The 9 Best AI Synthetic Data Generators of 2026 (Tested & Compared)
Getting enough clean, representative training data is the single biggest headache in any machine learning project. Your real-world data is either a privacy nightmare that'll get you fined under GDPR, or it's so messy and biased it's practically useless. Synthetic data generation is the industry's answer to this, promising perfectly balanced, privacy-compliant datasets on demand. The problem is, half these tools are little more than academic projects wrapped in a slick UI. We put nine of the top platforms through their paces to see which ones actually produce statistically sound data and which are just expensive random number generators.
Table of Contents
Before You Choose: Essential AI Synthetic Data Generators FAQs
What are AI Synthetic Data Generators?
AI synthetic data generators are advanced software tools that use machine learning models, like Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), to create artificial data. This generated data mathematically and statistically mimics the patterns, distributions, and relationships found in a real-world dataset, but without containing any of the original, sensitive information.
What do AI Synthetic Data Generators actually do?
These generators learn the underlying structure of a real dataset and then produce a new, artificial dataset from scratch. This process allows companies to create large volumes of realistic data for tasks like training machine learning models, testing software, or sharing data for research without violating privacy regulations like GDPR or HIPAA, as the synthetic data contains no real individual records.
Who uses AI Synthetic Data Generators?
A wide range of professionals use these tools. Data scientists and machine learning engineers use them to augment training sets and improve model accuracy. Software developers and QA testers use them to create realistic test environments. Healthcare and finance institutions use them to conduct research and develop products without exposing sensitive patient or customer data.
What are the key benefits of using AI Synthetic Data Generators?
The main benefits are enhanced data privacy, cost reduction, and improved AI model performance. They eliminate the privacy risks associated with using real PII (Personally Identifiable Information). They reduce the high cost and time required to collect and label real-world data. Finally, they can balance imbalanced datasets and create edge-case scenarios, leading to more robust and accurate machine learning models.
Why should you use an AI Synthetic Data Generator?
You need an AI synthetic data generator when real data is too sensitive, scarce, or expensive to use for model training or software testing. Think about training an AI for a self-driving car to recognize rare and dangerous events. To get real-world data of a tire blowout at 70 mph on a wet road, you'd have to stage dangerous, expensive tests or wait for accidents to happen. An AI synthetic data generator can create thousands of variations of that exact scenario—different speeds, lighting, and weather conditions—in a single afternoon, providing a rich, safe dataset that's impossible to collect manually.
How is synthetic data different from anonymized data?
Anonymized data is real data that has had personal identifiers removed or masked. However, it can often be "re-identified" by cross-referencing other datasets. Synthetic data is entirely new, artificially generated data. It holds the statistical properties of the original data but contains no one-to-one mapping to any real individual, making re-identification virtually impossible and offering a much higher level of privacy protection.
What are the risks or limitations of using synthetic data?
The primary risk is a potential loss of fidelity. If the generation model is not well-trained, the synthetic data might not perfectly capture all the complex correlations and outliers of the original data, which could introduce bias or lead to less accurate AI models. It's important to validate the quality of the synthetic data against the real data to ensure it's suitable for its intended purpose.
Quick Comparison: Our Top Picks
| Rank | AI Synthetic Data Generators | Score | Start Price | Best Feature |
|---|---|---|---|---|
| 1 | Gretel.ai | 4.3 / 5.0 | $1,500/month | The platform's strong differential privacy guarantees provide a defensible, mathematical foundation for data anonymization. |
| 2 | YData | 4 / 5.0 | $59/month | The synthetic data generator is genuinely impressive for creating privacy-safe datasets that actually retain statistical properties. |
| 3 | Syntheticus | 3.9 / 5.0 | Custom Quote | Creates statistically identical datasets without exposing real PII, which is a lifesaver for GDPR/CCPA compliance. |
| 4 | Tonic.ai | 3.9 / 5.0 | Custom Quote | Generates genuinely realistic data that preserves complex foreign key relationships, avoiding the broken, nonsensical data common with simpler masking tools. |
| 5 | Synthesized | 3.9 / 5.0 | Custom Quote | Generates high-fidelity test data that's actually safe to use, which keeps your compliance and legal teams from breathing down your neck. |
| 6 | Mostly AI | 3.8 / 5.0 | Custom Quote | Generates statistically accurate data without exposing real customer PII, simplifying GDPR/CCPA compliance. |
| 7 | Hazy | 3.8 / 5.0 | Custom Quote | Generates statistically representative data that preserves complex correlations, making it genuinely useful for realistic model training. |
| 8 | Statice | 3.8 / 5.0 | Custom Quote | The differential privacy implementation isn't just marketing fluff; it provides a mathematically sound basis for data sharing that will actually satisfy your compliance team. |
| 9 | GenRocket | 3.5 / 5.0 | Custom Quote | Models deeply complex business logic, maintaining referential integrity across dozens of tables without custom scripting. |
1. Gretel.ai: Best for Generating privacy-safe synthetic data
I've been watching Gretel for a while because they approach synthetic data from a developer's perspective. You're basically interacting directly with their `Gretel Synthetics` library to model and then generate a new, PII-free dataset. It's a good way to avoid the whole GDPR compliance song and dance for staging environments. Don't expect a fancy GUI to hold your hand, though. You'll need an engineer who is comfortable with the command line and isn't afraid to read the docs.
