MOSTLY AI leads recommendations for tabular data synthesis, capturing one-third of AI-generated mentions between March and April 2026. Gretel and Tonic each appear in roughly one-sixth of suggestions, highlighting their role as developer-focused alternatives. AI assistants prioritize tools with strong privacy compliance and high statistical fidelity for this workflow.
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.
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.
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?
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)
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.
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/)
Enterprise & Commercial Platforms (Best for Teams & Governance)
NVIDIA NeMo Data Designer : Part of NVIDIA's enterprise framework, highly optimized for scaling data generation pipelines using accelerated compute workloads.
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.
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.
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.
YData Fabric : Focused specifically on data quality and synthetic tabular/time-series generation to improve downstream machine learning model performance and handle class imbalances.
Enterprise / GenAI-driven generation
Can create datasets from scratch using natural language or code, augment existing data, and generate domain-specific tabular data at scale.
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.
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?
