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Analytics & BI

Heap vs Pendo

Which analytics & bi tool is right for you? Compare features, pricing, and user reviews to make the best choice.

H

Heap

4.0100 reviews

Product, growth, and analytics teams that want fast, low-instrumentation behavioral analytics for web and mobile apps, combining quantitative product analytics with session replay and strong data management.

Starting at Quote-based
P

Pendo

4.0100 reviews

Product teams (PMs, growth, UX, and customer success) at SaaS and digital product companies that need product usage analytics plus in-app guidance and feedback collection in one platform.

Starting at Quote-based

Side-by-Side Comparison

FeatureHeapPendo
PricingQuote-basedQuote-based
G2 Rating4.0 (100 reviews)4.0 (100 reviews)
Capterra Rating4.04.0
Best ForProduct, growth, and analytics teams that want fast, low-instrumentation behavioral analytics for web and mobile apps, combining quantitative product analytics with session replay and strong data management.Product teams (PMs, growth, UX, and customer success) at SaaS and digital product companies that need product usage analytics plus in-app guidance and feedback collection in one platform.

Pros & Cons

Heap

Pros

  • + Autocapture reduces engineering effort and speeds up analysis
  • + Powerful funnel/journey analysis for product and growth use cases
  • + Session replay helps diagnose UX issues and validate hypotheses
  • + Broad integration ecosystem for warehouses, CDPs, and BI tools

Cons

  • Pricing is quote-based and can be difficult to estimate upfront
  • Autocapture can create noisy datasets without strong governance
  • Advanced setups (mobile, complex SPAs, governance) may require significant configuration

Pendo

Pros

  • + Combines analytics + in-app guidance + feedback, reducing tool sprawl
  • + Strong segmentation and targeting for contextual in-app experiences
  • + No/low-code guide builder enables fast iteration without engineering
  • + Good visibility into feature adoption and user behavior for prioritization

Cons

  • Pricing is custom and can be expensive at scale (MAU-based)
  • Implementation and data governance (tagging, event strategy) can be complex
  • Some advanced analysis and reporting may require additional setup or higher-tier plans