DataSynthesizer

Also known as
datasynthesizer

What is DataSynthesizer?

An open-source Python library that creates synthetic datasets mimicking the statistical properties of real data while applying differential privacy to protect sensitive information. It enables collaboration between data scientists and data owners.

Independently observed

DataSynthesizer pricing

We don't have DataSynthesizer's full plan breakdown yet (its pricing page resisted automated reading). Here's what we could confirm. Always check live pricing for exact numbers.

What DataSynthesizer does

The capabilities that matter for synthetic data, normalised so it lines up with every alternative. “-” means we haven't confirmed it, not that it's missing.

Capabilities
Generation method
Statistical/copula
Relational integrity
-
Differential privacy
Privacy risk scoring
-
Fidelity utility reporting
-
Masking included
-
Deployment
Self host
Open source
Ci cd integration
-
Industry focus
Horizontal
SOC2
-
HIPAA available
-
Pricing model
Open source
Independently observed

Platform & deployment

Independently observed
Platforms
  • Web
Deployment
  • Self-hosted

Security & compliance

Known vulnerabilities: 0 (0 in the last 12 months) sourcea count reflects scale & disclosure, not quality

DataSynthesizer alternatives

Other synthetic data we track, ranked by the same independent score.

All DataSynthesizer alternatives, ranked →

Compare DataSynthesizer

Side by side against other synthetic data, attribute by attribute, with a source on every value.

Independent · unbought · dated

The Vioscale score: one lens on the evidence

Not user reviews and not a paid placement: a confidence-weighted blend of the independent signals below (adoption, activity, security posture, and more), which you can sort and re-weight yourself. Vendors can correct their listing but can never move their rank, and stars are weighted low as a vanity metric. It is one way to read the evidence for DataSynthesizer, not the verdict.

Balanced composite 43 / 100
low · 23%updating
Signal contributions to the composite score
SignalScoreWeightContributionEvidence
Pricing transparency800.086.7
Price level1000.055.2
Capabilities460.083.8
Stars460.031.2
Dependent projects80.060.5
Reliability00.070.0-
Integrations00.090.0-
Development activity00.090.0
Release cadence00.050.0-
Security posture00.070.0-
Package downloads00.140.0-
Security score00.040.0-
Developer Q&A activity00.060.0-

Computed . Re-weight it by intent, or see the full method.

All data & sourcesshow ↓

Every value we hold, with its source, retrieval date, and confidence. This is the evidence behind the score: don't trust it, verify it.

Activity

AttributeValueEvidence
Commits last 30d0mediumsource · 2026-08-26 · 65%

Adoption

AttributeValueEvidence
Github stars276highsource · 2026-08-26 · 90%
Dependent repos2highsource · 2026-08-26 · 85%

Features

AttributeValueEvidence
CapabilitiesDeployment: self_host · Open source: Yes · Pricing model: open_source · Industry focus: horizontal · Generation method: statistical/copula · Differential privacy: Yesmediumsource · 2026-08-21 · 60%

Language

AttributeValueEvidence
PrimaryHTMLhighsource · 2026-08-26 · 90%

License

AttributeValueEvidence
SpdxMIThighsource · 2026-08-26 · 95%

Pricing

AttributeValueEvidence
Free tierYesmediumsource · 2026-08-21 · 60%
Price levelfreemediumsource · 2026-08-21 · 60%
TransparentYesmediumsource · 2026-08-21 · 60%
Modelcommerciallowsource · 2026-08-26 · 40%

Security

AttributeValueEvidence
VulnerabilitiesCount: 0 · Source: https://advisories.ecosyste.ms/api/v1/advisories?ecosystem=pypi&package_name=datasynthesizer&per_page=100 · Last 12m: 0highsource · 2026-08-26 · 90%