Analytics and monitoring decision
Heap
A capable engineer can build a useful autocapture + storage + dashboard replacement of Heap’s core analytics features over several weeks, but reproducing Heap’s scale, built-in AI insights, session-replay at production scale, integrations, and enterprise support would be costly—so keep paying for those capabilities unless you only need a narrow analytics workflow.
Visit website↗Open-source builds that already do this
Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All Heap alternatives, with the arithmetic →
What a replacement has to do
- Collect user interactions via a small web snippet; store event streams; run queries to build funnels/cohorts; render dashboards and charts; basic session-recording playback (optional).
What it still won’t have
- Scale and reliability of Heap’s managed infrastructure
- Automatically captured, retroactive data processing at enterprise scale
- Built-in advanced data science / Illuminate insights and AI assistant
- Session Replay and heatmaps at production scale
- Hundreds of pre-built integrations and vendor support/SLAs
What remains hard
- Brand trust
Heap is used by over 10,000 companies to understand customers’ end-to-end journeys, improve conversion and activation, increase retention, and deliver a great user experience.
- Infrastructure at scale
Infrastructure How we build for scale
First-year cost
No published price
Heap does not publish a price we could read, so there is nothing to compare against. What building costs is below.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal product-analytics service using Node.js + Express for ingestion, ClickHouse for event storage and fast aggregates, React for a single-page dashboard, and S3 for session snapshot storage. In scope: (1) a lightweight autocapture JS snippet that records pageviews, clicks, and form events and posts to /ingest; (2) an ingestion service that validates events, writes raw events to S3 and streams to ClickHouse; (3) scheduled materialized queries for funnels and cohort counts and a simple REST API to return query results; (4) a React UI for creating a funnel, viewing a time-series chart, and saving a dashboard; (5) an optional session-replay recorder that stores DOM snapshots to S3 and a player that replays events against snapshots. Out of scope: enterprise multi-region scale, advanced AI insights, 100+ third-party integrations, and dedicated data-governance features. Include error handling, retries on ingestion failures, basic auth for the UI, and automated tests for ingestion, query API, and UI flows.
How we checked
How the score was reached
- Partly verdict base52
- An open-source build was found+5
- 5 cited sources+3
- 3/3 assessment runs agreed+4
- Hard moats found in the evidence-3
- Evidence score61
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.
How scoring works →Cited sources · 5
Every page the run actually retrieved.
- official productHeap - Better Insights. Faster.
- official pricingPricing | Heap
- official docsWhy Product Analytics - Heap
- open sourcePostHog/posthog
- open sourcematomo-org/matomo
Integrity checks
What held up, and what did not.
