> 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/~/changes/T3ZD1DOoRUEqkmrAGBZp/reference/explore-sdv.md). # Explore SDV SDV is available in Public or Enterprise formats. Use this page to determine which one is right for your project needs.


Public SDV	Explore Synthetic Data. Train a generative AI with your own, simple datasets as a proof-of-concept. Create synthetic data that has the same patterns.	Publicly available with a Business Source License. Get started today!
SDV Enterprise	Ready for scale? Expand synthetic data solutions in your enterprise. Create generate AIs for more complex datasets.	To learn more about pricing and plans, visit our website.

## Features ### AI-Based Synthesizers These synthesizers use AI to learn patterns from your data and use them to recreate synthetic data.

	Public SDV	SDV Enterprise
GaussianCopula statistical AI	✅	✅
CTGAN, TVAE, CopulaGAN neural networks	✅	✅
PAR for sequential data	✅	✅
HMA multi-table for limited tables (<5)	✅	✅
HSA multi-table for unlimited tables	❌	✅
Independent multi-table for unlimited tables	❌	✅

### Test Data Synthesizers These synthesizers create random test data based on metadata alone. They do not use AI so you do not need to input any training data.

	Public SDV	SDV Enterprise
DayZSynthesizer single table	❌	✅
DayZSynthesizer multi table	❌	✅

### Integrate You Data These features make it easy to integrate the SDV into your application and pipeline.

	Public SDV	SDV Enterprise
Auto-detect metadata using data CSVs or DataFrames	✅	✅
Auto-detect metadata with a DDL file from an SQL schema	❌	✅

### Pre-Process Statistical Information Transformers are used to pre-process your data, which can improve data quality. SDV synthesizers select transformers by default, but you can always customize these to your dataset.

	Public SDV	SDV Enterprise
FloatFormatter for missing value imputation, numerical columns	✅	✅
ClusterBased and Gaussian Normalizers statistical transforms	✅	✅
Uniform, Label, and OneHot Encoding for discrete variables (nominal and ordinal)	✅	✅
Datetime Encoding including datetime format parsing	✅	✅
OutlierEncoder for numerical outliers	❌	✅

### Understand & Anonymize Real-World Concepts Transformers are used to pre-process your data, which can improve data quality. SDV synthesizers select transformers by default, but you can always customize these to your dataset. These transformers are geared towards columns that correspond to industry or domain-specific concepts. Their structure may be human-created.

	Public SDV	SDV Enterprise
RegexGenerator, IDGenerator for keys and IDs	✅	✅
AnonymizedFaker general-purpose anonymization	✅	✅
PsuedoAnonymizedFaker for general pseudo-anonymization with a mapping	✅	✅
Emails understanding domains	❌	✅
Addresses understanding locations	❌	✅
Phone Numbers understanding country and area codes	❌	✅
[Coming soon!] GPS Coordinates understanding geographical areas and distances	❌	✅

### Constraints Constraints represent business rules and logic that you can apply to your synthesizer.

	Public SDV	SDV Enterprise
Predefined logic for individual columns: FixedIncrements, Negative, Positive, ScalarInequality, ScalarRange	✅	✅
Predefined logic for multiple columns: FixedCombinations, Inequality, OneHotEncoding, Range	✅	✅
Write your own custom constraints	✅	✅
Advanced, predefined logic: ChainedInequality	❌	✅
Support for custom constraints and additional predefined logic	❌	✅

### Synthetic Data Evaluation Evaluate your synthetic data by comparing it against the real data.

	Public SDV	SDV Enterprise
Access to SDMetrics library vendor-agnostic, open source	✅	✅
Diagnostic Report basic data validity checks , single and multi-table	✅	✅
Quality Report statistical similarity, single and multi-table	✅	✅
Privacy Metrics: CategoricalCAP, NewRowSynthesis, Inference Attacks	✅	✅
Visualization 1D and 2D bars, scatterplots, heatmaps and more	✅	✅
Use case-specific metrics: OutlierCoverage, SmoothnessSimilarity	❌	✅

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