Diffbot vs fastCRW: CV Extraction or LLM JSON

By the fastCRW team · Comparison/pricing verified 2026-05-18 · fastCRW launch pricing expires 2026-06-01 · Verify independently before buying.

Disclosure: we build fastCRW. This is a vendor-authored comparison, so weigh it accordingly — but we have kept the section on where Diffbot genuinely wins explicit, because a comparison that pretends the other tool has no advantages is useless to you.

Diffbot vs fastCRW at a glance

The core of Diffbot vs fastCRW is two different philosophies of turning a web page into structured data. Diffbot runs pre-trained computer-vision and ML models that look at a rendered page, classify it as an article, product, discussion, or other known type, and emit a fixed schema for that type — then feeds the result into its Knowledge Graph. fastCRW takes the opposite route: you hand it the exact JSON schema you want, and an LLM fills that schema from the scraped page. One gives you turnkey extractors and a graph; the other gives you a blank schema you define yourself.

Two extraction philosophies

Diffbot's bet is that most of the web falls into a small number of recognizable page types, so a model trained on millions of those pages can extract them without you writing any rules or schema. You call the Article API or the Product API, point it at a URL, and get back a consistent object — title, author, date, price, images — that Diffbot's vision models inferred from the rendered layout. The output shape is decided by Diffbot, not by you.

fastCRW inverts that. There is no library of pre-trained extractors; instead you send a request to /v1/scrape with formats: ["json"] and a jsonSchema describing the fields you want, and an LLM populates them from the scraped content. If you want a product's name, price, SKU, and three custom attributes that no generic extractor knows about, you simply put them in the schema. The trade is real: you do the schema design, but you are never constrained to a vendor's idea of what an "article" or "product" contains.

This maps cleanly onto when each tool is the right call. When your targets are mainstream page types and you want zero schema work, Diffbot's classifiers do a lot for free. When your fields are bespoke, span page types Diffbot does not model, or change between sources, a schema you control is the more flexible primitive.

Where Diffbot genuinely wins

Two advantages are real and we will not hand-wave them:

• Pre-built extractors at scale. Diffbot's page-type models are the product of years of training data. For high-volume extraction of standard article and product pages, you get structured output with no schema authoring and no per-page LLM cost — that is a genuine operational and cost advantage at scale.
• The Knowledge Graph. Diffbot does not just extract pages; it links entities (companies, people, products) into a queryable graph spanning much of the public web. fastCRW has no equivalent — there is no graph product, no entity-linking layer. If your use case is "give me the graph of companies and their relationships," that is squarely Diffbot's turf, not ours.

Where fastCRW wins

• You define exactly the JSON shape you want. A jsonSchema on /v1/scrape extracts precisely the fields you ask for — no remapping from a fixed vendor schema, no fields you do not need, no missing fields a generic extractor never modeled.
• Highest truth-recall in a 3-way scrape benchmark. On Firecrawl's own public dataset, fastCRW recovered correct content from 63.74% of 819 labeled URLs — the highest of the three engines tested (diagnose_3way.py, single run, 2026-05-08), ahead of Crawl4AI (59.95%) and Firecrawl (56.04%). It also posted a median latency of 1914 ms, beating Firecrawl's 2305 ms. We disclose the tail honestly: fastCRW's p90 was 14157 ms — the worst of the three — because the chrome-stealth fallback that recovers pages the others miss is the same mechanism that produces a slow tail. See the full p50/p90/p99 split on /benchmarks.
• Self-host the engine free under AGPL-3.0. fastCRW is a single static Rust binary you can run yourself; self-hosting costs $0 per 1,000 scrapes (you pay only your own server). Diffbot is cloud-only, so your URLs and extracted content always leave your network.
• One Firecrawl-compatible engine. The same engine does scrape, crawl, map, and search behind a Firecrawl-compatible REST surface — drop-in after a base-URL swap — so extraction is not a separate product bolted on.

Cost and credits

fastCRW prices a plain scrape at 1 credit, but any request that returns structured JSON — formats: ["json"], the extraction path — costs 5 credits. That is the number to budget against for Diffbot-style extraction: 5 credits per extracted page, plus your own LLM provider cost if you are using your own key path. The free tier grants 500 one-time lifetime credits to prototype on, and paid plans scale from there; rather than reprint a table that can drift, see live numbers on /pricing.

The economics that genuinely differ from a cloud-only tool show up at the floor, not the per-call price. Because fastCRW is AGPL-3.0 and self-hostable, the worst-case cost of high-volume extraction has a hard ceiling — your server bill — at $0 per 1,000 scrapes for the engine itself. Diffbot's cloud-only model has no such floor; you pay its metered rate for every page, forever.

Honest limits

Two fastCRW constraints matter directly for an extraction comparison, and we state them plainly so you are not surprised in production:

• LLM extraction is OpenAI/Anthropic only. fastCRW's JSON extraction calls out to OpenAI or Anthropic for the model step. If your stack standardizes on a different provider for extraction, that is a real constraint today. (The managed /v1/search answer mode defaults to DeepSeek, but that is search synthesis, not page extraction.)
• Single-URL extraction. The extract path is single-URL — there is no multi-URL batched /v1/extract endpoint like Firecrawl Cloud's. For many pages you iterate /v1/scrape concurrently or run a /v1/crawl job. Diffbot's Crawlbot is built for bulk by design, so for very large turnkey crawls that feed a graph, that is a point in Diffbot's favor.

Which to choose

Pick Diffbot when you want turnkey computer-vision extraction of standard page types with no schema work, or when you specifically need the Knowledge Graph and its entity linking. Those are real strengths fastCRW does not replicate.

Pick fastCRW when you need to define a custom JSON schema for fields a generic extractor will not model, when you want the highest-recall scrape engine in our 3-way benchmark feeding that extraction, when a predictable 5-credit-per-extract cost beats opaque metering, or when self-hosting under AGPL-3.0 — so URLs and content never leave your infrastructure — is a requirement rather than a nice-to-have. If you are already on Diffbot for extraction but want to bring crawling and scraping in-house, fastCRW's Firecrawl-compatible surface is the easiest landing point. For a deeper look at defining schemas, see our guide to structured JSON-schema extraction with fastCRW and the Firecrawl extract endpoint deep dive.

Sources

• fastCRW canonical facts: scrape benchmark (diagnose_3way.py, 819 labeled URLs, 2026-05-08), credit costs, honest gaps — github.com/us/crw
• see plan pricing and benchmarks: /pricing · /benchmarks
• Diffbot product and Knowledge Graph: diffbot.com (verified 2026-05-18)

Related: Diffbot alternative · Structured JSON-schema extraction · Best web scraping APIs