AmplitudevsHeap

Features Compared

Amplitude and Heap approach analytics from fundamentally different starting points, which shapes their feature sets. Amplitude excels at behavioral analytics paired with experimentation. Its built-in A/B testing capability allows product teams to run experiments directly within the platform alongside their analytics, eliminating the need for a separate experimentation tool. Amplitude also includes Compass, a unique feature that identifies which user actions predict retention—critical for teams focused on long-term engagement. Additionally, Amplitude offers robust data governance and compliance features designed for enterprise environments where data control and regulatory adherence matter. Funnel analysis and session replay round out its core toolkit.

Heap takes the opposite approach with auto-capture technology that records every user interaction automatically, requiring no manual event instrumentation. This means teams get analytics working immediately without the overhead of defining and implementing events. Heap's standout capability is retroactive funnel and cohort analysis—users can define funnels and build cohorts from historical data without waiting for new events to be instrumented. This retroactive power is a major time-saver for teams investigating past user behavior. Heap also emphasizes data science integrations, making it easier to connect analytics data to external modeling and analysis tools. Like Amplitude, Heap includes session replay and cohort analysis, but the auto-capture foundation sets it apart from tools requiring manual event setup.

Pricing & Value

Both platforms offer free tiers, but the limits and price-to-value proposition differ notably. Amplitude's free tier supports 50K monthly tracked users (MTUs), while Heap's free tier caps at 10K sessions—a significant difference that affects which teams can meaningfully start for free. As volume grows, both platforms shift to paid plans, but cost curves diverge. Amplitude is generally more predictable for scaling teams, whereas Heap's paid plans tend to carry higher per-unit costs for smaller companies, making it less budget-friendly for early-stage startups operating on tight analytics budgets. Enterprise customers benefit from Amplitude's data governance and compliance features, which justify higher spend. For teams with modest analytics needs but deep experimentation demands, Amplitude offers better value; for teams needing instant insights from historical user behavior without instrumentation overhead, Heap's pricing may justify the spend despite higher absolute costs.

• Amplitude free tier: 50K MTUs; Heap free tier: 10K sessions—5x difference favoring Amplitude for free users
• Amplitude pricing scales with MTUs; predictable for mid-market teams with standard growth
• Heap pricing often higher per unit for smaller companies; better negotiated rates at enterprise scale
• Amplitude includes experimentation at all tiers; Heap focuses purely on analytics without built-in testing

Ease of Use & Onboarding

Amplitude requires users to instrument events before analytics become useful—a necessary but time-consuming step that creates friction for new analysts. The platform itself has a steeper learning curve than some competitors like Mixpanel, particularly for users unfamiliar with behavioral analytics concepts. However, once learned, Amplitude's interface supports deep, advanced analysis. Heap eliminates the instrumentation barrier by auto-capturing all interactions, meaning teams see data flowing in immediately. This dramatically reduces time-to-first-insight and appeals to non-technical users or teams wanting fast answers without setup overhead. The tradeoff: auto-capture generates more data volume, including noise that teams must learn to filter and interpret. For data engineers or product analysts with time and expertise, Amplitude's flexibility pays off; for busy PMs or stakeholders wanting answers fast, Heap's plug-and-play approach wins.

Integration & Ecosystem

Amplitude positions itself as a hub for product intelligence, with session replay and funnel analysis built natively and A/B testing integrated into the core platform. This tight integration reduces tool-switching friction for product teams managing experimentation and analytics in tandem. Heap emphasizes data connectors to external tools, making it easier to push analytics data to data warehouses, BI platforms, and data science environments. Neither platform appears to have critical gaps in standard integrations, but Amplitude's strength lies in self-contained workflows (analytics + experimentation), while Heap's strength lies in being a feeder to downstream analytics and ML infrastructure. Teams heavily invested in their own data stack or data science workflows may prefer Heap's connector-first philosophy; teams wanting consolidated analytics and testing under one roof will favor Amplitude.

Who Should Choose Amplitude?

Amplitude is the right choice for product teams at mid-market to enterprise scale who run frequent A/B tests and need analytics and experimentation tightly coupled. Product managers and growth teams building retention-focused products benefit from Compass and its predictive insights around engagement drivers. Organizations with compliance, data governance, or regulatory requirements—such as fintech or healthcare companies—should prioritize Amplitude's enterprise-grade controls. Teams comfortable with instrumentation and willing to invest upfront in event taxonomy get compounding returns from Amplitude's power and flexibility. Budget-conscious early-stage startups benefit most from Amplitude's generous 50K MTU free tier.

Who Should Choose Heap?

Heap is ideal for product teams that need immediate, retroactive insights without instrumentation overhead. Non-technical stakeholders (PMs, designers, marketers) who want self-service access to user behavior will appreciate the zero-setup auto-capture approach. Teams conducting frequent ad-hoc investigations into past user flows—where Heap's retroactive funnel capability shines—get outsized value. Companies with strong data science teams or existing data warehouses should consider Heap if they value seamless data connectors and the ability to push analytics into their own modeling pipelines. Heap is also a good fit for organizations frustrated with manual event instrumentation at previous analytics platforms and willing to trade some data volume management for faster insights and lower implementation friction.