Heap is the stronger choice for product teams that need quantitative behavioral analytics with retroactive event definition and minimal instrumentation overhead. FullStory leads in session replay quality, frustration signal detection, and qualitative user experience research. Both tools answer "what are users doing?" — neither answers "which of those behaviors is driving or destroying margin?"
Key Takeaways
| Dimension | Heap | FullStory |
|---|---|---|
| Primary use case | Product analytics, funnel/retention | Session replay, UX research, support |
| Primary user | Product managers | Customer success, UX, support teams |
| Data capture method | Automatic (all interactions) | Session recording |
| Retroactive analysis | Yes — define events on historical data | Limited |
| Free plan | Yes (up to 10K sessions) | Yes (30K sessions/month) |
| Paid entry price | Custom (Growth tier, ~$3,600+/yr) | ~$199/month (Business) |
| Mobile app replay | Limited | Enterprise tier only |
| Frustration signals | Basic | Yes (rage clicks, dead clicks) |
| Data export / warehouse | Yes (managed ETL) | Limited |
| Self-hosting | No | No |
Heap: Overview
Heap was founded in 2013 with a core thesis: manual event tracking creates instrumentation debt, missed data, and analysis blind spots. Heap's answer was to capture every user interaction automatically — every click, every form submission, every page view — from the moment the Heap script loads. Teams can then label and define events after the fact using a visual editor, rather than writing tracking code upfront.
This approach, called autocapture, is Heap's primary differentiator. It means that if a business question comes up today — "how many users clicked that button three months ago?" — Heap can answer it retroactively. No other analytics tool on the market captures with the same breadth by default, except PostHog (which also autocaptures).
In 2021, Heap launched Illuminate, an AI-powered feature that automatically surfaces behavioral patterns and anomalies in your data without you needing to know what question to ask. Illuminate alerts teams to conversion drops, engagement changes, and unexpected segments — making it particularly valuable for teams that do not have a dedicated analyst.
Heap Pricing
Heap does not publish list prices for its paid plans. The pricing structure in 2026 is:
- Free: Core analytics charts, up to 10,000 sessions, 6 months data history, unlimited enrichment sources.
- Growth: Custom quote. Includes AI-powered assistant (Illuminate), unlimited users and reports, 12 months data history. Typical entry point is approximately $3,600 per year, varying significantly by MTU volume.
- Pro: Custom quote. Adds account analytics, engagement matrix, and report alerts. Pricing scales with MTU volume.
- Premier: Custom quote for large enterprises. Full access including professional services and dedicated support.
Heap's lack of published pricing creates friction in evaluation. Hidden costs can include MTU overages, session replay fees, professional services, and annual price escalation clauses — all worth addressing explicitly during negotiation.
Heap Strengths
- Autocapture eliminates instrumentation debt — all historical data is available immediately
- Retroactive event definition lets teams answer questions about past behavior
- Illuminate AI surfaces patterns without requiring a specific query
- Visual event editor enables non-technical users to define events without code
- Managed ETL for data warehouse export (Snowflake, BigQuery, Redshift)
- Account analytics enables B2B use cases out of the box
Heap Weaknesses
- No published pricing — evaluation requires a sales conversation
- Session replay is a secondary feature, not a primary strength
- Autocapture generates large data volumes that require governance to manage
- MTU-based pricing can become expensive for high-traffic consumer products
- Frustration signal detection is less mature than FullStory
- Mobile app support is limited compared to dedicated mobile analytics tools
FullStory: Overview
FullStory was founded in 2014 with a focus on session replay and digital experience intelligence. While Heap captures events for quantitative analysis, FullStory records the full pixel-perfect session — every mouse movement, scroll, click, and keypress — to allow teams to watch exactly what individual users experienced. This qualitative layer is FullStory's core strength and the reason it became the preferred tool for customer success and support teams.
FullStory's frustration signal detection — automatic flagging of rage clicks (rapid repeated clicks), dead clicks (clicks on non-interactive elements), and error clicks — gives teams a fast path to identifying UX problems without watching thousands of sessions manually. These signals can be filtered and segmented to prioritize which sessions to review.
In recent years, FullStory expanded its platform to include product analytics capabilities alongside session replay, blurring the line between its offering and tools like Heap. However, the session replay and qualitative research capabilities remain its primary competitive advantage.
FullStory Pricing
FullStory's pricing in 2026 is session-volume-based:
- Free: 30,000 sessions per month, 12 months data retention, core session replay, basic analytics, up to 10 users.
