Open-source data lakehouse for biology with lineage tracking and bio-format support.
By Tanmay Verma, Founder · Last verified 06 Jul 2026
In short
Lamin — Open-source data lakehouse for biology with lineage tracking and bio-format support. Best for Computational biology researchers managing large-scale multi-omics datasets, ML engineers building foundation models on biological data, Biotech R&D teams requiring reproducible data workflows. Free to start; paid plans from $30/mo.
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LaminDB is the most lineage-native data lakehouse for biology. Its open-source core and bio-format support make it ideal for computational biologists who need reproducibility across multi-omics datasets. But the Pro/Team hosted tiers can get pricey for small labs without institutional backing.
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Last verified: July 2026
Across the latest 10 updates: 5 feature updates and 5 changelog entries.
Hub 1.45.0 improves branch page with objects, comments, sidebar for status; provenance header; minimal page headers; lineage graph shows records; project-aware sheet default names.
hub 1.44.2 fixes sheets not staying editable with union dtype columns.
db 2.7.0 introduces schema.index support, schema transfer with related objects, .notes attribute, Feature.is_null(), RecordSet export, improved CLI speed, 2x faster Record.from_dataframe(), numerous bug fixes.
nf 0.8.3 adds max_workers setting, improved permission errors, upsert behaviour, exponential backoff with jitter, API refactor, stress testing workflow.
hub 1.44.0 adds Excel support to sheet imports, implements schema index feature handling in CSV editor, hides sheet index for sheets without one.
LaminDB mirrors Arc Virtual Cell Atlas (2.5B expression profiles, 600M cells) offering entity queries, GUI, zero-copy sharing.
PerturBench (NeurIPS 2025) tasks re-run with lineage tracking; datasets equivalence shown.
Guest post on managing single-cell RNA-seq data for foundation model building using LaminDB.
Native SpatialData support in LaminDB for cross-dataset queries, validation, lineage tracking.
LaminR package for traceability and reproducibility of data analyses in R, illustrated with single-cell example.
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
44 mentions across 3 sources (Bluesky, GitHub, Lemmy).
How likely is Lamin to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →LaminDB is an open-source data framework purpose-built to solve data management challenges in biology. It provides a unified query interface across diverse storage formats (Parquet, Zarr, AnnData, SpatialData) and databases (Postgres, SQLite), enabling researchers to trace, validate, and query millions of datasets. Designed for computational biology teams, LaminDB reduces data lineage tracking to a single line of code, supports bio-registries and ontologies, and offers LIMS/ELN capabilities. It is lineage-native, zero-lock-in, and scales from personal projects to enterprise deployments. Key features include automatic lineage capture for notebooks, scripts, pipelines, and functions; schema management and validation for biological datasets; git-like branching and merging for change management; and zero-copy data sharing across databases and storage. LaminDB integrates with Nextflow, Redun, Snakemake, Weights & Biases, MLFlow, and Vitessce, and supports local, S3, GCP, Azure, and R2 storage backends. The platform's hosted service, LaminHub, is in beta and offers free querying of public databases like the Arc Virtual Cell Atlas. Recent updates include faster Record.from_dataframe() in db 2.7.0, Excel support in sheet imports, and a database mirror for 600M-cell scRNA-seq data. LaminDB's open-source core and flexible storage options position it as a strong alternative to proprietary data management platforms for life sciences. Pricing follows a freemium model: Free (open-source core), Pro ($30/month), Team ($640+/month), and Enterprise (custom on-prem). Academic discounts are available. LaminDB best suits teams that prioritize reproducible research and need to manage heterogeneous biological data at scale.
LaminDB nails data lineage for biology — it's not just a lakehouse, it's built from the ground up to track every transformation on multi-omics data. We'd reach for this when reproducibility is non-negotiable: think scRNA-seq foundation model training or large-scale spatial omics projects. The git-like branching and zero-copy sharing are genuinely useful for collaborative research. That said, the free open-source tier is surprisingly capable — anyone can pip install and get lineage tracking immediately. The hosted LaminHub Pro tier adds convenience but at $30/month plus storage egress costs that can add up. For a five-person lab, the Team tier at $640/month might be overkill unless you need SOC2 and SSO. Compared to alternatives like Benchling or Databricks, LaminDB offers more bio-specific schema validation and ontology support, but less hand-holding on the UI side. If your lab runs on R, the new LaminR package (April 2026) closes a major gap. Where it bites: non-biology domains get no native support, and the hosted tiers are not cheap for cash-strapped academic groups. Still, for serious computational biology, it's hard to beat.
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Full product docs from lamin.ai
Step-by-step walkthrough from lamin.ai
Full product docs from lamin.ai
Full product docs from lamin.ai
Full product docs from lamin.ai
Full product docs from lamin.ai
Full product docs from lamin.ai
Full product docs from lamin.ai
Full product docs from lamin.ai
Full product docs from lamin.ai
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