Organizations increasingly need realistic data for analytics, software testing, artificial intelligence, and cross-team collaboration, yet the use of production data can create serious privacy, compliance, and security risks. Synthetic data generation platforms address this challenge by creating artificial records that preserve useful statistical patterns without directly exposing identifiable individuals. Gretel is one of the better-known platforms in this category, but it is not the only serious option for enterprises that need privacy-safe data creation.
TLDR: Synthetic data tools help teams create realistic, usable datasets while reducing exposure of sensitive personal or business information. Alternatives to Gretel include platforms such as Tonic.ai, Mostly AI, Syntho, Hazy, YData, and SDV. The right choice depends on your use case, including software testing, analytics, machine learning, compliance, or data sharing. No tool removes privacy risk automatically, so strong governance, validation, and security controls remain essential.
Why synthetic data matters for privacy-safe data creation
Synthetic data is not simply “fake data.” In a professional setting, it is data generated by statistical models, machine learning systems, rules-based engines, or a combination of these methods. The goal is to reproduce the structure and behavior of real datasets while minimizing the likelihood that specific individuals, customers, patients, employees, or transactions can be reidentified.
This is especially valuable in industries such as healthcare, financial services, insurance, retail, telecommunications, and software development. Teams often need data to build applications, train models, run QA tests, perform demonstrations, or collaborate with vendors. Using live production data for these activities can violate internal policies, increase breach exposure, or create unnecessary compliance burdens under frameworks such as GDPR, HIPAA, CCPA, and sector-specific regulations.
However, synthetic data should be treated as a risk-reduction technique, not a universal privacy guarantee. A trustworthy process includes data minimization, access control, privacy testing, utility testing, audit trails, and clear documentation of how synthetic records were generated.
What to look for in a Gretel alternative
Before choosing a platform, organizations should define what “privacy-safe” and “useful” mean for their specific environment. A synthetic dataset that works well for application testing may not be accurate enough for machine learning. Likewise, a dataset suitable for analytics may not preserve edge cases needed for fraud detection or clinical research.
Key evaluation criteria include:
- Data type support: Structured tables, time series, text, event logs, transactional data, and relational databases may require different generation methods.
- Privacy controls: Look for features such as differential privacy, memorization checks, reidentification risk testing, and sensitive attribute handling.
- Data utility: The synthetic data should preserve distributions, correlations, constraints, outliers, and business logic where appropriate.
- Deployment model: Some teams require cloud deployment, while others need on-premises or private cloud environments.
- Workflow integration: APIs, connectors, CI/CD compatibility, and role-based access control can determine whether a tool fits enterprise operations.
- Governance and reporting: Serious platforms should provide documentation, quality metrics, privacy reports, and repeatable generation pipelines.
1. Tonic.ai
Tonic.ai is a widely used platform for creating realistic, de-identified, and synthetic data for software development, testing, and analytics. It is often considered a strong option for engineering teams that need safe replicas of production databases without exposing sensitive information.
One of Tonic.ai’s strengths is its focus on maintaining referential integrity across complex relational databases. For example, if a customer table connects to orders, invoices, support tickets, and payments, the generated or transformed data should remain consistent enough for applications to run properly. This is important for QA teams that need test environments to behave like production.
Tonic.ai supports a mix of approaches, including data masking, subsetting, de-identification, and synthetic data generation. This flexibility can be helpful because not every field requires the same treatment. A name or email address may be fully replaced, while a transaction pattern may need to be preserved for realistic testing.
Best for: Engineering teams, QA environments, development databases, and organizations that need production-like test data with strong privacy controls.
2. Mostly AI
Mostly AI is an enterprise-oriented synthetic data platform designed to generate high-quality structured data for analytics, AI development, and data sharing. The platform emphasizes privacy-preserving synthetic data that retains statistical properties from the original dataset while reducing the exposure of real records.
Mostly AI is particularly relevant for organizations that want to use customer, financial, or behavioral data in machine learning workflows without distributing sensitive raw data. Its platform is designed to create synthetic datasets that can support exploratory analysis, model prototyping, and external collaboration.
A notable feature is its attention to privacy and accuracy reporting. Organizations evaluating synthetic data should not rely on assumptions; they should examine whether the generated records are too similar to original records and whether important statistical relationships are retained. Mostly AI’s reporting capabilities can support internal review, legal assessment, and compliance discussions.
Best for: Analytics teams, data science groups, financial institutions, and organizations sharing data across departments or with partners.
3. Syntho
Syntho is a synthetic data generation platform focused on privacy-safe data usage for analytics, testing, and AI. It is commonly positioned for organizations that want to unlock sensitive data while remaining aligned with privacy and compliance expectations.
Syntho’s approach includes generating artificial data based on patterns from original datasets, along with quality assurance and privacy assessment features. For regulated industries, these validation capabilities matter. Decision-makers need evidence that synthetic data is not only useful but also sufficiently separated from real individuals.
