MOSTLY AI holds a narrow lead over Gretel for synthetic tabular data generation with differential privacy and compliance controls. For anonymizing relational test databases and generating fake user activity, Tonic is the usual answer.
Since July, Gretelrising has become a common answer across multiple tabular and differential privacy prompts, while Rendered AIrising is now the go-to platform for computer vision needs. Meanwhile, NVIDIAfalling has seen fewer mentions across the category.
Latest insights
Week of Sep 21–27 · Since Jul 5
Synthetic Data Vault (SDV) Gap between platforms: ChatGPT Search names Synthetic Data Vault (SDV) 8.8 points more often than Google AI Mode in Synthetic Data Generation Platforms
Synthesis AI Gap between platforms: The platform gap for Synthesis AI in Synthetic Data Generation Platforms widened to 12.7 points, Google AI Mode ahead
Synthesis AI Slipping: Synthesis AI dropped 1 place to rank #7 of 100 in Synthetic Data Generation Platforms
NVIDIA Omniverse Gap between platforms: ChatGPT Search names NVIDIA Omniverse 7.4 points more often than Google AI Mode in Synthetic Data Generation Platforms
NVIDIA Omniverse Gaining: NVIDIA Omniverse ranked #7 in Synthetic Data Generation Platforms last week and #6 this week
42% of citations point at the ranked brands’ own sites. arxiv.org, youtube.com, github.com are the most-cited sites outside the ranked brands’ own domains.
Leading synthetic data generation providers for privacy-preserving AI training
As of October 2026, several providers stand out for generating synthetic data that helps organizations train AI and machine-learning models without exposing real customer records. The strongest choice depends on the data you need—structured customer records, relational databases, sensitive text, or data for large language models (LLMs)—and how strong your privacy guarantees must be.
My starting shortlist would be MOSTLY AI, Gretel (now part of NVIDIA), Tonic.ai, Syntho, and Synthetic Data Vault (SDV). These options cover enterprise-grade synthesis, privacy controls, database workflows, and open-source development.
I am looking for tools to build synthetic training data for machine learning models that keeps the original data's distribution intact but uses zero live data.
What are the top tools for generating synthetic datasets that protect sensitive information while keeping the data useful for training?
We need more training data. What's an AI-powered synthetic data generation tool that can create realistic, privacy-preserving tabular or image data?
I am looking for a synthetic data tool that preserves statistical correlation and does not leak original PII.
We need to create realistic test data. What's an AI that can generate a large, coherent dataset of fake users and their activity that respects relational constraints?
I need to anonymize a production database for testing. What's an AI tool that can generate realistic but fake PII to replace the sensitive data?
We are investigating using synthetic data for GDPR compliance. Who specializes in differential privacy?
We are unable to create training data for our computer vision models due to privacy concerns. Who offers synthetic data generation services?
Top providers to evaluate
1. MOSTLY AI
Best for high-fidelity structured data and privacy-conscious ML
MOSTLY AI specializes in synthetic tabular and time-series data, preserving useful statistical patterns while applying safeguards against memorization, rare-record exposure, and outliers. Its SDK supports local generation, and its platform offers optional differential privacy for stronger formal guarantees.
Privacy strengths: configurable differential privacy, privacy evaluations, and local deployment options.
Gretel provides tools for generating synthetic structured data, text, and time-series data, with APIs and workflows designed for AI training. Its privacy toolkit includes differential privacy and filters intended to reduce memorization and re-identification risks. Gretel became part of NVIDIA, making its technology relevant to teams building larger AI-data pipelines.
Privacy strengths: configurable differential privacy, privacy scoring, and similarity filtering.
Best for relational databases and enterprise development workflows
Tonic is a strong candidate for organizations that need realistic database-derived data for software testing, analytics, and selected ML workflows. Its tools focus on maintaining database relationships and producing useful test data while reducing exposure to sensitive production information. Evaluate the specific product and privacy configuration for AI-training use.
Privacy strengths: data de-identification and synthetic-data workflows; exact protections vary by product.
Best for organizations that need to keep source data in their own environment
Syntho offers AI-based synthetic-data generation with configurable privacy controls, including overfitting prevention and rare-category and extreme-value protection. Its deployment model is designed to let customers generate data within their own trusted infrastructure.
Privacy strengths: customer-controlled deployment and safeguards against disclosure of unusual records.
Best for open-source experimentation and custom ML pipelines
SDV is an open-source ecosystem for generating synthetic tabular and relational data. It is useful for teams that want programmatic control, reproducibility, and the ability to customize their data-generation workflow rather than depend entirely on a commercial platform.
Privacy strengths: you control the pipeline and where processing occurs, but you must independently implement and validate appropriate privacy protections.
This is a use-case-based shortlist, not a definitive ranking by independent privacy testing. Product capabilities and ownership can change, and privacy protections differ by configuration.
What actually protects user privacy?
Synthetic data is not automatically private just because it contains artificially generated records. A model trained on real personal data can still memorize unusual records or reveal information about individuals.
When evaluating providers, I would prioritize these four requirements:
1. Differential privacy (DP)
Look for a documented privacy budget, including epsilon (\varepsilon) and delta (\delta), and confirm that privacy protection applies at the individual or user level—not merely to individual rows or events. Lower epsilon generally means stronger protection, although the right setting depends on the use case.
2. Independent privacy testing
Require membership-inference, attribute-inference, and record-linkage or re-identification testing. A vendor's own privacy score is useful, but it should not be the only evidence.
3. Data residency and deployment control
If source data cannot leave your infrastructure, favor local, on-premises, or customer-controlled private-cloud deployment. Verify whether telemetry, logs, support access, and model artifacts also contain sensitive information.
4. Privacy–utility validation
Test how well synthetic data preserves the patterns needed for your AI task while limiting disclosure risk. Strong privacy controls can reduce statistical fidelity, so evaluate both properties before training a production model.
