Table of Contents
- Why Are Startups Like Gretel AI Leading The Synthetic Data Boom For Enterprise AI Teams?
- What Gretel AI Actually Does
- How the Synthetic Data Works
- Speed and Cost Advantage
- Funding, Scale, and Market Position
- Why Synthetic Data Matters Now
- Synthetic Data Market Growth
- Where Gretel AI Fits in This Trend
- Other Leading Synthetic Data Startups
- Tonic AI
- Mostly AI
- Synthesis AI
- Why This Matters for AI Teams and Businesses
Why Are Startups Like Gretel AI Leading The Synthetic Data Boom For Enterprise AI Teams?
Gretel AI is a company that helps teams create safe, realistic data for training large language models and other machine learning systems. It does this by generating synthetic data that behaves like real customer or business data, but without exposing any sensitive personal information. This makes it easier and faster for enterprises to build AI products while still following strict privacy rules.
What Gretel AI Actually Does
Gretel AI is a synthetic data platform built for developers, data scientists, and AI teams.
At a simple level, it helps teams:
- Create fake-but-realistic data from their own internal data.
- Keep private details hidden so no one can trace data back to real people.
- Feed AI models with large, high-quality datasets without waiting for long data approval cycles.
Key points in plain language:
- The data looks and behaves like real data.
- It follows privacy laws such as GDPR and other data protection rules.
- It is designed to plug into existing engineering and data workflows.
How the Synthetic Data Works
Gretel lets users describe the data they want using natural language prompts. Instead of writing complex code or building data pipelines from scratch, a user can type what type of dataset they need, including structure and behavior.
The platform can generate different data formats:
- Tabular data (like spreadsheets or database tables).
- Unstructured text (for language models and chatbots).
- Time-series data (for logs, financial data, sensor streams, etc.).
Behind the scenes, Gretel uses generative AI models trained on the company’s original data to learn patterns, distributions, and relationships. It then uses those patterns to generate new records that are statistically similar but not tied to any real person.
Speed and Cost Advantage
Traditional data preparation for AI can be slow and expensive because it often involves:
- Manual anonymization and cleaning.
- Risk reviews with legal, security, and compliance teams.
- Long approval workflows before developers can even touch the data.
Gretel reports that its synthetic data platform can:
- Give teams access to usable data up to about 15x faster than manual processes.
- Cut the cost of data preparation by around 5x compared with hand-curated datasets.
This time and cost leverage is particularly important for teams training large models or running many experiments, because they need constant access to new and diverse data.
Funding, Scale, and Market Position
Gretel launched in 2020 and quickly attracted venture backing from well-known investors. Public sources show that it has raised around 67–68 million dollars across its rounds, including a 50 million dollar Series B. This funding helped the company grow product features, expand customer reach, and position itself as one of the core synthetic data platforms for enterprise AI.
Industry news now places Gretel among the leading players in synthetic data for training large models and privacy-preserving analytics. Growing demand from sectors like financial services, life sciences, and gaming has reinforced its importance, as these industries must balance innovation with strict privacy and regulatory needs.
Why Synthetic Data Matters Now
Enterprises are running into a clear problem: they need more data than they can safely use. Large AI models require huge volumes of high-quality examples, but:
- Real-world data is limited or expensive to collect.
- Strong privacy laws restrict who can see or use sensitive information.
- Many use cases require rare or edge scenarios that barely appear in real datasets.
Several independent analyses project that AI models will consume most of the useful human-generated data sometime between the late 2020s and early 2030s. As real data becomes a bottleneck, synthetic data grows in importance because it can be generated on demand and tailored to specific use cases.
Synthetic Data Market Growth
The synthetic data generation market is on a fast growth path. Recent market research estimates:
- Global market size of about 267 million dollars in 2023.
- Projected growth to more than 4.6 billion dollars by 2032.
- An expected compound annual growth rate of around 33–37% through 2032.
This growth is linked to increased AI adoption, tighter privacy regulations, and the need for safer ways to share and collaborate on data. Sectors such as healthcare, finance, automotive, and retail are especially active because they handle sensitive personal or transactional information.
Where Gretel AI Fits in This Trend
Gretel sits within this broader synthetic data trend as a developer-first, multimodal platform built to integrate with modern AI stacks. It emphasizes:
- Strong privacy guarantees and compliance alignment.
- High-fidelity data that preserves important patterns for model training.
- APIs and workflows that fit how engineering and data teams already work.
Because synthetic data helps avoid direct use of raw personal data, Gretel’s tools can reduce the risk of data breaches and simplify internal governance around who can use what data. This aligns well with E-E-A-T and YMYL standards in sensitive domains, since organizations can test and build AI systems with less risk of exposing real individuals.
Other Leading Synthetic Data Startups
Several other startups are growing alongside Gretel and help show the shape of the market.
Tonic AI
Tonic AI focuses on synthetic and masked data for software and AI engineers. Its platform can:
- Identify sensitive fields and de-identify them.
- Generate realistic, relational test data that behaves like production data.
- Support regulated industries like finance, healthcare, and retail.
The company has raised several funding rounds, including a notable 35 million dollar round to accelerate its platform for DevOps and testing use cases.
Mostly AI
Mostly AI is a European synthetic data company that emphasizes data privacy and regulatory alignment, especially with rules like GDPR. Through its platform, users can:
- Build custom synthetic data generators.
- Share and reuse those generators across teams.
- Produce datasets that preserve statistical properties while staying anonymized.
The company has raised over 25–30 million dollars, positioning itself as one of the key European players in this space.
Synthesis AI
Synthesis AI specializes in synthetic data for computer vision. Its tools support:
- Training models for surveillance and security tasks.
- Retail and e-commerce uses like virtual try-on.
- Automotive and urban safety scenarios such as pedestrian detection.
By generating visual data at scale, Synthesis AI helps teams covers edge cases and rare events that are hard to capture with cameras in the real world.
Why This Matters for AI Teams and Businesses
Synthetic data platforms like Gretel AI, Tonic AI, Mostly AI, and Synthesis AI give organizations a new way to scale AI safely. Benefits include:
- Faster experimentation and model development.
- Lower risk of exposing private or regulated data.
- Access to richer, more diverse datasets than would be feasible to collect manually.
- Better alignment with privacy, security, and compliance expectations.
As regulations tighten and AI models grow, these capabilities are becoming part of standard data infrastructure rather than a niche add-on.