deploybase

Best Synthetic Data Generation Tools: Comparing Gretel, MOSTLY AI, Tonic, and More

Deploybase · April 7, 2025 · AI Tools

Contents

Synthetic data generation tools let developers generate training data that keeps privacy intact. Real data has sensitive stuff:names, financial details, medical histories. Can't share it. Synthetic data solves this: mathematically generated datasets that look and behave like real data without exposing individuals.

Use cases: test algorithms on data developers couldn't legally share, augment limited datasets, create benchmarks. Faster development, lower liability, fewer GDPR headaches.

This guide examines leading synthetic data platforms, compares their approaches, and breaks down pricing structures.

Why Synthetic Data Matters

Models trained on historical data don't generalize. New customer segments break them. New hospitals break medical AI. New fraud types slip past detection models.

Synthetic data fixes this. Generate underrepresented segments, edge cases, emerging patterns. Train on synthetic, get better coverage.

Privacy regulations (GDPR, HIPAA, CCPA) block raw data sharing. Synthetic removes that obstacle. Financial services company shares synthetic transactions without exposing customer IDs. Healthcare vendor gets synthetic patient data without HIPAA violations.

Bonus: data monetization. Create synthetic versions of proprietary datasets, license them, collect revenue.

Gretel: Privacy-First Synthetic Data

Gretel focuses on privacy guarantees. The platform generates synthetic data with formal differential privacy guarantees: mathematical proofs that specific individuals cannot be identified from the synthetic data.

How it works: Upload the dataset to Gretel's platform. The system trains generative models (typically transformers or diffusion models) on the data, learning statistical distributions. Gretel then generates new samples from these distributions, with differential privacy injections at training time and inference time ensuring no individual can be identified.

Key Features:

  • Differential privacy guarantees (mathematically provable privacy)
  • Multiple synthesis models (historical correlation, generative models)
  • Quality metrics (comparing synthetic vs. real distributions)
  • PII detection and masking
  • Integration with data warehouses (BigQuery, Snowflake)

Pricing: Gretel charges per row of synthetic data generated. Typical pricing starts at $0.01-0.05 per synthetic row depending on dataset complexity and privacy level. Generating 1M synthetic rows costs $10,000-50,000.

For comparison, acquiring real data of similar quality costs 10x more through data brokers. Privacy guarantees add premium pricing but justify the cost in regulated industries.

Best For: Financial services, healthcare, government agencies, and any organization with regulated data. Companies needing formal privacy certification (for compliance audits) should prioritize Gretel.

MOSTLY AI: Speed and Quality Balance

MOSTLY AI emphasizes balancing speed (quick synthesis) and quality (synthetic data closely matches original distribution).

How it works: Upload structured tabular data. MOSTLY AI trains on the data, then generates unlimited synthetic records. The platform supports conditional synthesis: "Generate synthetic customers similar to this segment" or "Generate edge cases unlike our current data."

Key Features:

  • Fast synthesis (minutes from upload to synthetic data)
  • Quality metrics (testing synthetic vs. real performance)
  • Conditional generation (generate specific patterns)
  • Privacy reports (estimating re-identification risk)
  • REST API for programmatic use

Pricing: MOSTLY AI charges per million synthetic rows generated, typically $0.10-0.30 per million rows depending on privacy level. Much cheaper than Gretel but fewer privacy guarantees. For 10M synthetic rows at $0.20/row: $2,000.

Best For: Companies prioritizing speed and cost over formal privacy guarantees, non-regulated industries, data augmentation for model training. Ideal for startup pilots and rapid prototyping.

Tonic: Data Masking and Synthetic Combination

Tonic takes a different approach, combining multiple techniques: masking (replacing sensitive values), synthesis (generating new data), and transformation (modifying data while preserving relationships).

How it works: Tonic connects directly to the database. For each table, developers configure rules: which columns to mask (replace with fake values), which to synthesize (generate new values), which to preserve (keep original). Tonic applies these rules at scale, generating new dataset with all sensitive information replaced or synthesized.

Key Features:

  • Database-native operation (works with the existing data warehouse)
  • Multiple masking strategies (shuffling, hashing, substitution, synthesis)
  • Relationship preservation (foreign keys, referential integrity maintained)
  • Testing automation (validate synthetic data quality before production use)
  • Continuous synthesis (scheduled updates as source data changes)

Pricing: Tonic charges per GB of data masked/synthesized, typically $1,000-5,000 monthly for databases under 1TB. The database-native approach makes Tonic practical for teams with existing data warehouse infrastructure.

Best For: Enterprises with established data warehouses, teams masking data for development environments, companies needing continuous data refresh (monthly updates).

Synthetic Data Vault (SDV): Open-Source Approach

Synthetic Data Vault is open-source software for generating synthetic data. Unlike commercial platforms, SDV runs locally or on the infrastructure, giving developers complete control.