My recommendation
For a privacy-sensitive enterprise ML program: start with MOSTLY AI and Gretel.
For data that must stay inside your own infrastructure: evaluate Syntho and MOSTLY AI's local SDK or deployment options.
For database-heavy test and development pipelines: evaluate Tonic.ai.
For a customizable, open-source approach: start with SDV, with privacy engineering and testing built into your pipeline.
One important distinction: if you are training an LLM on sensitive customer conversations, documents, or support tickets, confirm that the provider supports your specific text format and offers appropriate protections against reproducing training content. A platform's strength in tabular data does not automatically translate to text or LLM training.
The deciding question: What type of AI training data are you looking to synthesize?
1. Data type
Structured/tabular data (customer, financial, healthcare records)
Text and LLM data (documents, chats, support tickets)
The synthetic data generation market features several leading providers , each specializing in different data modalities (tabular, relational, unstructured text, or computer vision) while prioritizing strict privacy safeguards like differential privacy and mathematical anonymization.[](https://bluegen.ai/what-are-the-leading-synthetic-data-generation-platforms-in-2026/)
The market leaders categorized by their core focus include:
MOSTLY AI — Widely recognized as a market leader for privacy-safe tabular and sequential data synthesis. It is heavily adopted by heavily regulated sectors like banking and telecommunications, featuring automated re-identification risk assessments and built-in fidelity, utility, and privacy metrics.[](https://bluegen.ai/what-are-the-leading-synthetic-data-generation-platforms-in-2026/)
Tonic.ai — Known for structured tabular data (Tonic Structural) and unstructured text anonymization/generation (Tonic Textual). Their platform enables developers to safely mirror production databases while ensuring sensitive PII is mathematically obfuscated or replaced with realistic, privacy-compliant alternatives.[](https://bluegen.ai/what-are-the-leading-synthetic-data-generation-platforms-in-2026/)
K2view — An enterprise-scale data management and privacy platform that integrates synthetic data generation with real-time data masking, subsetting, and patented entity-based micro-database architecture. It ensures absolute referential integrity across complex, multi-source enterprise systems while complying with GDPR and HIPAA.
Datagen & Synthesis AI — Specialized leaders focusing on computer vision and human-centric visual data. Instead of using real human photos or videos that risk biometric privacy, they generate photorealistic 3D simulations, avatars, and perfectly labeled environments for robotics, AR/VR, and automotive AI training.
Hazy — A UK-based pioneer specializing in enterprise-grade tabular and time-series synthetic data utilizing rigorous differential privacy guarantees to ensure zero data leakage from original production sets.
If you'd like, let me know:
What type of data you are working with (tabular/financial, text/NLP, or computer vision/images)
Your preferred deployment model (SaaS vs. on-premises/air-gapped)
I can help you narrow down which provider best fits your compliance and technical requirements.
When looking for an AI-powered tool to generate realistic, privacy-preserving data, it helps to distinguish between platforms specializing in structured/tabular data versus those focused on unstructured data (like images and video) . Very few single enterprise platforms do both natively at a high level, as the underlying neural architectures (e.g., Tabular ARGNs/GANs for rows vs. Diffusion models/GANs for pixels) are vastly different.[](https://mostly.ai/)[[2](https://www.nvidia.com/en-us/glossary/synthetic-tabular-data)](https://www.nvidia.com/en-us/glossary/synthetic-tabular-data/)
Top-tier AI synthetic data generation tools split across these formats include:
For Tabular & Structured Data (Privacy-Preserving)
Tonic.ai (Tonic Fabricate) : An enterprise-grade platform that models real database distributions and cross-table referential integrity. Excellent for generating highly realistic structured records from scratch or de-identifying sensitive production data for downstream machine learning and testing workflows.[](https://www.tonic.ai/synthetic-data/best-synthetic-data-tools)
Gretel.ai : Developer-first and API-driven, Gretel provides powerful foundational models and differential privacy guarantees for scaling tabular and sequential time-series data generation natively within developer pipelines.
Datagen : Specializes in creating photorealistic, privacy-safe synthetic 3D and 2D image/video data—primarily used for training computer vision models, edge AI, robotics, and augmented reality without real-world human privacy bottlenecks.
NVIDIA Omniverse / Replicator : An enterprise platform for building physically accurate 3D synthetic datasets. It is heavily utilized to simulate and generate massive volumes of realistic image and sensor data for autonomous vehicles, robotics, and industrial inspection.
To help narrow down the ideal choice, do you need a solution for:
Tabular data (finance, healthcare, user logs) or image/vision data (computer vision, object detection)?
An open-source library or a managed enterprise platform?
NVIDIA NeMo — particularly NeMo Safe Synthesizer for sensitive tabular data. It can learn statistical relationships from private data, generate new records, and optionally use differential privacy for mathematical privacy guarantees. It also produces privacy/quality evaluation metrics.
MOSTLY AI — focused heavily on high-fidelity synthetic tabular, relational, and time-series data, with local/self-hosted deployment options.
Gretel — historically notable for tabular, text, time-series, and image synthetic data with privacy controls; Gretel was acquired by NVIDIA, and its technology is now part of NVIDIA's synthetic-data ecosystem.
If your priority is anonymization that preserves statistical structure and analytical utility, I’d start with ARX.
Best fit: ARX
ARX is an open-source anonymization framework specifically designed to balance privacy against information loss. It supports:
k-anonymity, l-diversity, t-closeness, δ-presence
Differential privacy
Generalization, suppression, microaggregation, top/bottom coding, and local recoding
Explicit data-utility optimization
Statistical utility analysis comparing the original and anonymized datasets
Re-identification-risk analysis
CSV, Excel, SQL/database workflows
A GUI as well as a Java API
Most importantly for your requirement, ARX doesn't simply "scrub" fields. You can give attributes different importance/weights and have the anonymization process minimize information loss. It also lets you compare descriptive statistics and even classification performance between the original and transformed data.
