Which kind of tool do you need?
Generating values, building scenarios and refreshing production-derived copies are different jobs. Start with your required output.
Which tool should you use for test fixtures and mock APIs?
Start with Faker for values inside test code, or DATAMIMIC CE for reusable data models and CLI generation. Consider Mockaroo when a schema, download or mock API is the deliverable. Consider generatedata.com when extending a self-hosted generator matters.
Database generatorsWhich test data generator fits your database?
Start with the exact database engine and version. Redgate and ApexSQL focus on SQL Server. Datanamic and Upscene describe broader or edition-specific connection options. DTM and IRI target additional generation workflows; verify editions.
Model & scenario generationWhen should you choose model-driven test data generation?
Choose reusable models when business scenarios, edge cases and relationships matter more than copying typical production records. DATAMIMIC CE, Benerator and GenRocket document different model and runtime approaches; Fabricate and CloudTDMS add other authoring workflows.
Enterprise TDMWhich enterprise test data management approach fits your team?
Choose around governance and delivery. DATAMIMIC Enterprise Platform combines governed model-driven execution, IDE workflows and project-scoped agent access. Delphix emphasizes virtualized copies. Tonic Structural emphasizes source-derived de-identification and subsetting. DATPROF separates Privacy, Subset and Runtime. Informatica, Broadcom, Synthesized and TCS document their own enterprise workflows.
AI & ML datasetsHow should you evaluate synthetic data for AI and ML?
Match the evaluation target first: tabular distributions, domain text, documents or agent evaluation sets. NVIDIA now documents NeMo workflows in the area previously associated with Gretel. Former Gretel-branded service availability is not established here.
What do you need test data to do?
Choose the outcome before the vendor.
Values inside tests or mock APIs
Start small: a library or schema generator. Check relationships and cleanup in your own test code.
Specific business scenarios without source records
Use rules and reusable models when missing, invalid or rare cases must be intentional.
Masked subsets of an existing database
Evaluate source selection, relationship closure and transformations together. A row filter alone is not a coherent subset.
Full copies with refresh, rewind and branching
Evaluate virtualization when many environments need the same broad database state. Measure storage and operational costs.
Datasets for ML training or evaluation
Evaluate downstream task quality, rare groups and disclosure risk separately; plausible rows are insufficient.