Top platforms for synthetic data generation fall into groups by job: test data and de-identification, statistical synthesis of existing tables, and training data for models. The right choice depends on your starting data and whether the output will train a model. Enterprise teams should compare required input, diversity, privacy, deployment, and training-use terms.
Key Takeaways
- Synthetic data generators differ by starting input: some mask or synthesize existing tables, some create mock records, and some generate model training data from seed data or a description.
- Diversity at scale needs a direct test in any generator that feeds model training, because narrow or repetitive synthetic data can degrade the model trained on it.
- Training-use terms vary by model provider, so teams should read each provider's terms before using generated data to train a commercial model.
- Synthetic data generation and adaptive model development connect through repeated data updates and retraining as goals, domains, and inputs change.
- Enterprise deployment options differ across platforms, from hosted APIs to installs in a customer's cloud, or on premise.
What Is Synthetic Data?
Synthetic data is artificially generated data that reproduces the structure, statistical patterns, or behavior of real data without copying real records. Teams use it to test software, analyze sensitive information, and train models. Synthetic data takes several forms: tabular and relational records, time series, conversations and instruction-response pairs, and free text. The form determines which generators apply.
Two related terms are often confused with it: mock data and anonymized data.
| Term | Starting point | Relationship to real records |
|---|---|---|
| Mock data | Field rules or a schema | None |
| Anonymized data | Real records with identifying fields masked or removed | Every row still derives from a real record |
| Synthetic data | A model, rules, or a description that generates new records | New records that reproduce patterns without copying rows |
Why Do Companies Need Synthetic Data?
Companies need synthetic data when real data is restricted, missing, rare, or slow to collect. Four reasons a company needs synthetic data:
- Privacy limits. Production records often cannot be used for development, testing, or analytics. Synthetic data gives teams realistic records to work with without exposing sensitive information.
- Missing data. Training data often does not exist in sufficient quantity or quality for a target task. The most common setting practitioners face is one where no data exists for the capability a team wants a model to learn.
- Rare cases. Synthetic generation lets teams target long-tail scenarios that are uncommon in real data.
- Collection cost. Collecting and labeling data is slow and manual, and the data a team already has rarely matches the behavior it wants a model to learn.
What Is a Synthetic Data Generator?
A synthetic data generator is software that produces artificial records from rules, a model trained on real data, or a description of the data a team needs. Generators differ most in the input they require.
| Generator Type | Input Required | Typical Use |
|---|---|---|
| Rule-based and mock data | Field rules or a schema | Test fixtures, demos, and seed data |
| Statistical synthesis | A real table or database | Analytics and machine learning on sensitive data |
| De-identification and test data management | Production databases | Development and QA environments without production records |
| Prompted generation | A prompt, seed data, or a pipeline configuration | Text training data such as conversations and instruction pairs |
| Dataset invention | A description of the behavior a model should learn | Training data when no data exists for a capability |
What Should Companies Look for in a Synthetic Data Generation Platform?
Companies should check eight properties, ordered here from input to outcome:
| Property | What To Check |
|---|---|
| Input required | Real data, a schema, seed data, or a description; this decides which teams can start |
| Data types | Tabular, relational, time-series, text, document, or image data |
| Fidelity and diversity | Whether column relationships survive and whether samples stay varied as volume grows; test diversity at the volume you plan to use |
| Privacy evidence | Whether the platform reports re-identification risk and ships quality reports with each run; ask for the quality and privacy reports a platform produces for each generated dataset |
| Deployment | Hosted service, your own cloud, Kubernetes, or on-premise installation |
| Integration | APIs, SDKs, connectors, and fit with existing pipelines |
| Training-use terms | Whether the generating model's terms allow commercial training on the output |
| Link to model training | Whether generated data feeds retraining when goals change |
How Do the Top Synthetic Data Generation Platforms Compare?
Eight platforms are compared below for mid-size and large enterprise buyers. The table lists each platform's primary job and does not rank them. Each note states what the platform does and one scope fact, drawn from the vendor's own pages or documentation.