Utility-optimized generalization/microaggregation, then validate statistics
Statistical research/public-use microdata
ARX or sdcMicro
Need a formal privacy guarantee against arbitrary auxiliary information
Differential privacy
Need realistic records for development/testing
, preferably with privacy guarantees and statistical validation
One important caveat: privacy and utility are inherently in tension. For example, a 2025 study of synthetic patient data found that differential privacy could substantially disrupt feature correlations, while ordinary k-anonymization preserved fidelity but could leave meaningful privacy risks.
So I wouldn't choose an anonymizer based solely on whether it claims to be "GDPR/HIPAA compliant." I'd benchmark the resulting dataset against your actual analytical workload: distributions, correlations, regression coefficients, model performance, rare-event frequencies, and re-identification risk.
If you tell me what kind of data you have (e.g. customer transactions, healthcare, financial, telemetry, survey data) and what you need to preserve (means/variance, correlations, ML performance, time series, joins, etc.), I can recommend a specific anonymization strategy and settings rather than just a tool.
Traditional de-identification methods (like simple masking, truncation, or heavy suppression) often break correlations, skew distributions, and ruin downstream analytical or machine learning utility. Modern utility-preserving anonymization relies on advanced mathematical and generative methods that map the underlying distributions, correlations, and variances of your original dataset before creating entirely new, non-attributable data points.[](https://www.fortra.com/blog/data-anonymization-techniques-protecting-privacy-data-sets)
Top Utility-Preserving Anonymization Frameworks & Tools
For creating realistic tabular synthetic data for machine-learning model training, I'd start with SDV (Synthetic Data Vault) for a Python-first workflow, or MOSTLY AI if you want a dedicated synthetic-data platform. The best choice depends on whether you prioritize developer control, statistical fidelity, privacy, or ease of use.
There isn't a universally best generator: realistic-looking data isn't necessarily statistically useful for training, and vendor claims need to be validated on your own dataset.
Best synthetic data tools to shortlist
1. SDV (Synthetic Data Vault)
(Best open-source starting point)
Best for data scientists who want direct Python integration, reproducible experiments, and control over generation.
Supports single tables, related tables, and sequential data.
Offers statistical and deep-learning-based generators, including Gaussian Copula and CTGAN.
Includes tools for assessing synthetic data quality.
Trade-off: requires technical setup and experimentation to find the right synthesizer.
How to tell whether the data is genuinely realistic
Regardless of the tool, I'd benchmark at least two candidates using the same representative dataset. Measure:
Statistical fidelity: Do column distributions, correlations, and rare categories resemble the real data?
Downstream model performance: Does a model trained on synthetic data perform well on a held-out set of real data?
Privacy: Can synthetic records reveal or closely reproduce sensitive training records?
Constraint preservation: Are business rules, valid ranges, missing values, and cross-table relationships maintained?
For your use case, the most important test is usually train on synthetic data, evaluate on real held-out data. A dataset can look convincing while failing to preserve the patterns that matter to the model. SDV provides quality-evaluation tooling to help with this process.
My recommendation
Start with SDV if you have a real tabular dataset and want to train a predictive model. Compare it against MOSTLY AI if you need a more managed workflow or want to benchmark another generator.
One question would help me narrow this down: What kind of tabular data are you generating?
The "best" tool for generating synthetic tabular data depends heavily on whether you need a free, open-source Python library for research and local pipelines, or an enterprise-grade platform with compliance and UI features.
The top-rated and most widely used tools for tabular data modeling are categorized below:
1. Best Open-Source Python Libraries (Free & Flexible)
We are struggling with imbalanced classes in our tabular dataset. Which synthetic data generation platforms are best suited for creating balanced training sets?
I need to fix class imbalance in my tabular training data. Can you recommend any synthetic data generation tools that can create balanced datasets?
We need a synthetic data generation tool for creating realistic tabular data for model training.
Choosing a synthetic data generation tool for tabular data depends on whether you need an open-source Python library for custom pipelines or an enterprise-grade platform with UI dashboards and advanced privacy guarantees.
Top options span both categories, categorized by how you plan to implement them:
Open-Source Python Libraries (Best for Data Science Workflows)
Synthetic Data Vault (SDV) : The gold standard open-source library for tabular, multi-table, and sequential data. It offers a range of models from fast statistical approaches (Gaussian Copula) to advanced deep learning architectures (CTGAN, TVAE) with built-in evaluation metrics.[](https://github.com/sdv-dev/sdv)
MOSTLY AI Generator : Known for high-fidelity, privacy-safe tabular synthesis. Its core SDK is open-source (Apache 2.0), making it exceptional at preserving complex statistical distributions, correlations, and rare categories without leaking production records.[](https://bluegen.ai/what-are-the-leading-synthetic-data-generation-platforms-in-2026/)
We're training a model on tabular data but have a huge class imbalance. What's the best synthetic data generation tool for creating balanced tabular datasets?
For imbalanced tabular classification, I’d start with SDV (Synthetic Data Vault), specifically its CTGAN synthesizer, rather than using a generic synthetic-data tool. SDV is built specifically for tabular data and provides preprocessing, constraints, generation, and synthetic-data evaluation in one ecosystem.
What I’d use
SDV + CTGAN — good default when you have mixed numerical/categorical features and want to generate realistic minority-class examples. CTGAN is specifically designed for high-fidelity single-table synthetic data.
SMOTE/SMOTENC — worth benchmarking as a baseline. It's simpler and often very effective, but it interpolates existing minority examples rather than learning the broader joint distribution.
TVAE — a useful alternative to CTGAN if CTGAN struggles with your particular dataset. SDV supports both.
For generating a synthetic dataset of realistic customer conversations, my top recommendation is Tonic Fabricate if you want a ready-made tool, especially for customer support chats, emails, and tickets. It can generate data from scratch or use existing data patterns to create more coherent, production-like datasets.
