Apache Paimon vs Bauplan
Apache Paimon
paimon.apache.orgBauplan
bauplanlabs.comSide by side
| Figure | Apache Paimon | Bauplan |
|---|---|---|
| Price from | Free | $700/mo |
| Free plan | Yes | Yes |
| Free trial | — | Yes |
| Open source | — | — |
| Web | — | Yes |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | — |
| Plans published | 1 | 3 |
| Spec score · Data Version Control Tools | 9.0 (#2) | 8.4 (#10) |
| Support | The project directs users to its user mailing list and GitHub issue tracker for help and issue reporting. | The Sandbox includes community support on Slack, Dedicated has private Slack support, and Enterprise includes dedicated customer support and a forward deployed engineer. |
Plans
In their own words
Apache Paimon
Apache Paimon is a lake format for realtime lakehouse architectures, supporting streaming and batch operations with Flink and Spark. Primary-key tables handle large-scale streaming updates through configurable merge engines, including deduplication, partial updates, aggregation, and first-row updates. Append tables support batch and streaming processing, small-file merging, and compaction with Z-order sorting. Paimon also provides ACID transactions, time travel, schema evolution, and metadata for petabyte-scale datasets and many partitions. Listed integrations include Flink, Spark, Hive, Trino, Presto, StarRocks, and Doris, though supported operations vary by engine and version. Documented CDC sources include MySQL, PostgreSQL, Kafka, MongoDB, and Pulsar. Other capabilities include blob and vector storage, full-text search, and global indexing. PyPaimon provides catalog, table, Arrow, and pandas APIs plus a command-line tool; core reads and writes do not require a JVM or a running Flink or Spark cluster. It is free at free, with users choosing connectors and storage plugins for their environment.
Bauplan
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.