# FullStory

> FullStory is a digital experience analytics platform that autocaptures every interaction on a website or app, clicks, scrolls, form entries, errors, page changes, without requiring events to be defined in advance, then makes that history searchable so teams can find and replay the exact sessions where users struggled. It combines session replay, heatmaps, funnels, and frustration signals with retroactive analysis of data collected before anyone knew to ask.

- Category: CRO & Experimentation (https://saastracker.org/categories/cro-experimentation)
- Website: https://www.fullstory.com
- Starting price: Free tier for low session volumes; paid plans quoted by session volume
- Free plan: Limited monthly sessions with core replay and analytics
- Free trial: Free tier plus trial access to paid capabilities
- Founded: 2014, HQ: Atlanta, Georgia, United States, Ownership: Private, venture-backed
- Profile last reviewed: 2026-08-22
- Canonical profile: https://saastracker.org/products/fullstory

## Overview

The founding idea of FullStory is autocapture: instead of instrumenting individual events and only being able to analyze what you thought to track, the script records everything, and analysis happens retroactively. When a support ticket arrives on Tuesday about a checkout failure that happened on Sunday, you do not need to have been tracking the relevant button, you search for it and watch it. That property is what separates the product from tools that require a tagging plan before they produce anything.

On top of that capture layer sits a search interface that behaves more like a query tool than an analytics dashboard. You can ask for sessions where a user rage-clicked on a specific element, or where a JavaScript error fired within ten seconds of an attempted payment, or where a defined segment abandoned a funnel step, and get back both the aggregate number and the individual replays behind it. Frustration signals, rage clicks, dead clicks, error clicks, thrashed cursors, are computed automatically and act as an early warning system for problems nobody reported.

FullStory has moved steadily upmarket over the years, and its pricing reflects that: the entry tier is generous enough for a small product team, but serious deployments are quoted by session volume and negotiated annually. Its natural buyers are product and engineering organizations that treat replay as a debugging and quality tool rather than a marketing one. Marketers looking primarily for cheap heatmaps will find the value proposition mismatched to the price.

## How it works

1. A JavaScript snippet or a mobile SDK is installed once. From then on the platform records the DOM and every interaction with it, reconstructing sessions as replayable pixel-accurate video without literally recording video, which keeps bandwidth and storage manageable and preserves searchability of the underlying elements.

2. Because capture is automatic, analysis is retroactive. Defining a new conversion event, funnel step, or segment applies to historical data as well as future data, so a question asked today can be answered with last month's sessions. This is the operational difference from event-based analytics, where an untracked action is permanently invisible.

3. Search and segmentation drive everything else. Sessions can be filtered by user attributes passed from your application, by behavior such as visiting a page or clicking an element, by technical conditions such as browser or error, and by computed frustration signals. Saved segments become monitored cohorts with alerting when their volume changes.

4. Findings route outward: replays link into Jira, Zendesk, Slack, and support tools so an engineer receives a reproducible session rather than a description, and data exports or warehouse connections let analysts join behavioral data with revenue in their own environment.

## Best for

Product, engineering, and support teams at software companies and larger ecommerce operations who need to reproduce and diagnose real user problems quickly, and who value retroactive analysis over the lower price of event-based tools.

## Not the right fit for

- Small marketing teams who mainly want heatmaps; the price and depth are aimed at a different job.
- Sites with very high traffic and small budgets, since session-based pricing scales directly with volume.
- Teams needing built-in A/B testing, which FullStory does not provide.
- Buyers who require published, self-serve pricing at every tier; serious volume is quoted.
- Highly regulated environments unwilling to accept autocapture, though extensive masking controls exist to address exactly this.

## Features

### Autocapture and replay

Record everything now, decide what mattered later.

- **Retroactive event definition**: Define a conversion, funnel step, or segment today and apply it to sessions captured months ago, because the underlying interactions were already recorded.
- **DOM-based session replay**: Sessions reconstruct the page rather than storing video, keeping playback fast, searchable, and light on storage.
- **Console and network context**: JavaScript errors, console output, and network activity appear alongside the replay timeline, which is what makes it usable as a debugging tool.
- **Mobile app replay**: Native iOS and Android SDKs bring the same capture and replay model to app sessions, including gesture and screen-transition detail.
- **Cross-session user history**: Every session for an identified user is linked, so a support conversation can be traced across visits rather than one isolated replay.

### Search and frustration signals

Finding the sessions that matter without watching thousands.

