Gretel enables organizations to generate synthetic data that preserves the statistical properties of real datasets while eliminating privacy risks. The platform uses generative models trained on source data to produce new records that maintain correlations, distributions, and patterns — making them suitable for ML model training, software testing, and cross-team data sharing without exposing sensitive information. Gretel supports tabular data, natural language text, and time-series formats with quality metrics that measure how well synthetic data represents the original.
The platform offers multiple synthesis approaches including LSTM-based generation for sequential data, ACTGAN for tabular data with complex relationships, and amplification for augmenting underrepresented classes in imbalanced datasets. Privacy controls include configurable differential privacy guarantees and automatic PII detection and transformation. Gretel's Transform pipeline can anonymize, redact, or replace sensitive fields before synthesis, providing defense-in-depth for privacy-critical workflows.
Gretel provides Python SDK and REST API access for integrating synthetic data generation into existing pipelines, with native connectors for BigQuery, Snowflake, Databricks, and S3. The platform offers a free tier with limited usage for experimentation and paid plans for production workloads. Open-source components including the Gretel Synthetics library are available on GitHub. For organizations constrained by privacy regulations, limited training data, or the need to share datasets across teams, Gretel provides a practical path to unlocking data utility without compromising privacy.
