Heap

What is Heap

Heap is a digital insights and product analytics platform that captures the full set of user interactions on web and mobile properties and applies automated modeling to surface opportunities for conversion and retention improvements. Unlike tag-based analytics tools, Heap’s instrumentation captures clicks, pageviews, form submissions, and other interactions automatically so analysts and product teams can create event definitions after data has been collected. Heap is used by product managers, growth teams, UX researchers, and data analysts who need rapid, unbiased visibility into user journeys.

Heap emphasizes both quantitative and qualitative context: structured event data is paired with session replay and automated analysis to answer both what happened and why it happened. The vendor positions the platform as a single source of truth for user behavior reporting, funnel analysis, and experimentation support. Heap is part of Contentsquare’s product set and is commonly evaluated alongside other product analytics and digital experience solutions; see the Contentsquare acquisition announcement for more context on the vendor relationship.

The platform claims broad adoption among mid-market and enterprise customers and highlights automated data capture and built-in data science as its distinguishing capabilities. For technical teams, Heap provides developer-focused controls and governance to map captured events into a maintained data model and to route data to downstream systems via integrations and exports.

Heap features

Heap bundles several feature areas that teams use to reduce time-to-insight and to make data-driven product decisions. Core capabilities include automatic event capture, funnel and retention analysis, behavioral cohorts, conversion attribution, session replay, and anomaly detection driven by statistical models. The combination is designed to let non-technical users run complex queries while preserving data governance for engineering and analytics teams.

From an implementation and data management perspective, Heap provides event schemas, data governance tooling, identity stitching across devices, and export connectors. These features support common enterprise needs: consistent event definitions, data lineage, and the ability to sync events to warehouses or downstream tools for advanced analytics. Heap also exposes administrative controls for privacy and consent management to help with compliance requirements.

Heap integrates with CDPs, marketing automation, experimentation platforms, and data warehouses so that behavioral signals captured in Heap can trigger personalized experiences or be analyzed in other systems. The product marketplace shows over 100 integrations across analytics, advertising, customer success, and tagging systems; see the Heap integrations directory for a full list and specific connector details.

What does Heap do?

Heap automatically records the universe of user interactions on a digital property so teams don’t need to predefine every event tag or wait for engineering changes to capture new signals. This approach surfaces “unknown unknowns” — actions and flows teams weren’t tracking previously — and enables retrospective analysis on those behaviors. Practically, that means you can ask new questions about past traffic without having had to instrument each metric in advance.

The platform offers funnel visualization and conversion analysis to quantify drop-off points, behavioral cohorting to track how specific segments perform over time, and retention curves to evaluate long-term engagement. Heap’s session replay ties those quantitative signals back to qualitative context: you can jump from an anomalous funnel step to the exact point in a user session to understand UI behavior or errors.

Heap also applies automated analytics — such as correlation and impact analysis — to prioritize actions. The product suggests which events correlate most strongly with conversions or churn, highlighting high-leverage fixes. For teams running experiments, Heap can measure variant performance by capturing event-based outcomes and linking experiments to behavioral metrics for a holistic evaluation.

Heap pricing

Heap offers flexible pricing tailored to different business needs, from individual analysts and small teams to enterprise organizations with advanced governance and security requirements. Pricing typically includes monthly and annual billing options and is structured around usage factors such as event volume, number of digital properties, and feature tiers (analytics, data exports, advanced data science, and enterprise controls). Heap also offers enterprise-grade contracts with negotiated terms for large customers.

Commonly referenced plan tiers in product analytics vendors include a Free Plan for evaluation or low-volume use, a Starter tier for small teams, a Professional tier for growth and product analytics teams, and an Enterprise tier with enhanced governance, security, and service levels. Heap follows a similar model where feature access and support levels increase with plan tier, and annual commitments typically lower the effective monthly cost compared with monthly billing.

Heap publishes specific pricing details on its site and frequently provides custom quotes for teams with large event volumes or special compliance needs. Check Heap's current pricing options for the latest rates and to compare monthly versus annual billing terms. Visit their official pricing page for the most current information.

