Choose by the job you need done. Compare documented capabilities, deployment models and limits — with the sources open beside you.
Choose the task first. Open the candidates to compare documented workflows and their sources.
Generate individual values or simple datasets.
Capabilities vary by edition and workflow; open the source and profile for scope.
Maintain rules, relationships and scenarios.
Capabilities vary by edition and workflow; open the source and profile for scope.
Select related records or mask sensitive values.
Capabilities vary by edition and workflow; open the source and profile for scope.
Provision, refresh or reset virtual data copies.
Capabilities vary by edition and workflow; open the source and profile for scope.
Generate records for database tests.
Capabilities vary by edition and workflow; open the source and profile for scope.
Train models on example data and assess outputs.
Capabilities vary by edition and workflow; open the source and profile for scope.
Alphabetical product families, no ranking. Editions have separate profiles. Not listed does not mean a capability is absent. View the full directory
Alphabetical, not ranked. Select 2–4 tools for a side-by-side comparison.
ApexSQL Generate creates SQL Server test data using random, regex, and incremental column generators. Quest presents it in SQL DevOps; latest located release notes are version 2022.02.x.
Broadcom documentation continues to identify CA Test Data Manager and current 4.11 support material. Its documented workflows include profiling, data masking, subset extraction and test-data provisioning; synthetic generation is described in vendor collateral.
CloudTDMS is a hosted, no-code synthetic test-data factory that profiles structures and patterns, defines data models, generates datasets, and connects to databases, files, and SaaS tools.
DATAMIMIC CE is a local Python runtime for model-based test data. XML models describe rules, relationships and output targets. It also offers Python and CLI interfaces for developer and CI workflows.
DATAMIMIC Enterprise Platform combines test-data engineering in the IDE with central governance and execution. XML models drive generation or transformation. The Platform manages projects, access and run evidence; modules depend on the license.
Datanamic Data Generator creates schema-aware rows for nine platforms, with direct inserts or SQL, customizable generators, and foreign-key integrity. Vendor says it is no longer a core product.
DATPROF Privacy is a proprietary database masking and generation tool. It can mask existing database values or generate new rows while preserving constraints; DATPROF Subset is a distinct product for database subsetting.
Perforce Delphix combines enterprise data virtualization, masking and centralized delivery controls. Its separate Synthetic Data product became generally available in release 2026.2, according to Perforce’s September 2026 release updates.
DTM Data Generator creates database test data and scrambled values with schema-aware relationships, repeatable mode, and SQL/file output. Features vary by edition; scrambling and a bundled masking-tool license are Enterprise-only.
Faker is a code library for generating locale-aware fake values such as people, addresses, dates, and identifiers. Python Faker and JavaScript Faker are separate implementations with distinct documentation and releases.
generatedata.com is a browser-based configurable generator and an open-source downloadable application. The current repository describes roughly 30 data types and 12 export types; accounts on the hosted site add saved datasets and higher-volume generation.
GenRocket is an enterprise synthetic test-data automation platform: engineers design reusable test-data cases in its cloud portal, then distributed runtimes generate data on demand.
Greenmask builds transformed PostgreSQL dumps and related subsets, then restores them into a target database. Your team owns the YAML configuration and any custom transformations.
Gretel was acquired by NVIDIA in 2025; NVIDIA now positions synthetic-data generation for agentic AI through NeMo. The availability of Gretel's former standalone platform is unconfirmed.
IBM InfoSphere Optim 11.7 extracts related subsets from existing databases and prepares them for development and tests. Assess the exact edition and product identifier: announced marketing-end dates differ. Modern IBM Optim TDM is a separate product line.
Informatica's documented TDM product is a web-based interface for data discovery, subsetting, masking and data generation. The 10.5.x documentation describes workflows and generation rules; distinguish this release line from separately marketed cloud services.
IRI RowGen synthesizes and populates structurally correct test data for relational databases, files, reports, and semi-structured targets. Jobs use metadata, generation rules, and the CoSort engine; it can blend generated and selected real values.
Jailer exports small connected slices of relational databases. A local extraction model controls which rows and related records are included. Export and import filters support transformations and key remapping.