| Platform | Primary Job | Starting Input | Data Types and Methods |
|---|---|---|---|
| Invent a Dataset | Model training data from a description | A behavior description | Text instruction datasets and preference pairs |
| NVIDIA NeMo Data Designer | Synthetic datasets for AI training pipelines | Seed data, samplers, or prompts in a declarative configuration | SFT chat data, tool-calling SFT data, and DPO preference pairs |
| Tonic | De-identification, test data, and generation from scratch | Production databases, documents, or prompts, by product | Structured, semi-structured, and unstructured data, plus mock APIs |
| MOSTLY AI | Statistical synthesis of structured data | A real dataset | Tabular and multi-table data |
| K2view | Test data and synthetic data management across connected systems | Source systems, organized by business entity | AI-based, rule-based, and entity-cloning generation |
| SAS Data Maker | Privacy-preserving synthetic data from existing datasets | A real dataset | Data that keeps statistical, relational, and temporal properties |
| YData Fabric | Data profiling plus synthetic data for machine learning | An uploaded dataset | Tabular, time-series, and multi-table data |
| Syntho | AI-generated, rule-based, and mock data for relational data | Source data | AI-generated, mock, and rule-based data, including time series |
Invent a Dataset
Invent a Dataset, Adaption’s dataset generation platform, builds text-only, training-ready datasets from a description of the behavior a model should learn, with no seed data, schema, or labeling guide. Because dataset invention generates a training dataset directly from a description, it moves dataset creation from collection to specification.
With Invent a Dataset, a team writes the description and optionally narrows it with domains and subdomains. The output is an instruction dataset or a set of preference pairs. It fits teams that need training data for a behavior and have no dataset to start from.
Teams need training data that stays varied and accurate as volume grows, because repetitive data degrades the model trained on it. In an October 2026 evaluation, the Invent a Dataset API was tested against five frontier model APIs on diversity and quality. Invent a Dataset consistently outperformed the highest baselines for maintaining quality and diversity, simultaneously.
Terms matter as much as quality when the resulting model is commercial. Many frontier model providers restrict training on their outputs. Invent a Dataset terms permits commercial use of generated datasets for downstream model training.
NVIDIA NeMo Data Designer
NeMo Data Designer is NVIDIA NeMo’s framework for generating synthetic datasets with LLMs. A team starts from seed data, statistical samplers, or prompts defined in a declarative configuration, and generates supervised fine-tuning (SFT) chat data, tool-calling SFT data, or preference pairs for direct preference optimization (DPO). The framework gives teams control over field relationships and data quality.
Tonic
Tonic divides its platform into three products by job. Structural de-identifies, subsets, and synthesizes structured and semi-structured data. Textual de-identifies, redacts, and synthesizes unstructured data, free text, and files. Fabricate synthesizes relational data, free text, and mock APIs from scratch, and its Validation Agent reviews and refines both the generation code and the generated data.
MOSTLY AI
MOSTLY AI is a data intelligence platform for working safely with sensitive structured data, using real or synthetic data. It centers on tabular and multi-table data, with guides for evaluating synthetic data quality and for privacy protection. It runs in a customer’s own environment, with Helm deployment on Kubernetes clusters including OpenShift, Amazon EKS, and Azure AKS.
K2view
K2view Synthetic Data Management combines generative AI and rule-based generation with a business-entity data model. It starts from source systems organized by business entity and covers software testing, machine learning model training, and data sharing, with entity cloning and subsetting, reserving, rolling back, and aging of synthetic data. It validates data and enforces referential integrity across source systems, and it deploys in cloud, on-premise, or hybrid environments.
SAS Data Maker
SAS Data Maker, available in the Microsoft Marketplace, generates synthetic data that replicates the statistical, relational, and temporal properties of real data. It works from existing data, using algorithms and a graphical interface to generate the amount a team needs. Visual statistical evaluation metrics let users compare synthetic and original data.
YData Fabric
YData Fabric is a data-centric AI development platform that pairs data quality profiling with synthetic data. It covers tabular, time-series, and multi-table datasets, and generation starts from an uploaded dataset after profiling. The guided interface flags imbalances and outliers and tracks fidelity, utility, and privacy during model training.
Syntho
Syntho combines AI-generated synthetic data, synthetic mock data, and rule-based generation. It works from source data, covers relational data and time series, and includes test data management for non-production environments. A quality assurance report assesses accuracy, privacy, and speed, and consistent mapping preserves referential integrity across relational data. Deployment options include on-premise, private cloud, and Syntho cloud.
How Does Synthetic Data Generation Support Adaptive Model Development?
Adaptive model development, also called continual learning, keeps a model current by updating its training data and retraining as goals, domains, and inputs change. Synthetic data generation supports it by producing new, targeted training examples whenever requirements change, without waiting to collect them.
The loop has four steps:
- Define or update the objective.
- Generate new targeted training data for it.
- Retrain the model against the objective.
- Evaluate, and repeat when goals or data change.
Invent a Dataset and AutoScientist, Adaption’s system for automating the research loop behind model training, make this possible. You describe the behavior you want, Invent a Dataset generates the training data, and AutoScientist co-optimizes that data and the training recipe against your objective. What used to take a labeled dataset and weeks of tuning now starts with a description and ends with a model you own.
Author
Adaption TeamDate