CTGAN vs REaLTabFormer
No clear leader: CTGAN (38.3) and REaLTabFormer (36.0) are within the 5-point margin; treat as a tie. The attribute-by-attribute breakdown below, with a source and date on every value, is the honest way to compare them.
Capabilities
Feature-by-feature on the axes that matter for synthetic data. “-” means undocumented, not absent.
What each one is
The product in its own terms, so the numbers below have context.
CTGAN
An open-source synthetic data generator that uses Conditional GANs to learn patterns from real tabular data and produce high-fidelity synthetic versions for analysis and testing.
REaLTabFormer
Open-source Python package that creates synthetic datasets by applying sequence-to-sequence transformers for relational data and GPT-2 for independent tables, with automatic stopping when synthetic distribution matches the original.
Pricing
List pricing as published by each vendor, with the date we read it. Always verify at the source before you buy.
Platform & deployment
Where each product runs and how it can be hosted. A dash means undocumented, not unsupported.
Integrations
What each product connects to. Counts come from the vendor's own integration directory where one exists.
CTGAN
- SDV ecosystem
- Python packages
REaLTabFormer
Not documented yet.
CTGAN or REaLTabFormer: which one depends on you
A composite score cannot know your constraints. Describe them and both get re-weighted against what you actually need, with the evidence behind every position.
Free to run, no account needed to start. How the evaluation works
Comparison generated from independently-sourced facts. Every value links to its source and retrieval date. See the method.