Top 10 Synthetic Data Generation Tools: Features, Pros, Cons & Comparison

Introduction

Synthetic data generation tools use advanced artificial intelligence and machine learning—such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)—to create entirely artificial datasets that mirror the statistical properties, correlations, and patterns of real-world data. Unlike traditional data masking or anonymization, synthetic data does not contain any actual records from the original source, making it a powerful solution for privacy-preserving AI development.

The importance of these tools lies in their ability to “unlock” data for innovation. In the real world, a bank might use synthetic data to train fraud detection models without ever touching sensitive customer transactions. A healthcare research team might generate synthetic patient records to accelerate clinical trials while maintaining absolute privacy. When evaluating these tools, users should prioritize mathematical privacy guarantees (like Differential Privacy), the accuracy of statistical distribution, ease of integration with MLOps pipelines, and the ability to handle complex relational database structures.

Best for: Data scientists, AI/ML engineers, and data privacy officers in mid-to-large enterprises, specifically within highly regulated sectors like finance, healthcare, insurance, and telecommunications. It is also ideal for software developers needing high-quality test data for QA and staging environments.

Not ideal for: Small businesses with minimal data needs or projects where the “ground truth” precision of every individual record is required (e.g., accounting audits or legal discovery). In these cases, traditional de-identification or manual data entry may be more appropriate.

Top 10 Synthetic Data Generation Tools

1 — MOSTLY AI

MOSTLY AI is widely considered the pioneer of AI-powered synthetic data. Its platform specializes in high-accuracy tabular and behavioral data synthesis, designed for enterprise-grade applications where preserving complex correlations is critical.

• Key features:
  • Automated synthesis of complex, multi-table relational databases.
  • Behavioral data synthesis for time-series and sequential patterns.
  • Integrated privacy-safe data sharing and collaboration hubs.
  • High-fidelity statistical mirroring with built-in quality reports.
  • Support for “fair” synthetic data by mitigating bias in original datasets.
  • Smart up-sampling to expand small datasets into massive training sets.
• Pros:
  • Unmatched accuracy in preserving complex relationships in tabular data.
  • Extremely user-friendly “magical” experience with high levels of automation.
• Cons:
  • Primarily focused on structured data; limited support for unstructured data (images/video).
  • Can be expensive for smaller teams or non-enterprise use cases.
• Security & compliance: SOC 2 Type II, HIPAA, and GDPR compliant. Includes mathematical privacy guarantees.
• Support & community: High-touch enterprise support, extensive technical documentation, and an active blog/webinar series.

2 — Gretel.ai

Gretel is a developer-first platform that focuses on making synthetic data accessible via APIs. It is known for its “Gretel Navigator,” an agentic AI tool that allows users to create data based on natural language prompts.

• Key features:
  • Gretel Navigator for prompt-based data generation.
  • Differential Privacy: Mathematically proven privacy guarantees built into models.
  • Support for Tabular, NLP (text), and Time-series data.
  • Gretel Cloud and Gretel CLI for seamless developer workflows.
  • Automated PII (Personally Identifiable Information) detection and removal.
• Pros:
  • Best-in-class API and developer experience; easy to integrate into CI/CD.
  • Strong capabilities for both structured and text-based synthetic data.
• Cons:
  • Advanced enterprise features can be complex to configure.
  • Resource-heavy for very large datasets if running locally.
• Security & compliance: SOC 2, HIPAA, and GDPR. Features built-in privacy filters and guardrails.
• Support & community: Active Discord community, regular workshops, and excellent documentation.

3 — Tonic.ai

Tonic.ai focuses heavily on the “Test Data Management” (TDM) use case. It is designed to help developers create secure, synthetic versions of production databases for use in development and staging environments.

• Key features:
  • Database Subsetting: Create smaller versions of massive databases while keeping relationships intact.
  • Smart Masking: Combines traditional masking with AI synthesis.
  • Continuous Sync: Refreshes synthetic environments as production data evolves.
  • Native support for massive database types (Postgres, MySQL, Oracle, MongoDB).
  • Integrated PII scanning and classification.
• Pros:
  • The most powerful tool for maintaining referential integrity across complex database schemas.
  • Streamlines the entire dev-to-production testing cycle.
• Cons:
  • Less focused on AI/ML training compared to Mostly AI or Gretel.
  • Setup can be lengthy for organizations with legacy infrastructure.
• Security & compliance: SOC 2 Type II, HIPAA, and GDPR.
• Support & community: High-touch enterprise support and detailed implementation guides.

4 — SDV (DataCebo)

Originally a research project at MIT, the Synthetic Data Vault (SDV) is now commercialized as DataCebo. It remains the most popular open-source ecosystem for synthetic data, offering unmatched flexibility for researchers.

