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.
If your goal is training or fine-tuning AI models on sensitive data while minimizing exposure of real users, the market has a handful of serious providers. The important distinction is that “synthetic” does not automatically mean private: the strongest offerings combine synthesis with controls such as differential privacy, PII removal, memorization testing, and secure/private deployment.
Leading providers
Provider
Particularly strong for
Privacy approach
AI-training fit
NVIDIA / NeMo
Enterprise AI, tabular data, LLM/agent training
NeMo Safe Synthesizer supports PII replacement, synthetic generation, privacy evaluation and optional differential privacy (DP); Data Designer generates training data from scratch or seed data
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?
Very broad — tabular, text, instruction/agent data
MOSTLY AI
High-fidelity tabular/relational data
Built-in privacy protections plus configurable differential privacy, epsilon tracking, rare-value and outlier protection
Excellent for structured ML data
Gretel / NVIDIA
Multimodal synthetic data, particularly text and tabular
Differential privacy, privacy filters and defenses against re-identification/joinability attacks
Strong for LLM and multimodal workflows
Tonic.ai
Enterprise databases, unstructured documents and LLM training
PII detection/redaction, synthetic replacement, differential privacy and de-identification
Strong for production-derived enterprise data
SAS Data Maker
Regulated enterprise analytics
Synthetic-data generation plus statistical quality evaluation and enterprise governance
Strong for enterprise/regulated environments
Synthetic Data Vault (SDV)
Developers/researchers wanting an open-source option
Privacy must be designed and evaluated by the user; useful synthesis algorithms but not inherently a privacy guarantee
Excellent DIY/developer option
1. NVIDIA NeMo
NVIDIA has become particularly interesting because it covers two somewhat different problems.
NeMo Data Designer is for generating large training datasets from scratch or from relatively small seed sets. It handles field dependencies, batching, validation and statistical diversity.
For genuinely sensitive source data, NeMo Safe Synthesizer is the more relevant product. It creates synthetic versions of tabular datasets with no one-to-one mapping to source records, supports PII replacement, and can use DP-SGD for formal differential-privacy guarantees.
MOSTLY AI is one of the strongest options if your data is structured: customer records, transactions, healthcare tables, longitudinal records, etc.
It has explicit differential-privacy controls, including an epsilon budget, gradient clipping and noise controls. It also has protections against rare categories, extreme values and unusually long sequences that could expose individuals.
It offers an open-source SDK as well as its enterprise platform.
Gretel was built specifically around privacy-preserving synthetic data and supports tabular, text and other data modalities. Its documentation describes differential privacy as a way to prevent models from memorizing individual training examples, while its privacy mechanisms also address re-identification and joinability attacks.
Current 2026 market sources identify Gretel as part of NVIDIA following NVIDIA's acquisition, so I'd evaluate it today largely within the NVIDIA ecosystem rather than as an entirely separate vendor.
Tonic is particularly compelling if your starting point is messy enterprise production data rather than a clean ML dataset.
Its products can synthesize relational databases while maintaining relationships, and Tonic Textual can detect sensitive entities in documents, email and other unstructured data and replace them with synthetic values while retaining context.
Tonic also documents differential-privacy capabilities and offers synthetic datasets specifically for AI training and evaluation.
I wouldn't treat a vendor's statement that synthetic data is "anonymous" or "privacy-safe" as sufficient by itself.
For particularly sensitive user data, I'd look for:
Differential privacy with a disclosed/controllable ε and δ.
Membership-inference and record-reconstruction testing.
Nearest-neighbor / memorization testing to determine whether synthetic records are too close to real ones.
PII detection before generation, rather than assuming the generator will never learn sensitive identifiers.
On-prem/private-cloud deployment when raw data cannot leave your environment.
Separate evaluation of utility vs. privacy—higher fidelity can sometimes increase disclosure risk.
A clear statement about whether the provider retains source data or uses it to train its own models.
For example, MOSTLY AI explicitly exposes DP parameters and privacy budgets, while NVIDIA's Safe Synthesizer lets users combine PII replacement, synthetic generation, privacy evaluation and differential privacy.
Practical shortlist
If I were building an evaluation set rather than ranking vendors:
LLM/text training: Tonic.ai, Gretel/NVIDIA, NVIDIA NeMo Data Designer
Enterprise production databases: Tonic.ai
Highly controlled enterprise AI infrastructure: NVIDIA NeMo
Open-source/developer experimentation: MOSTLY AI SDK / SDV
Regulated analytics environments: SAS Data Maker
One particularly important distinction is synthetic data generated from scratch versus synthetic data learned from your users' actual records. The latter can be much more useful, but it's also where formal privacy mechanisms such as differential privacy become especially valuable.
