Diffbot extracts pages with ML.
Everfetch gives you stable APIs.
Both turn websites into structured data for AI agents. The difference is whether a model infers fields from a page it has never seen, or you call a fixed schema for a site you use again and again.
By Everfetch · Published June 22, 2026 · Updated August 12, 2026
Methodology: Reviewed against current Diffbot documentation and the Everfetch catalog model.
Everfetch vs Diffbot, the honest version.
| Capability | Everfetch | ML extraction |
|---|---|---|
| Primary output | Stable site APIs that return JSON | ML-inferred fields from one page, or Knowledge Graph entities |
| Built for | Repeated structured data from known sites | Extracting from arbitrary pages, plus an org-level knowledge graph |
| Web-scale knowledge graph of entities | ||
| Pre-built site coverage | 1,900+ shared site schemas | No site catalog, ML model infers fields per page type |
| Fixed field names per site, every call | ||
| Open source | Catalog, CLI, and API under AGPL v3 | No, proprietary ML platform |
| Free start | No signup playground | Free plan, 10,000 credits per month |
In practice
Compare the output, not only the request.
Diffbot · ML extraction
Infer structured fields from a page type — article, product, discussion — without site-specific rules.
Everfetch · Source interface
Call a supported site workflow with declared parameters and structured fields.
Everfetch and Diffbot, answered.
Built for the sites you call again and again.
If you need ML extraction from an unknown page, or an org-level knowledge graph, use Diffbot. If your agent depends on the same structured site data over time, give it a stable API.