Hugging Face vs Lilac vs Unstructured vs Weights & Biases
A side-by-side look at scores, pricing and features — with RECATOOLS' ASEAN-aware verdict for each.
Hugging Face
The registry the open-source AI world runs on
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Lilac
Dataset curation for LLM training
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Unstructured
Open-source ETL for turning messy documents into LLM-ready data
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Weights & Biases
The near-default MLOps platform, now with CoreWeave inference
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| RECATOOLS Score | 8.5 / 10 | 6.5 / 10 | 7.4 / 10 | 8 / 10 |
| Capability | — | — | ||
| Value for money | — | — | ||
| Ease of use | — | — | ||
| ASEAN readiness | — | — | ||
| API quality | — | — | ||
| Pricing | Freemium | Open Source | Open Source | Freemium |
| Free tier | Unlimited public model/dataset access; 100GB private storage; limited daily ZeroGPU/inference credits | — | — | Free 'Basics' tier: ~5 model seats, 5GB storage, 1GB/mo Weave ingestion |
| Paid from | PRO $9/mo/user; Team $20/mo/seat; Enterprise $50+/mo/user | — | — | Pro from $60/mo (teams <50); usage billed for storage/Weave/inference |
| Has API | ✓ | ✗ | ✓ | ✓ |
| Open source | ✓ | ✓ | ✓ | ✗ |
| Free to use | ✓ | ✓ | ✓ | ✓ |
| Users | ~13M monthly active users; 50,000+ organizations | — | — | 1,400+ organizations (incl. AstraZeneca, NVIDIA) |
| Founded | 2016 | 2023 | 2022 | 2017 |
| Maker | Clément Delangue, Julien Chaumond, Thomas Wolf | — | — | CoreWeave (acquired 2025) |
| Verdict | The open-source AI world runs on this. If a model is public — Llama, Qwen, Whisper, a fine-tuned diffusion checkpoint — it almost certainly lives on the Hub, and pulling it down is a two-line call in the transformers lib... |
Lilac is an open-source tool for exploring, clustering, searching and cleaning unstructured text datasets — useful for LLM evaluation and for preparing data for RAG, fine-tuning and pre-training. Built by ex-Google engin... |
Unstructured has become close to a default choice for the messy first step in RAG: turning PDFs, Word docs, HTML, and scanned images into clean, chunked text before embedding. The open-source Python library (Unstructured... |
W&B is close to default infrastructure for ML teams — its experiment-tracking SDK is the sticky part, logging runs, metrics, and artifacts with a few lines of code. Weave extends that to LLM and agent observability w... |
| Full review → | Full review → | Full review → | Full review → |
Comparisons cover up to 4 tools. Scores are RECATOOLS editorial assessments; verify current pricing on each vendor's site.