Synthetic Data Platforms for Private Test and Training Data

Synthetic data platforms differ mainly in how they create data, which relationships they preserve, and how privacy is measured. Database-focused products generate or transform production-like records for development and QA, while training-data platforms concentrate on statistical fidelity, class balance, private text, model evaluation, or domain-specific examples. The right shortlist depends on whether the output must pass application tests, support machine learning, preserve a multi-table schema, or create new scenarios that do not exist in production.

Product and pricing details were checked in July 2026. Availability, deployment terms, usage credits, and enterprise plans can change by region or contract.

Table of Contents

Synthetic Data Platforms Do Not All Solve the Same Data Problem

A synthetic data platform may learn patterns from private source data, generate records from rules, replace sensitive values, create new examples with generative models, or combine several of these methods. These approaches can produce similar-looking outputs while serving very different engineering tasks.

Synthetic data is also different from basic mock data. Mock-data libraries can generate valid names, dates, addresses, or identifiers without learning the behavior of a source population. Data masking transforms existing records, while synthesis creates new ones. Some enterprise platforms combine generation, masking, subsetting, and cloning because test environments often require more than one method.

The word synthetic does not automatically mean anonymous. The UK Information Commissioner’s Office defines synthetic data as data generated from models of original data and notes that it may or may not be anonymous. [Data protection glossary]

Workflow Fit Matrix for Test and Training Data

The table separates platform fit rather than declaring one universal winner. A relational QA team and an LLM evaluation team may both request “private synthetic data,” but they need different validation methods, output controls, and delivery paths.

Platform Profiles and Their Real Selection Boundaries

Tonic.ai: One Vendor for Database, Fabricated, and Private Text Workflows

Tonic divides its data work across products rather than treating every dataset identically. Tonic Fabricate creates data from an idea, schema, or existing source and can operationalize the result through workflows and mock APIs. Tonic Structural is oriented toward production-like structured test data, while Tonic Textual detects and replaces sensitive entities in free text and audio.

This split makes Tonic a practical candidate when one organization needs both lower-environment databases and AI-ready unstructured data. Textual supports custom entity detection, consistent synthesis of sensitive values, dataset sharing, and self-hosted deployment through Kubernetes or Docker. [Unstructured data capabilities]

The free tier includes $5 in monthly usage credits, allowing a team to test generation before moving to a larger plan. The final cost depends on the selected Tonic product and usage pattern rather than one flat platform fee. [Official pricing]

Best fit: engineering organizations that want structured test data, generated databases, private documents, or model-training text under one vendor. Teams should still verify which Tonic product owns each workflow, because Fabricate, Structural, and Textual are not interchangeable modules.

MOSTLY AI: Tabular Synthesis with Local Execution and Differential Privacy Controls

MOSTLY AI concentrates on learning distributions, dependencies, and behavioral patterns from tabular source data. Its generators can be configured for multi-table datasets, foreign-key relationships, time-series behavior, conditional generation, rebalancing, and data imputation.

Differential privacy can be enabled when a team needs a mathematical privacy guarantee, with epsilon used to limit the influence of an individual record on the trained generator. [Generator documentation]

Multi-table relationships can be defined through foreign keys, which makes the platform more suitable for connected customer, account, transaction, and event tables than single-table generators. [Relationship documentation]

The Apache 2.0 Synthetic Data SDK can run in a local Python environment, keeping source data under the user’s control. The enterprise platform supports deployment on Kubernetes or OpenShift and adds shared assets, connectors, governance, and organizational workflows.

Best fit: data science, analytics, and regulated-data teams that need statistically faithful tabular datasets with explicit privacy settings. It is less directly centered on test-case design, file-format generation, or LLM conversation creation than products built for those jobs.

YData Fabric: Synthetic Data Inside a Wider Data Preparation Workflow

YData Fabric combines data profiling, catalogs, synthetic generation, experimentation, and pipelines. Its value appears when synthetic data is part of an iterative ML data workflow rather than a separate export performed after data preparation.

The platform supports tabular, time-series, multi-table, text, and document-oriented generation. Data teams can work through a visual interface or SDK, then use generated data for augmentation, model development, sharing, or controlled experiments.

The Community tier includes synthetic data generation and the Fabric SDK. Pay-as-you-go adds synthetic database generation, pipelines, self-hosting on AWS and Azure, unlimited concurrent users, and higher-scale operation. Enterprise arrangements can run in a private cloud or on-premises. [Plan comparison]

Best fit: ML teams that want profiling, data quality work, synthesis, and pipeline execution in the same product. A QA department seeking test-case-specific database provisioning may find Synthesized, GenRocket, K2view, or Tonic Structural closer to its operational model.

Synthesized: Database DevOps with Generation, Masking, and Subsetting

Synthesized is oriented toward provisioning safe versions of relational production data for development, testing, data engineering, load tests, and pipeline validation. It supports three related operations: masking existing data, creating data from a schema, and extracting smaller relationally complete subsets.

The platform analyzes schemas, constraints, data types, and relationships before applying configured transformations. Generated records can retain foreign-key relationships, while masking rules preserve formats and consistent replacement values across connected tables. Its documented sources include PostgreSQL, Oracle, MySQL, SQLite, Microsoft SQL Server, Snowflake, Db2, CSV, XML, and SAP HANA. [Platform documentation]

A separate self-service SDK creates tabular data for machine learning and analytics, including rebalancing, imputation, and bootstrapping. This gives Synthesized a wider scope than database masking alone, though its clearest distinction remains database-level test data automation.

Best fit: database, QA, DevOps, and engineering teams that need repeatable lower-environment provisioning while retaining relational behavior. Buyers should test schema-specific features such as sequences, triggers, partitioned tables, virtual relationships, and vendor-specific data types during evaluation.

