Best Gretel AI BetaÂ² Alternatives ranked by AI · updated Aug 2026

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Gretel is a synthetic data platform for developers, data scientists, and privacy teams. It generates, transforms, and evaluates synthetic datasets for machine learning, testing, and analytics while helping reduce exposure to sensitive data.

Developer: Gretel.ai Price: Free tier; paid plans with custom pricing 🎯 gretel.ai

🤖 Machine Learning Data Privacy Synthetic Data data generation

Top 6 Gretel AI BetaÂ² alternatives

MOSTLY AI

💡 Pick it for open-source flexibility, relational data support, and self-hosted deployment.

MOSTLY AI is an enterprise platform for generating synthetic tabular, time-series, and relational data. It targets organizations that need realistic privacy-preserving data...

Pros

Broad support for relational and sequential datasets
Strong enterprise governance and privacy features
More mature synthetic-data workflow than many smaller platforms

Cons

Enterprise features require a commercial plan
Can be more complex to deploy than developer-focused libraries
Less suitable for quick, lightweight experiments than Gretel

Free Community Edition; enterprise pricing custom

Alternatives to MOSTLY AI → Visit site

tonic

💡 Pick it when your priority is realistic, privacy-safe test data from existing databases.

Tonic is a comprehensive software solution for data masking and de-identification. It helps businesses protect sensitive data by anonymizing personally identifiable information.

Starting at $500/month

Alternatives to tonic → Visit site

Hazy

💡 Pick it for enterprise governance and regulated-industry synthetic data programs.

Hazy is an enterprise synthetic-data platform for creating realistic datasets from sensitive business information. It focuses on privacy-preserving data access for analytics,...

Pros

Designed for regulated enterprise data environments
Produces synthetic datasets for analytics and model development
Strong fit for organizations needing controlled data access

Cons

Limited public pricing and developer documentation
Less accessible for independent developers than SDV
Smaller community footprint than Gretel or MOSTLY AI

Custom pricing

Alternatives to Hazy → Visit site

Synthesized

💡 Pick it if synthetic data must fit into automated data-engineering and validation pipelines.

Synthesized is a data-engineering platform for generating, masking, and managing synthetic data for development, testing, and analytics. It focuses on preserving data...

Pros

Combines synthetic generation with data masking and transformation
Handles enterprise data pipelines and relational structures
More focused on repeatable data operations than simple random generators

Cons

More complex than Seedata for small test datasets
Enterprise-oriented pricing and deployment may limit accessibility
Requires data-pipeline configuration and operational ownership

N/A

Alternatives to Synthesized → Visit site

YData

💡 Pick it for an open-source-friendly Python workflow combining data quality and synthesis.

YData provides open-source and commercial tools for data profiling, quality improvement, and synthetic data generation. It serves data scientists and engineering teams...

Pros

Combines data quality workflows with synthetic data generation
Offers open-source components for local experimentation
Useful for teams that need profiling before modeling

Cons

Synthetic-data capabilities are less specialized than MOSTLY AI's
Commercial platform features may require additional setup
Smaller enterprise synthetic-data footprint than Tonic.ai

Free open-source tools; cloud and enterprise pricing custom

Alternatives to YData → Visit site

Datomize

💡 Pick it for an enterprise-first synthetic data program focused on structured business data.

Datomize is an enterprise synthetic data platform for creating privacy-safe datasets for analytics, software testing, and AI development. It focuses on structured...