> For the complete documentation index, see [llms.txt](https://docs.sdv.dev/sdv/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.sdv.dev/sdv/modeling/single-table-synthesizers.md). # Single Table Synthesizers The SDV offers a variety of synthesizers, which use different algorithms to generate synthetic data. This section describes single table synthesizers, which act on [single table data](/sdv/integration/overview/data-modalities.md). ## Basic Single Table Synthesizers These synthesizers are available in SDV Community. They build a generative AI model using your training data, and use it to create synthetic data. {% hint style="success" %} **We recommend starting with** [**GaussianCopulaSynthesizer**](/sdv/modeling/single-table-synthesizers/gaussiancopulasynthesizer.md) **for fast performance**, good quality, and customization. For higher fidelity, try a neural network-based synthesizer such as [CTGANSynthesizer](/sdv/modeling/single-table-synthesizers/ctgansynthesizer.md) or [TVAESynthesizer](/sdv/modeling/single-table-synthesizers/tvaesynthesizer.md). Modeling and sampling performance may be slower for these synthesizers, especially if you have categorical columns with many different values (high cardinality). {% endhint %}


GaussianCopulaSynthesizer	Use a classical ML algorithm to learn from real data. This is fast, transparent, and customizable.	/pages/G6E1sJhuBGe4TuIn8idv
CTGANSynthesizer	Use GAN-based ML algorithm to learn from real data. This may take longer to learn and be harder to debug.	/pages/PzX7CgB9wPzSsWDHvXEL
TVAE Synthesizer	Use a variational autoencoder ML model to learn from real data. This may take longer to learn and be harder to debug.	/pages/H8yXkKU4zLUV56jNDeQv

*Experimental synthesizer: The* [***CopulaGANSynthesizer***](/sdv/modeling/single-table-synthesizers/copulagansynthesizer.md) *combines classical statistics with GAN-based modeling.* ## Specialty Synthesizers Specialty synthesizers are available for special situations — such as improving speed, enhancing quality, or providing privacy guarantees. {% hint style="info" %} *Specialty synthesizers available for licensed, SDV Enterprise users (denoted by **＊**) or through purchasing additional bundles (denoted by ❖). For more information, see the* [*feature comparison*](/sdv/explore/sdv-enterprise/compare-features.md)*.* {% endhint %}


❖ XGCSynthesizer	Use extra features on top of Gaussian Copula for higher quality synthetic data and improved performance.	/pages/Q0CltahGDjlJOqADLwYU
❖ BootstrapSynthesizer	A synthesizer that is optimized to learn from a smaller number of training rows.
❖ SegmentSynthesizer	Use this synthesizer when your real data is highly segmented, with different patterns for each.	/pages/jKnYQXaZIbP60QOIzPLQ
＊DayZSynthesizer	Generate synthetic data from scratch. Use this when you don't have a lot of real data.
❖ DPGCSynthesizer	Use Gaussian Copula while guaranteeing differential privacy.	/pages/ov6SvclI4G67aRaH2TI5
❖ DPGCFlexSynthesizer	[Experimental] Use and customize Gaussian Copula while guaranteeing differential privacy.	/pages/Psw4WvC1fCOMzxDTbGF3

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