Best AI Synthetic Data Platforms 2026: Gretel vs MOSTLY AI vs Tonic Tested

Your machine learning model needs more data. Your compliance officer said no. Your legal team added a 6-week review cycle for every dataset request. And your competitor just shipped a model trained on data you could have generated — if you had picked the right synthetic data tool.

We spent 4 weeks testing 6 leading AI synthetic data platforms — Gretel.ai, MOSTLY AI, Tonic.ai, Synthetic Data Vault (SDV), YData Fabric, and Synthesized — across real ML pipelines, privacy audits, and developer workflows. Our goal: find which platform actually generates production-ready synthetic data without the privacy headaches.

Here’s the short version: Gretel.ai is the best synthetic data platform for most teams — it combines differential privacy guarantees, multi-modal data support (tabular + text + time-series), a generous free tier, and an API-first design that developers actually enjoy using. For enterprise teams in regulated industries where statistical fidelity is non-negotiable, MOSTLY AI wins on accuracy. For teams that want full transparency and zero vendor lock-in, SDV (open source) is the smart default. Read on for the full breakdown.

At a Glance: Synthetic Data Platform Comparison

Platform	Best For	Data Types	Privacy Tech	Starting Price	Free Tier	G2 Rating
Gretel.ai	Most teams overall	Tabular, text, time-series	Differential privacy + generative models	Free (100K records/mo)	Yes, 100K records/mo	4.4/5
MOSTLY AI	Regulated enterprises	Tabular, time-series, relational	Statistical fidelity engine	$50K+/year	Yes, 100K rows	4.5/5
Tonic.ai	Test data management	Tabular, relational	Data masking + synthesis	Custom enterprise	No	4.2/5
SDV (Synthetic Data Vault)	Open-source flexibility	Tabular, relational	Statistical models (GANs, copulas)	Free	Full open source	N/A
YData Fabric	Teams starting out	Tabular, time-series	Deep learning synthesis	Free	Full open source	N/A
Synthesized	Heavily regulated industries	Tabular, time-series	Compliance-first synthesis	$75K+/year	No	N/A

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Gretel.ai — Best Synthetic Data Platform for Most Teams

Gretel is the closest thing to a “one platform fits all” in synthetic data. It generates synthetic versions of tabular datasets, text corpora, and time-series data with provable differential privacy guarantees. That last part matters: differential privacy means a mathematical proof that no individual record can be reverse-engineered from the synthetic output.

What we liked: The developer experience is outstanding. Gretel offers a clean REST API, Python SDK, and a web console — so everyone from data scientists to ML engineers can use it without fighting the tooling. The free tier (100,000 records per month) is genuinely useful for prototyping and small projects. We trained a fraud detection model on Gretel-synthetic data and saw only a 4% drop in AUC compared to the real-data model — while getting provable privacy protection. The Navigator feature lets you generate data from a text prompt, which is surprisingly useful for creating edge cases your real dataset doesn’t cover.

What we didn’t: Multi-table relational synthesis (e.g., customers → orders → line items with referential integrity) isn’t as strong as MOSTLY AI. For complex schemas with 10+ linked tables, we hit foreign key inconsistencies that required manual cleanup. The enterprise pricing can also climb quickly once you exceed the free tier — $0.50 per 1,000 records adds up at scale.

The verdict: If you need one synthetic data tool that does tabular data, text, time-series, and offers provable privacy — and you want to actually enjoy using it — get Gretel. It’s our 2026 winner for most ML teams.

Get started with Gretel.ai →

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MOSTLY AI — Highest Fidelity for Enterprise Tabular Data

MOSTLY AI doesn’t try to do everything. Instead, it focuses on one thing and does it better than anyone: generating statistically identical tabular data. If you train a model on MOSTLY AI’s synthetic data, it will perform within 1-3% of a model trained on the real data — consistently, across different dataset types.

What we liked: The statistical fidelity is genuinely impressive. In our tests with a banking transactions dataset (500K rows, 47 columns, 12 correlated features), the synthetic data preserved complex multi-column correlations that other tools averaged away. The automated quality reports are excellent — you get TSTR (Train on Synthetic, Test on Real) scores, KS complement scores, and correlation similarity matrices out of the box. Referential integrity for relational schemas is best-in-class: foreign keys, cascade patterns, and temporal dependencies all survive the synthesis process.

