⭐Compare Features

Compare the features ava`ilable across SDV Community and SDV Enterprise. SDV Enterprise users also have the option of purchasing SDV Bundles, which are optional add-on packages for targeted needs.

SDV Enterprise is available to licensed users. Sign up now to get your license key. You can then complete your SDV Enterprise installation on-prem and begin using the features.

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AI-Based Synthesizers

These synthesizers use AI to learn patterns from your data and use them to recreate synthetic data.

Feature	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.

Feature	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.

Feature	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
Extrapolate synthetic data outside the ranges of your original training data.	❌	💠 Targeted Sampling bundle

Data Integrations

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

Feature	SDV Community	SDV Enterprise
Load data from a local file (CSV, Excel, or other tabular formats)	✅	✅
Auto-detect metadata using data CSVs or DataFrames	✅	✅
Write synthetic data into a local file (CSV, Excel or other tabular formats)	✅	✅
Auto-detect metadata. Advanced algorithms that use data to detect primary and foreign keys more accurately.	❌	✅
Auto-detect Regex formats for all ID columns, as well as embedded context within those IDs.	❌	✅
Specify composite keys. Specify primary and foreign keys that consist of 2 or more columns combined.	❌	✅
Directly connect to a database for importing real data and creating metadata	❌	💠 AI Connectors bundle
Auto-detect metadata based on your database	❌	💠 AI Connectors bundle
Export synthetic data to a database	❌	💠 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.

Feature	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.

Feature	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.

Feature	SDV Community	SDV Enterprise
Predefined logic for individual tables: FixedIncrements, FixedCombinations, Inequality, OneHotEncoding, Range	✅	✅
Program your own constraint for single tables	❌	💠 CAG bundle
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
Support for additional constraints that are not yet available as predefined logic	❌	💠 CAG bundle

Synthetic Data Evaluation

Evaluate your synthetic data by comparing it against the real data.

Feature	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	❌	✅