How it works: Install SDV via Python. Load the dataset, specify metadata (column types, constraints), and call fit() to train the model. Then call sample() to generate synthetic data. Multiple models available: Gaussian mixture models for simple data, neural networks for complex relationships.

Key Features:

  • Open-source (free, transparent, customizable)
  • Multiple synthesis models (choose appropriate model for the data)
  • Constraint specification (enforce business rules in synthetic data)
  • Evaluation metrics (quantitatively assess synthetic data quality)
  • Time series support (synthesize sequential data like sensor readings)

Pricing: Open-source, free. Developers pay only for compute infrastructure (GPU if using neural network models).

Best For: Teams comfortable with open-source software, research institutions, teams wanting full control over synthesis process, companies with limited budgets.

Technical Approach Comparison

Different platforms use different underlying techniques, each with tradeoffs.

Differential Privacy (Gretel):

  • Adds mathematical noise ensuring privacy
  • Reduces utility slightly (synthetic data is slightly less accurate)
  • Provides formal privacy guarantees
  • Best for regulated industries

Generative Models (MOSTLY AI, Gretel):

  • Train neural networks to learn data distribution
  • Generate completely new samples from learned distribution
  • Better quality when dataset is large (10k+ rows)
  • Requires more computation

Masking (Tonic):

  • Replace sensitive columns with fake values
  • Preserve data relationships perfectly
  • Faster than synthesis
  • Lower privacy for some columns (fake values might not be unique enough)

Hybrid (SDV):

  • Combine multiple techniques
  • Tailor approach to each column type
  • Maximum flexibility
  • Requires more configuration

Quality Metrics and Evaluation

How do developers know synthetic data is good? Platforms provide several metrics:

Distribution Similarity (Kolmogorov-Smirnov test, Wasserstein distance): Compares statistical distributions of real vs. synthetic columns. Synthetic data should have similar distribution shape, mean, and variance.

Correlation Preservation: Real data has column correlations (customer age correlates with credit limit). Synthetic data should maintain these relationships. Pearson correlation of real columns should match synthetic columns.

Downstream Performance: Train machine learning models on real data, then on synthetic data, and compare performance. If a fraud detection model achieves 95% accuracy on real data and 93% on synthetic data, the synthetic data is high-quality.

Privacy Metrics: Risk of re-identification, how many real records could be reverse-engineered from synthetic data. Lower scores are better. Gretel provides formal differential privacy (best), others estimate empirical risk.

Regulatory Compliance and Privacy by Design

Synthetic data helps teams meet privacy regulations.

GDPR compliance: GDPR restricts how personal data is processed. Synthetic data that contains no personal information bypasses many GDPR requirements. European teams can generate synthetic datasets and share freely without GDPR friction.

HIPAA compliance (healthcare): HIPAA restricts access to patient data. Synthetic patient data maintains statistical properties without exposing protected health information. Healthcare providers can share synthetic datasets with researchers and vendors without HIPAA violation risk.

CCPA compliance (California privacy law): Similar to GDPR. Synthetic data reduces compliance burden while enabling data use.

SOC 2 Type II audit readiness: Auditors review how companies handle sensitive data. Synthetic data demonstrates privacy-first approach, strengthens audit results.

Synthetic data is increasingly part of compliance strategy, not just nice-to-have. Regulated teams should treat synthetic data generation as core infrastructure.

Use Cases in Practice

Data Augmentation: A healthcare AI developer has 5,000 patient records, too few for reliable model training. Generate 100,000 synthetic records representing similar distributions. Train on combined real and synthetic data, achieving better model performance.

Development Environment Privacy: A fintech company with 1 billion production transactions can't share real data with developers (privacy regulations, competitive concerns). Mask and synthesize 100 million transactions, create development database with synthetic data. Developers train models without touching real customer information.

Benchmark Datasets: Create public synthetic datasets for competition/benchmarking without privacy concerns. Healthcare researchers can publish synthetic patient data publicly, enabling global collaboration on algorithms.

Edge Case Testing: The fraud detection model lacks examples of a specific emerging fraud type. Generate synthetic fraud patterns matching the heuristics, augment training data with these edge cases, retrain model.

Cross-Border Data Transfer: Transfer patient or customer data across borders violates privacy laws in many countries. Generate synthetic versions, transfer those (legal in all jurisdictions), train models.

Selecting the Right Tool

Choosing among platforms requires evaluating the priorities:

Privacy Critical? Choose Gretel (formal guarantees) or Tonic (masking approach preserves relationships). MOSTLY AI and SDV work but provide fewer guarantees.

Large-Scale Continuous Processing? Choose Tonic (database-native, scheduled runs). MOSTLY AI works but at higher cost for continuous generation.

Cost-Sensitive? Choose SDV (free), MOSTLY AI (cheap), or Gretel/Tonic (premium).

Ease of Use? Choose MOSTLY AI (web interface) or Tonic (wizard-driven setup). SDV requires coding.