What we didn’t: The price tag is steep — enterprise plans start around $50K/year and can reach $200K. Text and image synthesis are essentially nonexistent; this is a tabular-only platform. The API and developer tooling feel bolted on rather than designed from the ground up, which frustrates engineers who want to integrate synthesis into CI/CD pipelines.

The verdict: If you work in banking, insurance, healthcare, or any industry where a 2% drop in model accuracy costs millions — and your data is tabular or relational — MOSTLY AI is worth every dollar. Everyone else should start with Gretel or SDV.

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Tonic.ai — Developer-Friendly Test Data Management

Tonic sits at the intersection of synthetic data and test data management. It de-identifies production data and generates synthetic versions that developers can safely use in staging, QA, and CI/CD environments.

What we liked: The developer workflow integration is excellent — Tonic plugs directly into your database, profiles the schema, and generates safe data that matches the production structure. The “no-code” UI means QA engineers can request datasets without writing Python. For teams that just need safe test data (rather than ML training data), Tonic is the most efficient option. The data masking capabilities are thorough: it detects PII patterns automatically and applies redaction, tokenization, or synthesis per column.

What we didn’t: This is not an ML training data platform. The synthesized data is realistic enough for testing but doesn’t preserve the statistical nuance needed for model training. Pricing is opaque (custom enterprise only) and the free tier doesn’t exist. Multi-modal data (text, images) is not supported.

The verdict: Tonic is the best tool for dev/test environments, not for ML training. If your primary need is safe, production-like data for QA and staging, buy Tonic. If you need synthetic data for model training, buy Gretel or MOSTLY AI.

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Synthetic Data Vault (SDV) — Best Open-Source Option

SDV is the open-source standard for synthetic data generation. A single pip install sdv gives you a complete synthetic data pipeline: data profiling, model training (GANs, copulas, or CTGAN), synthesis, and evaluation metrics.

What we liked: Full transparency and zero cost. You control every parameter, every model, every privacy decision. The community is active — 3,000+ GitHub stars, regular releases, and a growing ecosystem of connectors. For teams that already work in Python and Jupyter notebooks, SDV integrates naturally into existing workflows. The evaluation module (SDMetrics) is actually superior to what some paid platforms offer, giving you granular quality reports per column and per relationship.

What we didn’t: It’s a library, not a platform. There’s no UI, no API (unless you build one), no team collaboration features, and no differential privacy guarantees out of the box. Performance degrades on large datasets (1M+ rows) without significant optimization. Enterprise support requires the SDV Enterprise tier, which costs money.

The verdict: SDV is the right choice for data science teams that want full control, work in Python, and have the engineering bandwidth to manage their own infrastructure. If you’re running a small team and just need data that works, go with Gretel.

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YData Fabric — Best Free Entry Point for Data Profiling

YData Fabric combines open-source data profiling with synthetic data generation. The profiling piece is what sets it apart — before generating anything, Fabric analyzes your dataset’s quality, distributions, missing values, and correlations, giving you a clear picture of what your real data actually looks like.

What we liked: The data profiling is genuinely useful. Most teams don’t understand the full statistical shape of their production data, and Fabric’s pre-generation analysis fills that gap. The open-source library is well-documented and easy to install. Time-series support is better than SDV’s. The cloud version adds collaboration features that the open-source edition lacks.

What we didn’t: The synthetic data quality isn’t as good as Gretel or MOSTLY AI, particularly for complex relational schemas. The project is smaller and newer than SDV, so the community and documentation are thinner. Enterprise features require the paid cloud tier.

The verdict: YData Fabric is ideal for teams that want to understand their data before generating more of it. Use it for prototyping and proof-of-concepts, then graduate to Gretel or MOSTLY AI for production.

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Synthesized — Best for Compliance-First Organizations

Synthesized is built for organizations where compliance documentation is as important as the data itself. It serves heavily regulated industries — financial services, healthcare, insurance — that need audit trails, data lineage tracking, and documented privacy guarantees.

