Lamin vs MLflow GenAI Evaluation

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.

MLflow GenAI Evaluation is a free, open-source API and UI for measuring, improving, and monitoring LLM applications and AI agents from development through production. Evaluation Datasets centralize test cases, expected answers, and evaluation data. Teams can collect feedback from end users and domain experts, attach it to traces, and retain metadata. Scoring includes LLM-as-a-Judge, custom LLM judges, and code-based scorers; automatic evaluation supports LLM judges but not code-based scorers. Results appear in the MLflow UI with per-record details and comparisons between agent versions. MLflow Tracing records production metrics such as latency and token usage. Integrations are listed for more than 40 LLM and agent libraries and frameworks, including OpenTelemetry, LangChain, OpenAI, and Anthropic. Judge adapters include Anthropic, Bedrock, Google, and xAI. API, self-hosted, and web options are available, with Python SDK support, prompt history and testing, regression runs, safety evaluations, tool-call checks, deployment controls, and optional basic HTTP authentication.