Tonic vs K2view synthetic data generation: One table at a time, or your whole data landscape?

Choose Tonic for single-database synthetic data generation, or K2view for enterprise-wide consistency across multiple systems and core applications. 

Introduction 

Teams don’t struggle because they lack test data tools. They struggle because they can’t get safe, realistic data fast enough to support testing and AI across the systems that power real customer journeys. 

Tonic is ideal for developer-led, departmental synthetic data generation, for a singular database.  

K2view is designed for enterprises whose synthetic data requirements span multiple systems, including SQL and NoSQL databases, SaaS platforms, legacy systems, flat files, and more. Its entity-based architecture ensures synthetic data maintains referential integrity across all systems and provisions compliant data to the downstream data stores in minutes.

The real decision is simple: 

• If your test data is simplistic and lives in one place, Tonic fits as a developer-friendly, synthetic data tool.

• If your synthetic test data spans your broader data ecosystem, K2view delivers the speed and realism you need. 

Who each tool really serves 

The following table compares the what, who, and where for each tool: 

How Tonic approaches synthetic data 

Tonic is a table-centric engine. You point it at a database, it discovers schemas, and you define masking and synthetic data generation rules at the column and table level.  

This works well when your data stack is restricted to: 

• One primary application database

• A handful of tables with predictable relationships

• A developer or data engineer maintaining the configuration 

But as soon as your synthetic data generation requirements span more than one system, friction grows:

• Cross-system consistency must be configured manually. 

• Parent-child hierarchies and timelines break easily. 

• Every new source adds more rules and more places for drift.

Tonic protects columns but not context. 

How K2view approaches synthetic data 

K2view starts from the business, not the schema.

It treats data as business entities: customer, policy, worker, device. A schema is auto discovered for each entity, pulling its definition from every connected system. Subsetting, data masking, synthetic data generation, and provisioning all operate on this entity model. 

This means: 

• You always generate a complete view of the entity.

• Data relationships across heterogeneous systems are maintained automatically.

• Requests for synthetic data become business-oriented, instead of schema-driven (e.g., 200 VIP customers in NYC with orders and payments). 

K2view eliminates the stitching and scripting typically required to create usable test data across your application ecosystem. 

What this means for test data management 

With Tonic.ai 

Tonic gives you solid capabilities for masking and synthetic data for common databases. But it is not designed to run the full test data management lifecycle.

Apart from the tool, teams still must manage: 

• Which data to pull for each test

• Cross-system joins and filters

• Environment refreshes, reservation, rollback, and versioning

• Pipeline orchestration and glue code 

With K2view

K2view provides a full TDM platform based on business entities. It includes: 

• Sensitive data discovery, classification, and cataloging

• Static, dynamic, and in-flight masking

• Business-rule-driven subsetting

• Reservation, versioning, and rollback  

• Built-in synthetic data generation, including AI-based synthetic data generation

• Automated provisioning into any environment 

Instead of assembling a toolchain, K2view gives teams one control plane for all test data operations. 

Data masking architecture 

In terms of data masking and privacy, architectural choices show up at cycle time: 

With Tonic.ai 

• Masking is table-focused. 

• Rules are defined per column and tuned per schema. 

• Adding systems increases copies of sensitive data. 

• Consistency across systems requires ongoing manual effort. 

With K2view 

• Masking is entity-centric and is applied in flight, minimizing risk. 

• Sensitive fields are auto discovered and cataloged once (with automated and ongoing schema drift detection). 

• Masking functions are defined and applied at the catalog level: defined once, applied everywhere – maintaining the referential integrity of the masked data across systems and technologies. 

• Custom masking functions are easy to create, even for the most complex, unique enterprise requirements.

• Audits are simpler, because privacy logic is unified. 

Synthesis 1-table-at-a-time vs whole-entity realism 

Both tools support synthetic data generation, but the experience and depth are very different. 

Tonic synthetic data is appropriate for simple database schemas, but requires: 

• Manual work, to keep keys aligned 

• Extra logic, for parent-child hierarchies 

• Scripting, to maintain system-to-system continuity 

• Tuning, to preserve timeline accuracy 

• Manual post-processing, for AI-generated synthetic data generation 

K2view synthetic data generation is built on the entity model, so it naturally preserves: 

• Cross-system keys 

• Hierarchies and relationships 

• Temporal logic 

• Complete business journeys

And because K2view supports multiple methods – rules, cloning, masking-based generation, and GenAI, all in the same platform – you can choose the right one for each scenario, or even combine them.

When Tonic is the right answer 

Tonic fits when your environment looks like this: 

• One primary database 

• A small footprint with simple relationships 

• Workflow owned by developers 

• Developer-friendly tool for anonymizing data 

You should expect significant scripting and manual work as your footprint grows, and self-service provisioning of test data by non-developers is not available. 

When K2view is the better fit 

K2view becomes the clear choice when your testing spans:

• CRM, billing, orders, payments, products, logs, SaaS, and files 

• Regulated data with strict privacy expectations 

• QA, Dev, analytics, and AI teams 

• Self-service provisioning by non-developers 

• Automated delivery cycles and CI/CD pipelines 

You get: 

• Shorter cycles, from meaningful, right-sized subsets 

• Higher stability, because relationships hold across systems 

• Lower operational overhead, with a single platform 

• Stronger privacy, through in-flight masking and unified controls 

Bottom line 

Tonic is a decent choice for developer-led masking and synthetic data for simple use cases. 

K2view is built for enterprises that need realistic, compliant test data across their whole data landscape – from legacy systems to SaaS and modern cloud stores. 

Choose based on: 

• How many systems your test cases span 

• Who needs to provision and use the data 

• How much work you’re willing to do outside the tool to get the job done 

• How much do broken data relationships and manual data prep cost today 

Experience K2view Synthetic Data Generation 
first-hand in our interactive product tour.