- **Rage, dead, and error clicks**: Automatically computed frustration signals surface elements users repeatedly attack, elements that do nothing, and clicks that trigger errors.
- **Behavioral search**: Query sessions by combinations of actions, attributes, technical conditions, and timing, then jump straight to the replays behind any number.
- **Segments with alerting**: Save a cohort and get notified when its size shifts, turning replay from reactive investigation into monitoring.
- **Heatmaps from captured data**: Click, scroll, and engagement maps generated from the same autocaptured interactions, with element-level detail on dynamic pages.
- **Funnels and conversion paths**: Build funnels retroactively, see drop-off per step, and inspect the sessions that abandoned each one.

### Data platform

Behavioral data that leaves the tool rather than staying trapped in it.

- **Warehouse export**: Structured event data delivered to your own warehouse for joining with revenue, subscription, or support data.
- **Custom user attributes and events**: Pass plan tier, account ID, role, or any business dimension into the session record for segmentation that matches how you actually think about customers.
- **APIs and webhooks**: Programmatic access to sessions, segments, and events, plus webhooks for pushing signals into other systems.
- **Integrations with support and engineering tools**: Attach the exact replay to a Zendesk ticket, a Jira bug, or a Slack alert so the next person sees the problem rather than reads about it.
- **AI-assisted summarization**: Automatic surfacing and narrative summarization of anomalous behavior, aimed at teams with more sessions than time.

### Privacy and governance

The controls that make autocapture defensible.

- **Private-by-default element rules**: Text and inputs can be excluded or masked wholesale, with allowlist rather than blocklist configurations available for sensitive applications.
- **Consent gating**: Capture can be conditioned on a consent management platform's signal so recording never begins without permission.
- **Data deletion and retention controls**: Per-user deletion requests and configurable retention windows to satisfy data subject rights and internal policy.
- **Role-based access**: Granular permissions on who may view replays, since session recordings are among the most sensitive datasets a company holds.
- **Compliance certifications**: SOC 2, ISO 27001, and support for HIPAA and other regulated deployments on appropriate plans.

## Use cases

- **Support lead handling an unreproducible bug**: A customer reports that saving fails, but the team cannot reproduce it and the description is vague. Outcome: The user's session is found by email attribute, replayed with console errors visible, and the failing request is identified in minutes; the Jira ticket carries a link to the exact moment.
- **Product manager auditing a new feature launch**: Adoption is lower than expected a week after release and nobody instrumented the feature properly beforehand. Outcome: Retroactive event definition measures actual usage from launch day onward without waiting for new tracking, and replays show users failing to find the entry point rather than rejecting the feature.
- **Ecommerce operations manager watching for silent failures**: Revenue dips on one browser version and no error monitoring has flagged anything. Outcome: A saved segment of error clicks on the payment step alerts on volume change, and replays confirm a rendering fault isolated to that browser.
- **UX researcher preparing a redesign case**: Needs evidence that current navigation confuses users, beyond a handful of moderated sessions. Outcome: Dead-click and thrash signals quantify confusion across thousands of real sessions, and representative replays are clipped into the research readout.

## Pricing

Subscription priced by captured sessions and product modules, with a free entry tier and quoted contracts above it. Annual agreements are standard, and add-ons such as mobile app capture or data export are typically priced separately.

- **Free**: $0 per month. Capped monthly session capture; Session replay, basic search, and frustration signals; Enough for a small product team to prove the workflow.
- **Business**: Quoted annual, by session volume. Higher session volumes and longer retention; Funnels, segments, alerting, and integrations; Custom attributes and event definitions.
- **Advanced and Enterprise**: Quoted annual. Mobile app capture, warehouse export, and API access; Advanced privacy controls and regulated-industry support; Dedicated success management.

Billing notes:

- Sessions are the meter, so both traffic growth and longer average sessions raise cost, a dynamic worth modeling before committing annually.
- Mobile capture, data export, and some advanced modules are commonly separate line items rather than included capabilities.
- Published self-serve pricing is limited to the free tier; everything meaningful is a negotiated annual quote.
- Retention windows differ by tier, which matters for teams investigating issues weeks after they occur.
- Because the tool is bought by product and engineering as much as marketing, budget ownership is often a procurement question in itself.

Value assessment: FullStory is expensive relative to heatmap tools and cheap relative to the engineering hours spent reproducing bugs from bad descriptions. The value case rests almost entirely on retroactive autocapture: teams that regularly need to answer questions they did not anticipate get something no event-based tool can offer at any price. Teams whose questions are all known in advance are paying a premium for optionality they will not use, and should look at cheaper replay products or free options instead.

