GrowthBook vs Heap: Which Is Better in 2026?

GrowthBook is an open-source feature flagging and A/B testing platform that lets teams run experiments and manage feature rollouts using their existing data warehouse, rather than relying on a separate analytics silo. This warehouse-native, open-source approach is its defining feature: GrowthBook connects to the data you already have (in warehouses like Snowflake, BigQuery and others), so your experiment results are based on your real, trusted metrics, and you maintain control and ownership over your data and infrastructure.

The platform provides feature flags to roll out and control features safely — gradual releases, targeting and instant rollbacks — alongside a powerful experimentation engine for running A/B tests and analyzing their statistical results. Because it queries your data warehouse for results, you can measure the impact of experiments on the metrics that matter to your business using data you trust, without duplicating tracking or depending on a vendor's numbers. Being open source, GrowthBook can be self-hosted for full control, privacy and cost efficiency, with a managed cloud option available, and it benefits from transparency and community contributions.

GrowthBook is popular with product, engineering and data teams that want a rigorous, data-driven approach to experimentation and feature management while keeping their data in their own warehouse and avoiding lock-in. Its combination of feature flags, warehouse-native experimentation and open source appeals especially to data-mature teams and those who value ownership and flexibility. As experimentation becomes a core practice for building better products and companies increasingly centralize their data in warehouses, warehouse-native, open-source experimentation tools are a natural fit. For teams that want to manage feature flags and run trustworthy A/B tests using their existing data warehouse — with the freedom of open source — GrowthBook offers a capable, transparent and well-designed solution.

Heap is a product analytics platform distinguished by its autocapture approach, which automatically records every user interaction on your website or app without requiring you to manually define and track each event in advance. Traditional analytics force teams to decide upfront what to measure and write code for each event, which means you can only analyse what you thought to track — and you're stuck if you later want to study something you didn't anticipate. Heap solves this by capturing everything automatically, so the data is always there when a new question arises.

This autocapture foundation gives teams remarkable flexibility. Because every click, page view, and interaction is recorded, analysts and product managers can explore user behaviour retroactively, defining and analysing events after the fact rather than predicting their needs months earlier. On top of this complete dataset, Heap provides the analytics teams expect — funnels, retention, user paths, and segmentation — plus features that help surface insights and identify opportunities and friction in the user experience. The result is faster, more complete answers to questions about how people actually use a product.

Heap is used by product, growth, and data teams that want thorough behavioural insight without the limitations and ongoing engineering effort of manual event tracking. The value is completeness and agility: because the data is captured automatically and comprehensively, teams can answer new questions immediately instead of instrumenting tracking and waiting weeks to collect data. This is especially valuable for fast-moving teams that constantly explore new hypotheses about user behaviour. For organisations that want to deeply understand their users and iterate quickly on their products without being constrained by what they remembered to track, Heap's autocapture approach offers a powerful and flexible foundation for product analytics.