue starts the same way.

Someone reports that something is broken.

The first thirty minutes aren't spent fixing it. They're spent figuring out what actually happened.

Which users were affected?

When did it start?

Was it the latest deployment?

Can anyone reproduce it?

Monitoring shouldn't stop at telling you something is wrong. It should help you understand why it's happening and give you enough context to fix it.

In this guide, we'll set up HeronSignal from scratch and walk through the features that help you move from detecting an issue to actually resolving it.

Step 1: Connect Your Domain

Before your application can send data, tell HeronSignal which domains belong to your project.
Open Domains from your dashboard and add every origin where your application runs. Most teams will at least add their production and staging environments.
Keeping environments separate makes it much easier to test new releases without mixing test traffic with production data.

Step 2: Install the Tracker

Install the Web SDK.

Create a provider.

"use client";

import { useEffect } from "react";
import { initHeronSignal } from "@heronsignal/web";

export function HeronSignalProvider() {
  useEffect(() => {
    void initHeronSignal({
      publicKey: "YOUR_PUBLIC_KEY",
      service: "storefront",
      environment: "production",
    });
  }, []);

  return null;
}

Mount it inside your root layout.
That's it.
From this point, HeronSignal starts collecting production signals automatically.

Step 3: Verify Everything Is Working

Open your website.
Browse a few pages.
Click buttons.
Fill in a form.
Use the application exactly like one of your users would.
Return to the dashboard.
Within a few moments you should start seeing activity.

What You Can Do Next

Installing the SDK unlocks the platform, but each feature solves a different problem.
Here's a quick tour.

Insights
Need a quick overview of your application's health?
Insights shows visitor activity, page views, and performance trends, with breakdowns by browser, country, and device.
Instead of guessing whether an issue affects everyone or just a subset of users, you can immediately see where the impact is happening.

Sessions
Sometimes a stack trace isn't enough.
Sessions let you replay exactly what a user experienced before an issue occurred.
Instead of asking users how they reproduced a bug, you can watch the journey yourself.

Logger
Logs become much more useful when they're searchable.
Attach structured attributes to your logs.

log("warn", "Checkout retry", {
  step: "payment",
  plan: "pro"
});

Later, search directly for:

@step:payment

@plan:pro

instead of scrolling through hundreds of plain-text messages.

Health Checks
Some pages are too important to fail silently.
Health Checks continuously monitor critical endpoints like your homepage, login page, or checkout.
If something goes down, HeronSignal alerts you.
When it recovers, you'll know that too.

Funnels

Monitoring tells you when something breaks.

Funnels tell you where users stop progressing.

Build journeys such as:

Landing Page
↓
Signup Started

↓
Signup Completed

event("landing_page_viewed");

event("signup_started");

event("signup_completed", {
  plan: "pro"
});

You'll immediately see conversion rates, drop-off points, and trends over time.

AI Analysis
Once production data starts flowing, AI Analysis helps prioritize what deserves attention first.
Instead of looking through every dashboard yourself, AI reviews the available signals and surfaces the issues with the greatest impact.
For example, it might highlight:
LCP regression on the pricing page
Checkout failures
Slow cart API responses
Helping you decide where to begin instead of where to click next.

Connect Your Coding Agent
When you're debugging a production issue, one of the biggest frustrations is giving your AI assistant enough context.

You copy an error message, paste a stack trace, explain what the user experienced, attach screenshots, and hope it has enough information to help.

With HeronSignal's MCP integration, your coding agent can access live production context directly.

Instead of working from code alone, tools like Cursor, Claude, or Codex can understand what's happening in your application while helping you investigate an issue.

Connecting your coding agent only takes a few minutes. Create an MCP token from your HeronSignal dashboard, then add it to your AI client.

{
  "HeronSignal": {
    "url": "https://api.heronsignal.com/mcp?workspaceId=YOUR_WORKSPACE_ID",
    "headers": {
      "Authorization": "Bearer paste-token-shown-once"
    }
  }
}

By default, MCP tokens are read-only, allowing your coding agent to safely inspect production context without modifying anything.

If you want your agent to help create Funnels, you can enable Funnel write permissions when generating the token.

Complete the Setup
Before you finish, make sure you've completed these final steps:

Add every production and staging domain from Domains.
Visit your website and interact with it like a real user.
Return to the dashboard and verify that data is appearing in Insights, Sessions, Logger, Funnels, and AI Analysis.
At this point, your HeronSignal setup is complete.

Imagine a customer reports that checkout feels slower than usual.

You open Insights and notice a performance regression.
A Session Replay shows exactly what the customer experienced.
Logger helps you search related logs using structured attributes.
AI Analysis highlights the issue affecting the most users, and your coding agent already has the production context needed to investigate further.

That's the workflow you've just built.

From installation to investigation, HeronSignal gives your team the visibility and context needed to understand production faster and spend more time solving problems.