Best AI Synthetic Data Generation Tools in 2026: Gretel vs Mostly AI vs Synthesized vs YData

Your ML team has a great model idea, but the dataset you need is locked behind privacy regulations, HIPAA restrictions, or a legal team that takes six weeks to approve access. Meanwhile, competitors are shipping. This is exactly the problem that AI synthetic data generation tools were built to solve, and in 2026 the category has matured enough that there's a real choice to make between platforms with very different strengths.

Synthetic data is artificially generated data that mirrors the statistical properties of real data without containing any actual personal information. The best AI synthetic data generation tools in 2026 go beyond simple noise injection, they use generative models (GANs, diffusion, VAEs) to produce data that's statistically indistinguishable from the original while being fully privacy-safe. Whether you're training fraud detection models, testing database migrations, or satisfying GDPR auditors, the right tool can unblock months of work in hours.

What Are AI Synthetic Data Generation Tools?

These platforms take real data as input and produce artificial data as output. The output preserves correlations, distributions, and relationships from the original but contains no real personal records. Most enterprise platforms also include privacy guarantees (differential privacy, k-anonymity) and quality metrics so you know how close the synthetic data is to the real thing. The use cases range from augmenting small training sets to replacing production databases in test environments.

Quick Comparison: Best AI Synthetic Data Generation Tools in 2026

Tool	Best For	Starting Price	Free Plan	Data Types
Gretel.ai	Privacy-first enterprise teams	Free tier available	✓ Yes	Tabular, text, relational, time-series
Mostly AI	Financial services, high-fidelity synthesis	~$500/mo (Pro)	✓ Limited	Tabular, relational
Synthesized	Developer teams, SDK-first workflows	Contact for pricing	✗ No	Tabular, relational, time-series
YData Fabric	Data scientists, open-source budgets	Free (open-source)	✓ Yes (OSS)	Tabular, time-series, text

Gretel.ai, Best for Privacy-First Enterprise Teams

Gretel is the most flexible synthetic data platform in 2026 and the only one that handles tabular, text, and relational data in a single API. It was founded by ex-Amazon privacy engineers and shows: the platform bakes differential privacy guarantees into every generation job, and it produces a detailed privacy report alongside each synthetic dataset so your compliance team has the documentation they need without a fight.

What Makes Gretel Stand Out

• Multi-modal data support: One platform for structured tables, free text, and relational (multi-table) data, so you're not juggling three different tools for different data types.
• Privacy scores built-in: Every generated dataset comes with a privacy protection score and a data quality score. You can tune the privacy-fidelity tradeoff with a single parameter.
• Cloud and on-premise: Gretel runs in their cloud, in your AWS/GCP/Azure VPC, or fully on-premise, which matters a lot for regulated industries where data can't leave the building.
• Pre-built connectors: Native integrations with Snowflake, BigQuery, S3, and most enterprise data warehouses. You can pull data in and push synthetic data out without writing custom ETL.
• Gretel Transforms: Beyond synthesis, Gretel includes a data transformation pipeline for masking, tokenization, and redaction if you need partial anonymization rather than full synthesis.

Pricing

• Developer (Free): 5 credits/month, up to 5,000 records per run, access to all models including ACTGAN and LSTM
• Teams: ~$295/month, 60 credits, higher record limits, SLA support
• Enterprise: Custom pricing, on-premise deployment, SSO, dedicated support

Best For

Teams in healthcare, finance, or any regulated space where privacy guarantees need to be documented and auditable. Also the best choice if you're working with text data (PII redaction, synthetic NLP training sets) alongside structured tables. Not ideal if your entire use case is a single simple table and you want the absolute cheapest option.

Mostly AI, Best for Financial Services and High-Fidelity Synthesis

Mostly AI produces some of the highest-fidelity synthetic tabular data available, and it's the platform of choice for banks, insurers, and telcos that need synthetic data they can actually trust for model training. The company has been focused on tabular and relational data since 2017, which shows in the quality of their models.

