What is the best synthetic data generation tool for LLM fine-tuning?

As of November 10, 2026, NVIDIA NeMo Data Designer (Apache-2.0, v0.10.0 released November 2, 2026) is the strongest actively developed open-source option: it combines statistical samplers, LLM and image-model generation, seed datasets, field dependencies, MCP tool calls and validation with Python, SQL or LLM judges. distilabel (Apache-2.0) is the established alternative for instruction and preference datasets, but its original authors have moved on and it is now community-maintained.

What is the best tool for synthetic tabular data?

For privacy-safe copies of real tables, the MOSTLY AI Synthetic Data SDK (Apache-2.0, 6.2.1) trains generators locally on single-table, multi-table and time-series data. SDV (v2.2.0, November 6, 2026) is the most widely used Python library, but it is licensed under the Business Source License 1.1, which forbids using it to offer a synthetic data service.

Is Gretel still available for synthetic data?

Gretel's open-source gretel-synthetics repository is archived on GitHub, with its last release, v0.22.20, in June 2025. Do not start new projects on it; NVIDIA's NeMo Data Designer is the natural replacement for LLM-based generation.

How do I keep synthetic data from degrading a model?

Seed generation with real examples, constrain fields with samplers and dependencies so outputs stay diverse, validate every record with code or an LLM judge, deduplicate, and always keep a held-out set of real data for evaluation. Mix synthetic data with real data rather than training on synthetic data alone, and never evaluate a model only on data generated by the same model.

Quick Answer

Best Synthetic Data Generation Tools for LLMs in 2026

Published: November 10, 2026

The short answer

Use NeMo Data Designer for LLM training and eval data, the MOSTLY AI SDK for privacy-safe tabular data, and SDV only if its Business Source licence fits your use. As of November 10, 2026:

NVIDIA NeMo Data Designer — best overall for LLM datasets: text, structured records and images, with validation built in. Apache-2.0.
distilabel — best for instruction and preference (AI feedback) pipelines if you accept community maintenance. Apache-2.0.
MOSTLY AI Synthetic Data SDK — best for synthetic copies of real tables, trained locally. Apache-2.0.
SDV — most popular tabular library, but BSL 1.1.
Argilla — not a generator: the review tool to put humans on top of any of these.

The comparison

Versions, stars and licences from GitHub on November 10, 2026.

	NeMo Data Designer	distilabel	MOSTLY AI SDK	SDV
Latest release	v0.10.0 (Nov 2, 2026)	1.5.3 (Jan 28, 2025)	6.2.1 (Oct 29, 2026)	v2.2.0 (Nov 6, 2026)
GitHub stars	~2,400	~3,500	~800	~3,600
Licence	Apache-2.0	Apache-2.0	Apache-2.0	Business Source License 1.1
Maintenance	Active (NVIDIA)	Community collaborators	Active (MOSTLY AI)	Active (DataCebo)
Data types	Text, structured, images; audio/video context	Text (instructions, dialogue, preferences)	Tabular, multi-table, time series, text	Tabular, multi-table, sequential
Generation method	Samplers + LLMs + image models + seed data	LLM pipelines with any provider	Trained generative models (TabularARGN)	Statistical and deep-learning models
Built-in quality checks	Python, SQL, custom validators, LLM judges	AI feedback / judging steps	Fidelity and privacy reports	Quality and diagnostic reports
Best for	Fine-tuning and eval sets	Instruction/preference data	Privacy-safe data sharing	Prototyping tabular data

1. NeMo Data Designer — best for LLM datasets

Data Designer goes past “prompt an LLM a thousand times”. You declare columns: some drawn from statistical samplers (so categories and lengths stay balanced), some generated by an LLM conditioned on other columns, some from seed data you supply. Dependency-aware generation keeps fields consistent, and every record can be checked by Python, SQL, custom validators or an LLM judge before it lands in the dataset. Since v0.10.0 it covers multimodal workflows, connects to local or remote MCP servers to capture tool-use traces, and can preview, resume and monitor long runs. Install with pip install data-designer; NVIDIA publishes a technical report on the design.

Pick it if you are building fine-tuning sets, tool-calling traces or evaluation data and want quality gates in the same pipeline.

2. distilabel — proven for instruction and preference data

distilabel popularized pipelines for synthetic instructions and AI feedback, grounded in published recipes, with one API across model providers. Its README now says the original authors have moved on and community members maintain it; the last tagged release is from January 2025, with fixes on the develop branch. It still works well, but budget for pinning versions and patching yourself.

Pick it if you already have distilabel pipelines or need preference pairs for DPO-style training.

3. MOSTLY AI SDK — best for privacy-safe tables

The SDK trains a generator on your existing tables, locally by default, and produces any number of synthetic rows with the same statistical shape. It handles mixed types, multi-table relationships and time series, and can fine-tune Hugging Face language models for text columns. It is the pick when the goal is to share or test with realistic data that contains no real customer.

4. SDV — popular, but check the licence

SDV is the best-known Python library for synthetic tabular data and stays actively developed. Since its move to the Business Source License 1.1, you may use it and modify it, but not to run a “Synthetic Data Service”. Fine for internal use; read the licence before building a product on it.

What about Gretel?

The open-source gretel-synthetics repository is archived, last released June 2025. New LLM-generation projects should go to Data Designer.

How to choose

Training or eval data for an LLM? NeMo Data Designer.
Preference pairs, existing pipelines? distilabel.
Realistic copies of production tables? MOSTLY AI SDK, or SDV for internal use.
Need humans to review samples? Add Argilla on top.
Then fine-tune: see best LLM fine-tuning frameworks and how to run LLM evals in CI/CD.

Generation is billed by your model provider, so price a run first on our current API prices table.

Last verified: November 10, 2026.