Tonic.ai vs GenRocket: Masked Copies or Synthetic Test Data?

Quick verdict

Short answer

Choose Tonic.ai if your developers need realistic, de-identified copies (or subsets) of existing relational databases and cloud warehouses, refreshed through an API, and you want the option of a small published plan (Fabricate) for from-scratch data. Choose GenRocket if a QA organisation runs large automated suites and wants each test to request exactly the data it needs, including edge cases, files, EDI or mainframe formats, without copying production at all. Both core products are quote-only; GenRocket also has a 20-project minimum, so it is an enterprise purchase from the start.

How we know: This comparison is research-based: products, deployment options, connectors, generation approach and pricing models were checked on 7 November 2026 against Tonic.ai's pricing page, Structural documentation and release notes, Tonic's April 2025 press release, and GenRocket's home, pricing and architecture pages. GenRocket's detailed support matrices sit behind a login and were not used. We have not run either product, so nothing here is a measured result.

Tonic.ai sells three separate products. Tonic Structural connects to a source database, applies a generator to each sensitive column (masking, consistent replacement or synthesis), can subset the data, and writes a de-identified copy to a destination. Tonic Fabricate, a product built by Mockaroo that Tonic acquired in April 2025, generates relational data, mock APIs and exports from a prompt or schema. Tonic Textual redacts sensitive values in free text and files. Structural ships frequent numbered releases; v1899 is dated 6 November 2026.

GenRocket is a synthetic test data platform built around a model. You describe Domains (like tables) and Attributes (like columns), assign each attribute a Generator, and choose Receivers that 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 keep parent, child and sibling records consistent. Around this core GenRocket adds subsetting, masking, a self-service portal and an API for test automation.

Copy or design? Masked copies keep the shape and quirks of real data but still start from production. Designed synthetic data never touches production but needs modelling effort and can miss real-world distributions. Many test programmes use both. For a lightweight generator for seed files, see Mockaroo.

Side by side

Aspect	Tonic.ai	GenRocket
Core approach	De-identify and subset existing data (Structural); from-scratch synthesis in Fabricate	Model-driven synthetic generation per test case; subsetting and masking added
Needs production data?	Structural reads a production or staging source; Fabricate does not	No for generated data; yes only when you use its subsetting or masking
Data sources and targets	PostgreSQL, MySQL, SQL Server, Oracle, Db2 for LUW, Snowflake, Redshift, BigQuery, Databricks, MongoDB, Salesforce and others	Any JDBC database; subsetting named for Oracle, Db2, SQL Server, MySQL, PostgreSQL, Sybase; files incl. VSAM and EDI; Salesforce; REST and SOAP
Subsetting	On most connectors; not on Databricks, BigQuery, Iceberg, files or Spark	Relational database subsetting for the engines it names
Masking	Column generators in Structural; Textual for unstructured text	Masking with subsetting, in-place database masking and file masking
Referential integrity	Subsetting keeps related rows; consistent generators keep join values aligned	Scenarios keep parent, child and sibling records consistent
Test automation hook	Structural API for refresh jobs; specific CI plugins not documented on pages checked	API for test frameworks and CI/CD; tests can request their own data on demand
Deployment	Tonic Cloud; self-hosted Docker Compose or Kubernetes (Helm) on Enterprise plans, x86 only	Hosted web platform (multi-tenant included, single-tenant by quote); generation runtime on your own machines
Licensing unit	Structural: annual, by source data size; Fabricate: credits; Textual: per 1,000 words	Annual licence per test data project, minimum 20 projects, unlimited users
Published prices	Fabricate Free and Plus are published; Structural and Textual rates are not	None; every item is Request Quote
Main trade-off	Fast route to realistic data, but production data has to flow through the tool	No production dependency, but data must be designed and the purchase is large

Key differences

Masked copies versus designed data

The products answer different questions. Tonic Structural asks: how do I give developers a copy of this database that behaves like production but contains no real personal data? You connect a source, Structural flags sensitive columns (its Privacy Hub and schema change review help with new columns before a run), you choose a generator per column, and it writes a de-identified copy, optionally subsetted. Data distributions, odd values and volume patterns come from the real source, which is why masked copies tend to surface realistic bugs.

