A detailed 2025 guide to the best synthetic data generators for testing, analytics and AI. Compares features, pricing, compliance and performance so teams can pick the right tool for dev, QA and data-science use cases.
The best synthetic data generators in 2025 are Tonic.ai, Gretel.ai, and Delphix. Tonic.ai excels at realistic, schema-aware masking; Gretel.ai offers generative AI for rapid dataset creation; Delphix is ideal for enterprise-scale, policy-driven provisioning.
Synthetic and masked test data has shifted from a nice-to-have to a requirement in 2025. Stricter privacy laws, AI model hunger for varied datasets, and shorter release cycles demand data that is safe to share yet production-realistic. Modern generators let teams provision compliant data in minutes, unblock automated testing pipelines, and speed analytics without risking personally identifiable information.
Our ranking scores each platform on seven equal-weight factors: breadth of data types, ease of use, pricing transparency, integration depth, performance at scale, governance capabilities, and community traction. Data comes from official documentation, public roadmaps, verified customer reviews, and hands-on trials performed in January 2025.
Tonic.ai tops the 2025 list for balancing developer-first workflows with enterprise-grade controls. Column-level generators, subset rules, and realistic text synthesis come pre-built. Native connectors for Postgres, Snowflake and MongoDB let engineers create masked subsets straight from CI pipelines. SOC 2 Type II and GDPR reports ease audits, while its new GPT-powered data recipe builder lowers the learning curve for non-SQL users.
Gretel.ai focuses on AI-generated synthetic data. In 2025 it added streaming support and differential-privacy guarantees, making it a favorite for time-series and event data. A CLI, Python SDK and managed SaaS give flexibility. Model cards explain privacy metrics, which supports risk teams during compliance reviews.
Delphix remains the go-to for large enterprises. The 2025 release folded its masking engine into the DataOps platform, allowing policy-driven provisioning to any Kubernetes namespace. Built-in retention policies and parallel refresh jobs handle multi-terabyte Oracle and SAP landscapes with minimal DBA effort.
Synthesized accelerates analytics by creating statistically valid, privacy-preserving tables. Its 2025 DataCopilot feature automatically detects bias and reruns synthesis until fairness thresholds are met. Data contracts export directly to Lakehouse formats like Delta and Iceberg, reducing integration work.
GenRocket emphasizes deterministic, rule-based generation. Teams define scenarios in YAML, then parallelize across grids to output billions of rows for load or performance testing. The 2025 release introduced reusable component libraries and OpenTelemetry metrics so QA leads can monitor generation jobs in Grafana.
DATPROF focuses on compliance. The 2025 version bundles pre-configured GDPR and HIPAA masking templates and a low-code web studio. Role-based access controls and audit trails simplify regulator conversations, though large-scale performance lags behind higher-ranked tools.
Datomize applies Generative Adversarial Networks to tabular data and visualizes privacy versus utility in real time. March 2025 updates added S3 data lakes and Azure SQL support. Pricing is aggressive for startups, but limited connector coverage keeps it at rank seven.
Mockaroo is popular for quick CSV or API payload mocks. The April 2025 upgrade doubled field types and introduced a local Docker runner. It remains the most affordable option, though it lacks automated compliance checks and enterprise support.
SDV is an open-source library that academics and data scientists love. Version 2.1, released in 2025, added Transformer-based synthesizers and categorical constraint support. Community resources are rich, but running and tuning models at scale demands machine-learning expertise.
K2View wraps masking, virtualization and micro-DBs into a unified fabric. Its 2025 release boosted CDC performance and added Kafka sinks. However, the proprietary engine requires specialized training, placing it lower in day-one usability.
Teams prioritizing developer velocity and realistic subsets should trial Tonic.ai or Gretel.ai. Enterprises with sprawling legacy estates lean toward Delphix or DATPROF for robust governance. QA groups needing deterministic loads favor GenRocket, while data-science teams experimenting with AI models often start with SDV or Datomize. Budget-conscious users might begin with Mockaroo then graduate as their needs mature.
Galaxy helps engineering and data teams manage the lifecycle of the SQL that powers these generators. By centralizing version-controlled queries, Galaxy lets practitioners orchestrate masking or synthesis jobs, audit data transformations, and expose trusted results via shared collections. When a Tonic.ai recipe or Gretel.ai job relies on complex SQL for subsetting, Galaxy’s lightning-fast editor and AI copilot ensure the query is accurate, reviewed and discoverable.
As organizations evolve toward a unified data platform, Galaxy becomes the control plane connecting raw data, synthetic data and the analytics that depend on both.
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Synthetic test data is artificially generated information that mimics real production data without revealing sensitive details. In 2025 it is critical because privacy laws and AI workloads require realistic yet compliant datasets for development, testing and analytics.
Mockaroo remains the most cost-effective option, offering free tiers and a $50 per month Pro plan. It handles basic CSV or JSON mocks quickly, letting small teams bootstrap testing before investing in advanced compliance tools.
Tonic.ai focuses on schema-aware masking and database subsetting, ideal for dev and QA environments. Gretel.ai leverages generative models to create entirely new datasets with differential privacy guarantees, making it stronger for AI training and analytics.
Galaxy provides the version-controlled SQL layer that many generators rely on for data subsetting and transformation. Teams write, review and share the queries that feed tools like Tonic.ai directly inside Galaxy, ensuring accuracy, governance and collaboration.
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