# Compare Features Compare the features available across SDV Community and SDV Enterprise. SDV Enterprise users also have the option of purchasing [**SDV Bundles**](https://docs.sdv.dev/sdv/explore/sdv-bundles), which are optional add-on packages for targeted needs. ### AI-Based Synthesizers These synthesizers use AI to learn patterns from your data and use them to recreate synthetic data.

	SDV Community	SDV Enterprise
GaussianCopula statistical AI	✅	✅
CTGAN, TVAE, CopulaGAN neural networks	✅	✅
XGC advanced Copula modeling with flexible shapes, faster runtime and more	❌	💠 XSynthesizers bundle
SegmentSynthesizer for separately modeling highly segmented data	❌	💠 XSynthesizers bundle
BootstrapSynthesizer for modeling data with a few rows	❌	💠 XSynthesizers bundle
DPGC, and DPGCFlex Synthesizers for creating synthetic data with differential privacy guarantees	❌	💠 Differential Privacy bundle
PAR for sequential data	✅	✅
HMA multi-table for limited tables (<5)	✅	✅
HSA multi-table for unlimited tables	❌	✅
Independent multi-table for unlimited tables	❌	✅
Performance estimates for multi-table synthesizers with various dataset sizes	❌	✅

### 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.

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

### Synthetic Data Creation Create synthetic data that matches the patterns in your real data — as well as other specifications that you have for your project.

	SDV Community	SDV Enterprise
Sample realistic data that matches the patterns in your real data, and scale it up.	✅	✅
Single-table conditional sampling for fixed values in single-table and sequential datasets	✅	✅
Multi-table conditional sampling for fixed values in multi-table datasets.	❌	💠 Targeted Sampling bundle

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

	SDV Community	SDV Enterprise
Auto-detect metadata using data CSVs or DataFrames	✅	✅
Auto-detect metadata. Advanced algorithms that use data to detect primary and foreign keys more accurately.	❌	✅
Specify composite keys. Specify primary and foreign keys that consist of 2 or more columns combined.	❌	✅
Auto-detect metadata based on your database	❌	💠 AI Connectors bundle
Directly connect to a database for importing real data and creating metadata	❌	💠 AI Connectors bundle
Connect to a database for exporting synthetic data	❌	💠 AI Connectors bundle

### 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.

	SDV Community	SDV Enterprise
FloatFormatter for missing value imputation, numerical columns	✅	✅
ClusterBasedNormalizer and GaussianNormalizer statistical transforms	✅	✅
XGaussianNormalizer with support for 100+ statistical distributions	❌	💠 XSynthesizers bundle
ECDFNormalizer to normalize any distribution with high fidelity	❌	💠 XSynthesizers bundle
Uniform, Label, and OneHot Encoding for discrete variables (nominal and ordinal)	✅	✅
Datetime Encoding including datetime format parsing, and converting timezones	✅	✅
Learning timezones that are attached to your datetime column	❌	✅
OutlierEncoder for numerical outliers	❌	✅
DPLaplaceNoiser, DPTimestampLaplaceNoiser, DPResponseRandomizer, DPWeightedResponseRandomizer for adding noise to a column to guarantee differential privacy	❌	💠 Differential Privacy bundle
DPECDFNormalizer, DPDiscreteECDFNormalizer for normalizing a column while guaranteeing differential privacy	❌	💠 Differential Privacy bundle

### 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.

	SDV Community	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	❌	✅
GPS Coordinates understanding geographical areas and distances	❌	✅

### Constraint-Augmented Generation Input business rules into your synthesizer using constraints. This ensures high-quality, valid synthetic data, 100% of the time.

	SDV Community	SDV Enterprise
Predefined logic for individual tables: FixedIncrements, FixedCombinations, Inequality, OneHotEncoding, Range	✅	✅
Program your own constraint for single tables	✅	✅
Advanced predefined logic for individual tables: FixedNullCombinations, MixedScales, ChainedInequality	❌	💠 CAG bundle
Advanced, predefined logic for multi-table tables: CarryOverColumns, DenormalizedTable, PolymorphicRelationship, and more.	❌	💠 CAG bundle
Automatically detect predefined constraint logic and apply it to your synthesizer.	❌	💠 CAG bundle
Program your own constraint for multi-table	✅	✅
Support for programming your constraint 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	✅	✅
Measure the privacy of your data: DisclosureProtection and DisclosureProtectionEstimate	✅	✅
Verify the differential privacy of any synthesizer algorithm	❌	💠 Differential Privacy bundle
Visualization 1D and 2D bars, scatterplots, heatmaps and more	✅	✅
Use case-specific metrics: OutlierCoverage, SmoothnessSimilarity	❌	✅

[^1]: Categories without any order, such as ice cream flavor or gender [^2]: Categories with an order such as school grades ("C+", "B-", "B", etc.)