# Tools for generating synthetic data

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# Tools for generating synthetic data: An in-depth overview

 By Ivo Donker — compiled with AI support (Claude & Gemini) · Last updated: 6 August 2026

 The use of synthetic data has taken off strongly in recent years within software development, data science and the training of artificial intelligence. Where previously organizations mainly worked with anonymized versions of real production data, privacy legislation and the need for scalable test environments are now forcing organizations to explore artificially generated alternatives. For a broader picture of general market developments, you can consult the article on [synthetic data](https://nieuws.llmnet.nl/en/synthetische-data) on the news platform. This directory page focuses specifically on the landscape of available tools, the underlying technologies and the criteria for selecting the right solution.

 
## What is synthetic data used for in practice?

 Synthetic data is not a universal solution, but serves specific purposes within different phases of the development cycle. The most common applications can be divided into four main scenarios:

 
 
 
- Populating test environments: Developers need realistic datasets to test applications for performance, edge cases and logical flows. Synthetic data provides a representative structure without any real personal data ending up in the test environment.
 
- Supplementing underrepresented classes: In machine learning projects, there is often an unbalanced distribution (class imbalance). Think of fraud detection or rare medical diagnoses. Synthetic generators can create additional examples of these scarce classes to improve model accuracy.
 
- Building evaluation sets: When deploying language models (LLMs), high-quality test sets are crucial to prevent regression. Synthetic data helps systematically build these sets. This is particularly relevant when working with sensitive [evaluation data from production](https://benchmark.llmnet.nl/en/evaluatiedata-uit-productie), which may not be directly exposed to external model APIs.
 
- Working with non-transferable data: Within heavily regulated sectors, such as healthcare or the financial sector, data often may not leave the physical or legal boundaries of an organization. By generating a synthetic variant locally that preserves the statistical properties, external partners can still perform analyses without gaining access to the original source files.
 

 
## The three main categories of generation tools

 The market for synthetic data generation tools breaks down into three technological approaches. Each category has its own mechanisms, advantages and limitations.

 
 
 
 Category | 
 Core Technology | 
 Primary Data Type | 
 Typical Use Case | 
 

 
 
 
 Rule- and schema-based | 
 Heuristics and fixed templates | 
 Structured (databases, CSV) | 
 Software testing and database seeding | 
 

 
 Statistical (distribution-preserving) | 
 Probabilistic models, GANs, Copulas | 
 Tabular data and time series | 
 Machine learning and statistical analysis | 
 

 
 LLM-based | 
 Generative AI and transformer models | 
 Unstructured text and code | 
 NLP training and evaluation sets | 
 

 
 

 
### 1. Rule- and schema-based generators

 These traditional systems work based on predefined rules, data types and constraints. They analyze the database architecture or a schema (such as JSON Schema or an SQL DDL file) and fill the fields with pseudo-random values that meet the specified criteria. The tools often use libraries with predefined lists for names, addresses and phone numbers.

 Well-known open-source libraries in this segment are Faker (available for, among others, Python, JavaScript and Ruby) and Mimesis. On the commercial side, traditional database vendors offer integrated modules within their test data management (TDM) suites. These tools are particularly suitable when the structure and referential integrity of the database matter more than the deeper statistical correlations between columns.

 
### 2. Statistical generators (distribution-preserving)

 Statistical generators take an existing dataset as their starting point. They analyze the mathematical distributions of individual columns and, more importantly, the correlations between them. They then build a mathematical model that can sample new records exhibiting the same statistical properties.

 Commonly used open-source frameworks include the Synthetic Data Vault (SDV) ecosystem and libraries based on Copulas or Conditional Tabular GANs (CTGAN). Commercial platforms in this category often offer more advanced interfaces and enterprise functionality for scanning entire relational database systems. Examples of providers in this segment are YData and Mostly AI. The generated data is highly suitable for analytical applications, since patterns such as "average income rises with age" are preserved in the synthetic export. Once this data has been generated, it can be deployed directly in systems for [AI Tools for Data Analysis](https://directory.llmnet.nl/en/ai-tools-data-analyse).

