FEVER vs Unstructured
Unstructured gives you a document intelligence system, bring your own storage. FEVER does that and more, all-in-one.
| FEVER | Unstructured | |
|---|---|---|
| What it is | Self-hosted multimodal media database: ingest, embed, dedupe, and search in one API | ETL pipeline that parses and chunks documents, then loads them into your own store |
| Scope | Images, video, audio, documents, and more - media-native | Document-centric (PDFs, Office files, email, HTML) |
| Embeddings | Built-in ultra-fast AI - you send media, FEVER deals with it | Bring your own embedding models and evaluate them too |
| Storage & search | All inclusive - vector database, hybrid search, easy filters | You assemble and operate the storage layer yourself |
| Dedupe | Near-duplicate detection and clustering on ingest | Not included - you build it downstream |
| Media enrichment | OCR, transcripts, metadata extraction, synthetic scoring and more included | OCR and layout parsing for documents; media enrichment is out of scope |
| Ops burden | One appliance to run; the ease of Postgres at the core | You own the pipeline workers plus the database and search stack |
| Cost model | License + Bring Your Own Compute | Usage-based platform + the database and compute you still have to build |
Choose Unstructured when…
- You are building a retrieval pipeline over complex documents and already have a vector database and embedding strategy.
- You need deeper document parsing - tables, layout, OCR - as a preprocessing step that hands off to another store.
Choose FEVER when…
- You want the database, embeddings, enrichment, and search all-in-one without yet another set of components to assemble.
- Your corpus is multimodal media and you need it all - images, video, audio, documents.
- You need dedupe, compliance scoring, and natural language search running in your own cloud with no data egress.