Synthetic data generation is revolutionizing machine learning by providing secure, scalable, and privacy-compliant datasets for training AI models. With Gartner predicting that synthetic data will surpass real data usage by 2030, the demand for high-quality synthetic data generation tools is skyrocketing. Whether you’re tackling data privacy concerns, addressing dataset scarcity, or enhancing model robustness, these tools are essential for machine learning practitioners. This article explores the best synthetic data generation tools for machine learning in 2025, offering detailed reviews, comparisons, and insights to help you choose the right solution for your AI projects.
Why Use Synthetic Data in Machine Learning?
Synthetic data, artificially generated to mimic real-world datasets, addresses critical challenges in machine learning:
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Privacy Compliance: Protects sensitive information, ensuring compliance with GDPR, CCPA, and other regulations.
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Data Scarcity: Generates diverse datasets when real data is limited or expensive.
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Bias Reduction: Creates balanced datasets to improve model fairness and performance.
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Cost Efficiency: Reduces the need for costly data collection and annotation.
By leveraging synthetic data generation tools, data scientists can train robust machine learning models without compromising ethics or quality. Below, we review the top tools available in 2025, focusing on their features, pricing, and suitability for machine learning tasks.
Top 10 Synthetic Data Generation Tools for Machine Learning
1. Mostly AI
Overview: Mostly AI is a leading platform for generating high-fidelity synthetic data, particularly for tabular datasets. Its AI-driven approach ensures realistic data distributions, making it ideal for machine learning applications in finance, healthcare, and retail.
Key Features:
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Generates synthetic tabular data with statistical fidelity.
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Supports differential privacy for enhanced data security.
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Integrates with Python, R, and cloud platforms like AWS and Azure.
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User-friendly interface for non-technical users.
Pros:
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High-quality data with minimal information loss.
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Scalable for large datasets.
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Strong privacy guarantees.
Cons:
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Premium pricing may deter small teams.
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Limited support for unstructured data (e.g., images).
Pricing: Starts at $5,000/year for enterprise plans; custom pricing for larger deployments.
Use Case: Ideal for training machine learning models in regulated industries requiring privacy-compliant datasets.
2. Synthetic Data Vault (SDV)
Overview: SDV is an open-source Python library designed for generating synthetic data for machine learning. It supports tabular, relational, and time-series data, making it versatile for various AI applications.
Key Features:
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Supports multiple data types (numerical, categorical, datetime).
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Implements deep learning-based models like GaussianCopula and CTGAN.
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Open-source with active community support.
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Customizable for specific dataset constraints.
Pros:
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Free and open-source, perfect for startups and researchers.
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Highly customizable for advanced users.
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Regular updates and community-driven improvements.
Cons:
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Steep learning curve for non-technical users.
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Performance may lag with very large datasets.
Pricing: Free (open-source).
Use Case: Best for data scientists building custom machine learning pipelines on a budget.
3. DataSynthesizer
Overview: DataSynthesizer is an open-source tool for generating synthetic datasets with differential privacy guarantees. It’s widely used in academic research and small-scale machine learning projects.
Key Features:
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Implements differential privacy to protect sensitive data.
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Generates tabular data with customizable parameters.
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Lightweight and easy to integrate with Python workflows.
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Supports random, independent, and correlated data generation modes.
Pros:
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Free and open-source.
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Strong privacy features for compliance.
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Simple setup for small datasets.
Cons:
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Limited scalability for enterprise use.
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Basic feature set compared to commercial tools.
Pricing: Free (open-source).
Use Case: Suitable for researchers and small teams needing privacy-preserving synthetic data for machine learning experiments.
4. Synthea
Overview: Synthea is an open-source tool focused on generating synthetic medical data, such as patient records, for healthcare machine learning applications.
Key Features:
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Generates realistic patient data based on medical ontologies.
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Supports FHIR and CSV output formats.
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Customizable disease progression models.
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Community-driven with regular updates.
Pros:
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Free and tailored for healthcare use cases.
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High-fidelity medical data.
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Easy to use for non-technical healthcare professionals.
Cons:
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Limited to healthcare-specific data.
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Less flexible for general machine learning tasks.
Pricing: Free (open-source).
Use Case: Perfect for training machine learning models in healthcare, such as predicting patient outcomes.
5. Gretel.ai
Overview: Gretel.ai offers a cloud-based platform for synthetic data generation, leveraging advanced AI models to create high-quality datasets for machine learning.
Key Features:
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Supports tabular, time-series, and text data.
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Uses generative AI models like GANs and Transformers.
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Integrates with cloud platforms and data warehouses.
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Built-in privacy metrics and compliance checks.
Pros:
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User-friendly with robust cloud integration.
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High-quality synthetic data for diverse use cases.
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Scalable for enterprise needs.
Cons:
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Higher cost for advanced features.
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Cloud dependency may concern some users.
Pricing: Freemium plan available; paid plans start at $500/month.
Use Case: Ideal for enterprises needing scalable, cloud-based synthetic data for machine learning.
6. YData
Overview: YData provides an AI-powered platform for synthetic data generation, with a focus on tabular and time-series data for machine learning and data science.
Key Features:
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Automated data profiling and synthesis.
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Supports structured and semi-structured data.
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Integrates with Python and popular ML frameworks.
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Privacy-preserving synthesis options.
Pros:
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Intuitive interface for data scientists.
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Strong focus on data quality.
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Scalable for large datasets.
Cons:
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Limited support for unstructured data.
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Pricing can be high for small teams.
Pricing: Starts at $1,000/month for enterprise plans.
Use Case: Best for data science teams working on tabular data-driven machine learning projects.