## Strengths

- Autocapture means analysis is retroactive, so an untracked action is still analyzable later.
- Search over behavior is genuinely powerful, turning replay from browsing into querying.
- Automatic frustration signals surface problems nobody reported, functioning as passive quality monitoring.
- Console and network context alongside replay makes it a credible engineering debugging tool, not just a marketing one.
- Mature privacy controls including allowlist masking, consent gating, and per-user deletion.
- Strong integrations into support and engineering workflows so findings arrive where the fix happens.

## Limitations

- Priced well above behavior analytics tools aimed at marketers, with quoted contracts at any real volume.
- No A/B testing, so validating a fix requires another platform.
- Autocapture requires deliberate privacy configuration before it can be turned on in regulated contexts.
- Session-based metering makes cost a direct function of traffic, which penalizes growth.
- Depth of configuration means onboarding takes longer than a heatmap tool a marketer installs in an afternoon.
- Data volume can overwhelm teams without a clear question, since the tool is best when interrogated rather than browsed.

## Comparisons

- **FullStory vs LogRocket**: The closest comparison, and the choice usually turns on which discipline owns the budget. LogRocket leans harder into frontend engineering, with deeper error monitoring, network inspection, and performance tooling bundled into the same product. FullStory leans into behavioral search, retroactive analytics, and cross-functional use by product and support. Engineering-led teams tend to prefer LogRocket; product-led organizations tend to prefer FullStory.
- **FullStory vs Hotjar**: Different products despite superficial overlap. Hotjar is a marketer's diagnostic tool with a free tier, heatmaps, and surveys, priced for small sites. FullStory is a data platform for product and engineering teams that happens to include replay, priced accordingly. A team choosing between them on price has almost certainly not scoped the job the same way twice.
- **FullStory vs Microsoft Clarity**: Clarity gives away unlimited session capture and heatmaps, which makes it the correct starting point for any budget-constrained site. What it does not offer is retroactive event definition, deep behavioral search, custom attribute segmentation, warehouse export, or the governance apparatus that regulated buyers require. FullStory is what teams move to when replay stops being a curiosity and becomes part of an operational workflow.
- **FullStory vs Smartlook**: Both autocapture sessions and let events be defined after the fact, so the split is scope and commercial model. FullStory is a quoted data platform for product and engineering teams, with deeper behavioral search, warehouse export, and the governance regulated buyers require. Smartlook is self-serve, free to around 3,000 monthly sessions and roughly $55 a month after that, and treats native mobile (iOS, Android, React Native, Flutter, and game engines) as a first-class recording surface rather than a separately priced add-on. An app-first company on a small budget lands on Smartlook; a team building replay into an operational workflow ends up on FullStory.

## Implementation

- Setup time: The snippet installs in under an hour, but a responsible deployment includes a privacy configuration pass, masking sensitive fields and gating on consent, which typically takes a few days of coordination.
- Learning curve: Moderate. Replay is intuitive; building useful segments and search queries takes practice, and the retroactive model rewards teams that learn to ask precise questions.
- Onboarding: Self-serve on the free tier, with structured onboarding and success management on quoted plans. Documentation and template segments cover most early needs.
- Migration: Behavioral history does not transfer between vendors, so plan for a gap where the new tool has no back catalogue. Custom attribute schemas should be designed before install, since consistent identifiers are what make cross-session analysis valuable later.

## Platform, API & security

- Platforms: Web (JavaScript), iOS and Android SDKs, React Native, Google Tag Manager
- API: REST APIs for sessions, segments, users, and events; server-side event ingestion; webhooks; and data export connectors for warehouses.
- Compliance: GDPR, CCPA, SOC 2 Type II, ISO 27001, HIPAA support on qualifying plans
- Data residency: US and EU data region options depending on plan.
- SSO: SAML single sign-on with SCIM provisioning on higher tiers.
- Security notes: Supports allowlist-style capture where nothing is recorded unless explicitly permitted, which is the configuration regulated buyers usually require. Element-level exclusion, consent gating, and per-user data deletion are standard.

## Support

- Channels: Email and ticket support, Dedicated success management on higher tiers, Documentation and community
- Documentation: Thorough technical documentation covering SDKs, privacy configuration, and data export, aimed at developers rather than only at analysts.
- Community: An established user community with published playbooks, plus significant presence in product management and frontend engineering circles.

## Company

- Founded: 2014
- Founders: Scott Voigt, Bruce Johnson, Joel Webber, Jaeson Fleming
- Headquarters: Atlanta, Georgia, United States
- Ownership: Private, venture-backed
- Employees: ~500 (est. 2026)
- Funding: Raised substantial venture funding across multiple rounds including a large late-stage round in 2021.