Pros
- The platform's strong differential privacy guarantees provide a defensible, mathematical foundation for data anonymization.
- Developer-friendly tools like the Python SDK and pre-built 'Gretel Classifiers' for quality scoring make it easy to automate synthetic data generation.
- Handles complex data well, including time-series and relational database structures via its 'Gretel Relational' feature.
Cons
- Steep learning curve; this is a developer-first platform requiring Python knowledge and a solid grasp of data science concepts, not a simple GUI tool.
- The usage-based pricing model can become surprisingly expensive and hard to predict when dealing with large datasets or frequent model training.
- Generating truly high-fidelity data isn't automatic; it often requires significant manual tuning of the models (like its LSTM or ACTGAN options) to preserve complex statistical relationships.
2. YData: Best for AI/ML data preparation.
YData is aimed squarely at data science and ML teams, not your average app developer. If your model training is stalled because you can't get enough clean (or legally compliant) data, this is the kind of tool you look at. Their whole platform, `YData Fabric`, is designed to generate high-quality synthetic data to fill those gaps or replace PII-laden datasets entirely. Don't confuse this with a simple database masking tool. It's a complex system for a complex problem, and you should expect a significant learning curve before your team is proficient with it.
Pros
- The synthetic data generator is genuinely impressive for creating privacy-safe datasets that actually retain statistical properties.
- Its data profiling feature is a huge time-saver, automatically flagging quality issues like outliers and missing values before they mess up a model.
- Having a usable Community Edition and open-source libraries like `ydata-synthetic` lets data science teams test the core concepts without a big budget.
Cons
- Steep learning curve requires dedicated data science expertise to maximize value.
- Pricing can quickly become prohibitive for startups or smaller-scale data projects.
- Highly specialized; it's overkill if your main problem isn't synthetic data generation or advanced data quality profiling.
3. Syntheticus: Best for Generating Synthetic AI Data
Syntheticus feels like it was built by data scientists, for data scientists. There are no frills here. Your team gets a `Syntheticus SDK` and is expected to use it to generate the statistically-sound artificial datasets they need for model training. The whole point is to give them the freedom to build and test without ever requesting access to production data, which makes the governance people happy. The UI, if you can call it that, is barebones, but it's effective for its core job: creating safe tabular and time-series data.
Pros
- Creates statistically identical datasets without exposing real PII, which is a lifesaver for GDPR/CCPA compliance.
- Excellent at augmenting small or imbalanced datasets, helping to correct for bias and improve model accuracy.
- The Syntheticus Generative Model (SGM) offers fine-grained control over the output, which is rare in this space.
Cons
- Steep learning curve for non-data scientists; requires a solid statistical background to generate truly useful data.
- Can be computationally intensive, leading to high cloud-compute costs if you're generating massive datasets.
- Risk of generating data that misses subtle real-world correlations, leading to models that fail on production data.
4. Tonic.ai: Best for Generating safe test data.
I've lost count of the number of engineering VPs who tell me their biggest bottleneck is getting safe data for staging. Tonic.ai is built to fix exactly that. It hooks into your production database, mimics the schema, and generates fake data that's safe to use. What I actually like is their `Subsetter` tool; it lets you create small, specific datasets for a CI run without having to generate a massive database clone. It's a real time-saver. The setup isn't a walk in the park—you'll curse it when you're mapping foreign key constraints—but it's better than explaining a data spill to your CISO.
Pros
- Generates genuinely realistic data that preserves complex foreign key relationships, avoiding the broken, nonsensical data common with simpler masking tools.
- The database subsetting feature is incredibly practical, letting you create smaller, targeted, yet fully functional dev datasets without cloning entire production databases.
- Offers a strong library of pre-built data generators for specific PII types (e.g., names, addresses, SSNs), which dramatically speeds up configuration for compliance.
Cons
- Enterprise-level pricing can be prohibitive for startups and mid-sized companies.
- Initial setup and schema configuration demand significant engineering effort and expertise.
- Data generation jobs for very large databases are resource-intensive and can be slow to complete.
5. Synthesized: Best for Creating Safe Test Data
Forget your brittle, in-house data anonymization scripts; they're going to fail eventually. Synthesized is the proper way to handle test data. It generates an entirely new, artificial dataset that is statistically identical to your production environment. That means QA can actually do their job without you having a compliance meltdown. Their Test Data Kit (TDK) is the key feature here, designed specifically to feed CI/CD pipelines without ever touching PII. It's an expensive tool and you'll need someone who knows what they're doing to configure it, but the cost is nothing compared to a breach.
Pros
- Generates high-fidelity test data that's actually safe to use, which keeps your compliance and legal teams from breathing down your neck.
- Dev and QA teams can get realistic test datasets in minutes instead of submitting a ticket and waiting two weeks for a sanitized database dump.
