Apache Paimon vs Lance
Apache Paimon
paimon.apache.orgLance
lance.orgSide by side
| Figure | Apache Paimon | Lance |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | — |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Data Version Control Tools | 9.0 (#2) | 7.8 (#22) |
| Data management | Paimon supports ACID transactions, time travel, schema evolution, and metadata for petabyte-scale datasets and many partitions. | The table format supports ACID transactions, schema evolution, time travel, and incremental updates through MVCC. |
| Multimodal data | The project lists blob storage, vector storage, full-text search, and global indexing capabilities. | Lance stores images, videos, audio, text, and embeddings alongside tabular data in one format. |
| Purpose | Apache Paimon is a lake format for unified batch, streaming, and multimodal AI workloads. | Lance is an open lakehouse format for multimodal AI, built for data on object storage. |
| Security reporting | The security page asks users to report vulnerabilities privately to the Apache Security Team at [email protected] before public disclosure. | The community page asks users to report security vulnerabilities privately to the PMC mailing list and avoid public disclosure until the issue is resolved. |
| Storage | Documented filesystem options include local files, HDFS, Aliyun OSS, S3, Tencent COS, Azure Storage, Huawei OBS, and Google Cloud Storage. | Lance can commit directly to object storage, and its Directory Catalog supports deployments directly on object stores without additional infrastructure. |
| Support | The project directs users to its user mailing list and GitHub issue tracker for help and issue reporting. | The contributor guide directs users with questions to the Lance Discord for real-time support. |
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.