Top Synthetic Data Generation Tools for AI and Testing in 2025

How does synthetic data generation work?

Most platforms estimate the distribution of real data, sample new records that follow the learned distribution, then apply post-processing to meet constraints or business rules. Techniques include statistical modeling, machine learning, and deep learning.

Key advantages

• Protects sensitive data and strengthens privacy programs
• Helps teams meet regulatory obligations
• Shortens development and testing timelines
• Lowers the cost of data provisioning and sharing
• Improves collaboration by sharing safe-to-use datasets
• Can help reduce unwanted bias when fairness controls are applied
• Gives developers and testers more control over dataset shape and quality

Top synthetic data generation options in 2025

1. K2view — best for all-in-one enterprise testing and data privacy
Recognized as a “Visionary” in Gartner’s 2024 Magic Quadrant for Data Integration Tools, K2view combines synthetic data generation with test data management and data masking to support secure testing at scale. It offers no-code workflows so testers can parameterize datasets for specific scenarios.

2. Gretel — best for AI and machine-learning workflows
Gretel provides APIs and models for generating privacy-preserving synthetic data across tabular data, text, JSON, events, and more. It is well-suited to developer pipelines and research workflows.

3. Synthesis AI — best for computer-vision datasets
Synthesis AI focuses on high-fidelity, labeled visual datasets for training perception models. It supports complex, human-centric, and in-vehicle scenarios to accelerate CV development where real data collection is slow or costly.

4. Sogeti — best for enterprise services across multiple domains
Sogeti (part of Capgemini) offers consulting and tooling for synthetic and test data, with guidance tailored to regulated industries and large enterprise landscapes.

5. MOSTLY AI — best for compliant data sharing and fairness controls
MOSTLY AI generates privacy-safe synthetic datasets that preserve statistical properties of source data. It includes fairness tooling to target parity on sensitive attributes, which can help reduce disparate outcomes in downstream models.

6. Synthea — best open source option for healthcare
Synthea is an open source synthetic patient generator used for research, validation, and testing without exposing real patient data. It produces rich, labeled health records, making it a strong fit for clinical and health IT use cases.

7. Hazy — best for financial services datasets
Hazy specializes in high-quality synthetic data for financial services and other regulated sectors, delivered as an enterprise platform.

Open Source Options for Linux Teams

If you’re running workloads on Linux or working in highly regulated environments, open source tools can be especially valuable. Synthea remains the best-known choice for healthcare, producing detailed synthetic patient records for research and testing without exposing real PHI.

Another strong option is the Synthetic Data Vault (SDV) ecosystem, a set of Python libraries designed for generating tabular, relational, and time-series synthetic data. SDV is widely used in academic and enterprise projects and benefits from a growing open source community.

Open source solutions give teams full control over the software stack, greater transparency for audits, and the flexibility to extend or adapt tools for custom test cases — advantages that align closely with the priorities of Linux security practitioners.

Bottom line for 2025

Choose a platform that matches your compliance needs, data types, and delivery model. For enterprises consolidating test data, K2view and Hazy are strong fits. For AI-heavy teams, Gretel and Synthesis AI accelerate training. If you prefer open source, start with Synthea for healthcare or other community-driven options for tabular and time-series data.