# Synthesizers

The SDV offers a variety of synthesizers, which use different algorithms to generate synthetic data.

## Basic Single Table Synthesizers

These synthesizers are available in the SDV Community package. They build a generative AI model using your real data, and use it to create synthetic data.

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**We recommend starting with** [**GaussianCopulaSynthesizer**](https://docs.sdv.dev/sdv/single-table-data/modeling/synthesizers/gaussiancopulasynthesizer) **for fast performance**, good quality, and customization.

For higher fidelity, try a neural network-based synthesizer such as [CTGANSynthesizer](https://docs.sdv.dev/sdv/single-table-data/modeling/synthesizers/ctgansynthesizer) or [TVAESynthesizer](https://docs.sdv.dev/sdv/single-table-data/modeling/synthesizers/tvaesynthesizer). Modeling and sampling performance may be slower for these synthesizers, especially if you have categorical columns with many different values (high cardinality).
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<table data-view="cards"><thead><tr><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><a href="synthesizers/gaussiancopulasynthesizer"><strong>GaussianCopulaSynthesizer</strong></a></td><td>Use a classical ML algorithm to learn from real data. This is fast, transparent, and customizable.</td><td><a href="synthesizers/gaussiancopulasynthesizer">gaussiancopulasynthesizer</a></td></tr><tr><td><a href="synthesizers/ctgansynthesizer"><strong>CTGANSynthesizer</strong></a></td><td>Use GAN-based ML algorithm to learn from real data. This may take longer to learn and be harder to debug.</td><td><a href="synthesizers/ctgansynthesizer">ctgansynthesizer</a></td></tr><tr><td><a href="synthesizers/tvaesynthesizer"><strong>TVAE Synthesizer</strong></a></td><td>Use a variational autoencoder ML model to learn from real data. This may take longer to learn and be harder to debug.</td><td><a href="synthesizers/tvaesynthesizer">tvaesynthesizer</a></td></tr></tbody></table>

*Experimental synthesizer: The* [***CopulaGANSynthesizer***](https://docs.sdv.dev/sdv/single-table-data/modeling/synthesizers/copulagansynthesizer) *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.

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*Specialty synthesizers available for licensed, SDV Enterprise users (denoted by **＊**) or through purchasing additional bundles (denoted by ❖). For more information, see* [*SDV Enterprise*](https://docs.sdv.dev/sdv/explore/sdv-enterprise) *and* [*SDV Bundles*](https://docs.sdv.dev/sdv/explore/sdv-bundles)*.*
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<table data-view="cards"><thead><tr><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td>❖ <a href="synthesizers/xgcsynthesizer"><strong>XGCSynthesizer</strong></a> </td><td>Use extra features on top of Gaussian Copula for higher quality synthetic data and improved performance.</td><td><a href="synthesizers/xgcsynthesizer">xgcsynthesizer</a></td></tr><tr><td>❖ <a href="synthesizers/bootstrapsynthesizer"><strong>BootstrapSynthesizer</strong></a></td><td>A synthesizer that is optimized to learn from a smaller number of training rows.</td><td></td></tr><tr><td>❖ <a href="synthesizers/segmentsynthesizer"><strong>SegmentSynthesizer</strong></a></td><td>Use this synthesizer when your real data is highly segmented, with different patterns for each.</td><td><a href="synthesizers/segmentsynthesizer">segmentsynthesizer</a></td></tr><tr><td><strong>＊</strong><a href="synthesizers/dayzsynthesizer"><strong>DayZSynthesizer</strong></a></td><td>Generate synthetic data from scratch. Use this when you don't have a lot of real data.</td><td></td></tr><tr><td>❖ <a href="synthesizers/dpgcsynthesizer"><strong>DPGCSynthesizer</strong></a></td><td>Use Gaussian Copula while guaranteeing differential privacy.</td><td><a href="synthesizers/dpgcsynthesizer">dpgcsynthesizer</a></td></tr><tr><td>❖ <a href="synthesizers/dpgcflexsynthesizer"><strong>DPGCFlexSynthesizer</strong></a></td><td><em>[Experimental]</em> Use and customize Gaussian Copula while guaranteeing differential privacy.</td><td><a href="synthesizers/dpgcflexsynthesizer">dpgcflexsynthesizer</a></td></tr></tbody></table>