GenRocket asks: what exact data does this test need? You model the data once, and each test case or scenario generates the rows it needs, including negative and edge-case values that rarely appear in production. Nothing is copied, so there is no masking risk in that path. The cost is modelling effort up front, and generated data is only as realistic as the model. GenRocket does also sell subsetting and masking, and Tonic does sell from-scratch synthesis (Fabricate), so each covers the other's main job to a degree; the difference is which job the product is built around.

Sources, targets and formats

Tonic.ai is strongest on modern relational and analytical sources. Structural connectors cover the main transactional engines, the major cloud warehouses (Snowflake, Redshift, BigQuery, Databricks), MongoDB and Salesforce. Check subsetting per connector: Tonic's connector summary shows it is not available for Databricks, BigQuery, Iceberg, file connectors or Spark. Fabricate Enterprise lists expanded exports including SQL Server and Oracle.

GenRocket is strongest on breadth of output. Receivers write to any JDBC database and to many file formats, including mainframe-style VSAM files, healthcare X12 EDI (through a paid accelerator), XML, JSON and CSV, as well as web services and Salesforce. GenRocket's own pages quote different counts for generators and formats, so treat headline numbers loosely and confirm the formats you need in a demo. Its detailed database matrix sits in a knowledge base that now requires a login.

Where it runs and where data goes

Tonic.ai runs Structural on Tonic Cloud on the Professional plan, or self-hosted on the Enterprise plan with Docker Compose or Tonic's Helm chart on Kubernetes. Tonic's requirements ask for an x86 host with 8 vCPUs, 16 GB RAM (32 GB recommended) and 100 GB disk, and state that ARM is not supported. If production data cannot leave your network, budget for Enterprise.

GenRocket splits the work: models are built in a hosted web platform (multi-tenant included in the licence, single-tenant by quote), while the generation runtime runs on your own computers or servers. Because the data is generated where it is used, the hosted platform holds designs rather than production data. If you also use GenRocket subsetting or masking, confirm with the vendor where that processing runs.

Buying model

Tonic.ai prices each product separately. Structural is custom priced on an annual contract based on the on-disk size of connected source data (excluding logs and indexes), with lower per-GB pricing at larger volumes. Fabricate has published entry plans billed in usage credits. Textual is billed per 1,000 words processed or by Enterprise contract. So a small team can start on Fabricate, but the core masking product always needs a quote.

GenRocket 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. Hosting, onboarding hours, support and training are bundled, and every price is by quote. In our view the 20-project minimum makes GenRocket a programme-level purchase, while Tonic.ai can start with one team and grow by data volume.

Pricing and licensing

Tonic.ai. Listed on tonic.ai/pricing on 7 November 2026 (amounts shown with a dollar sign; the page does not name the currency). Tonic Fabricate: Free at $0 per month with $5 of monthly credits; Plus at $29 per month with $25 of credits and pay-as-you-go beyond that; Enterprise custom priced. Both Free and Plus run on Tonic Cloud only. Tonic Structural: Professional (up to 10 TB of source data, 10 users, 2 data source types, Tonic Cloud only) and Enterprise (unlimited, cloud or self-hosted) are both custom priced, based on source data size, with annual contracts. Tonic Textual: a flat rate per 1,000 words processed (rate not published) or an Enterprise contract. No free trial is listed for Structural.

GenRocket. Listed on genrocket.com/pricing on 7 November 2026: an annual licence per test data project, minimum 20 projects, with the per-project price and the licence total both shown as "Request Quote". Included: 5 versions per project, unlimited users, unlimited test data sets in lower environments, multi-tenant hosting, 20 hours of onboarding, 5x24 online support, self-paced training and a key account manager. Single-tenant hosting, the X12 EDI and unstructured data accelerators, Navigator service hours (blocks of 40) and 5-project add-ons are also quote-only. No trial is mentioned on the pricing page.