- Business: Approximately $199 per month for annual commitments. Exact price varies by session volume.
- Advanced: Approximately $499 per month (annual). Additional features, higher session limits, extended retention.
- Enterprise: Custom pricing. Typical annual contract values average approximately $80,000 based on benchmark data. Mobile SDK access (iOS and Android) is Enterprise-only.
Extending data retention beyond the default 30–90 days adds 10–20% to base contract value. Mobile app support requires the Enterprise tier.
FullStory Strengths
- Best-in-class session replay with pixel-perfect fidelity
- Automatic frustration signal detection (rage clicks, dead clicks, error clicks)
- Strong adoption in customer success and support workflows
- Segment and filter sessions by user attributes or behavioral signals
- Good for UX research and design validation
- Expanding product analytics layer complements the session data
FullStory Weaknesses
- Mobile app support restricted to Enterprise tier
- No equivalent to Heap's retroactive event definition on historical data
- Data export options are more limited than Heap's managed ETL
- Session-based pricing can be expensive for high-traffic products
- Extended data retention increases cost materially
- Product analytics capabilities are newer and less mature than dedicated analytics tools
Feature-by-Feature Comparison
| Feature | Heap | FullStory |
|---|---|---|
| Session replay | Yes (secondary) | Yes (primary, best-in-class) |
| Autocapture | Yes (full event capture) | Session recording only |
| Retroactive event analysis | Yes | No |
| Frustration signals | Basic | Yes (rage clicks, dead clicks, error clicks) |
| Funnel analysis | Yes, advanced | Yes, basic |
| Cohort analysis | Yes | Limited |
| Heatmaps | Limited | Yes |
| Mobile app support | Limited | Enterprise only |
| Data warehouse export | Yes (managed ETL) | Limited |
| Account / B2B analytics | Yes (Pro+) | Limited |
| AI anomaly detection | Yes (Illuminate) | Frustration signal alerts |
| Visual event editor | Yes | No equivalent |
| User journey visualization | Yes | Yes (session-based) |
| Pricing transparency | No (quote-based) | Partial (Business/Advanced published) |
Use Case Recommendations
Choose Heap if:
- You need retroactive event analysis — the ability to answer questions about past behavior without pre-instrumentation
- Your team lacks the bandwidth to maintain a detailed tracking plan and you want autocapture to handle it
- You are a B2B SaaS company that needs account-level analytics and user-level analytics in the same platform
- You want to export behavioral data to a warehouse for deeper analysis (Snowflake, BigQuery, Redshift)
- Product managers, not engineers, are the primary analytics consumers
Choose FullStory if:
- Session replay and qualitative user experience research are the primary use cases
- Customer success or support teams need to review individual user sessions to diagnose issues
- You need automatic frustration signal detection (rage clicks, dead clicks) to prioritize UX fixes
- You are doing UX research and need to watch real user sessions to validate design decisions
- Your primary questions are about specific user experiences, not aggregate behavioral patterns
The Operating Intelligence Gap
Heap tells you which funnels are leaking and which features drive retention. FullStory shows you exactly how individual users navigated those funnels. Both are answering questions about user behavior inside your product.
Neither tool answers the question that operators and founders need answered: which of these user behaviors is translating into revenue, and which is consuming operational cost without returning margin?
A Heap funnel analysis can show you that a certain onboarding step has a 60% drop-off rate. A FullStory session replay can show you exactly how confused users look when they hit that step. What neither tool tells you is whether the users who do complete onboarding are profitable — or whether the acquisition channel driving most completions has a 90-day payback that is destroying your unit economics.
Fairview is the operating intelligence layer above both tools. It connects your product data, your revenue data, and your operational cost structure into a single view — so you know not just what users are doing, but what those behaviors mean for your business. COOs and operators use it to identify margin leaks, prioritize the right customer segments, and make decisions with financial context — not just engagement metrics.
Fairview starts at $149 per month.
See Fairview in ActionVerdict
Heap and FullStory serve different primary jobs. If the question is "what are users doing in aggregate and where are they dropping off?", Heap is the better answer — especially if you need retroactive analysis or data warehouse export. If the question is "what did this specific user experience?", FullStory is the better answer — especially for customer success and support workflows.
Many mature product teams run both: Heap for quantitative product analytics and FullStory for session-level qualitative research. If budget requires choosing one, the decision point is simple — are your most pressing questions aggregate (Heap) or individual (FullStory)?