7. Hazy
Overview: Hazy specializes in synthetic data generation for financial services, offering tools to create privacy-compliant datasets for machine learning.
Key Features:
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Generates synthetic financial data with high fidelity.
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Supports differential privacy and anonymization.
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Cloud and on-premises deployment options.
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Tailored for regulatory compliance (e.g., GDPR).
Pros:
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Industry-specific focus on finance.
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Strong privacy and compliance features.
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Flexible deployment options.
Cons:
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Niche focus limits versatility.
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Expensive for non-financial use cases.
Pricing: Custom pricing, typically $10,000+/year.
Use Case: Ideal for financial institutions training machine learning models on sensitive data.
8. Syntho
Overview: Syntho is a versatile platform for generating synthetic data, supporting tabular, text, and image data for machine learning applications.
Key Features:
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AI-driven synthesis for diverse data types.
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Supports GDPR-compliant data generation.
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Integrates with SQL databases and cloud platforms.
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Customizable data generation workflows.
Pros:
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Broad data type support.
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User-friendly for enterprise users.
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Strong compliance features.
Cons:
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Higher cost for advanced features.
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Limited open-source community support.
Pricing: Starts at $2,000/month.
Use Case: Suitable for enterprises needing synthetic data across multiple data types.
9. TGAN
Overview: TGAN is an open-source Python library for generating synthetic tabular data using Generative Adversarial Networks (GANs).
Key Features:
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Leverages GANs for realistic data generation.
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Supports numerical and categorical data.
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Lightweight and Python-based.
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Active open-source community.
Pros:
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Free and open-source.
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High-quality synthetic data for tabular datasets.
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Easy to integrate with ML pipelines.
Cons:
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Requires technical expertise to use effectively.
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Limited scalability for large datasets.
Pricing: Free (open-source).
Use Case: Best for advanced users building custom machine learning models with tabular data.
10. NVIDIA DLSS (Deep Learning Synthetic Data Service)
Overview: NVIDIA’s DLSS is a premium tool for generating synthetic image and video data, tailored for computer vision machine learning tasks.
Key Features:
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Generates high-resolution synthetic images/videos.
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Integrates with NVIDIA’s AI ecosystem (e.g., Omniverse).
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Supports custom 3D environments for data generation.
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Optimized for GPU acceleration.
Pros:
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Unmatched quality for visual data.
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Ideal for computer vision applications.
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Scalable for large-scale projects.
Cons:
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Expensive and hardware-intensive.
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Limited to image/video data.
Pricing: Custom pricing, typically $20,000+/year.
Use Case: Perfect for computer vision teams training machine learning models on synthetic visual data.
Comparison of Synthetic Data Generation Tools
|
Tool |
Data Types Supported |
Pricing |
Privacy Features |
Best For |
|---|---|---|---|---|
|
Mostly AI |
Tabular |
$5,000+/year |
Differential Privacy |
Regulated Industries |
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SDV |
Tabular, Relational, Time-Series |
Free |
Basic Privacy |
Researchers, Startups |
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DataSynthesizer |
Tabular |
Free |
Differential Privacy |
Academic Research |
|
Synthea |
Medical Records |
Free |
Basic Privacy |
Healthcare ML |
|
Gretel.ai |
Tabular, Text, Time-Series |
$500+/month |
Advanced Privacy |
Enterprises |
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YData |
Tabular, Time-Series |
$1,000+/month |
Privacy Options |
Data Science Teams |
|
Hazy |
Financial Data |
$10,000+/year |
Differential Privacy |
Financial Services |
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Syntho |
Tabular, Text, Image |
$2,000+/month |
GDPR Compliance |
Multi-Data Enterprises |
|
TGAN |
Tabular |
Free |
Basic Privacy |
Advanced ML Users |
|
NVIDIA DLSS |
Image, Video |
$20,000+/year |
Limited Privacy |
Computer Vision |
How to Choose the Best Synthetic Data Generation Tool
Selecting the right tool depends on your machine learning project’s needs:
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Budget: Open-source tools like SDV and DataSynthesizer are ideal for startups, while Mostly AI and Gretel.ai suit enterprises.
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Data Type: Choose NVIDIA DLSS for images/videos, Syntho for mixed data, or SDV for tabular data.
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Privacy Needs: Prioritize tools with differential privacy (e.g., Mostly AI, Hazy) for regulated industries.
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Scalability: Gretel.ai and YData excel for large-scale projects.
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Ease of Use: Mostly AI and Syntho offer user-friendly interfaces for non-technical users.
Consider trialing free or freemium versions to evaluate compatibility with your machine learning workflows.
FAQs
What is synthetic data generation?
Synthetic data generation creates artificial datasets that mimic real-world data, used for training machine learning models without privacy risks.
Are there free synthetic data generation tools?
Yes, SDV, DataSynthesizer, Synthea, and TGAN are free, open-source options for machine learning.
Which tool is best for privacy-compliant data?
Mostly AI and Hazy offer robust differential privacy features, ideal for GDPR and CCPA compliance.
Can synthetic data improve machine learning model performance?
Yes, synthetic data can reduce bias, increase dataset diversity, and address data scarcity, enhancing model accuracy.
Conclusion
The best synthetic data generation tools for machine learning in 2025 cater to diverse needs, from privacy-compliant tabular data to high-resolution synthetic images. Tools like Mostly AI and Gretel.ai lead for enterprise use, while SDV and DataSynthesizer are perfect for budget-conscious researchers. By selecting a tool aligned with your data type, budget, and privacy requirements, you can unlock the full potential of synthetic data for your machine learning projects. Explore these tools, try free versions, and share your experiences in the comments below!