Evidence vs Heap: Which Is Better in 2026?

Evidence is an open-source, code-based business intelligence tool that lets data teams build polished reports, dashboards and data products using just SQL and Markdown. It takes a refreshingly different approach from traditional drag-and-drop BI: instead of clicking through a GUI to build charts, you write SQL queries and Markdown, and Evidence renders beautiful, interactive data reports — bringing the benefits of software engineering, like version control, code review and reproducibility, to analytics and reporting.

The workflow is elegant for technical analysts. You write SQL to pull and shape your data, then write Markdown with simple components to lay out narrative text, charts, tables and interactive elements. Evidence turns this into a clean, professional web report or data app that updates as your data does. Because everything is code, your reports live in a git repository — they can be reviewed, versioned, tested and collaborated on like any software project, which brings rigor and maintainability that point-and-click BI tools struggle to provide.

This code-first philosophy resonates strongly with the modern, engineering-minded data community. It's ideal for building things like recurring reports, internal data products, metrics narratives and customer-facing analytics, where you want polish, consistency and the ability to manage reports as code. As open source, Evidence can be self-hosted and customized freely, avoiding lock-in, with options for easier hosting too. It fits naturally alongside tools like dbt in the modern data stack. For data analysts and engineers who are comfortable with SQL and want to build elegant, maintainable, version-controlled reports and dashboards — and who find traditional BI tools clunky or hard to govern — Evidence offers a powerful, developer-friendly and genuinely modern approach to turning data into clear, shareable insight.

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.