AI assistants and search decision
Diffbot
A single engineer can assemble a useful page-extraction + NLP + search pipeline from open-source components in a few months, but Diffbot’s value rests on its massive prebuilt Knowledge Graph and production-scale crawling infra which are costly to replicate, so replacing the full product is impractical.
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 Diffbot alternatives, with the arithmetic →
What a replacement has to do
- Crawl pages, fetch and render HTML, extract structured fields (title, product, article, images), run NLP/entity extraction, index results for search/query.
What it still won’t have
- Access to Diffbot’s massive pre-built Knowledge Graph of the public web
- High-volume, low-latency, production-hardened crawling and processing infrastructure
- Out-of-the-box credit-based metering, rate-limits, and enterprise support
What remains hard
- Proprietary data
Search the largest structured database of the public web — billions of people, organizations, products, and articles — like one big table.
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 2 seats.
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 self-hosted web-extraction and lightweight KG pipeline using Python/Node, Postgres (or Elasticsearch) and a vector DB (Milvus or Weaviate). Include: (1) a polite, rate-limited crawler with robots.txt support; (2) headless rendering via Playwright to retrieve rendered HTML and screenshots; (3) an HTML/content extractor that outputs a normalized schema (article/product/image fields); (4) an NLP step using spaCy/transformers for NER and simple relation/ triplet extraction; (5) indexing in Postgres + vector store and a small REST API to run searches and export JSON. Out of scope: building or matching Diffbot’s multi-billion-entity global Knowledge Graph and large-scale distributed shards. Deliverables: runnable Docker Compose, setup script, error handling, instrumentation (logging/alerts), and unit + integration tests for crawler, extractor, and NLP pipeline.
How we checked
How the score was reached
- Pay verdict base20
- An open-source build was found+5
- 5 cited sources+3
- Price verified on pricing page+3
- 3/3 assessment runs agreed+4
- Hard moats found in the evidence-6
- Evidence score29
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 productDiffbot (home)
- official docsDiffbot Docs - Introduction
- official pricingDiffbot Pricing
- open sourcegetmaxun/maxun
- open sourcefirecrawl/firecrawl
Integrity checks
What held up, and what did not.
