Mostly AIOffers formal privacy guarantees with differential privacy and certified anonymisation reports95.0NEWCustom
Gretel.aiSupports multiple data types including tabular, time-series, and text with integrated privacy controls92.7NEWFree (limited), $500/month (paid plans)
Synthesis AIGenerates fully annotated photorealistic human images with precise demographic and pose control90.3NEWCustom
Tonic.aiIntegrates with major databases and data warehouses for automated synthetic data pipeline creation88.0NEWCustom
DatagenGenerates multi-modal sensor data including camera, lidar, and radar for autonomous systems85.7NEWCustom
YDataIntegrates data quality assessment with synthetic data generation in a unified platform83.3NEWFree (limited), Enterprise custom pricing
HazyProvides certified privacy reports and governance frameworks for regulated industries81.0NEWCustom
Clearbox AIEmphasises transparent and interpretable synthetic data generation with validation workflows78.7NEWCustom
MOSTLY AIAchieves synthetic data validity scores whilst guaranteeing subject-level privacy at enterprise scale.76.3NEWCustom
Largely AISpecialises in generating diverse synthetic data specifically to address ML model bias and improve generalisation.74.0NEWCustom
Mostly AI specialises in generating high-quality synthetic versions of tabular data with formal privacy guarantees. Their platform uses generative AI to create datasets that maintain statistical properties while ensuring differential privacy compliance.
Gretel.ai provides a comprehensive platform for generating synthetic data across multiple modalities including structured tables, sequential data, and text. They emphasise privacy by design with built-in differential privacy and synthetic data classification.
Synthesis AI specialises in generating photorealistic synthetic images of human faces and bodies with automatic annotations. Their platform is designed for computer vision and facial recognition model training with complete control over demographics and pose.
Tonic.ai combines data masking with synthetic data generation to create realistic but privacy-protected datasets for development and testing. Their platform integrates with common databases and data warehouses to streamline the process.
Datagen (formerly known as Synthesis AI's robotics division) generates synthetic training data for autonomous systems, robotics, and simulation environments. They provide photorealistic sensor data including camera feeds, lidar, and radar.
YData provides tools for generating synthetic data to address data quality issues and accelerate model training. Their platform includes synthetic data generation, data profiling, and data quality assessment for machine learning workflows.
Hazy specialises in generating privacy-protected synthetic data for enterprises with strong regulatory requirements. Their platform includes comprehensive data governance and privacy compliance features suitable for financial services and healthcare.
Clearbox AI focuses on synthetic data generation with emphasis on explainability and interpretability. Their approach helps users understand and validate the synthetic data generation process with transparent algorithms.
Leading enterprise platform providing unstructured and structured synthetic data generation with proprietary AI algorithms to maintain data utility whilst ensuring privacy and compliance across multiple sectors.
Largely AI focuses on generating diverse synthetic datasets to improve machine learning model robustness and reduce bias, particularly for computer vision and natural language processing applications.
Data masking (or de-identification) obscures sensitive values in real data whilst maintaining structure, whereas synthetic data is entirely new data generated algorithmically that mimics real data's statistical properties without containing actual records. Synthetic data is generally more flexible for AI training but requires more validation for quality assurance.
Leading platforms use differential privacy, formal privacy guarantees, and third-party audits to certify privacy protection. They generate data that is mathematically proven to be privacy-preserving and can withstand reconstruction attacks. Organisations should verify the specific privacy certifications relevant to their regulatory requirements (GDPR, HIPAA, etc.).
Synthetic data can effectively supplement real data and is particularly valuable for addressing imbalanced datasets, rare classes, or scenarios where real data is unavailable or restricted. However, purely synthetic training often requires validation that generated patterns align with actual distribution. Hybrid approaches combining both datasets typically yield optimal results.
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