Over the past few years, teams working with data have seen a fundamental shift: real production data is no longer the default choice for many use cases. Privacy regulations, long provisioning cycles, and increasingly complex approval processes have made it harder to use real data safely and efficiently. As a result, many organizations are turning to Synthetic Data Generation (SDG) as a practical alternative.
SDG creates artificial datasets that mirror the patterns, relationships, and statistical properties of real data – without exposing sensitive information. Often described as a “digital twin” of production data, synthetic data is realistic enough for analytics, software testing, and AI training, while avoiding regulatory and compliance risks.
By 2026, synthetic data has moved well beyond a niche innovation. It has become a core capability for organizations that want to innovate with data while staying compliant. Below are five synthetic data generation tools that consistently rank at the top based on enterprise adoption, technical capabilities, and user feedback.
1. K2view
K2view leads this list as more than just a data generator – it’s a full suite of synthetic data generation tools. The K2view solution covers the entire synthetic data lifecycle, from source data extraction and subsetting to synthetic data generation and delivery into downstream environments.
Its patented, entity-based architecture preserves referential integrity across complex, multi-system environments, making it especially valuable for enterprise applications with deeply interconnected data models.
What stands out:
Enterprise-grade scalability and strong handling of complex, interrelated datasets.
What users highlight:
Fast and accurate synthetic data delivery, support for both AI-based and rules-based generation, and integrated masking and anonymization within the same platform. Some users note that local support is primarily focused on Europe and the United States.
Best for:
Large organizations operating in complex data environments that require self-service access to realistic, compliant synthetic data across multiple systems.
2. MOSTLY AI
MOSTLY AI is often chosen by teams that want high-quality synthetic data without a steep learning curve. Its user interface is clean and intuitive, allowing teams to generate realistic datasets quickly for analytics and machine learning use cases. Privacy safeguards are a strong focus of the platform.
Where it shines:
Ease of use, fast processing, and a smooth experience for non-engineering users.
Where it falls short:
Limited flexibility when working with highly complex or deeply hierarchical data models.
User feedback:
Easy to adopt and effective, though some users would like more advanced configuration options.
Best for:
Mid-size to large organizations generating synthetic data primarily for machine learning and analytics.
3. YData Fabric
YData Fabric positions synthetic data generation as part of a broader data quality and machine learning pipeline. In addition to generating synthetic data, it offers profiling, quality assessment, and support for tabular, relational, and time-series data.
Why teams choose it:
Strong versatility and a focus on improving data readiness for modeling and analytics.
Why some hesitate:
The platform requires data science expertise and may not fully address all privacy or regulatory requirements in highly regulated environments.
User feedback:
Effective at balancing datasets for model training, but best suited for technically skilled teams.
Best for:
Machine-learning-driven teams working across multiple data domains.
4. Gretel Workflows
Gretel Workflows is designed with developers in mind. It is API-first, automation-friendly, and built to integrate synthetic data generation directly into CI/CD, Dev/Test, and ML pipelines. The platform supports both structured and unstructured data.
Why engineers like it:
Strong automation, workflow scheduling, and pipeline integration.
Limitations:
Less effective for highly complex data models and heavily dependent on cloud infrastructure, which may limit flexibility for some organizations.
User feedback:
Streamlines development workflows, though some teams would prefer stronger local or on-premises options.
Best for:
Engineering teams embedding synthetic data generation into automated development and testing pipelines.
5. Hazy (now part of SAS Data Maker)
Hazy has long focused on privacy-preserving synthetic data generation and continues that approach as part of SAS Data Maker. It is commonly used in environments where privacy and regulatory compliance are non-negotiable, such as banking and financial services.
What makes it stand out:
Differential privacy techniques, strong anonymization capabilities, and enterprise-grade integration.
Drawbacks:
Setup can be complex, and costs may be prohibitive for smaller teams.
User feedback:
Highly reliable for compliance-driven use cases, though implementation can take time.
Best for:
Highly regulated industries that prioritize privacy, governance, and control.
Conclusion
Synthetic data generation is no longer a “nice to have” capability. It has become essential for training AI models, enabling realistic testing, and reducing regulatory risk in modern data operations. Choosing the right SDG tool depends on factors such as data complexity, privacy requirements, team skill sets, and integration needs.
Enterprise platforms like K2view emphasize scale, governance, and referential integrity, while other tools specialize in usability, machine learning pipelines, or developer-centric automation. Understanding your requirements clearly is the key to selecting the solution that will deliver long-term value.