 
### 3. LLM-based generators for text

 For unstructured data, such as complaint letters, medical reports or transcripts of customer conversations, rule-based and purely statistical methods fall short. LLM-based generators use large language models to produce contextually plausible text. This can be done via targeted prompting on commercial APIs or by locally fine-tuning open-weight models (such as Llama or Mistral variants).

 Platforms such as Gretel.ai combine statistical methods with deep learning and transformer models to generate both text and tabular data. In the open-source community, frameworks such as LangChain or DSPy are used to build pipelines that enforce structured JSON outputs from unstructured language models. This type of generation is essential for training natural language processing (NLP) models and setting up agent systems.

 
## Overlap and hybrid architectures

 In practice, the categories mentioned do not exclude each other. On the contrary, many enterprise environments use hybrid architectures in which multiple methods are deployed side by side. This is because business systems rarely consist of just one type of data.

 A typical example is a customer database (CRM). This contains structured columns such as customer numbers and dates of birth, statistically correlated columns such as purchase amounts, and unstructured fields such as account manager notes. A team can choose to:

 
 
- Generate the unique identifiers (IDs) and email addresses with a rule-based tool to guarantee uniqueness and syntax validity.
 
- Simulate the financial transaction history with a statistical generator to preserve seasonal influences and credit risk patterns.
 
- Fill the free-text fields in the customer interaction logs using a locally running language model, fed with contextual variables from the previous steps.
 
 By combining these methods, the database remains both technically valid and statistically representative for complex testing purposes.

 
## Selection criteria for the right tool

 When evaluating tools for generating synthetic data, teams need to take various technical and organizational factors into account. The choice is closely tied to the intended end goal of the dataset.

 
### Data type: Tables versus unstructured text

 If the source consists mainly of relational SQL tables with strict foreign-key relationships, priority goes to tools that can maintain these relationships across multiple tables (referential integrity). If the data consists mainly of free text or semi-structured documents (such as PDFs or JSON logs), tools that use transformer models or LLM architectures are necessary.

 
### Statistical similarity versus structural validity

 For software testers, it's often enough that an email address has the correct naam@domein.nl structure and that the citizen service number (BSN) passes the eleven-test. The correlation between the BSN and the address is irrelevant to them. For data scientists training predictive models, on the other hand, the mathematical distribution is decisive. In that case, the tool must be able to accurately reproduce the multivariate distributions.

 
### Reproducibility and determinism

 In regression testing within software development, it's essential that a test run is repeatable. This requires the generator to be able to work deterministically by fixing a random 'seed'. When generating training data for machine learning, a higher degree of variation and non-determinism is actually desirable to prevent overfitting.

 
### Local execution and infrastructure

 Organizations under strict oversight cannot, for security reasons, upload data to external cloud APIs. For them, it's a hard requirement that the generation software can run locally (on-premises) or within their own protected cloud infrastructure (VPC). Open-source libraries or commercial-off-the-shelf software that can be deployed in Docker containers are preferred in these scenarios.

 
## Privacy is not an automatic consequence

 A persistent misconception is that synthetic data is by definition privacy-friendly and fully anonymous. Because statistical generators and LLMs are trained on real data, there's a risk that they directly copy or memorize specific characteristics of individuals from the training set. This is known as outlier leakage.

 For example, if a database contains only one person with an extremely high income in a specific postal code, a powerful statistical model can reproduce this unique pattern in the synthetic dataset. A malicious actor could then trace this information back to a natural person.

 
 
 Definition: Differential Privacy
 Differential privacy is a mathematical framework that offers formal guarantees against the leakage of individual records. By adding targeted noise to the statistical parameters during the training process, it is guaranteed that the presence or absence of one specific individual in the source set doesn't significantly affect the generator's output.

 
 
 When selecting a tool, it's therefore important to check whether it offers explicit support for differential privacy and whether you can manually configure the privacy parameters (such as the privacy-budget parameter epsilon, ε).