How much is Heap per month

Heap offers flexible monthly billing and customized quotes for usage-based plans. Pricing is typically quoted per number of events or sessions tracked and by feature tier; teams with low-volume needs may be able to use a limited free tier or entry-level monthly plan, while mid-market and enterprise customers receive custom monthly or annual pricing based on usage and service requirements. For precise monthly rates for your environment, contact sales or review the Heap pricing page.

The monthly cost varies with the scale of data capture (events per month), whether session replay and advanced data science features are required, and whether the account needs enterprise security features like SSO, audit logs, or dedicated support. Teams evaluating Heap should estimate event volume and required feature set before requesting a quote to get an accurate monthly number.

If you expect growth in traffic, plan for incremental costs as event volume grows; many organizations choose annual billing to get predictable expense and discounts.

How much is Heap per year

Heap offers annual contracts that typically include discounts compared to month-to-month billing. When customers commit to a year, vendors commonly reduce effective unit costs by a percentage that varies by vendor and deal size; Heap follows this market practice and negotiates enterprise discounts for larger, multi-year agreements. Annual plans also make procurement simpler for organizations that require fiscal-year budgeting.

Yearly pricing depends on the same variables as monthly pricing: event volume, features (such as session replay or advanced analytics), SLAs, and data governance requirements. Heap’s sales team will provide a formal annual quote after sizing event traffic and required integrations or export needs.

To compare exact yearly rates and potential savings from annual billing, check Heap's current pricing options or contact Heap’s sales team for a custom estimate.

How much is Heap in general

Heap pricing ranges from a low-volume or free tier for small projects to enterprise-level contracts for large organizations with heavy event usage. Typical costs scale with data volume (events or sessions) and feature requirements (data exports, advanced modeling, dedicated account support). For small teams or proof-of-concept work, Heap may be accessible at minimal or no monthly cost under a limited tier, while established product and analytics teams should expect to budget for a paid tier.

When comparing alternatives, include not just the subscription price but expected costs for data export, warehouse usage, and any professional services required for migration and governance. Organizations often perform a cost projection based on monthly events, number of properties, and desired retention window to forecast annual spend.

For an accurate cost assessment tailored to your traffic and feature needs, consult Heap’s published guidance and request a custom quote: visit Heap's current pricing options. Visit their official pricing page for the most current information.

What is Heap used for

Heap is used primarily for product analytics, conversion and funnel analysis, retention measurement, and session-level investigation. Product teams use it to answer questions such as which features drive activation, where onboarding funnels fail, and which user segments show higher lifetime value. Growth and marketing teams use behavioral cohorts and attribution data to connect acquisition efforts to product outcomes.

User experience (UX) researchers and customer success teams use Heap for qualitative context: session replay lets analysts see exactly how users interacted with interfaces around events of interest, such as a failed form submission or a drop in a key funnel. This mix of quantitative cohorting and qualitative replay helps teams prioritize issues that have measurable business impact.

Engineering and analytics teams use Heap as a source of event-level truth and as a way to centralize tracking definitions. Heap can export cleaned event data to warehouses for SQL-based analysis or feed events into experimentation and personalization tools. That makes it a practical platform for organizations seeking to align product metrics, experimentation, and downstream analytics.

Pros and cons of Heap

Heap's strengths include comprehensive automatic capture, reduced reliance on pre-planned instrumentation, and integrated session replay that links behavior to context. These capabilities speed exploratory analysis and help teams find behavioral patterns they were not explicitly tracking. Heap’s automated data science can surface high-impact correlations that guide prioritization.

Limitations to consider are data volume costs and the operational overhead of managing large datasets. Automatic capture produces a high cardinality of events, and teams must define governance and retention policies to control costs and maintain performance. Also, organizations with strict privacy or regulatory requirements must configure Heap carefully to ensure PII is handled according to policy.

From an integration standpoint, Heap is strong but not a complete replacement for systems such as a mature data warehouse or a CDP; many teams use Heap in a hybrid architecture, sending curated event sets downstream. Finally, some teams prefer tag-based control when they want minimal data capture for privacy reasons or to tightly control dataset size.

Heap free trial

Heap commonly offers entry-level access or time-limited trials to let teams evaluate the platform with real traffic. Trials typically include core analytics capabilities and a limited event or session quota so teams can run funnel and cohort analysis on actual user behavior. Trials are intended to demonstrate Heap’s automatic capture model and to surface the differences compared with tag-based analytics.

