Bauplan vs DagsHub

Bauplan is a data lakehouse for running Python and SQL pipelines directly on Apache Iceberg tables in a user's object storage, without managing servers or clusters. It uses serverless execution for Python pipelines and supports Python, SQL, and mixed workloads on the same tables. Data branches, isolated runs, atomic publishes, and rollback are intended to safeguard changes to production data. Bauplan also describes workflows where AI agents build pipelines, explore data, and manage a lakehouse through its MCP Server and Skills. Integrations listed include Airflow, Prefect, Dagster, Temporal, DBOS, Jupyter, Marimo, Streamlit, Snowflake, BigQuery, Metabase, Superset, Fivetran, and Airbyte. Bauplan says customer data stays in the customer's S3 bucket and offers PrivateLink or bring-your-own-cloud options. Its free Sandbox uses a shared environment where all data is public, which the company recommends for toy projects and demos rather than company data. Dedicated costs $700 a month after the first 14 days free and includes a dedicated private instance, data in the customer's S3, 10GB memory, and unlimited queries, users, and agents. Enterprise pricing is custom. Bauplan states that it is SOC 2 Type II compliant.

DagsHub helps developers and teams manage machine-learning projects, from data collection and dataset curation through annotation, experimentation, and model management. A project workspace can hold code, data, experiments, models, pipelines, and annotations together. The platform is designed for text, images, audio, video, documents, medical imaging, and binary files. Experiment tracking supports run comparison, metric and parameter visualization, real-time progress monitoring, and reproducibility. Data Engine provides curation, visualization, quality review, lineage, hard-example mining, labeling, auto-labeling, and data streaming. Official integrations include tools such as GitHub, Google Colab, DVC, MLflow, Jenkins, Label Studio, Google Drive, Weights & Biases, and Hugging Face; hosted projects also have support for the MLflow and Label Studio APIs. DagsHub hosts data but does not provide compute, so projects rely on local, cloud, or edge infrastructure. Enterprise installations can run on-premises, in a VPC, in air-gapped environments, and in OpenShift environments. The Individual plan is free, with stated private-repository limits; paid pricing starts at $99/user/mo billed annually, and a 14-day trial is available.