Fidelity, at the Cost of Flexibility

Mostly AI's strength is its Accuracy Scores. Every synthetic dataset gets evaluated across three dimensions: univariate distributions, bivariate correlations, and privacy protection against membership inference attacks. For a large customer transaction table, Mostly AI consistently achieves 95%+ accuracy scores, meaning the synthetic data is nearly statistically identical to the real data. That's rare.

The tradeoff is that Mostly AI is purpose-built for tabular/relational data. You won't use it for synthetic text generation or image synthesis. If your ML pipeline is entirely structured data (customer records, financial transactions, sensor readings), that narrowness is actually an advantage: the team has spent years optimizing exactly that use case.

Pricing

• Free Trial: Up to 100,000 rows free, access to most models
• Pro: ~$500-800/month depending on volume, includes SLA and priority support
• Enterprise: Custom pricing, on-premise, private cloud, SOC 2 compliance documentation

Best For

Data science teams at financial institutions, insurance companies, and telcos where model accuracy depends heavily on statistical fidelity. If you're training a credit risk model and need training data that won't introduce distribution shift, Mostly AI is worth the higher price. Less useful if you need text synthesis or have a tight budget.

Synthesized, Best for Developer Teams and SDK-First Workflows

Synthesized takes a different approach from the other tools on this list: it's designed to fit into your existing Python or SQL workflow rather than being a standalone platform you log into. The Synthesized SDK installs with pip and integrates directly into notebooks, CI/CD pipelines, and dbt workflows.

The Developer-First Angle

Where Gretel and Mostly AI are primarily web platforms with APIs, Synthesized leads with its SDK. You define your data generation rules in code, commit them to your repo, and run synthesis as part of your data pipeline. This makes it the natural choice for data engineering teams that already version-control their transformations and want synthetic data generation to work the same way.

Synthesized also has a strong focus on data testing: you can generate synthetic edge cases (rare events, outlier distributions, specific demographic slices) to validate model strongness. This is particularly useful for testing fraud detection systems against scenarios that don't appear often in real training data. If you're familiar with tools like AI data labeling platforms, Synthesized slots in naturally at the pre-labeling stage of the ML pipeline.

Pricing

• No public pricing, Synthesized is enterprise-focused with custom contracts
• Free trial available for qualified teams
• Pricing is typically usage-based on rows generated per month

Best For

Data engineering and ML platform teams that want synthetic data generation to live inside their existing pipelines rather than as a separate workflow. Best fit for Python-heavy shops with existing dbt or Airflow orchestration. Not ideal for business users who need a no-code interface, or for teams that need text synthesis.

YData Fabric, Best for Data Scientists on Open-Source Budgets

YData Fabric is the only tool on this list with a genuinely open-source foundation, and it's the most accessible entry point for data scientists who want to experiment with synthetic data generation without budget approval. The open-source ydata-synthetic library is pip-installable, Pandas-compatible, and includes implementations of CTGAN, WGAN, and CopulaGAN out of the box.

Open Source Core, Enterprise Layer

The free YData community tier and open-source library give you enough to generate synthetic tabular and time-series data for most experimentation use cases. The paid Fabric platform adds a visual interface, data profiling (automatic quality reports), dataset versioning, and team collaboration features that make it practical for production use.

YData's data profiling reports are worth highlighting specifically. Before you generate anything, Fabric profiles your source data and surfaces distributions, correlations, missing value patterns, and data type anomalies. That upfront visibility often catches data quality issues that would otherwise pollute your synthetic data. For teams building predictive models, this pairs naturally with AI predictive analytics platforms where training data quality directly determines model accuracy.

Pricing

• Open-source (ydata-synthetic): Free, no limits, self-managed
• Community Cloud: Free, limited to smaller datasets, YData-managed infrastructure
• Enterprise: Custom pricing, includes SLAs, SSO, dedicated support, on-premise option

Best For

Data scientists and ML engineers who want to get started quickly with Python without vendor lock-in. The open-source library works well for academic research, small team experimentation, and prototyping. The enterprise Fabric tier suits teams that need the open-source approach scaled up with collaboration and governance features.

