lift vs unstract
Datalab's 9B vision model that extracts JSON matching your schema from PDFs and images in one pass — schema-constrained decoding, multi-page values, vLLM or HF inference, Schema Studio. — versus — LLM-driven platform turning unstructured documents into structured data: a no-code Prompt Studio to define extractions, then deploy as APIs or ETL pipelines. Self-hosted, AGPL + enterprise.
Both turn unstructured documents into structured data; Unstract is a no-code platform deploying extractions as APIs/ETL, lift a single local model and CLI.
| lift | unstract | |
|---|---|---|
| Stars | 1.2k | 7.3k |
| Forks | 110 | 718 |
| Language | Python | Python |
| License | Apache-2.0 | AGPL-3.0 |
| Last activity | 6 days ago | 3 days ago |
| Topics | ocr, data | ocr, rag |
| Curated connections | 4 | 7 |
lift — the curator's take
Use it when you need fields, not Markdown — invoices, contracts, filings into a fixed JSON schema — locally: 90% field accuracy on its own 225-doc benchmark at ~3x Gemini Flash's speed on one GPU. Read the license first: commercial self-hosting needs a paid Datalab license. Keep schemas simple (no enum, oneOf or $ref, or the decoder guarantee weakens), and expect whole-document perfection only ~1 time in 5.
unstract — the curator's take
The platform play in document extraction: where parsers hand you markdown, Unstract hands you the FIELDS — Prompt Studio lets a domain expert define and test per-document-type extraction prompts, then ships them as stable APIs or ETL pipelines into warehouses, with the connector surface (S3, databases, DMS) that ops teams actually need. Docker-compose up and it runs. NOT a parsing engine: quality rides on the LLM you wire in and per-page LLM calls at volume cost real money — for clean-layout bulk conversion a deterministic parser is cheaper; and mind the AGPL + enterprise split when embedding.