Gretel.ai, MOSTLY AI, and Tonic.ai are the three leading synthetic data generation platforms for enterprise use in 2026 — each with distinct strengths that make them optimal for different use cases and data types. Gretel.ai excels at multi-modal synthetic data generation (tabular, text, time series) with strong differential privacy support; MOSTLY AI delivers the highest statistical fidelity for structured tabular data; Tonic.ai is optimised for database-level test data generation with referential integrity preservation. This comparison guides data engineering and ML teams through the selection decision.
Platform Comparison
| Platform | Data Types | DP Support | Deployment | Best For |
|---|---|---|---|---|
| Gretel.ai | Tabular, text, time series, relational | Yes — DPCTGAN, DP-GPT | SaaS + self-hosted | Multi-modal; ML training data; Python-first teams |
| MOSTLY AI | Tabular, relational (multi-table) | Yes | SaaS + self-hosted + cloud VM | Highest statistical fidelity; enterprise governance |
| Tonic.ai | Relational databases (PostgreSQL, MySQL, Snowflake) | Limited | SaaS + self-hosted | Dev/test database generation; referential integrity |
| SDV (open source) | Tabular, relational | No native DP | Self-hosted (Python library) | Open source; evaluation; no-budget teams |
MOSTLY AI
Consistently top-ranked for statistical fidelity in independent evaluations — MOSTLY AI's GAN-based synthesis preserves complex multi-column correlations better than alternatives, making it the preferred choice for ML training data where fidelity to real-data distributions is critical
Tonic.ai
The database-level synthetic data tool — Tonic.ai connects directly to production databases and generates synthetic copies that preserve referential integrity (foreign key relationships), data type constraints, and distribution patterns. Used by engineering teams who need realistic dev/test databases without PII
Gretel
The most developer-friendly synthetic data platform — Gretel's Python SDK, Jupyter notebook examples, and CLI tools make it the preferred choice for ML engineers and data scientists who want to generate synthetic data programmatically in their existing workflows
Gretel.ai Workflow
Gretel Python SDK:
pip install gretel-client. Configure: from gretel_client import Gretel; gretel = Gretel(project_name="healthcare-synth"). Upload data and train: trained = gretel.submit_train("tabular-actgan", data_source=df). Generate: generated = gretel.submit_generate(trained.model_id, num_records=10000). Evaluate: quality and privacy report generated automatically — check Synthetic Data Quality Score (SQS) and Privacy Protection Level. Gretel's ACTGAN (Approximate Conditional Tabular GAN) is the default model for tabular data; LSTM for time series; GPT-based for text generation. Our ML team uses Gretel for training data generation.MOSTLY AI for High-Fidelity Financial Data
MOSTLY AI's enterprise differentiator: multi-table relational synthesis that preserves cross-table correlations. For financial data: generate synthetic customer + transaction + account tables where the transaction amounts correlate with the customer income tier, and account types correlate with customer demographics — relationships preserved from the real data, no real PII in the output. MOSTLY AI's QA report shows: column statistics comparison (real vs synthetic), correlation heatmap comparison, and pairwise relationships. Target: >80% similarity score on MOSTLY AI's quality metrics for production ML training use.
Tonic.ai for Dev/Test Databases
Tonic.ai connects to your PostgreSQL/MySQL production database, analyses the schema and referential integrity constraints, and generates a synthetic copy that: preserves all foreign key relationships (orders reference valid customer IDs), respects data type constraints (valid email formats, phone number formats), and matches statistical distributions. Engineers get a realistic dev/test database with no real customer data. Setup: point Tonic.ai at production read replica, configure generators per column, schedule daily synthetic database refresh. Typical use: 100 engineers each getting their own schema-accurate synthetic PostgreSQL database for integration testing.
Selection Decision Guide
Choose by use case: MOSTLY AI for highest-fidelity tabular ML training data (healthcare outcomes, financial fraud, customer churn); Gretel.ai for multi-modal generation (tabular + text + time series) and Python-native ML workflows; Tonic.ai for dev/test database generation where referential integrity across tables is the primary requirement; SDV (open source) for evaluation, learning, and teams without budget for commercial tools. All three commercial platforms offer free tiers or trial access — run a 2-week evaluation with your actual data before committing to a platform.
Synthetic Data Platform Selection and Implementation
Our ML development and data analytics teams evaluate and implement synthetic data generation platforms for ML training, privacy compliance, and software testing. Book a free advisory session.