Timeline:

- 2014: Founded in Atlanta with autocapture session replay as the core idea.
- 2018: Establishes itself in product and support workflows as replay becomes standard practice at software companies.
- 2021: Raises a large late-stage round and expands into mobile app capture and data export.
- 2023: Repositions as a behavioral data platform rather than a replay tool, emphasizing warehouse export and analytics depth.
- 2025: Adds AI-assisted anomaly surfacing and session summarization for teams with more data than review capacity.
- 2026: Sits at the product and engineering end of the experience analytics market, above marketer-oriented replay tools.

## Integrations

Jira, Zendesk, Slack, Segment, Salesforce, HubSpot, Mixpanel, Amplitude, Snowflake, BigQuery, Intercom, Sentry

## FAQ

### What is FullStory?

FullStory is a digital experience analytics platform that automatically captures every interaction on a site or app and makes that history searchable and replayable. Because capture is automatic rather than event-by-event, teams can define new metrics and segments after the fact and apply them to data already collected.

### How much does FullStory cost?

There is a free tier with a capped number of monthly sessions, and paid plans are quoted based on session volume, retention, and modules such as mobile capture or warehouse export. Serious deployments are annual contracts rather than self-serve purchases, so expect a sales conversation once you exceed the free allowance.

### What does autocapture mean and why does it matter?

Autocapture means the script records all interactions rather than only pre-defined events. The practical consequence is retroactive analysis: if you decide today that you want to measure clicks on a button nobody tagged, FullStory can answer using sessions recorded weeks ago. Event-based analytics tools cannot, because untracked actions were never stored.

### FullStory vs LogRocket: which is better?

They overlap heavily and differ in center of gravity. LogRocket is stronger for frontend engineering, with deeper error, network, and performance tooling. FullStory is stronger for behavioral search, retroactive analytics, and cross-team use spanning product, support, and design. The right answer usually follows whichever team is driving the purchase.

### Is session replay legal under GDPR?

It can be, but it processes personal data and generally requires a lawful basis and, in most European interpretations, prior consent. FullStory supports this with consent gating, aggressive masking including allowlist-only capture, retention limits, and per-user deletion. The obligations to disclose the recording in your privacy notice and to honor data subject requests remain yours.

### Does FullStory work with mobile apps?

Yes, through native iOS and Android SDKs and React Native support, capturing gestures, screen transitions, and interaction detail in the same replay and search model as the web product. Mobile capture is generally licensed separately from web sessions.

### What are rage clicks and dead clicks?

Rage clicks are rapid repeated clicks on the same element, a reliable signal of frustration when something appears broken or unresponsive. Dead clicks are clicks on elements that do nothing, typically text or images users assume are links. Error clicks are clicks that trigger a JavaScript error. FullStory computes all three automatically and lets you find the affected sessions instantly.

### Can I get FullStory data into my own warehouse?

Yes, on appropriate plans, through structured data export to destinations such as Snowflake or BigQuery. This is often the deciding capability for analytics teams, because it lets behavioral data be joined with revenue, subscription, and support data rather than analyzed in isolation.

### Does FullStory include A/B testing?

No. It measures and explains behavior but does not run experiments. Teams pair it with a testing platform such as VWO, Convert, or an open-source framework, using FullStory to find problems and generate hypotheses and the testing tool to validate fixes.

### How does FullStory handle sensitive data like credit cards?

Input values are excluded by default and any element can be masked or blocked by selector, with an allowlist mode where nothing is captured unless explicitly permitted. Regulated deployments typically start from the allowlist configuration, gate capture on consent, and restrict replay viewing to specific roles.

## Editorial verdict

FullStory is the strongest argument in the category for capturing everything and deciding later, and retroactive analysis is a genuine capability rather than a marketing distinction: the questions you did not know to ask are exactly the ones that turn up when something breaks. Its search and frustration signals make replay operational rather than anecdotal, and its privacy controls are mature enough to survive a regulated review. What it asks in return is money and rigor: quoted annual contracts scaled by session volume, and a privacy configuration pass before launch. Product and engineering organizations that use it as a shared source of truth get their money back in reproduction time alone. Marketers who wanted heatmaps should shop elsewhere.

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Source: SaaSTracker (https://saastracker.org), an independent editorial project. This profile is compiled from public information, carries no peer reviews or paid placement, and was last reviewed 2026-08-22. Awards are judged on published criteria: https://saastracker.org/methodology