GenRocket: Scenario-Designed Data for Large Testing Programs

GenRocket uses a design-driven model in which teams define the domains, generators, relationships, scenarios, and output formats required by a test. This is useful when data must be created for a precise test case rather than statistically sampled from a production population.

The platform lists more than 750 synthetic data generators and more than 110 data formats. It also supports database and file masking, subsetting, PII detection, team permissions, SSO, MFA, CI/CD integration, schema-change detection, and a self-service portal.

Pricing is based on annual test-data projects, with a listed minimum of 20 projects. The published package includes unlimited lower-environment datasets and unlimited users, while the license amount is supplied by quote. [Test data model]

Best fit: larger quality-engineering programs that need repeatable scenario data across APIs, files, messages, databases, packaged applications, or industry-specific formats. The project minimum makes it less aligned with a small team seeking occasional tabular synthesis.

K2view: Business-Entity Test Data Across Distributed Systems

K2view organizes test data around business entities such as a customer, device, policy, account, or order. The entity model gathers the connected records required for that entity across source systems, helping preserve context that may be lost when each database is handled independently.

Its test data generator combines four methods: generative AI, user-defined rules, entity cloning, and masking. A tester can create data for new functionality through rules, produce larger volumes through cloning, use masked production subsets for model input, or generate new production-like scenarios. Portal and API access support self-service and CI/CD delivery. [Test data generation methods]

Best fit: enterprises where one test case spans several systems and databases. The main buying question is whether entity-centered provisioning matches the organization’s application topology and data ownership model.

NVIDIA NeMo Data Designer: Synthetic Text and Structured Examples for LLM Development

NeMo Data Designer serves a different need from enterprise test data management. It creates datasets for conversational AI, agent development, evaluation, benchmarks, low-resource domains, synthetic documents, and domain-specific question-answer work.

Teams configure models, samplers, columns, inference settings, prompts, and dependencies between generated fields. Seed data can steer the output toward an internal domain, while automated metrics and model-based judges can help inspect generated examples. NVIDIA also positions NeMo Safe Synthesizer for privacy-safe versions of sensitive data. [Training-data use cases]

A hosted free trial is available through an NVIDIA API key. Trial limits and supported models differ from a production deployment, so teams should calculate model inference, review, storage, and regeneration costs rather than treating dataset generation as a fixed subscription.

Best fit: AI teams creating controlled text, conversations, benchmark cases, synthetic documents, or agent training examples. It is not a direct replacement for a platform that must clone an enterprise customer and all related transactions into a QA database.

Private Source Data Requires More Than a “Synthetic” Label

A vendor may generate entirely new rows, but privacy risk can remain if the model memorizes rare records, reproduces unusual combinations, or reveals whether a person appeared in the source dataset. The acceptable privacy test also changes with the release audience. Data used by a restricted internal team may follow a different risk threshold from data shared with customers, research partners, contractors, or the public.

NIST’s SDNist reporting tool evaluates both utility and privacy metrics for structured synthetic datasets, illustrating why a visual quality report alone is not enough for a release decision. [Synthetic data evaluation]

Deployment location is another part of privacy control. A local SDK, private-cloud installation, or on-premises platform can prevent raw source data from leaving the organization, but it does not prove that the generated output is safe. Conversely, a managed service may offer mature security controls while requiring a data-processing review. Architecture and output privacy must be assessed separately.

Relational Fidelity and Model Utility Need Different Tests

Teams often ask whether a synthetic dataset is “realistic” without defining what realism means. The answer should be tied to the downstream job.

For Application and Integration Testing

• Primary and foreign keys remain valid.
• Required parent records exist before dependent records.
• Values satisfy database constraints and application validation.
• Business states and transitions are possible.
• The same synthetic identity remains consistent across systems.
• Negative cases can be generated deliberately.
• Test runs can recreate the same data when a defect must be reproduced.
• The platform can deliver data into the required database, file, queue, or API format.

For Model Training and Analytics

• Univariate and multivariate distributions remain useful.
• Correlations and temporal dependencies match the intended population.
• Minority classes and rare cases are represented intentionally.
• A model trained on synthetic data performs acceptably on held-out real data.
• Generated labels remain correct after augmentation.
• Missing values, outliers, and long-tail behavior are handled deliberately.
• Privacy tests show an acceptable release risk.
• Bias is measured rather than assumed to disappear during generation.

A database platform may preserve every foreign key while producing weak training distributions. A statistical synthesizer may preserve model performance while missing the exact state combinations required for an integration test. The selection should follow the acceptance test, not the number of generation features on the product page.

Which Synthetic Data Platform Fits Each Workflow?

When a Commercial Platform May Be Unnecessary

A lightweight library may be enough for unit tests, demos, seed databases, or public sample data when no private source distribution must be learned. Commercial platforms become more useful when the workflow includes private production data, multi-table dependencies, privacy reports, access controls, repeatable provisioning, deployment inside controlled infrastructure, or generation at organizational scale.

What to Test During a Proof of Concept

1. Use a representative source slice containing ordinary records, rare cases, missing values, and difficult relationships.
2. Define the downstream acceptance test before generating data.
3. Measure privacy and utility separately.
4. Test direct delivery into the actual development, analytics, or training environment.
5. Record manual configuration work, generation time, compute use, storage, and regeneration cost.
6. Change the source schema and observe how much maintenance is required.
7. Test whether developers or data scientists can request datasets without exposing production access.
8. Review exported generators, logs, permissions, deletion controls, and source-data retention.

The strongest proof of concept is not the dataset with the most realistic sample rows. It is the one that demonstrates acceptable privacy, repeatable delivery, and measurable performance in the intended test or training task.