Gretel vs Mostly AI vs Synthesized vs YData: Head-to-Head

Category	Gretel.ai		Mostly AI	Synthesized	YData Fabric
Tabular data	★★★★★	★★★★★	★★★★	★★★★
Text/NLP data	★★★★★	★	★★	★★★
Privacy guarantees	★★★★★	★★★★★	★★★★	★★★
Developer experience	★★★★	★★★	★★★★★	★★★★★
No-code UI	★★★★	★★★★★	★★	★★★
On-premise option	★★★★★	★★★★★	★★★★	★★★★
Free tier	★★★★★	★★★	★	★★★★★
Data quality reporting	★★★★★	★★★★★	★★★★	★★★★★

Which AI Synthetic Data Tool Should You Choose?

• ✓ Choose Gretel.ai if you need multi-modal synthesis (tabular + text) and need documented privacy guarantees for compliance audits. It's also the best choice for teams who want flexibility without giving up on-premise options.
• ✓ Choose Mostly AI if statistical fidelity is your top priority and your data is entirely tabular or relational. Financial services teams in particular should shortlist this one first.
• ✓ Choose Synthesized if your team lives in Python and wants synthetic data generation to be version-controlled and CI/CD-integrated like any other part of your data pipeline.
• ✓ Choose YData Fabric if you're starting with no budget, want to experiment with an open-source library, or need strong data profiling built into your generation workflow.

Frequently Asked Questions About AI Synthetic Data Generation

Is synthetic data legal to use for GDPR compliance?

Synthetic data itself doesn't contain personal data, so it generally falls outside the scope of GDPR. However, the generation process involves processing real personal data, which does require a lawful basis. Most enterprise platforms (Gretel, Mostly AI) provide documentation showing differential privacy guarantees that help satisfy regulators, but you should have your legal team review any deployment before treating synthetic data as a compliance silver bullet.

How close is synthetic data to real data in terms of model accuracy?

The best platforms achieve 90-97% statistical fidelity for tabular data, meaning a model trained on synthetic data performs within a few percentage points of one trained on real data. That gap varies significantly by dataset complexity, the quality of your source data, and how much you've tuned the privacy-fidelity tradeoff. Running baseline experiments with both real and synthetic data is always worth doing before committing to a full synthetic data workflow.

Can synthetic data generation tools handle time-series data?

Yes, though with varying quality. Gretel and YData both have explicit time-series models (TimeGAN, DoppelGANger) that preserve temporal correlations. Mostly AI handles time-series through its sequential data module. Synthesized has time-series support but it's a newer addition. For financial time-series specifically (stock prices, transaction sequences), expect some manual tuning regardless of the platform you choose.

What's the difference between synthetic data and data masking?

Data masking (tokenization, redaction, pseudonymization) modifies real data to hide sensitive values. The underlying records still exist. Synthetic data generation creates entirely new records from scratch, so there's no one-to-one mapping back to real individuals. Synthetic data is stronger from a privacy standpoint but harder to produce at high fidelity. Many teams use both: masking for databases that need to stay in a recognizable format, synthesis for ML training sets.

How much does it cost to generate a million synthetic records?

It depends heavily on the platform and data complexity. On Gretel's free tier you can generate roughly 5,000 records per credit. Mostly AI's free trial covers 100,000 rows. YData's open-source library is free and limited only by your compute. Enterprise contracts for large-scale synthesis (tens of millions of records monthly) typically run $1,000-5,000/month, though Mostly AI and Synthesized price on a custom basis for volumes like this.

Conclusion

The best AI synthetic data generation tool for your team depends on what you're building and how you work. Gretel.ai is the most versatile option, Mostly AI sets the standard for tabular fidelity, Synthesized fits developer-first workflows, and YData gives you a genuinely free starting point. All four are mature enough in 2026 to use in production. Start with whichever matches your data type and budget, then benchmark fidelity scores on your actual dataset before committing to an enterprise contract. Bookmark Techno-Pulse for daily AI tool comparisons to keep your stack current.