During a trial, expect to test common tasks: building funnels, creating behavioral cohorts, using session replay to inspect specific sessions, and exporting a subset of events to a warehouse or downstream tool for validation. Trials also give product and analytics teams a chance to evaluate administrative features such as user roles and data governance controls.

If your evaluation requires larger event volumes, long retention windows, or enterprise security features, request a proof-of-concept or pilot engagement with Heap’s sales and technical teams. That route provides a controlled environment to validate integration, performance, and compliance expectations prior to a full purchase.

Is Heap free

Heap offers a Free Plan or limited free access for low-volume or evaluation use. The free tier is designed for small teams or proofs-of-concept and typically limits event volume, retention periods, and access to premium features like advanced data science and enterprise governance. For production-grade usage and higher volumes, organizations will need a paid plan.

If you evaluate Heap’s free tier, confirm limits on event volume, session replay minutes, and retention to ensure it covers your test scenarios. For broader usage or enterprise requirements, review paid tier features and request pricing aligned to your expected traffic.

For the latest details on what the free tier includes, consult Heap's current pricing options. Visit their official pricing page for the most current information.

Heap API

Heap exposes programmatic access through APIs and SDKs that let engineering and analytics teams extract event data, define or manage schemas, and integrate Heap with other systems. The API layer supports exporting raw event data, querying aggregated metrics, and controlling administrative aspects such as user permissions. This makes Heap usable both as an analytics UI for non-technical users and as a data source for automated processes.

Developers can instrument applications with Heap SDKs for web, iOS, Android, and server-side environments to ensure consistent data capture across platforms. Heap’s SDKs typically handle identity stitching so that user actions across multiple devices can be associated with a single user profile when appropriate. For production deployments, teams should evaluate SDK behavior in terms of performance and privacy controls.

For teams that require warehouse-first architectures, Heap provides export capabilities to send cleansed event data to data warehouses and downstream analytics systems. Check Heap’s documentation for API reference and best practices: view the Heap developer documentation to understand endpoints, rate limits, and examples for common integration patterns.

10 Heap alternatives

Paid alternatives to Heap

• Mixpanel — Event-based analytics focused on product and retention measurement with strong funnel and cohort capabilities and a developer-friendly API. Mixpanel often competes on real-time event processing and advanced segmentation.
• Amplitude — Product analytics platform with behavioral graph, advanced funnel analysis, and a focus on product intelligence. Amplitude is commonly used by companies with mature product analytics practices.
• FullStory — Combines session replay with analytics and conversion analysis; FullStory emphasizes qualitative session insights paired with analytics to diagnose UX issues.
• Contentsquare — Digital experience analytics with heatmaps, zone-based analytics, and behavioral insights at scale; Contentsquare acquired Heap to broaden combined capabilities across the customer journey.
• Hotjar — Simpler session recording, heatmaps, and feedback tools targeted at UX teams and smaller product organizations seeking qualitative insights.
• Adobe Analytics — Enterprise-grade analytics platform that integrates with Adobe Experience Cloud for complex implementations and advanced attribution and reporting needs.

Open source alternatives to Heap

• PostHog — Open source product analytics that offers event capture, cohorts, funnels, and session recording with the ability to self-host and control data retention and privacy.
• Matomo — Privacy-first web analytics platform that can be self-hosted for full data ownership; Matomo provides page analytics, funnels, and tracking with GDPR-oriented features.
• Plausible — Lightweight, privacy-focused analytics for websites that prefer aggregate metrics and minimal event capture; Plausible is appropriate for teams that do not require full session-level detail.
• Ackee — Simple self-hosted analytics for sites where lightweight event tracking and ownership matter; Ackee focuses on straightforward page-view and referrer metrics.

Frequently asked questions about Heap

What is Heap used for?

Heap is used for product analytics and behavioral insights. Teams use Heap to analyze funnels, measure retention, create behavioral cohorts, and link quantitative events to session replay to understand why users behave the way they do. It is particularly useful when you want to discover events you did not explicitly instrument in advance.

How does Heap capture events?

