Documents and notes decision

Heptabase

A competent developer can recreate a useful subset (notes, whiteboard, PDF parsing, LLM chat) in about a week and run it cheaply, but replicating Heptabase's full polished multi-platform product, integrations, and AI Tutor workflow is larger and would likely remain reason to keep the paid product.

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Subscription$8.99/month ✓ verified
Initial build30 hours
Monthly upkeep8 hours + $50
Evidence2/3 runs agree

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.

Code Heptabase publishes itself

Not a way out of the subscription — these are the vendor’s own repositories. Worth a look for how they build, and for anything you would have to integrate with.

What a replacement has to do

  • Ingest documents and web content, create and link notes/cards on a visual whiteboard, and ask an AI chat to summarize/explain selected sources.

What it still won’t have

  • Polished cross-platform native apps and installers (Mac/Windows/Linux/iOS/Android)
  • Heptabase-hosted AI Tutor workflows and curated learning sessions
  • Built-in premium model access/credit system and vendor-managed scaling
  • Integrations advertised (Readwise, Zotero, YouTube transcripts) and their convenience

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

Keep paying

Paying ischeaper in year one.

On cash alone, building overtakes the subscription at 7 seats.

Paid seatsseats

Money you would actually spend

Keep paying

Subscription price × seats × 12

Build it

AI build APIs + hosting

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

Not run yet
Build a minimal self-hosted Heptabase-like web app using: React (frontend), Node.js + Express (API), PostgreSQL (data), and an LLM API (OpenAI/Anthropic). In scope: user auth (single-user or basic accounts), upload and OCR/parse PDFs into text, create and position note cards on a zoomable whiteboard canvas, store bi-directional links between notes, a web-clipper endpoint to save page text+URL, and an AI chat endpoint that sends selected document snippets to an LLM and returns summaries. Out of scope: native desktop/mobile installers, multi-tenant billing, sophisticated AI Tutor sequencing, and third-party sync integrations. Include error handling for uploads and API failures, basic tests for parsing, DB migrations, and Docker-based deployment instructions.
How we checked4 sources · 2/3 runs agreed · evidence score 63

How the score was reached

  • Partly verdict base52
  • An open-source build was found+5
  • 4 cited sources+3
  • Price verified on pricing page+3
  • Evidence score63

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 · 4

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

✓ Price read off the page! 2 of 3 runs agreed; the verdict is the majority✓ Citations limited to fetched pages! 1 moat recorded