# 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. {% hint style="success" %} **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). {% endhint %}


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

*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. {% hint style="info" %} *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)*.* {% endhint %}


❖ XGCSynthesizer	Use extra features on top of Gaussian Copula for higher quality synthetic data and improved performance.	xgcsynthesizer
❖ 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.	segmentsynthesizer
＊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.	dpgcsynthesizer
❖ DPGCFlexSynthesizer	[Experimental] Use and customize Gaussian Copula while guaranteeing differential privacy.	dpgcflexsynthesizer

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