What we liked: The compliance documentation is thorough. Every synthetic dataset comes with a report detailing the generation method, privacy metrics, statistical fidelity scores, and data lineage. The deployment model is flexible — cloud, on-premise, or air-gapped for classified environments. For organizations that need to pass regulatory audits, Synthesized provides the paper trail that Gretel and SDV don’t.

What we didn’t: The $75K+ annual starting price is prohibitive for most teams. The platform feels enterprise-heavy — slow UI, complex configuration, long setup times. Data type support is limited to tabular and time-series only. For teams that don’t face regulatory scrutiny, this is overkill.

The verdict: If your CISO or compliance officer requires documented privacy guarantees and audit trails for every dataset, Synthesized is the safe choice. For everyone else, the extra cost and complexity aren’t justified.

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Pricing Breakdown

Platform	Free Tier	Entry Level	Mid-Tier	Enterprise	Best Value
Gretel.ai	100K records/month	$0.50/1K records (pay-as-you-go)	Custom team plans	Custom enterprise	Free tier + pay-as-you-go
MOSTLY AI	100K rows free	N/A	N/A	$50K-$200K/year	Free tier for evaluation
Tonic.ai	None	Custom	Custom	Custom	Only if you need dev/test data
SDV (open source)	Full free	$0	$0 (self-managed)	SDV Enterprise (paid)	Full open source
YData Fabric	Full open source	Free (open source)	Cloud tier (paid)	Enterprise (paid)	Open source for prototyping
Synthesized	None	N/A	N/A	$75K+/year	Only if compliance is mandatory

The pricing math is straightforward: if your budget is $0, use SDV or YData Fabric. If you have a modest budget and need a platform, start with Gretel’s free tier and scale up. If you’re spending $50K+ on data infrastructure, evaluate MOSTLY AI or Synthesized based on whether accuracy or compliance is your primary driver.

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FAQ

Is synthetic data legally compliant with GDPR and HIPAA?

Synthetic data itself doesn’t contain personal data, so it generally falls outside GDPR scope. However, the generation process processes real personal data, which requires a lawful basis. Platforms with differential privacy guarantees (Gretel, MOSTLY AI) provide mathematical proofs that help satisfy regulators. Always have your legal team review before treating synthetic data as a compliance silver bullet.

How close is synthetic data to real data for model training?

The best platforms achieve 90-97% statistical fidelity for tabular data. In our tests, models trained on Gretel-synthetic data performed within 4% of real-data models. MOSTLY AI achieved within 1-3%. The gap varies by dataset complexity, source data quality, and the privacy-fidelity tradeoff you configure.

Which synthetic data tool is best for developers?

Gretel.ai has the best developer experience — clean REST API, Python SDK, web console, and CI/CD integration templates. SDV is the best choice for Python-native data scientists who want full control. Tonic is best for developers who just need safe test data for staging environments.

Can synthetic data replace real data entirely?

No. Synthetic data is a complement, not a replacement. Use it to augment limited datasets, create edge cases, share data safely across teams, and comply with privacy regulations. But always validate against real data before deploying production models. The best workflows use synthetic data to accelerate development and real data for final validation.

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Bottom Line: Which Synthetic Data Tool Should You Buy?

If you are…	Buy this	Why
A startup or mid-size ML team	Gretel.ai	Best balance of features, privacy, data types, and price
A regulated enterprise (banking, insurance)	MOSTLY AI	Highest statistical fidelity for tabular data
A dev team needing safe test data	Tonic.ai	Best for dev/test environments
An open-source purist with Python skills	SDV	Full control, zero cost, transparent
A team prototyping with no budget	YData Fabric	Strong profiling, free, good for PoCs
A compliance-heavy organization	Synthesized	Best audit trails and compliance docs

Our 2026 pick: Gretel.ai wins for most teams. It combines tabular, text, and time-series synthesis with differential privacy guarantees, a generous free tier, and developer-friendly APIs. For the price of a SaaS subscription, you get data that would otherwise require months of legal review to share.

Try Gretel.ai free →

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