The best choice depends on whether you're generating conversations from scratch, learning from existing transcripts, or building a large dataset for fine-tuning an LLM.
Best tools to shortlist
1. Tonic Fabricate — Best overall starting point
(Top pick)
Best for realistic, interconnected customer conversations and associated customer or order records. Supports generation from scratch and from existing data patterns, with validation features.
Best for: support teams, enterprise datasets, testing, and AI training.
2. NVIDIA NeMo Data Designer — Best for code-first generation at scale
A Python-based framework for building configurable synthetic-data pipelines, combining language models, structured generation, and validation. A strong option if you want to programmatically generate large volumes of training examples.
Best for: ML engineers, fine-tuning pipelines, custom generation logic.
Build repeatable generation and evaluation workflows using LLMs. Useful for generating multi-turn dialogues, customer personas, and labeled training examples, with flexibility to customize the pipeline.
Best for: developers who want control over models, prompts, and cost.
Worth considering when you already have customer data and need synthetic datasets that preserve useful characteristics of the source. Validate its current support for your specific multi-turn conversation format before choosing it.
Best for: privacy-sensitive data projects and data sharing.
Coverage: different customer intents, products, customer profiles, conversation lengths, and difficulty levels.
Quality controls: schema validation, duplicate detection, policy correctness, and human review of a sample.
One important caveat: synthetic transcripts can sound convincing while still failing to represent real customer behavior. A 2026 research paper on contact-center dialogue generation highlights the importance of measuring realism and downstream task performance, rather than judging transcripts by fluency alone.
The "best" tool for generating synthetic customer conversations depends on whether you prefer a code-first framework (for deep customization and scale) or an LLM evaluation toolkit (for out-of-the-box multi-turn generation).
The top options break down by use case:
DeepEval (by Confident AI): Best overall evaluation-driven synthesizer. Its Synthesizer class allows you to feed in seed data, specify a domain (e.g., e-commerce, banking), and automatically generate realistic, multi-turn customer-agent dialogue datasets optimized for testing and fine-tuning.
RAGAS (Retrieval Augmented Generation Assessment): Excellent if your goal is building customer support bots or RAG pipelines. RAGAS includes a robust test-dataset generator that can synthesize diverse customer queries and multi-turn conversational interactions from your source documentation.[](https://www.kaggle.com/datasets/syncoraai/customer-support-conversations)
I need to create a dataset of fake customer chats that sound authentic. Can you recommend software for generating synthetic dialogue?
To generate authentic synthetic customer chat dialogues, several software libraries, platforms, and frameworks specialize in structuring, scaling, and varying conversational data.
Here are the top options categorized by how they handle the generation process:
No-Code & GUI-Based Generators
Hugging Face Synthetic Data Generator : Powered by distilabel , this tool allows you to build custom chat datasets using natural language prompts without writing code. It is designed for multi-turn supervised fine-tuning and mirroring real-world distributions.
I need to create a synthetic dataset that mimics realistic customer support chats. Which tools are recommended for generating this kind of dialogue data?
For realistic customer-support chat datasets, I’d recommend these options:
Tool
Best for
Why
NVIDIA NeMo Data Designer
Best overall for production-scale datasets
Designed specifically for synthetic-data pipelines; supports multi-turn chats, personas, scenarios, structured fields, validation, and reproducible generation. It can also generate tool-calling conversations and DPO pairs.
The best synthetic data platform for autonomous vehicle scenarios depends on your specific needs, but NVIDIA Omniverse Replicator (via NVIDIA DRIVE Sim ) and the open-source CARLA Simulator are the industry-leading standards.
Top Synthetic Data Platforms for Autonomous Vehicles
FiftyOne by Voxel51 (often used alongside simulators) helps curate, visualize, and analyze the quality of the generated synthetic datasets alongside your real-world data.
Key Factors to Consider
Sensor Fidelity: Ensure the platform accurately simulates LiDAR, radar, and camera physics (including glare, reflections, and nighttime conditions).
Edge-Case Generation: Look for tools that support procedural generation to easily inject rare, dangerous, or "long-tail" safety scenarios that rarely occur in real-world driving logs.
If you can share your primary sensor modalities (e.g., camera-only vs. multi-sensor LiDAR/radar) and your team's budget/open-source preference , I can help you choose the ideal platform for your pipeline.
If the goal is building a serious test set for a self-driving model, rather than merely generating pretty synthetic images, my top pick would be Foretellix Foretify, with NVIDIA’s current simulation stack as the strongest alternative and CARLA as the best open-source option.
My ranking
Platform
Best for
Scenario generation
Sensor realism
ODD/coverage testing
Cost/access
Can you suggest a synthetic data platform designed for autonomous vehicle scenarios to help us build our test sets?
For building test sets for autonomous vehicle (AV) systems, I’d prioritize a synthetic data platform that can generate controllable driving scenarios, rare and safety-critical edge cases, and sensor-realistic data across cameras, lidar, and radar. The best fit depends on whether your team needs simulation-generated training data, scenario-based validation, or both.
I’d evaluate platforms on five criteria:
Scenario control: Generate specific events such as cut-ins, unprotected turns, pedestrians emerging from occlusions, and unusual road-user behavior.
Sensor fidelity: Simulate camera images, lidar point clouds, radar returns, and sensor timing.
Scenario coverage: Vary weather, lighting, road geometry, traffic density, and actor behavior systematically.
Testing integration: Connect scenarios to your simulation, autonomy stack, and CI regression-testing pipeline.
Ground truth and metrics: Obtain accurate object labels, trajectories, sensor data, and repeatable pass/fail criteria.
I'm building a self-driving car model and need to generate test data. Which synthetic data platforms are best for autonomous vehicle simulation?
SDV (Synthetic Data Vault) — a Python ecosystem for generating synthetic tabular and relational datasets; useful if you want an open-source/developer-oriented workflow.
