Synthetic data generation tools help teams create artificial datasets that resemble real-world data without always using direct production records. They can support software testing, analytics, model development, privacy-focused data sharing, and other data workflows.
The right solution depends on the type of data being generated, the required level of realism, privacy needs, and the team's technical environment. Businesses should focus on data quality and privacy rather than choosing a tool based only on how much data it can generate.
1. Gretel
Gretel provides tools for generating synthetic data and developing privacy-focused data workflows. It can support different types of structured data use cases and help teams create datasets for development and testing.
Best for: Teams looking for flexible synthetic data generation and privacy-focused workflows.
2. Mostly AI
Mostly AI focuses on generating synthetic data that can preserve useful patterns found in real datasets. It is designed for organizations that need synthetic versions of sensitive data for analytics, testing, and development.
Best for: Businesses working with sensitive structured data.
3. Tonic.ai
Tonic.ai provides synthetic data and data de-identification capabilities for development and testing environments. It can help teams work with data that resembles production information while reducing direct exposure to sensitive records.
Best for: Software and data teams that need safer test data environments.
4. Hazy
Hazy focuses on synthetic data generation for organizations working with sensitive datasets. Its approach can help businesses create artificial data while retaining useful statistical characteristics from source data.
Best for: Organizations that need synthetic data for privacy-sensitive business use cases.
5. Synthesized
Synthesized provides data generation and data management capabilities designed to help teams create useful datasets while considering privacy and data governance requirements.
It can be useful when synthetic data needs to fit into broader data engineering workflows.
Best for: Data teams looking for synthetic data within broader data workflows.
What Features Should Teams Compare?
Choosing a synthetic data platform requires more than checking how quickly it generates datasets. Teams should evaluate whether the generated data remains useful, realistic, and safe.
Important areas include:
- Data realism: Generated data should preserve useful patterns and relationships from the original data.
- Privacy protection: Check how the platform reduces the risk of exposing sensitive or identifiable information.
- Data types: Make sure the tool supports the required structured, transactional, time-series, or other data formats.
- Relationship preservation: Important links between fields and records should remain meaningful where required.
- Data quality: Teams should compare missing values, distributions, correlations, and other quality measures between source and synthetic data.
- Scalability: The platform should handle the required dataset size and generation frequency.
- Customization: Teams may need control over schemas, constraints, distributions, or generation settings.
- Testing support: Check whether generated data works well for application testing, quality testing, and development environments.
- ML usefulness: For machine learning, compare whether models trained with synthetic data maintain useful performance.
- Integration: The solution should fit existing databases, cloud storage, data pipelines, and analytics tools.
- Governance: Access controls, audit capabilities, documentation, and compliance support can matter for sensitive datasets.
- Cost: Consider platform pricing, infrastructure requirements, storage, and ongoing management effort.
Final Thoughts
Synthetic data can help organizations reduce dependence on sensitive production datasets while supporting development, testing, analytics, and machine learning. Gretel, Mostly AI, Tonic.ai, Hazy, and Synthesized are worth considering for different synthetic data requirements.
Before selecting a solution, teams should compare realism, privacy, data quality, supported data types, relationship preservation, scalability, customization, ML usefulness, integrations, governance, and cost. The best solution should produce data that is useful for the intended task while managing privacy and security risks carefully.