Custom Requirements? Choose SDV (fully customizable) or open-source Gretel (also available open-source for non-commercial use).

Building Synthetic Data into The Workflow

Successful synthetic data adoption requires integrating it into the development process:

  1. Assess Privacy: Which columns are sensitive? Which are safe to share?
  2. Generate Synthetic Dataset: Use the chosen platform to create synthetic versions
  3. Validate Quality: Compare distributions, correlations, downstream model performance
  4. Replace Real Data: Use synthetic data in development, testing, demos
  5. Update Processes: Each time raw data changes, regenerate synthetic versions

Teams building reliable ML systems incorporate synthetic data as part of their data pipeline, alongside monitoring and evaluation infrastructure.

Cost-Benefit Analysis

Synthetic data cost varies widely but should be evaluated against benefits:

  • Data acquisition cost: Real data costs $1M+ if external
  • Privacy liability: Data breach costs average $4M
  • Time to market: Synthetic data enables rapid development (weeks vs. months)
  • Model quality: Good augmentation improves production model accuracy 5-15%

For most teams, synthetic data ROI is positive within 6-12 months.

Advanced Quality Assessment and Benchmarking

Beyond basic metrics, mature teams conduct sophisticated quality evaluation.

Downstream task evaluation trains models on real data and synthetic data separately, comparing final model performance. If a fraud detection model achieves 95% accuracy on real data and 93% on synthetic data, synthetic data is high-quality. This is the most business-relevant metric.

Temporal consistency evaluates whether synthetic data maintains time-series properties. Real data often has temporal correlations (customer spending patterns vary by season). Synthetic data should preserve these patterns. Evaluate by training time-series forecasting models on synthetic data and testing on real data.

Adversarial evaluation tests whether individuals can be re-identified from synthetic data. This is stronger than statistical re-identification risk. Attempt to match synthetic records to original records using available information. Success indicates privacy leakage.

Sensitivity testing introduces variations in training data and measures impact on synthetic data. Small training data changes should result in small synthetic data changes. Large divergence indicates overfitting/memorization.

Fairness evaluation ensures synthetic data preserves demographic representation. Original data has specific gender/racial/age distributions. Synthetic data should match these distributions. Failure indicates bias in generation.

Ethical Considerations and Responsible Synthetic Data

Synthetic data enables responsible AI, but creates new ethical questions.

Bias amplification: If synthetic data comes from biased real data, synthesis might amplify bias. A generation model trained on biased hiring data might produce more biased synthetic hiring data. Evaluate fairness metrics carefully.

Misrepresentation: Synthetic data might misrepresent reality if the generation model has gaps. Gaps in training data lead to gaps in synthetic data. Be transparent when using synthetic data for important decisions.

Liability: Who is responsible if synthetic data harms someone? This is legally murky. Document assumptions and limitations when deploying systems trained on synthetic data.

Consent: Generating synthetic versions of people's data, even with privacy guarantees, raises ethical questions. Users might not expect their data to be synthesized. Consider consent even when not legally required.

Document these considerations when deploying synthetic data. Communicate clearly with stakeholders about what data is synthetic and what assumptions underlie its generation.

Implementation Best Practices

Start small: Pilot with one dataset, one use case. Measure impact before expanding.

Validate thoroughly: Don't trust generation blindly. Run downstream models, compare metrics, ensure quality justifies the investment.

Document assumptions: Record what generation model was used, what hyperparameters, what privacy level. This enables reproducibility and auditing.

Version synthetic datasets: Track which version of generation code created synthetic data. Regenerate periodically as platforms improve.

Maintain lineage: Track which synthetic data came from which real data. If real data is deleted (GDPR right to be forgotten), can derived synthetic data be affected?

Integrate with ML pipeline: Synthetic data shouldn't be one-off. Integrate into automated pipelines. Generate weekly/monthly updated synthetic datasets as real data evolves.

Final Thoughts

Synthetic data generation is moving from research to production practice. Teams serious about responsible AI, privacy-first development, and efficient data utilization should invest in synthetic data capabilities.

Gretel offers the strongest privacy guarantees for regulated industries. MOSTLY AI balances speed and quality cost-effectively. Tonic integrates with existing data warehouse infrastructure. Synthetic Data Vault provides open-source flexibility and cost efficiency.

Start with a pilot project: select one dataset, generate synthetic version, validate quality, measure impact on the workflow. Use this experience to guide platform selection and expansion to additional datasets. Within months, synthetic data will become a standard component of the data pipeline, enabling faster development, better privacy, and higher-quality models.

Build synthetic data into the data infrastructure alongside data labeling, validation, and version control. Treat synthetic data as a core asset: version it, document it, monitor its quality. As the ML systems mature, synthetic data becomes increasingly valuable for testing, augmentation, and privacy-preserving development.

The competitive advantage belongs to teams that synthesize data strategically while maintaining quality and ethical standards. Start early, start small, learn deeply from the pilot projects.