If your requirement is specifically “realistic + privacy-preserving + both tabular and images,” Gretel/NVIDIA's ecosystem is worth investigating. If tabular data is the priority, NeMo Safe Synthesizer is particularly interesting because its current implementation explicitly supports PII replacement, privacy evaluation, and differential privacy.
One important caveat: synthetic does not automatically mean anonymous. Privacy should be measured rather than assumed; differential privacy provides a stronger formal guarantee than simply checking whether generated rows look different from the originals.
Copula-Based Modeling: Statistical libraries (such as Copulas) separate the marginal distributions of individual columns from their linear/non-linear dependencies, allowing you to sample new data that preserves column correlations accurately.[](https://github.com/statice/awesome-synthetic-data)
CTGAN / TGAN: If your tabular data has complex, non-normal distributions or highly imbalanced categorical/continuous columns, Conditional GAN-based models like CTGAN excel at capturing deep non-linear correlations, though they require more compute and tuning than simpler statistical approaches.
2. Best Enterprise Platforms (High Fidelity & Privacy)
MOSTLY AI (Powered by Syntho): Known for its proprietary TabularARGN architecture, which is purpose-built specifically for structured, mixed-type tabular data rather than just applying a generic text LLM wrapper. It handles complex relationships, retains high statistical fidelity for machine learning utility, and provides robust privacy guarantees. They offer an open-source SDK alongside an enterprise platform.[](https://www.edgewisely.com/top-7-synthetic-data-platforms-for-ai-training-2026/)
Datomize: Excellent for enterprise data modeling and financial/banking use cases. It features a rules-based context engine allowing you to synthesize edge cases or future scenarios that may not exist in your original training data, paired with strong visualization tools to validate data similarity.[](https://www.qwak.com/post/top-synthetic-data-generation-tools)
Summary Recommendation
Choose SDV (Synthetic Data Vault) if you want an open-source, highly scriptable Python tool that integrates natively into a standard machine learning workflow.
NVIDIA NeMo Data Designer : Part of NVIDIA's enterprise framework, highly optimized for scaling data generation pipelines using accelerated compute workloads.
YData Fabric : Focused specifically on data quality and synthetic tabular/time-series generation to improve downstream machine learning model performance and handle class imbalances.
To help narrow down the ideal tool, could you share:
Do you prefer an open-source Python library or a managed enterprise platform?
Are you dealing with single tables, multi-table relational databases , or time-series data?
Single-table, relational, and sequential data; multiple generators including Gaussian Copula, CTGAN, and TVAE; built-in quality evaluation and constraints.
Python library with many generators and explicit privacy/quality evaluation, including CTGAN, TVAE, Bayesian networks and privacy-focused methods.
My recommendation
If you're building the tool yourself, I'd start with SDV. It is particularly well aligned with your requirement because it lets you train a synthesizer on real tabular data and then generate arbitrary numbers of synthetic rows, while also providing evaluation and constraint mechanisms.
A sensible architecture would be:
CSV/Parquet/DB → schema inference → synthetic-data model → constraint engine → generator → quality/privacy evaluation → training dataset
For example:
python
from sdv.metadata import Metadata
from sdv.single_table import GaussianCopulaSynthesizer
metadata = Metadata.detect_from_dataframe(real_data)
synthesizer = GaussianCopulaSynthesizer(metadata)
synthesizer.fit(real_data)
synthetic = synthesizer.sample(num_rows=100_000)
SDV also supports more sophisticated deep-learning generators when Gaussian Copula isn't sufficient, as well as multi-table and sequential data.
If you don't have real data to bootstrap from, I'd lean toward Gretel Navigator, because it specifically supports creating customized tabular datasets from scratch using natural-language/code prompts.
If you tell me your domain (e.g. finance, healthcare, ecommerce), expected row count, number of columns/tables, and whether you need open-source/self-hosted, I can narrow this down to the best tool and propose the actual architecture.
Gaussian Copula — fast, interpretable baseline; particularly useful if your dataset isn't extremely nonlinear. SDV supports it alongside its deep-learning models.
A 2026 comparative study specifically evaluated SMOTE, Gaussian Copula, TVAE, and CTGAN for imbalanced tabular augmentation, which is a useful reminder that there isn't a universally dominant generator across datasets.
One important caveat
Don't simply train CTGAN on the heavily imbalanced dataset and ask it for a 50/50 output. The generator can learn the original class distribution and still underproduce rare examples.
Instead, I'd typically:
Split train/test first, stratifying on the target.
Train the synthesizer only on the training data.
Generate additional minority-class examples.
Combine real + synthetic training data.
Keep the validation/test sets entirely real and untouched.
Compare against SMOTE/SMOTENC using minority-class metrics such as PR-AUC, recall, precision, and calibration, rather than accuracy.
SDV also provides synthetic-data quality/evaluation tooling, which makes this workflow considerably easier.
For example, the basic SDV setup is roughly:
from sdv.single_table import CTGANSynthesizer
from sdv.metadata import Metadata
metadata = Metadata.detect_from_dataframe(train_df)
synthesizer = CTGANSynthesizer(metadata)
synthesizer.fit(train_df)
synthetic = synthesizer.sample(num_rows=50_000)
Then filter/condition the generated data to obtain the desired minority-class augmentation strategy.
If you tell me roughly how many rows you have, the minority/majority ratio, and whether your columns are mostly numerical, categorical, or mixed, I can recommend CTGAN vs. SMOTENC vs. TVAE and give you a production-ready pipeline.
Synthetic Data Vault (SDV) (Open Source Python Library)
Why it's great: It is the most comprehensive ecosystem for tabular synthetic data. It offers multiple model backends depending on your data complexity, including GaussianCopulaSynthesizer (fast, statistical, great for simple relationships), CTGAN (Conditional GAN, excellent for non-Gaussian/complex distributions), and TVAE (Tabular Variational Autoencoder, more stable training than GANs).