Heap captures events automatically via a single snippet and SDKs. The platform records clicks, pageviews, form interactions, and other interactions so analysts can define events retroactively. SDKs are available for web, iOS, Android, and server-side environments to maintain consistent capture across platforms.

Does Heap integrate with data warehouses?

Yes, Heap supports exports to data warehouses and downstream systems. You can sync cleaned event datasets to warehouses for SQL-based analysis and to feed other tools like experimentation platforms, CDPs, and BI systems. See the Heap integrations directory for details on supported destinations.

Can Heap be used for experimentation measurement?

Yes, Heap can measure experiment outcomes using event-based metrics. Teams can use Heap to track variant performance, compare funnels across experiment groups, and analyze retention or engagement changes attributable to experiments. Integration with common experimentation platforms is also available.

Is Heap compliant with data privacy regulations?

Heap provides controls to support privacy and compliance needs. The platform includes features for data governance, consent management, and PII suppression, but compliance requires configuration and processes on the customer side. For enterprise use, review Heap’s documentation and legal terms to confirm alignment with GDPR, CCPA, or other applicable regulations.

Why would a team choose Heap over other analytics tools?

Teams choose Heap when they want automatic event capture and rapid exploratory analysis. Heap reduces the dependency on pre-defined instrumentation, enabling analysts to ask new questions of historical data without re-instrumentation. Organizations that value fast iteration and discovery often find this model beneficial.

When should a company consider Heap for its analytics stack?

Consider Heap during product growth or when discovery is a priority. If your team frequently needs to test hypotheses, analyze unexpected user behavior, or run cross-platform analysis without lengthy instrumentation cycles, Heap is a strong candidate. For very high-volume or strictly privacy-constrained environments, evaluate governance and cost trade-offs first.

Where can I find Heap documentation?

Heap’s developer and product documentation is available online. Developers and analysts can consult the SDK guides, API reference, and implementation best practices in the official documentation at the Heap docs site to plan an evaluation or deployment.

How much does Heap cost for small teams?

Heap offers flexible pricing with entry-level options for small teams. Small teams can often begin on a free or low-volume tier for evaluation, while paid plans scale by event volume and features. For a precise cost estimate based on expected events and retention, consult Heap’s sales team or view current rates at Heap's current pricing options. Visit their official pricing page for the most current information.

Does Heap offer support and onboarding?

Heap provides support and onboarding services scaled to the plan level. Paid tiers and enterprise customers typically receive faster response SLAs, dedicated onboarding resources, and technical account management, while lower tiers have standard documentation and community support. For details on service levels and available support packages, review Heap’s customer resources and contact sales.

Heap careers

Heap maintains a careers page with roles across engineering, product, sales, and customer success. Candidates can find openings, role descriptions, and hiring practices through the company careers portal. Job postings often describe required experience levels, remote or office expectations, and the hiring process.

Heap’s hiring priorities commonly include product analytics expertise, software engineering skills for SDK and platform work, and roles focused on customer success to help customers derive value from behavioral data. The company may also list roles in data science, privacy, and compliance to support enterprise customers.

To evaluate current openings and application steps, check the Heap careers listing on their corporate site or job boards where the company posts opportunities.

Heap affiliate

Heap does not publish a widely publicized affiliate program for general resellers; partnership opportunities are typically handled through formal channel or enterprise partner programs. Organizations interested in reselling, referral partnerships, or systems-integration work should contact Heap’s partner or sales team to explore authorized partnership models. Enterprise partnerships may include go-to-market alignment, referral fees, or co-selling arrangements.

If you represent an agency or integration partner, ask Heap for partner program details, technical enablement resources, and commercial terms. Heap also works with implementation partners who provide migration, instrumentation, and analytics services for customers.

Where to find Heap reviews

Heap reviews and ratings are available on major software review sites and industry publications. You can consult platforms such as G2 and TrustRadius for user-submitted reviews, use-case comparisons, and aggregated ratings. These sites provide insights into customer satisfaction, common pain points, and feature comparisons against competitors.

For enterprise evaluations, combine public reviews with reference calls and a pilot to validate performance on your traffic profile. Also consult case studies and benchmarks that Heap publishes to understand how similar organizations have deployed the platform.