Lamin vs Rubicon-ML

LaminDB is Lamin's open-source, format-agnostic lakehouse and programmable memory layer for research and development. It tracks data, code and agent activity across notebooks, scripts and workflows, and provides schema-based records for LIMS and ELN use cases, including ontologies and notes. Users can validate and annotate datasets, then manage changes through branching and versioning data and code. Documented integrations include Claude Code, GitHub Copilot, Cursor, Git, Nextflow with Seqera, PyTorch Lightning, Weights & Biases, MLflow and Vitessce. Read/write storage includes local filesystems, AWS S3, S3-compatible services such as MinIO and Cloudflare R2, and Google Cloud Storage; HTTP/HTTPS and Hugging Face are read-only. Lamin provides web, API and self-hosted options. The Free plan is free for open assets and public database queries, with no hosted database listed. Pro is $20 a month billed annually. Team and Enterprise are each $200 a month billed annually per seat; Enterprise includes full on-prem deployment in the customer's AWS account. Lamin says it is SOC 2 certified and encrypts Lamin-managed deployments in transit and at rest. The maker was founded in 2022 and is headquartered in Munich and New York City.

Rubicon-ML is a free, open-source Python package for capturing model training and execution details. It records inputs, outputs, parameters, and outcomes in searchable projects and experiments, while Git integration can attach repository origin, branch, and commit information for auditability. Its dashboard and widgets let users explore, filter, visualize, compare, and share logged work. Multiple experiments can be logged in parallel, and asynchronous S3 communication keeps network reads and writes from blocking. Storage backends include local files, Amazon S3, in-memory storage, and Weights & Biases. Integrations are provided for Git, Prefect, and Scikit-learn; RubiconPipeline can log parameters and metrics when Scikit-learn pipelines are fit and scored. Installations use pip or conda-forge. Visualization is an optional pip extra, while conda installs optional extras by default. The dashboard is a Dash app that can be dockerized, and remote logging does not require a hosted server. It is licensed under Apache License 2.0.