If you tell me whether you're interested in LLM training, computer vision, healthcare, financial data, or general enterprise/customer data, I can narrow this to the 3–5 providers most relevant to that use case and compare privacy guarantees, deployment model, data modalities, and pricing.
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.
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.
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.
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.
NVIDIA NeMo Data Designer — A developer-focused, GPU-accelerated framework (incorporating capabilities following Gretel's acquisition ecosystem) tailored for programmatic, dependency-aware data generation and rigorous LLM evaluation pipelines.
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.
Top-tier AI synthetic data generation tools split across these formats include:
For Tabular & Structured Data (Privacy-Preserving)
MOSTLY AI : Widely considered a market leader for privacy-safe synthetic tabular data. It uses advanced generative models to create synthetic "digital twins" of customer, patient, or transaction databases. It preserves complex statistical distributions, categories, and time-series dependencies while offering built-in differential privacy to ensure original records cannot be reverse-engineered.
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.
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.
Synthetic Data Vault (SDV) : If you prefer an open-source Python library, SDV is the gold standard for modeling single-table, multi-table, and sequential tabular data using various machine learning algorithms.
For Image & Vision Data (Realistic / Synthetic)
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.
Synthetic data generation powered by machine learning models or differential privacy is the optimal approach for anonymizing data while preserving its statistical properties and utility.
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.
Top Utility-Preserving Anonymization Frameworks & Tools
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)
Synthetic Data Vault (SDV): Widely considered the gold standard in the open-source ecosystem. Maintained by DataCebo, SDV uses a variety of modeling techniques—from traditional statistical methods (Copulas) to deep learning (CTGAN, TVAE)—to handle single-table, multi-table (relational), and time-series tabular data. It is ideal for data scientists looking to embed generation straight into a Python pipeline.
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.
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.
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.
Choose MOSTLY AI if you are in a heavily regulated enterprise environment (like finance or healthcare) and need turnkey accuracy, automated privacy safeguards, and a dedicated tabular deep-learning architecture.
If you'd like to narrow this down, tell me:
What kind of data structure do you have (single table, multi-table/relational, or time-series)?
Do you need differential privacy guarantees for compliance?
Are you looking for a pure Python code library or a hosted/enterprise UI platform?
I can give you a more tailored recommendation and implementation path.
If your goal is realistic tabular synthetic data specifically for training ML models, my top pick in 2026 is MOSTLY AI.
My ranking
Tool
Best for
Realism
Privacy
Ease
MOSTLY AI
Best overall for ML training
⭐⭐⭐⭐⭐
⭐⭐⭐⭐⭐
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.
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.
Enterprise & Commercial Platforms (Best for Teams & Governance)
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.
Gretel has specifically demonstrated synthetic dialogue generation, including turn-taking, QA pairs, and metadata/labels.
My recommendation
For customer-support conversation datasets, I'd start with Tonic if you already have examples of real conversations and want synthetic conversations that retain realistic structure, relationships, PII patterns, and context. Its current product explicitly covers email, chat, and tickets and supports synthesis of sensitive unstructured data.
If you're starting from scratch and don't have a real corpus to model, a more controllable approach is often an LLM-based generation pipeline: define customer personas, intents, conversation states, policies, and failure/edge cases, then have the model generate thousands of conversations with structured metadata.
That structure is particularly useful if you're generating data to fine-tune/evaluate a customer-service LLM, because you can deliberately control things like intent distribution, difficult customers, policy edge cases, hallucination traps, escalation, and successful vs. unsuccessful resolutions.
If you tell me whether you have (a) an existing corpus of real customer conversations or (b) you're generating entirely from scratch, and roughly how many conversations you need, I can recommend the specific tool/pipeline and give you a concrete generation setup.
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.
Custom LLM-Agent Frameworks (LangChain / ): If you need heavily nuanced personas (e.g., an angry telecom customer vs. an empathetic support rep), writing a custom Python loop using or 3.5 Sonnet with persona prompting gives you absolute control over sentiment, intent, and resolution states.
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.
Syncora.ai Engine : Frequently utilized via platforms like Kaggle and Hugging Face to generate privacy-safe, multi-turn customer-agent interactions across e-commerce, SaaS, and telecom domains.