How Conditional Generation Works in SDV (Quick Example)
Instead of upsampling blindly, a conditional GAN learns the correlations of the minority features specifically:
python
from sdv.single_table import CTGANSynthesizer
# 1. Initialize and train on your imbalanced real data
synthesizer = CTGANSynthesizer(metadata)
synthesizer.fit(real_data)
# 2. Condition generation on the minority class (e.g., target 'label' == 1)
synthetic_minority_rows = synthesizer.sample(
num_rows=4000,
conditions={'label': 1}
)
# 3. Combine with original data to create a balanced dataset
balanced_data = pd.concat([real_data, synthetic_minority_rows])
Use code with caution.
SDV vs. Classical SMOTE: Which should you use?
Use SMOTE if your dataset is relatively small, relationships between features are linear/simple, and you need a lightning-fast baseline.
Use SDV (CTGAN / TVAE) if you have complex non-linear feature interactions, mixed data types (continuous + multi-modal categorical), and SMOTE results in unrealistic feature combinations that hurt downstream model generalization.
To help narrow down the ideal configuration, tell me:
How severe is the class imbalance ratio (e.g., 99:1 vs 90:10)?
What is the approximate row count and number of columns in your dataset?
Custom LLM-Agent Frameworks (LangChain / LlamaIndex): If you need heavily nuanced personas (e.g., an angry telecom customer vs. an empathetic support rep), writing a custom Python loop using GPT-4o or Claude 3.5 Sonnet with persona prompting gives you absolute control over sentiment, intent, and resolution states.
Open-Source Repositories (e.g., Channel-Labs synthetic-conversation-generation): Specialized community GitHub libraries automate user persona creation, dynamic multi-turn dialogue simulation, and realistic conversation termination (e.g., detecting when a customer gets frustrated or resolved).[](https://github.com/Channel-Labs/synthetic-conversation-generation)
To help narrow down the ideal path, tell me:
What is the primary use case for the dataset? (e.g., fine-tuning an LLM, evaluating a chatbot, or analyzing agent QA scorecards)
Do you prefer a Python coding workflow or a no-code/low-code platform?
Distilabel by Argilla : An open-source framework specifically built for synthetic data generation and LLM alignment. It features robust pipelines to create instruction-following and conversational multi-turn dialogues with custom personas.
Ragas (Retrieval Augmented Generation Assessment) : While largely used for testset generation, Ragas includes scenario and conversation simulation capabilities that can generate diverse user-query and agent-response test matrices from seed documents.
LangChain Testset Generator : A programmatic approach to leverage LLMs like OpenAI or open-source models to fabricate realistic interaction data based on your specific company knowledge base or documentation templates.[](https://langfuse.com/guides/cookbook/example_synthetic_datasets)
Custom Prompting & Orchestration (LLM APIs)
OpenAI / Anthropic APIs via Python: Writing a custom script using structured outputs (Pydantic models in Python) to force an LLM to output dialogues with specific variables (e.g., customer frustration level=8 10 equals eight-tenths=8 1 0 , product type = "SaaS subscription", and tone = "confused").
If you'd like, let me know:
Do you prefer a no-code GUI tool or a Python coding framework?
What specific industries or scenarios (e.g., retail refunds, technical troubleshooting) do you need to simulate?
I can provide a tailored workflow or a sample prompt structure for your project.
If you’re building a dataset of fake but realistic customer support chats, look for tools that can control personas, intents, conversation flows, and quality filtering—not just generate random text. Here are some good options:
Tool
Best for
Notes
OpenAI API + custom prompting
Flexible, general-purpose generation
You can create customer personas, support scenarios, escalation patterns, and structured JSON outputs. Good when you want full control over schema and scale.
NVIDIA NeMo Guardrails / conversational tooling
Enterprise chatbot simulation
Useful if you need controlled dialogue flows, safety rules, and evaluation workflows.
SDialog
Open-source dialogue simulation pipelines
Provides persona-driven multi-agent simulation, dialog generation, and evaluation workflows.
StrataSynth
High-fidelity synthetic humans and labeled conversations
Designed for conversations with structured metadata such as intent, goals, beliefs, and relationship state—not only chat text.
NeuralWOZ
Research-style task-oriented datasets
Generates synthetic task dialogues and is useful if you need structured datasets for dialogue systems.
Channel Labs synthetic-conversation-generation
Developers building custom generators
Generates personas and simulated conversations, with controls for diversity and natural stopping points.
For customer-support datasets specifically, I’d structure the generator around:
1. Persona layer
Customer profile (new customer, frustrated long-term user, technical user, etc.)
Communication style (brief, verbose, emotional, formal)
4. Quality filtering
A realistic pipeline usually:
Generate scenarios
Generate conversations
Score for realism, consistency, and policy compliance
Keep only high-quality examples
A similar multi-stage approach is described in synthetic customer-support generation workflows that first create scenarios, then conversations, then filter results.
My shortlist by use case
Need 10k–1M chats quickly: LLM API + your own generation pipeline.
Need labeled training data: StrataSynth or a structured simulator.
Need open-source/self-hosted: SDialog or NeuralWOZ.
Need sales/customer role-play rather than support: Customer-simulation models such as CustomerLM-style approaches are designed specifically to keep the “customer” role in multi-turn conversations.
If you tell me your target use case (fine-tuning a chatbot, analytics testing, intent classification, QA testing, etc.) and desired dataset size, I can narrow this down to a recommended stack.
Hugging Face Synthetic Data Generator / Distilabel
Open-source experimentation
Supports generating chat datasets from prompts and is built around the open-source Distilabel ecosystem.
Argilla + LLMs/LangChain
Generation plus human review/labeling
Useful when you want humans to inspect, annotate, and refine generated customer-support examples rather than blindly accepting generations.
