Lamin

Lamin's pitch is narrow and it knows it: traceability and governance for multimodal biological data. LaminDB traces results across agent sessions, notebooks, scripts, workflows, shell sessions, and now Cursor IDE sessions and multi-dev directories per compute environment (2.11.0). The lakehouse generalizes core guarantees you'd expect from Iceberg or Delta — ACID transactions, snapshot isolation, time travel, schema evolution — to non-tabular formats like parquet, zarr, AnnData, and SpatialData, while letting you query with Polars, DuckDB, or your own data loaders. That decoupling is the point: you run your compute engine, Lamin governs the data. Strengths that show up in practice: zero-copy data sharing across databases and storage; git-style branching and Change Requests that let an agent's contribution be reviewed before it merges; co-versioning of data and code so a published analysis can be retraced; FAIR validation via schemas with one-line annotation for files, DataFrames, AnnData, and SpatialData; LIMS and ELN records management with ontologies, notes, and registries; and fine-grained permissions and audit logs covering humans and agents — relevant if you're working toward GxP (the docs cite 21 CFR Part 11 and EU Annex 11). Recent benchmarks show the ecosystem is keeping pace with data size: annbatch reaches 60k samples/second on terabyte-scale anndata, and Lamin published agentic variant analysis of 1000 Genomes using Polars and DuckDB over 100M+ variants. Weaknesses are structural, not bugs. LaminDB is a data management layer with no underlying AI model of its own, so it won't do your modeling for you. It requires Python or R proficiency — pip install lamindb or install.packages('laminr') — and it deliberately offers direct pydata/R access with no REST API in the path, which means there is no managed-warehouse experience for analysts who don't code. Self-hosting has a real operational surface: you administer databases and storage at the SQLite/Postgres and S3/GCP/Azure/R2 level. Team and Enterprise are $200 per seat per month billed annually, a per-seat commitment small labs should not take on before collaboration hurts. Enterprise full on-prem deployment is delivered in your own AWS account. Where it fits: computational biology teams doing single-cell, spatial, imaging, or perturbation work that need provenance; ML engineers training foundation or perturbation models on multimodal omics; biopharma R&D groups needing FAIR datasets and schema validation across many experiments; academic labs and public-data initiatives that qualify for reduced pricing. Where it doesn't: teams outside biology that don't need ontologies or biological format support; groups who want a fully managed warehouse they can run without writing Python or R; anyone shopping for simple file sync or storage-only tooling.