K2view prepares test data around business entities such as customers and orders. It selects related data, masks fields and provisions datasets to test environments. Rule-driven generation and a separate AI generation path are documented.
Mockaroo is a hosted test-data generator with configurable schemas, browser downloads, APIs, mock API routes, datasets, and de-identification. Output formats and row or request limits depend on the selected workflow and plan.
MOSTLY AI’s Python SDK trains on tabular or language data and generates samples locally or through a remote SDK endpoint. Syntho acquired the brand assets on 9 June 2026. Confirm former commercial-platform availability separately.
Redgate SQL Data Generator creates schema-aware synthetic rows for SQL Server and supported hosted deployments, with foreign-key consistency, custom generators, command-line automation, and fixed-seed repeatability.
Redgate Test Data Manager prepares masked, subsetted database copies through Anonymize and Subsetter. It is a separate product from Redgate SQL Data Generator.
RNDGen offers web-based random, synthetic and simulated data generators. Detailed capabilities, limits and licensing are not sufficiently described in the accessible official material.
SB Data Generator was Softbuilder's GUI tool for generating and loading realistic data into selected database tables or whole databases. The vendor retired it as a standalone product and integrated generation into ERBuilder.
SDV learns patterns from existing tables and samples synthetic data through Python. Community includes single-table, multi-table and sequential workflows. Enterprise capabilities and license terms are separate.
Solix's current product datasheet centers Test Data Management on configurable, reusable database subsetting and cloning with relational integrity. Solix separately markets data masking; current reviewed TDM collateral does not establish synthetic generation as a core feature.
Spawner is an abandoned desktop test-data generator last released in 2016. Its project page documents delimited text and SQL output, configurable random values and sequences, and direct insertion into MySQL 5.x.
Synthesized's current TDK platform transforms, generates, and subsets production data using configurable workflows, preserving statistical distributions and referential integrity across supported pipelines.
Syntho prepares database test data using learned generation, mock values and masking. Teams configure column settings in a workspace and write results to a destination database. This profile covers the Syntho platform separately from the MOSTLY AI SDK.
TCS MasterCraft DataPlus is the DataPlus product within the MasterCraft suite. TCS documents enterprise test-data management spanning sampling, discovery, masking, synthetic generation, versioning, reservation, and reuse.
Tonic Fabricate generates test datasets from descriptions through its Data Agent and exports them in several formats. Uploaded examples can enrich generation; assess this separately from Structural’s source-derived workflow.
Tonic Structural transforms production-derived relational and semi-structured data for development and testing, combining configurable de-identification with referential integrity and data subsetting.
Upscene Advanced Data Generator creates reusable localized sample data across related tables. Pro uses ADO/ODBC; editions target InterBase, Firebird, MySQL, or Access. Listed version 4.2.0 was released March 2025.
No tools match. Try a broader search or another category.
The difference that changes the decision.
Small fixtures: values in code or hosted schemas
ComparisonProvision test environments: entity subsets or virtual database copies
ComparisonLocal learned datasets: model fit, utility and license limits
ComparisonTest database data: generate without a production copy or transform source rows
Check the sources, compare the exact editions and use the same business acceptance criteria. A product name alone does not tell you which workflow is included.
Sources and comparison criteriaPrimary sourcesOfficial documentation and current product material are linked.
Clear boundariesEdition, release and preview restrictions stay attached to the claim.
No invented winnersNo expert scores, fake review stars or untested performance rankings.
The best fit depends on the required workflow. Shortlist a values library for small fixtures, a model generator for explicit business cases, or a managed product for shared provisioning. Require evidence for your exact database, edition and deployment.
Read the decision guideThey can cover the generation task when your team already manages execution, access and cleanup. Python Faker and DATAMIMIC CE offer different starting points. Compare the operational work you retain before choosing a platform or building those services yourself.
Read the decision guideA seed controls one input. Runtime versions, source data, reference time, execution order and serialization can also matter. Specify whether you need the same cases, values or bytes, then test that property alongside business correctness.
Read the decision guide