Real data is expensive, restricted, and often unusable. Privacy regulations block access to customer records. Data sharing agreements prevent using production data in development environments. Class imbalance makes training datasets biased. Edge cases are underrepresented because they are rare by definition. Synthetic data generation addresses all of these problems by producing artificial data that preserves the statistical properties of real data without containing any real records.
Three tools have emerged as serious options: Gretel, Mostly AI, and Tonic. They all generate synthetic tabular data. They differ in their privacy guarantees, their fidelity metrics, their integration patterns, and the trade-offs they make between statistical accuracy and privacy protection.
The Fundamental Trade-off
Synthetic data generation faces an inherent tension: the more faithfully the synthetic data reproduces the statistical patterns of the real data, the higher the risk that individual records can be re-identified. Perfect fidelity means the synthetic data is effectively a copy of the real data, which defeats the privacy purpose. Perfect privacy means the synthetic data bears no statistical relationship to the real data, which defeats the utility purpose.
Every synthetic data tool navigates this trade-off differently. Some prioritize fidelity and provide privacy as a configurable parameter. Some prioritize privacy and accept fidelity loss as the cost. The right choice depends on whether your primary use case is data sharing (privacy matters most), model training (fidelity matters most), or development environments (both matter moderately).
Gretel: Developer-Focused Platform
Gretel positions itself as a developer platform for synthetic data, not just a generation tool. The platform provides APIs for generating synthetic data, detecting PII, transforming data, and evaluating the quality of synthetic datasets. The broader scope makes Gretel useful for data teams that need more than just generation.
Gretel’s model selection is automatic. You provide a dataset, and Gretel selects the appropriate generative model (GAN, transformer, or differential-privacy-based), trains it, and generates synthetic data. The abstraction reduces the expertise required — you do not need to understand generative adversarial networks or variational autoencoders to use Gretel.
The privacy evaluation is Gretel’s strongest feature. After generating synthetic data, Gretel runs privacy attacks (membership inference, attribute inference, linkage attacks) against the synthetic data and reports the results. You see a quantitative assessment of how well the synthetic data protects individual privacy, not just a promise that the algorithm is privacy-preserving.
Gretel’s PII detection and transformation capabilities are useful for teams that need to clean data before using it. The platform can detect names, addresses, emails, phone numbers, SSNs, and other PII patterns, and either redact or replace them with synthetic equivalents. This capability is valuable even for teams that do not need full synthetic data generation.
The limitation is fidelity. Gretel’s privacy-first approach means that the synthetic data may lose statistical properties that are important for model training. For use cases where fidelity is the primary concern (training a fraud detection model where rare patterns matter), Gretel’s privacy-protecting approach may degrade model performance.
Gretel’s pricing is usage-based (per record generated), which is predictable but can become expensive for large datasets. The free tier is sufficient for experimentation but not for production-scale generation.
Mostly AI: Fidelity-First
Mostly AI takes the opposite approach: maximize fidelity and provide privacy controls as configuration options. The platform’s generative models are designed to capture the statistical relationships in the real data as accurately as possible, including correlations between columns, temporal patterns, and conditional distributions.
The fidelity metrics that Mostly AI provides are the most detailed of the three. Column-level distribution comparisons, correlation matrix preservation, predictive utility scores (train a model on synthetic data, test on real data, compare to training on real data), and privacy risk scores. You see exactly how much statistical information is preserved and at what privacy cost.
Mostly AI’s handling of relational data is a differentiator. Real datasets often have multiple tables with foreign key relationships. Mostly AI can generate synthetic data for an entire relational schema while preserving the inter-table relationships. Other tools typically flatten the data or handle tables independently, losing the relational structure.
The privacy controls are configurable. You set a privacy level (from strict to relaxed), and Mostly AI adjusts the generation process to balance fidelity against privacy. At the strict setting, the synthetic data is more private but less statistically accurate. At the relaxed setting, the synthetic data is more accurate but carries higher re-identification risk.
Mostly AI’s limitation is the developer experience. The platform is more enterprise-focused than Gretel, with a UI-driven workflow that is less amenable to programmatic integration. The API exists but is less comprehensive than Gretel’s developer platform.
The pricing is enterprise-oriented with custom contracts rather than transparent per-unit pricing. This makes cost prediction harder for teams that want to estimate spend before committing.
Tonic: Development Environments
Tonic specializes in a specific use case: creating realistic development and testing environments from production data. Instead of generating entirely synthetic data, Tonic transforms production data by replacing sensitive values with realistic synthetic equivalents while preserving the statistical relationships and referential integrity.
This approach is different from Gretel’s and Mostly AI’s. Tonic starts with real data and transforms it, rather than generating data from scratch. The result is data that looks and behaves like production — the same distributions, the same edge cases, the same referential relationships — but contains no real PII.
Tonic’s strength for development environments is that the synthetic data preserves the properties that matter for testing: edge cases are present, data distributions match production, and referential integrity is maintained. A test that passes against Tonic-generated data is more likely to pass against production than a test that passes against randomly generated test data.
Tonic’s subsetting capability is practical. Instead of generating a full synthetic copy of a production database (which may be terabytes), Tonic can generate a representative subset that preserves the statistical properties at a fraction of the size. Development environments that cannot afford terabyte-scale databases benefit from this feature.
The limitation is that Tonic is not designed for model training. The transformation approach (starting with real data and modifying it) is different from the generation approach (creating data from scratch), and the privacy guarantees are different. Tonic’s data is suitable for development and testing but may not satisfy the privacy requirements for data sharing or model training in regulated environments.
Tonic’s integration with databases is the strongest of the three. Direct connectors for Postgres, MySQL, SQL Server, MongoDB, and Snowflake allow Tonic to read production data and write synthetic data directly to development databases. The workflow is more automated than Gretel’s or Mostly AI’s API-driven approach.
Use Case Alignment
The three tools map to different use cases:
Gretel fits data sharing and analytics. You need synthetic data to share with partners, publish for research, or use in environments where real data cannot go. The privacy evaluation gives you confidence that the synthetic data is safe to share.
Mostly AI fits model training. You need synthetic data that preserves the statistical properties of real data well enough to train models that generalize to production. The fidelity metrics tell you exactly how much accuracy you lose by training on synthetic data.
Tonic fits development environments. You need realistic data for development and testing that behaves like production without containing production PII. The database integration makes the workflow automated and the referential integrity preserves testing validity.
Decision Framework
Use Gretel when privacy is the primary concern, you need developer-friendly APIs, and you want built-in privacy evaluation. Best for data sharing, analytics on sensitive data, and teams that need PII detection alongside synthetic generation.
Use Mostly AI when fidelity is the primary concern, you need synthetic data for model training, and your data has relational structure that must be preserved. Best for ML teams that need the most statistically accurate synthetic data and can accept the enterprise pricing model.
Use Tonic when the use case is development and testing environments, you need to transform production data rather than generate from scratch, and database integration is important. Best for engineering teams that want realistic test data without managing production data in non-production environments.
The wrong approach is treating synthetic data as a drop-in replacement for real data without evaluating the quality. Every synthetic dataset has fidelity gaps — statistical properties that are not accurately reproduced. Before using synthetic data for any critical purpose, run the evaluation metrics and verify that the gaps do not affect your use case.
