At a glance
| Supported databases | Any JDBC database; subsetting named for Oracle, Db2, SQL Server, MySQL, PostgreSQL, Sybase |
| Data generation | Core capability: 750+ generators, data designed from models rather than copied |
| Masking | Masking with subsetting, in-place database masking and file masking |
| Referential integrity | Scenarios maintain parent, child and sibling relationships |
| CI/CD integration | API for pipelines and test frameworks; specific CI tools not named on the pages checked |
| Test automation | Test cases can call for their own data on demand |
| Data volume or subsetting | Row counts set per scenario; database subsetting available |
| Deployment | Hosted web platform (multi-tenant included, single-tenant by quote); runtime on your machines |
| Licensing | Annual per test data project, minimum 20 projects, price by quote |
GenRocket suits QA organisations that run large automated test suites and want each test case to request exactly the data it needs, generated on demand, instead of refreshing masked production copies. The 20-project minimum and quote-only pricing put it in enterprise territory, so it is a poor match for a single team that only needs a few seed files.
Overview
GenRocket generates synthetic test data from a design rather than from a copy of production. A Domain is modelled like a table and its Attributes like columns; each attribute gets a Generator (GenRocket cites 750+ generators), and Receivers write the output to databases over JDBC, to files such as XML, JSON, CSV and VSAM, or to web services and Salesforce. Scenarios hold the instructions, not the data, and set how many rows to produce while keeping parent, child and sibling records consistent.
Around the generator the platform adds database subsetting and masking (GenRocket names Oracle, Db2, SQL Server, MySQL, PostgreSQL and Sybase for subsetting), in-place masking of whole databases, data profiling, a self-service portal for testers, a multi-user server and an API for test automation frameworks and CI/CD pipelines. Models are built in the GenRocket web platform, which is hosted multi-tenant by default with a single-tenant option, while the generation runtime runs on your own machines.
Key features
- Model-driven generation: domains, attributes, generators and receivers, with seeded generators for repeatable output
- Receivers that load databases over JDBC or write XML, JSON, CSV, SQL, VSAM and other formats (GenRocket cites 100+ formats)
- Scenarios that keep referential integrity across parent, child and sibling data while setting row counts
- Subsetting and masking for relational databases, plus in-place masking of whole databases and masking for files
- Self-service portal and questionnaire so testers can request data without building models
- Accelerators for healthcare X12 EDI, banking and Salesforce data
- API for calling generation from test automation frameworks and CI/CD pipelines
Integrations and ecosystem
Pros and cons
Pros
- Generates data per test case on demand, so tests do not depend on a shared production copy
- No personal data needs to leave production when tests run on fully synthetic data
- Wide range of output targets, including mainframe-style files, EDI and Salesforce
- Unlimited users and test data sets within the licensed projects
Cons
- Minimum purchase of 20 test data projects, with every price by quote
- Modelling domains and generators takes upfront design work compared with masking an existing copy
- Detailed support matrices sit in a knowledge base that now requires a login
Pricing
GenRocket publishes its pricing model but not its prices. It sells an annual licence per test data project, with a minimum of 20 projects, 5 versions per project, unlimited users and unlimited test data sets in lower environments. Multi-tenant hosting, a 20-hour onboarding block, 5x24 online support, self-paced training and a key account manager are included. The per-project price, single-tenant hosting, the X12 EDI and unstructured data accelerators, Navigator service hours (blocks of 40) and 5-project add-ons are all "Request Quote". No free trial is mentioned on the pricing page.
Pricing checked on the vendor's official page on 7 November 2026. Prices change; confirm before buying.
Who it is best for
Enterprise QA and test automation teams that want synthetic, per-test-case data across databases, files and APIs, and can commit to a multi-project annual licence.
Typical use cases
- Feeding each automated regression test its own freshly generated data set
- Producing negative and edge-case data that rarely appears in production
- Generating X12 EDI healthcare transactions for claims system testing
- Loading synthetic data into Salesforce or mainframe file formats for integration tests
Who should look elsewhere
- Developers who need a one-off file of fake rows, where a free generator is enough: see Mockaroo.
- Teams whose main need is a masked, subsetted copy of production rather than designed data: see Tonic.ai or DATPROF.
Verdict
GenRocket is a strong fit for organisations that want test data designed for each test rather than copied from production, especially where files, EDI or mainframe formats matter. The trade-offs are modelling effort and an enterprise purchase (20-project minimum, quote-only), so smaller teams or masking-first programmes should look at Tonic.ai or DATPROF.
Frequently asked questions
How is GenRocket priced?
By annual licence per test data project, with a minimum of 20 projects and unlimited users. GenRocket does not publish the per-project price; the pricing page asks you to request a quote.
Does GenRocket copy production data?
Its main approach is to generate synthetic data from models, so no production copy is needed. It also offers subsetting and masking for teams that want to work from existing databases.
Which databases does GenRocket support?
GenRocket loads data into databases through JDBC receivers and names Oracle, Db2, SQL Server, MySQL, PostgreSQL and Sybase for subsetting. It also writes many file formats, including XML, JSON, CSV and VSAM.
How does GenRocket compare with Tonic.ai?
GenRocket centres on designing synthetic data per test case, licensed per project. Tonic.ai centres on de-identifying and subsetting existing databases with Tonic Structural, priced on source data size, and adds Fabricate for from-scratch synthesis.
Sources
- GenRocket home page
- GenRocket pricing
- GenRocket component-based architecture
- GenRocket solution overview (PDF)
Checked November 2026.
Listed in: Database Testing & Test Data.
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