• Key features:
  • Gaussian Copula and CTGAN models for high-fidelity synthesis.
  • Multi-table relational data support with complex constraints.
  • Evaluation metrics library to score synthetic data quality.
  • Capability to handle categorical, numerical, and date/time data types.
  • Lightweight Python library (sdv) for easy integration into notebooks.
• Pros:
  • The “gold standard” for open-source flexibility and academic research.
  • Allows for deep customization of the underlying generative models.
• Cons:
  • Can struggle with very large-scale datasets compared to proprietary engines.
  • Lacks a polished GUI in the core open-source version.
• Security & compliance: Varies (Open Source); commercial version (DataCebo) offers enterprise security.
• Support & community: Massive GitHub community, active Slack channel, and extensive academic documentation.

5 — K2view

K2view takes a unique “entity-centric” approach to data. It organizes data around specific entities (like a “Customer”) across disparate silos, then synthesizes them as a whole to ensure perfect consistency.

• Key features:
  • Micro-database architecture for entity-centric synthesis.
  • Real-time generation for dynamic testing and simulation.
  • Support for hundreds of data sources, including legacy mainframes.
  • Integrated data masking, tokenization, and synthesis.
  • Advanced subsetting and transformation capabilities.
• Pros:
  • Ideal for massive enterprises with highly fragmented data across many systems.
  • Ensures 100% referential integrity at the “entity” level.
• Cons:
  • Significant learning curve due to the unique architectural approach.
  • Typically requires a larger-scale deployment.
• Security & compliance: SOC 2, HIPAA, GDPR, and ISO 27001.
• Support & community: Enterprise-focused support with dedicated account managers and professional services.

6 — Syntho

Syntho is a European platform that focuses on high-performance synthesis for analytics and data sharing. It offers a “Syntho Engine” that is particularly effective at mimicking statistical distributions for BI and dashboarding.

• Key features:
  • PII Scanner: Automatically highlights sensitive fields before synthesis.
  • Syntho Engine: High-performance AI model for large tabular datasets.
  • Validation Reports: Detailed reports to prove “non-PII” status to legal teams.
  • Support for time-series data and up-sampling.
  • Integration with major BI tools like PowerBI and Tableau.
• Pros:
  • Very strong focus on European regulatory compliance (GDPR).
  • Excellent reporting features for governance and audit purposes.
• Cons:
  • Less widely adopted in the North American market compared to competitors.
  • UI is functional but less “developer-centric” than Gretel.
• Security & compliance: GDPR-first design, SOC 2, and ISO 27001.
• Support & community: Direct access to experts, regular webinars, and specialized GDPR consultation.

7 — Hazy

Hazy, based in the UK, specializes in synthetic data for the financial services sector. It is known for its ability to handle complex, sequential financial transactions while maintaining strict differential privacy.

• Key features:
  • Specialized algorithms for banking and credit risk data.
  • Differential Privacy mechanism integrated into the generative models.
  • Sequential Data: Maintains complex dependencies in time-ordered records.
  • Privacy-Utility Trade-off control: Adjust balance between privacy and accuracy.
  • Automated model selection for different data types.
• Pros:
  • Deep domain expertise in finance and banking use cases.
  • Highly realistic generation of time-ordered transactional data.
• Cons:
  • Primarily focused on structured/tabular data.
  • Pricing and setup are geared toward larger financial institutions.
• Security & compliance: SOC 2, GDPR, and high-level banking security standards.
• Support & community: High-touch enterprise support with deep industry knowledge.

8 — YData

YData focuses on the “Data Quality” aspect of synthetic data. It positions itself as a platform for improving AI performance through data augmentation and automated data profiling.

• Key features:
  • Data Augmentation: Create more examples of rare events (e.g., fraud).
  • Auto-balancing: Generates data to fix imbalanced datasets automatically.
  • Smart Profiling: Deep analysis of original data quality before synthesis.
  • Integration with MLOps stacks (Kubeflow, MLflow).
  • Streamlit-powered GUI for a no-code experience.
• Pros:
  • Excellent for improving the performance of existing ML models.
  • Strong “Data-Centric AI” philosophy that helps users understand their data.
• Cons:
  • Less emphasis on “test data management” compared to Tonic.ai.
  • Some features require a technical background to fully leverage.
• Security & compliance: SOC 2, GDPR, and SSO support.
• Support & community: Active Discord, regular MLOps webinars, and a high-quality technical blog.

9 — Betterdata.ai

Betterdata uses a combination of generative AI and privacy-enhancing technologies (PETs) to help organizations share and collaborate on sensitive data globally.

• Key features:
  • Relational Integrity: Focus on structural links between multi-table datasets.
  • Automated compliance scoring and reporting.
  • Support for synthetic data generation in multiple languages.
  • Collaborative workspaces for cross-border data sharing.
  • Privacy-preserving AI model training directly on synthetic data.
• Pros:
  • Strong focus on global data residency and cross-border collaboration.
  • Good balance of ease of use and advanced privacy features.
• Cons:
  • Newer player with a smaller market footprint than industry giants.
  • Documentation is still maturing compared to Gretel or Mostly AI.
• Security & compliance: SOC 2 (in progress), GDPR, and HIPAA.
• Support & community: Direct access to founding engineers and proactive customer success.