Pricing checked on the vendors' official pages on 7 November 2026. Prices change; confirm before buying.

Where each one leads

Tonic.ai strengths

Produces production-shaped, de-identified copies of real databases and warehouses
Broad connector list across transactional engines, cloud warehouses, MongoDB and Salesforce
Subsetting to shrink large sources into related, laptop-sized slices
Fabricate offers a free plan and a low published monthly plan for from-scratch data
Self-hosted option on Enterprise for data that must stay in your network

GenRocket strengths

Generates data per test case on demand, so tests do not share or wait for a production copy
No production data involved when tests run on fully generated data
Wide range of outputs, including VSAM files, X12 EDI, Salesforce and web services
Unlimited users and test data sets within the licensed projects
Good fit for negative and edge-case data that production rarely contains

Limitations

Tonic.ai limitations

Structural, the core product, has no published price and is sized by source data volume
Self-hosting requires the Enterprise plan; Professional is Tonic Cloud only
Subsetting is unavailable on several connectors, including Databricks and BigQuery
Three products with three pricing models make the total harder to estimate

GenRocket limitations

Minimum purchase of 20 test data projects, with every price by quote
Modelling domains, generators and scenarios is upfront design work
Detailed database and format matrices sit behind a support login
Generated data reflects the model, not real-world distributions, unless you invest in profiling

When to choose each

Choose Tonic.ai if

Developers need realistic copies of production databases or warehouses with personal data removed
You want to shrink a multi-terabyte database into a related subset for local development
Your estate includes Snowflake, Redshift, BigQuery, Databricks or MongoDB
You want to start small (Fabricate) before committing to a larger contract
You also need to redact unstructured text, for example before AI training

Choose GenRocket if

A central QA team runs large automated regression suites and wants data generated per test
Policy forbids using production data in test, even masked
You need mainframe file formats, X12 EDI or Salesforce test data
You can commit to an annual programme of at least 20 test data projects
Edge cases and negative data matter more than production realism

When neither is right

You only need a quick file of fake rows for a demo or seed script: Mockaroo or a single-engine generator such as Redgate SQL Data Generator is far cheaper.
You need database virtualization or entity-based provisioning alongside masking: compare Delphix vs K2view.
You want a European, fixed-price masking and subsetting suite for mainstream relational engines: look at DATPROF.
Your estate is mainframe-heavy with Db2 for z/OS or IMS: look at IBM Optim.

Final recommendation

Bottom line

Tonic.ai is the more direct fit when the goal is to get realistic, privacy-safe copies of the databases you already run into developers' hands quickly, especially across cloud warehouses and MongoDB. GenRocket is the more direct fit when a QA organisation wants test data designed and generated per test case, keeps production out of testing entirely, or needs file, EDI or mainframe formats. Both core products are sold by quote, so ask each vendor to price your actual scope: source data size for Tonic Structural, number of test data projects for GenRocket.

Frequently asked questions

Does GenRocket copy production data like Tonic Structural?

Not by default. GenRocket's main approach is to generate synthetic data from models, so no production copy is needed. It also sells subsetting and masking for teams that want to work from existing databases. Tonic Structural, by contrast, is built around de-identifying and subsetting a real source.

Is either product free?

Only part of Tonic.ai. Tonic Fabricate has a Free plan at $0 per month with $5 of monthly credits. Tonic Structural and Textual are paid, and GenRocket lists no free plan or trial on its pricing page; its licence starts at 20 test data projects, priced by quote.

Can both run on premises?

Tonic Structural can be self-hosted with Docker Compose or Kubernetes on the Enterprise plan. GenRocket's design platform is hosted (multi-tenant, or single-tenant by quote), while its generation runtime runs on your own machines.

Did Tonic.ai buy Mockaroo?

Tonic.ai's April 2025 press release says it acquired Fabricate, a product built by Mockaroo, and that Mockaroo's founder joined Tonic to lead it. It does not say Mockaroo itself was acquired, and Mockaroo remains available separately.

Sources

Checked November 2026.

How we research comparisons: our editorial method.