Lilac vs Hugging Face
A side-by-side look at scores, pricing and features — with RECATOOLS' ASEAN-aware verdict for each.
Lilac
Dataset curation for LLM training
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Hugging Face
The registry the open-source AI world runs on
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| RECATOOLS Score | 6.5 / 10 | 8.5 / 10 |
| Capability | — | |
| Value for money | — | |
| Ease of use | — | |
| ASEAN readiness | — | |
| API quality | — | |
| Pricing | Open Source | Freemium |
| Free tier | — | Unlimited public model/dataset access; 100GB private storage; limited daily ZeroGPU/inference credits |
| Paid from | — | PRO $9/mo/user; Team $20/mo/seat; Enterprise $50+/mo/user |
| Has API | ✗ | ✓ |
| Open source | ✓ | ✓ |
| Free to use | ✓ | ✓ |
| Users | — | ~13M monthly active users; 50,000+ organizations |
| Founded | 2023 | 2016 |
| Maker | — | Clément Delangue, Julien Chaumond, Thomas Wolf |
| Verdict | 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... |
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... |
| Full review → | Full review → |
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Comparisons cover up to 4 tools. Scores are RECATOOLS editorial assessments; verify current pricing on each vendor's site.