10 — MDClone

MDClone is a highly specialized platform dedicated entirely to the healthcare vertical. It allows medical researchers to access patient data in a synthetic format that is completely non-identifiable.

• Key features:
  • Synthetic healthcare records (EHR) generation.
  • Specialized for patient longitudinal data (medical history over time).
  • Query-based synthetic data extraction.
  • Integrated healthcare analytics platform.
  • Secure sandbox environments for clinical research.
• Pros:
  • The undisputed leader for healthcare-specific synthetic data.
  • Accelerates medical research by bypassing the 6-12 month IRB approval cycle.
• Cons:
  • Not applicable for any industry outside of healthcare and life sciences.
  • High cost associated with its specialized medical data engine.
• Security & compliance: HIPAA, GDPR, and rigorous healthcare data standards.
• Support & community: Professional clinical support teams and a dedicated healthcare research community.

Comparison Table

Evaluation & Scoring of Synthetic Data Generation Tools

The following rubric provides a weighted scoring system to help you evaluate which synthetic data solution meets your specific requirements.

Which Synthetic Data Generation Tool Is Right for You?

Selecting the right tool depends on your technical maturity and the specific problem you are trying to solve.

• Solo Users & Academic Researchers: The SDV (Synthetic Data Vault) open-source library is your best starting point. It is free, highly customizable, and has a massive community to help you navigate complex research scenarios.
• Small to Medium Businesses (SMBs): Gretel.ai or YData are ideal. Gretel’s pay-as-you-go API model is perfect for teams that don’t want to commit to massive enterprise contracts, while YData is fantastic for improving model performance on a budget.
• Mid-Market Enterprises: MOSTLY AI is the best choice if you need the highest possible accuracy for your AI models without hiring a team of privacy engineers. It provides the most “out of the box” value for tabular data.
• Large-scale Enterprises with Legacy Data: K2view or Tonic.ai are preferred. If you have data spread across mainframes, SAP, and modern SQL databases, these tools have the architectural “muscle” to keep everything in sync.
• Healthcare & Finance Verticals: If you are in medical research, MDClone is a specialized must-have. For banking and high-risk credit modeling, Hazy offers the deep domain expertise required to satisfy financial regulators.

Frequently Asked Questions (FAQs)

1. Is synthetic data actually as good as real data for AI training?

Yes, in many cases. Modern AI synthesis preserves statistical correlations so well that models trained on synthetic data often perform within 1-2% of those trained on real data—sometimes even better if the synthetic data was used to “balance” out biases or add rare edge cases.

2. Is synthetic data truly anonymous?

Technically, “anonymity” is a legal term, but synthetic data is inherently privacy-preserving because it contains no original records. Using tools with Differential Privacy adds a mathematical guarantee that no individual from the original dataset can be re-identified.

3. What is the difference between data masking and synthetic data?

Data masking obscures or redacts specific fields (like changing “John Doe” to “User 123”). Synthetic data generates entirely new, fake records from scratch. Synthetic data is safer because it eliminates the risk of “re-identification attacks” that can often break masking.

4. How long does it take to generate a synthetic dataset?

For simple datasets, it can take minutes. For massive, multi-table relational databases with millions of rows, the initial “training” of the AI model might take several hours, but once the model is trained, generating new data is almost instantaneous.

5. Can I use synthetic data for images and video?

Yes, but most of the tools on this list specialize in structured/tabular data. For synthetic images or video (Computer Vision), you would typically look for specialized tools like NVIDIA Omniverse or Synthesis AI.

6. Does synthetic data help with GDPR compliance?

Absolutely. Under GDPR, synthetic data is often classified as “non-personal data” because it does not relate to an identified or identifiable natural person. This allows organizations to use the data for research and development without the same restrictions as PII.

7. What is “Referential Integrity” in synthetic data?

It means that if a “Customer ID” in Table A links to a “Transaction” in Table B, that link remains consistent in the synthetic version. If this is broken, the synthetic data is useless for testing or complex analytics.

8. Can synthetic data be biased?

Yes. If the original data is biased (e.g., favoring one demographic), the synthetic data will likely mirror that bias. However, tools like MOSTLY AI and YData allow you to “de-bias” the data during the generation process.

9. Is there a free version of these tools?

SDV is entirely open-source and free. Gretel.ai and MOSTLY AI also offer generous free tiers for developers and small-scale experimentation.

10. Do I need a GPU to run these tools?

For small datasets, a standard CPU is fine. For high-speed generation of massive datasets or complex time-series data, most tools will leverage NVIDIA GPUs to accelerate the AI training process.

Conclusion

The transition from “Real-Data-First” to “Synthetic-Data-First” is one of the most significant shifts in modern AI. By moving away from the risks and limitations of sensitive real-world data, organizations can finally innovate at the speed of the cloud. Whether you choose the developer-friendly API of Gretel, the enterprise power of MOSTLY AI, or the medical precision of MDClone, the “best” tool is the one that aligns with your industry’s unique regulatory and technical requirements.