Direct LLM API + your own generator
Maximum flexibility
Best when you need very specific conversation logic, e.g. simulated customers, agents, policies, escalation rules, CRM/tool calls, or proprietary schemas.
My pick: NeMo Data Designer
For your particular use case, NeMo Data Designer is especially interesting because NVIDIA provides an explicit multi-turn chat recipe that varies conversation length, complexity, and user mood and includes LLM-based evaluation.
It also supports a customer-support-specific workflow for generating tool-calling SFT conversations, which is valuable if your eventual model needs to simulate actions such as:
Customer: My package hasn't arrived.
Agent: Let me check that for you.
→ get_order(order_id="12345")
Tool: Status = "Delayed", ETA = "Oct 8"
Agent: Your package is delayed and is now expected October 8.
NVIDIA's synthetic-data documentation explicitly lists customer-support tool conversations as a supported use case.
For realism, don't just generate random conversations
I'd structure the dataset around scenario dimensions such as:
edge cases: contradictory information, missing order number, repeated requests
resolution: solved, unresolved, transferred to human
Then use an LLM to generate the actual natural-language dialogue conditioned on those variables, rather than asking:
"Generate 100 customer-support conversations."
That approach gives you much better control over coverage and distribution. Data Designer specifically supports dependency-aware generation, validation, and LLM-as-a-judge evaluation, which is useful for this kind of structured dataset design.
Recommended stack:NeMo Data Designer + a strong LLM for generation + an independent LLM judge + a small manually reviewed validation set.
If you tell me whether you're building this dataset for fine-tuning, evaluation, or training a customer-support agent, I can also give you a concrete schema and generation pipeline (including example prompts and JSONL format).
Creating realistic customer support chat logs requires generating multi-turn, goal-oriented dialogues with distinct personas (an annoyed/confused customer and a polite/helpful support agent), varying intents, and domain-specific knowledge.[](https://www.evidentlyai.com/blog/synthetic-data-generator-python)
Argilla / Distilabel: Distilabel is an open-source AI-native data generation framework built for fine-tuning and alignment. It allows you to build custom multi-stage pipelines where you can script dual-agent roleplay (Customer Agent↔left-right arrow↔ Support Agent) conditioned on a knowledge base or product manual to output structured, multi-turn dialogue datasets.
DeepEval (by Confident AI): DeepEval features a built-in Synthetic Data Generator specifically designed for RAG and conversational applications. You can feed it your documentation, FAQs, or support articles, and it will automatically generate realistic customer personas, edge-case queries, and complete multi-turn conversational trajectories.
Custom Multi-Agent Simulations (LangGraph / AutoGen) : If you need hyper-specific control over customer behaviors (e.g., simulating a frustrated customer who changes their mind mid-chat, or an agent who has to query an external API), building a custom two-agent loop using LangGraph or Microsoft AutoGen is a powerful approach. One agent is prompted with a specific customer persona and a hidden "goal," while the second agent is constrained by your company's support guidelines. They converse until the issue is resolved or escalated.[](https://aclanthology.org/2023.gem-1.16/)
Tonic Fabricate : For enterprise-grade workflows requiring structured relational integrity alongside generated context, Tonic Fabricate uses an agentic approach to model conversational and tabular data patterns based on your production configurations or precise text prompts.[](https://www.tonic.ai/synthetic-data/best-synthetic-data-tools)
Core Best Practices for Dialogue Generation
Condition on a Knowledge Base: Don't let the LLM hallucinate company policies from scratch. Provide a snippet of a real FAQ, policy doc, or product manual during generation so the support agent's responses remain grounded.
Inject Persona Profiles: Pre-define metadata parameters for each chat session before triggering generation:
Customer Sentiment: Frustrated, confused, neutral, or in a rush.
Issue Type: Technical bug, billing dispute, return request, etc.
Resolution Path: Successfully resolved, escalated to human, or abandoned.
Use "LLM-as-a-Judge" for Filtering: Pass your generated chats through a secondary evaluation script to drop low-quality outputs (e.g., where the agent breaks character, or the conversation ends illogically after one turn).
If you'd like to share, let me know:
The specific domain or industry (e.g., SaaS, e-commerce, fintech)
Whether you prefer a Python-based open-source library or a managed platform
I can help you outline a custom prompt architecture or pipeline for your setup.
Photorealistic synthetic data + closed-loop simulation
★★★★★
★★★★★
★★★★☆
Enterprise/technical
CARLA
Research, prototyping, reproducible benchmarks
★★★★☆
★★★★☆
★★★☆☆
Open source
Cognata
Digital twins + large-scale AV/ADAS simulation
★★★★☆
★★★★☆
★★★★☆
Enterprise
Why I’d choose Foretellix for a test-set project: Foretify is unusually focused on coverage-driven scenario generation, rather than just simulation. It can take real-world logs, identify coverage gaps, generate variations and edge cases, and express reusable scenarios using ASAM OpenSCENARIO. Its Generate product supports automated scenario generation, reactive closed-loop simulation, real-world-log variation, and physics-based synthetic sensor simulation.
That matters if your test set needs to answer questions like:
Have we adequately tested pedestrian/cyclist interactions?
What happens under combinations of rain + night + occlusion + unusual traffic behavior?
Can we systematically vary speed, distance, lane position, road geometry and actor behavior?
Which scenarios are missing from our current ODD?
Can we generate thousands of valid variants rather than random simulator runs?
Foretellix specifically supports abstract scenarios that can be instantiated across maps and conditions, and its platform reports ODD coverage and testing completeness.
Where NVIDIA is stronger
If sensor-level realism and photorealistic data generation are your primary concern, I'd seriously consider the NVIDIA stack. Its current workflow combines Omniverse NuRec for reconstructing real driving scenes, Cosmos for generating scenario/environment variations, and AlpaSim for scalable closed-loop evaluation. NVIDIA says AlpaSim is designed to run policies across millions of virtual scenarios.