- Maintains complex database relationships and statistical patterns, so you're testing against something that behaves like real-world data, not just junk values.
Cons
- The learning curve is steep; it's not a plug-and-play tool for junior developers and requires data engineering knowledge.
- Pricing can be prohibitive for smaller teams or initial pilot projects, making it an enterprise-first consideration.
- Can struggle to replicate the complex, messy 'long-tail' edge cases found only in real-world production data.
6. Mostly AI: Best for Anonymizing sensitive datasets
Think of Mostly AI as the enterprise-grade synthetic data generator for big banks and insurance companies. If your developers are waiting weeks for a ticket to get approved by legal just to access production data, you're the target audience. It's designed to generate statistically accurate copies of complex customer info, so QA isn't completely useless. Honestly, setting up a generator is straightforward, but tweaking it for something like time-series data will take some actual effort. It's not a tool for startups, it's a tool for ending bureaucratic standoffs.
Pros
- Generates statistically accurate data without exposing real customer PII, simplifying GDPR/CCPA compliance.
- Provides on-demand synthetic data, dramatically cutting down the wait time for development and testing teams.
- Capable of creating balanced datasets and simulating rare edge cases that are difficult to isolate from production data.
Cons
- The learning curve is steeper than marketing suggests; generating high-fidelity synthetic data requires genuine data science expertise.
- Enterprise-level pricing makes it a non-starter for smaller teams or straightforward testing scenarios.
- Can be overkill if your only need is basic data masking or anonymization, not full statistical replication.
7. Hazy: Best for Enterprise Synthetic Data Generation
Let's be honest, most 'data anonymization' is just shoddy masking. Hazy is one of the few that gets it right by creating genuinely new, synthetic data. The process feels more deliberate: you connect your sources to the `Hazy Hub`, define your privacy rules, and then it generates the data. This isn't a simple script; it's a proper platform for ending the war between your dev team wanting data and your compliance team saying 'no'. It's not for beginners and requires some real data science expertise, but it works.
Pros
- Generates statistically representative data that preserves complex correlations, making it genuinely useful for realistic model training.
- Radically simplifies GDPR and PII compliance for development environments by completely replacing sensitive production data.
- Their 'Hazy Tabular' tool is effective at maintaining referential integrity across multiple tables, a common failure point in other synthetic data generators.
Cons
- Requires deep data science expertise for meaningful results; this is not a tool for general business analysts.
- Generated data can miss nuanced, real-world edge cases and outliers, potentially skewing model training.
- Opaque enterprise pricing makes it difficult to budget for without a lengthy sales process.
8. Statice: Best for Enterprise synthetic data generation.
The minute you need to share data with a third party, your legal team starts losing its mind. Statice is designed for exactly that scenario. It's a serious platform for creating synthetic data with a heavy focus on differential privacy, ensuring nothing can be reverse-engineered. This is not a point-and-click affair; your data scientists will be living in the `Statice SDK` to get the schema mapping and utility metrics right. The payoff is being able to hand over a dataset for a POC or to a partner without scheduling a three-hour meeting with compliance.
Pros
- The differential privacy implementation isn't just marketing fluff; it provides a mathematically sound basis for data sharing that will actually satisfy your compliance team.
- Generates statistically representative data that's genuinely useful for ML model training, not just junk data for filling test tables.
- The Python SDK is well-documented, allowing data teams to programmatically integrate data synthesis into their existing CI/CD or MLOps pipelines without a ton of manual intervention.
Cons
- Steep learning curve; requires significant data science expertise to properly configure and validate synthetic data models.
- Primarily a tool for large enterprises; the pricing structure and feature set are not geared for small teams or individual researchers.
- Generating high-fidelity data for very large or complex datasets can be computationally intensive and time-consuming.
9. GenRocket: Best for Enterprise test data automation.
Do not buy GenRocket if you need a simple CSV file of fake names. This is a heavyweight data generation engine, and its learning curve is, frankly, brutal. Setting up your first `GenRocket Scenarios` will be a frustrating experience. However, once you understand its component-based logic using `Data Generators`, you can create unbelievably complex test data that actually respects referential integrity. It's built for enterprise QA departments who are sick of tests failing because their data environment is garbage. This is a specialist's tool, not a generalist's.
Pros
- Models deeply complex business logic, maintaining referential integrity across dozens of tables without custom scripting.
- Generates millions of rows of synthetic data in minutes, completely bypassing the security and time-sink of masking production data.
- Its component-based system using 'Generators' and 'Receivers' allows for precise control over data format and destination, from flat files to direct database inserts.
Cons
- The learning curve is exceptionally steep; mastering the GenRocket Runtime and its component-based architecture (Domains, Attributes, Generators) is not a weekend project.
- Its power is also its weakness: creating complex 'Test Data Cases' (G-Cases) can feel like a full-time development job in itself, adding significant overhead to testing cycles.
- The tool is priced for large enterprises, making the cost prohibitive for smaller teams or organizations that don't have a constant, high-volume need for synthetic data.