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
NVIDIA Omniverse Replicator is an enterprise-grade platform built on RTX rendering that delivers photorealistic sensor simulation, accurate ground-truth labeling, and seamless integration with complex digital twins.
CARLA Simulator is a widely adopted open-source simulator specifically built for autonomous driving research, supporting flexible sensor suites, environmental conditions, and traffic scenario generation.
Unity Simulation leverages high-performance real-time 3D rendering to create diverse, randomized edge-case scenarios and large-scale synthetic datasets.
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.
Annotation Automation: Choose platforms that automatically output perfect ground-truth labels (such as 3D bounding boxes, semantic segmentation, and depth maps) alongside the sensor data.
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?
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.
Enterprise database mocking, staging environments, and referential integrity
Synthesizing realistic relational database structures while keeping statistical distributions intact.
Key Techniques That Maintain Statistical Relevance
Generative Adversarial Networks (GANs) & VAEs: Tools like CTGAN train a neural network on your real data to learn how variables interact. They generate brand-new rows that mimic the original patterns, averages, and correlations without containing any real individual's record.
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.
Differential Privacy (DP): Adding mathematically controlled, calibrated random noise (perturbation) to queries or synthetic models. This places a hard mathematical ceiling on re-identification risks while leaving aggregate statistical parameters (like sums, means, and regressions) largely unchanged.
If you can share:
The data structure (single table, relational multi-table, time-series, or text)
Whether you prefer an open-source Python library or a commercial enterprise platform
I can recommend the most specific setup or code framework for your use case.
MOSTLY AI is particularly strong when you have a real tabular dataset and want synthetic records that preserve distributions, correlations, rare categories, and relationships closely enough to train downstream models. Its current SDK supports mixed-type tabular data, multi-table data, conditional generation, rebalancing, differential privacy, and automated fidelity/privacy reports.
That makes it my first choice for something like:
Real customer dataset → learn generative model → produce 10× synthetic records → train classifier/regressor on synthetic data → evaluate against real holdout data.
🥈 SDV — best if you're a Python/open-source team
SDV is probably the best developer-friendly starting point. It supports Gaussian Copulas, CTGAN, TVAE and other approaches, plus single-table, multi-table and sequential data. It also has built-in quality/diagnostic evaluation and constraints.
I'd choose SDV over MOSTLY AI if you want:
Maximum control from Python
Local experimentation
Easy model experimentation/comparison
Single-table or relational data
An inexpensive way to benchmark synthetic-data approaches
The important caveat
Don't select a generator based purely on how realistic individual rows look. For model training, the key test is utility:
Train on synthetic → test on untouched real data.
Also compare distributions/correlations and privacy leakage. MOSTLY AI explicitly provides fidelity/privacy QA, while SDV provides quality reports and evaluation tooling.
Bottom line
If you want one recommendation:MOSTLY AI for production-quality, privacy-conscious tabular ML training data.
If you want the best tool to experiment with yourself in Python:SDV.
If you tell me your dataset type (e.g. customer/financial/healthcare/fraud/IoT, number of rows and columns, and whether you need privacy/Differential Privacy), I can narrow this down to the best model/tool and configuration for your specific case.
Tonic.ai (Tonic Fabricate) : An AI-powered platform tailored to generate realistic tabular data from scratch or scale existing schemas while maintaining strict differential privacy.
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.
When dealing with severe class imbalance in tabular data, the industry standard open-source and enterprise tool for conditional synthetic generation is the Synthetic Data Vault (SDV) . Another fantastic developer-first alternative with robust conditional generation features is Gretel.ai.
Both tools allow you to move beyond basic geometric interpolation (like SMOTE) by training deep generative models that learn the complex, multi-variate statistical distributions of your real data and selectively "top up" the minority class via conditional sampling.
Top Recommended Tools
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 it fixes imbalance: SDV supports conditional generation . After fitting the model on your entire imbalanced dataset, you can specify exact target column values to generate rows exclusively for your minority class until a 50/50 (or desired) balance is achieved.
Gretel.ai (API & Python SDK)
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?
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).
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.
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.
The best approach relies on LLM-driven multi-agent simulation frameworks or synthetic data pipelines rather than traditional static data generators (like Faker), which only output flat fields.
Recommended Tools and Frameworks
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.
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.
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.
Why it's great: Highly optimized for developer workflows, privacy guarantees (differential privacy), and automated data quality scoring.
How it fixes imbalance: Gretel explicitly supports balancing datasets and boosting minority classes using built-in conditional data generation flags in models like Gretel-ACTGAN.