 
## Quality control and validation

 Generating the data is only the first step; validating it is at least as important. Quality control focuses on three aspects:

 
 
### 1. Comparing distributions

 To verify whether the synthetic data is statistically usable, the distributions of the synthetic columns must be compared with the original columns. This is done visually via histograms and mathematically via statistical tests such as the Kolmogorov-Smirnov test or Jensen-Shannon divergence for categorical variables. Advanced tools automatically generate quality reports quantifying these deviations.

 
### 2. Referential integrity and logical rules

 Synthetic data is useless if it contains logical inconsistencies. A record where the date of birth is in 1995 and the start date of the first job is in 1998 is biologically and legally implausible. Validation pipelines must check whether such business rules and foreign-key relationships remained intact after the generation process.

 
### 3. Train on Synthetic, Test on Real (TSTR)

 The ultimate test for the quality of statistically generated data in machine learning is the TSTR method. Here, you train a predictive model (for example a classifier) exclusively on the synthetic dataset. You then test the performance of this model on a separate set of *real* data. If the model performs well on the real data, this shows that it has genuinely learned the underlying patterns and not just the generator's noise.

 
## The risk of model collapse

 When synthetic data is used extensively and at scale over a long period, especially in training generative AI and LLMs, a specific danger lurks: model collapse. This phenomenon occurs when a model is recursively trained on data generated by an earlier generation of the same or a similar model.

 Because synthetic generators tend to ignore rare outliers in the tail of the distribution (the 'long tail') in favor of the most likely averages, variation shrinks with each successive generation step. Over time, the model 'forgets' the less common patterns. The output narrows into a repetitive stream of average results and can eventually degenerate into unusable gibberish. For developers of AI systems, this means that carefully archiving and protecting authentic, human-generated source data remains a critical priority.

 
## Synthetic data for evaluation sets

 When building applications that use LLMs, continuously evaluating answer quality is a requirement. Manually writing hundreds of test questions and expected answers is, however, time-consuming. Synthetic generation offers a solution here by automatically generating varied evaluation sets based on documentation or production databases.

 However, it is critically important to keep these synthetic evaluation sets strictly separate from the data used to train or fine-tune the models. Setting up such a separation is crucial to guarantee a representative [test set without data leaks](https://benchmark.llmnet.nl/en/testset-zonder-datalek) . If overlap occurs between the evaluation and training data, the model will score artificially high on the test, while performance in practice falls short.

 
## Legislation and regulation: The GDPR and the EU AI Act

 Although synthetic data is often presented as the solution to privacy issues, using it does not release organizations from their legal responsibilities. The General Data Protection Regulation (GDPR) still applies to the *process* of generating the data. After all, to create synthetic data, the generator must first be trained on the original personal data. This processing requires a valid legal basis (such as legitimate interest or consent).

 In addition, the EU AI Act sets specific requirements for the quality of datasets used to train and test high-risk AI systems. The law requires training data to be representative and free of systematic biases. If an organization uses synthetic data to correct bias or expand the dataset, this process must be fully documented and traceable. To determine whether the chosen method complies with the legal requirements, a prior [AI risk analysis and DPIA](https://consultancy.llmnet.nl/en/ai-risicoanalyse-dpia) can provide clarity.

 
## Further reading

 
 
- [Synthetic data: Market developments and strategic trends](https://nieuws.llmnet.nl/en/synthetische-data)
 
- [Overview of AI tools for data analysis](https://directory.llmnet.nl/en/ai-tools-data-analyse)
 
- [Guidelines for setting up a test set without data leakage](https://benchmark.llmnet.nl/en/testset-zonder-datalek)
 
- [Handling evaluation data from production safely](https://benchmark.llmnet.nl/en/evaluatiedata-uit-productie)
 
- [Carrying out an AI risk analysis and DPIA](https://consultancy.llmnet.nl/en/ai-risicoanalyse-dpia)
 
- [Overview of model and dataset hubs](https://directory.llmnet.nl/en/modellen-en-datasets-hubs)
 

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