Lilac vs Weights & Biases
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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Weights & Biases
The near-default MLOps platform, now with CoreWeave inference
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| RECATOOLS Score | 6.5 / 10 | 8 / 10 |
| Capability | — | |
| Value for money | — | |
| Ease of use | — | |
| ASEAN readiness | — | |
| API quality | — | |
| Pricing | Open Source | Freemium |
| Free tier | — | Free 'Basics' tier: ~5 model seats, 5GB storage, 1GB/mo Weave ingestion |
| Paid from | — | Pro from $60/mo (teams <50); usage billed for storage/Weave/inference |
| Has API | ✗ | ✓ |
| Open source | ✓ | ✗ |
| Free to use | ✓ | ✓ |
| Users | — | 1,400+ organizations (incl. AstraZeneca, NVIDIA) |
| Founded | 2023 | 2017 |
| Maker | — | CoreWeave (acquired 2025) |
| 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... |
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 → |
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Comparisons cover up to 4 tools. Scores are RECATOOLS editorial assessments; verify current pricing on each vendor's site.