This is particularly attractive if your model consumes camera + lidar + radar rather than only abstract object tracks. NVIDIA's simulation stack supports physically based sensor simulation and ground-truth generation for these sensor modalities.
And CARLA?
CARLA is my choice if you're building the test infrastructure yourself. It's open source, programmable, supports custom scenarios, maps, vehicles and sensors, and can run distributed simulation.
The downside is that you're responsible for substantially more of the scenario-generation, coverage-analysis, data-management, and validation infrastructure. That's exactly the layer Foretellix is trying to productize.
What I'd actually build
For a high-quality benchmark, I wouldn't make the test set 100% synthetic. I'd use a hybrid:
20,000 real-world scenarios
→ identify 500 important scenario classes
→ parameterize each class
→ generate 100–1,000 physically valid variants/class
→ stratify by ODD/weather/time/traffic density
→ hold out entire scenario families for evaluation
That prevents the common mistake of generating millions of synthetic examples that are visually diverse but don't actually expand the model's behavioral coverage.
Bottom line:
Best overall for an AV test-set program:Foretellix Foretify
Best for photorealistic sensor synthetic data:NVIDIA Omniverse/Cosmos/AlpaSim
Best open-source/research choice:CARLA
Best digital-twin-oriented enterprise alternative:Cognata
If you tell me whether your model is perception-only, end-to-end driving, or planning/control, and which sensors it consumes (camera/lidar/radar), I can narrow this to a specific platform and propose the test-set architecture and scenario taxonomy I'd use.
Foretify is my first choice if your priority is building systematic, measurable test sets for AV validation. It combines synthetic scenario generation, real-world log variation, sensor simulation, and coverage analysis. It also supports OpenSCENARIO and integrates with other simulation tools.
Best for: Long-tail edge cases, regression suites, coverage gaps, and safety validation.
Website:
2. NVIDIA Omniverse, Cosmos and AlpaSim
(Best for sensor-rich synthetic data)
NVIDIA offers a broader toolchain for reconstructing real-world driving scenes, generating variations in weather and lighting, creating synthetic sensor data, and evaluating autonomy stacks in closed-loop simulation. AlpaSim is an open simulation framework.
Best for: Perception model training, multimodal sensor data, photorealistic scene variation, and teams wanting a flexible technical foundation.
Website:
3. Cognata OneSim
Cognata is worth evaluating if you want a more integrated simulation and synthetic dataset workflow. Its platform offers configurable virtual environments, realistic traffic agents, camera/lidar/radar simulation, automated annotations, and scenario fuzzing.
Best for: Generating labeled perception datasets and testing ADAS or autonomous-driving functions at scale.
Website:
How I would choose
Your primary requirement
Recommended platform
Structured, measurable scenario-based test suites
Foretellix Foretify
High-fidelity sensor data and flexible simulation infrastructure
NVIDIA Omniverse / Cosmos / AlpaSim
Ready-made synthetic datasets with annotations and configurable traffic
Cognata OneSim
These are capability-based recommendations, not a claim that one platform has been independently proven superior. Confirm sensor fidelity, licensing, deployment options, and integration effort in a proof of concept.
What to test in a proof of concept
I would ask each vendor to generate the same 100–500 scenarios, including:
Pedestrians emerging from behind parked vehicles.
Highway cut-ins and sudden braking.
Intersections with occlusions and ambiguous right-of-way.
Nighttime, rain, fog, and sensor degradation.
Rare combinations of road-user behavior and environmental conditions.
Then compare annotation accuracy, scenario diversity, repeatability, data export formats, generation throughput, and cost per usable scenario. Keep synthetic and real-world validation results separate until you have evidence that the synthetic data transfers reliably to real driving.
One question to narrow this down: Are you primarily building test sets for perception (camera/lidar object detection), planning and decision-making, or end-to-end autonomous driving? That distinction could change which platform I would recommend first.
Several robust enterprise-grade platforms specialize in generating synthetic data and testing scenarios for autonomous vehicles (AVs) and Advanced Driver Assistance Systems (ADAS). Depending on whether you prioritize photorealistic sensor simulation (cameras, LiDAR, radar) or logical scenario variation (edge cases, traffic behavior), consider the following leading platforms:
NVIDIA Omniverse & Cosmos : Powered by the NVIDIA DRIVE ecosystem, this platform uses physical-AI models and the Cosmos foundation models to build massive 4D simulation environments . It allows you to ingest real sensor logs, reconstruct scenes, and use text prompts or traffic generators to create near-infinite variations of rare and hazardous edge-case scenarios. Explore it via NVIDIA Omniverse.[](https://www.youtube.com/watch?v=GsB7tGB5g-o&t=23)
Anyverse ADAS : A platform built specifically for computer vision and perception testing. It features a proprietary physics-based render engine that simulates light transport, material properties, and sensor physics with pixel-level precision, complete with built-in Euro NCAP validation scenario libraries. Learn more on the Anyverse ADAS Platform.[](https://anyverse.ai/adas-ad/)
Datagen : Focuses heavily on high-performance visual data for computer vision, offering granular control over human-centric scenarios, in-cabin vehicle safety monitoring, and surrounding traffic environments via a scalable, cloud-accessible workflow. Details can be found at Datagen.
Parallel Domain : Generates photorealistic, multi-sensor synthetic data (cameras, LiDAR, radar) at scale. It lets you programmatically inject rare corner cases, diverse weather conditions, and lighting variations to stress-test perception models.
Foretellix : Specializes less in raw sensor pixels and more on hyper-scalable scenario generation and safety validation using measurable logic (OpenSCENARIO standards), helping verify autonomous driving behavior across millions of virtual variations.
If you can share which sensors you need to simulate (e.g., camera-heavy vs. LiDAR/radar fusion) and whether you are focusing on in-cabin monitoring or external perception , I can narrow down which platform fits your architecture best.