The platform can be useful when organizations are trying to reduce reliance on traditional anonymization techniques. Conventional anonymization can fail when datasets contain rare combinations of attributes or when external data can be linked to the anonymized records. Synthetic data, when properly generated and tested, can lower those risks while preserving analytical value.
Best for: Regulated organizations, analytics programs, AI initiatives, and teams that require documented privacy and data quality assessments.
4. Hazy
Hazy is a synthetic data platform with a strong emphasis on enterprise privacy, financial services, and secure data collaboration. It has been used in contexts where organizations need to produce data that resembles sensitive customer or transaction datasets without exposing actual confidential records.
Hazy is often relevant for banks, insurers, and large enterprises that need to balance innovation with strict governance. These organizations may need to provide realistic datasets to data scientists, developers, or external partners, but cannot freely distribute live customer data. Synthetic data can help reduce bottlenecks while keeping sensitive records protected.
For enterprise buyers, the appeal is not only data generation but also operational control. Serious adoption requires repeatable pipelines, measurable privacy safeguards, and alignment with internal approval processes. Hazy’s positioning in the enterprise market makes it worth considering for organizations with mature data governance requirements.
Best for: Financial services, insurance, enterprise analytics, and privacy-sensitive data collaboration.
5. YData
YData provides tools for synthetic data generation and data quality workflows, particularly for teams working on machine learning and artificial intelligence projects. It is designed to help data scientists improve access to usable data while addressing privacy and scarcity challenges.
In many AI projects, the problem is not simply that data is sensitive. The available data may also be incomplete, imbalanced, or difficult to use. Synthetic data can support model development by creating additional examples, representing rare scenarios, or enabling experimentation before production data access is approved.
YData is relevant for teams that want to combine synthetic data generation with broader data preparation and quality management. This can be useful because synthetic data is only as valuable as the process around it. If the source data is poorly understood, biased, or structurally inconsistent, the generated data may reproduce those issues.
Best for: Data science teams, machine learning workflows, data quality initiatives, and projects that require synthetic data for experimentation.
6. SDV
SDV, short for Synthetic Data Vault, is an open-source synthetic data ecosystem originally developed to help generate synthetic tabular, relational, and time-series data. Unlike many commercial platforms, SDV is especially attractive to technical teams that want flexibility, transparency, and programmatic control.
SDV supports multiple modeling approaches and can be integrated into Python-based data workflows. This makes it a practical option for researchers, data scientists, and engineering teams that want to build custom synthetic data pipelines rather than rely exclusively on a managed commercial platform.
Open-source tools can be powerful, but they also shift more responsibility to the user. Teams must carefully manage security, privacy evaluation, deployment, documentation, and compliance review. SDV can be an excellent choice for experimentation and controlled internal use, but organizations with strict governance requirements may need additional tooling, expert review, or enterprise support.
Best for: Technical teams, Python users, research environments, custom pipelines, and organizations that prefer open-source synthetic data generation.
Comparing the tools by use case
Each platform has a different center of gravity. Tonic.ai is especially strong for development and testing environments where database realism and referential integrity are critical. Mostly AI and Syntho are strong candidates for analytics, AI, and compliant data sharing. Hazy is well aligned with enterprise and financial services use cases. YData fits data science teams working on AI readiness and data quality. SDV is a flexible open-source option for teams with the skills to manage their own pipelines.
The best tool is not necessarily the one with the most advanced model. It is the one that matches your data, risk profile, regulatory environment, and operational workflow. A small data science team may value flexibility and speed, while a bank may prioritize auditability, access control, deployment options, and vendor risk management.
Important privacy and governance considerations
When evaluating synthetic data platforms, organizations should ask direct questions about privacy testing. Can the vendor measure whether synthetic records are too similar to real records? Does the system detect memorization? Are rare individuals or outliers protected? Can privacy settings be adjusted according to risk level?
It is also important to test utility. Synthetic data should be compared against original data using meaningful metrics, not just visual inspection. Distribution similarity, correlation preservation, model performance, constraint validity, and downstream task accuracy can all be relevant.
Finally, synthetic data should be covered by normal data governance. This includes access permissions, retention policies, usage limitations, documentation, and approval workflows. Even if synthetic data is lower risk than production data, it may still reveal sensitive business patterns or contain residual privacy risk.
Conclusion
Synthetic data generation is becoming an essential capability for organizations that want to innovate responsibly. Tools like Gretel have helped popularize the category, but serious alternatives exist for different needs and maturity levels. Tonic.ai, Mostly AI, Syntho, Hazy, YData, and SDV each offer credible paths toward privacy-safe data creation.
The right decision should be based on a careful assessment of data utility, privacy protection, deployment requirements, governance capabilities, and integration needs. Synthetic data can reduce risk and accelerate access, but it should be deployed with discipline. When combined with strong controls and clear validation, it can help organizations use data more safely without bringing critical development, analytics, and AI work to a halt.
