Tonic.ai is a leading synthetic data platform designed to help engineering teams generate realistic, de-identified test data from production databases. It excels at maintaining referential integrity and statistical properties of the original data while stripping out sensitive PII, making it invaluable for development, testing, and compliance workflows. The platform supports a wide range of databases including PostgreSQL, MySQL, Oracle, SQL Server, and Snowflake, with robust API access for CI/CD pipeline integration. Its subsetting capabilities allow teams to create smaller, manageable datasets that still reflect production complexity. The privacy controls are sophisticated, offering multiple transformation strategies per column type. On the downside, Tonic.ai's custom enterprise pricing puts it out of reach for smaller teams and startups, and the initial setup can require significant configuration effort for complex schemas. Documentation is solid but the learning curve is moderate. Compared to alternatives like Gretel or Mostly AI, Tonic.ai stands out for its database-centric approach and enterprise-grade reliability, though it's less suited for purely generative AI training data use cases.