Gretel’s mission is to enable developers and organizations to innovate with data while upholding strict privacy standards. Gretel aims to solve the critical challenge of accessing high-quality, privacy-preserving data for AI and machine learning by providing tools that make data safe and useful. Its core goal is to build the best developer platform for synthetic data, allowing users to generate artificial datasets that mirror the statistical properties of real-world data without exposing sensitive information. This facilitates faster innovation, research, and development by removing data access bottlenecks.
Gretel is recognized as a pioneering company in privacy engineering and synthetic data generation. Gretel brands itself as a “developer-first” company, offering accessible tools like APIs and a cloud-native console to integrate synthetic data generation into existing workflows. The company is known for its focus on creating high-quality, statistically accurate synthetic data that can be used to train AI models, test software, and share data securely. Gretel’s approach has attracted significant investment and a growing user base, including Fortune 500 companies. Recent reports in early 2025 indicated that Nvidia acquired Gretel, signaling a strategic move to bolster AI development capabilities by addressing data scarcity and privacy concerns.
Offerings, Capabilities, and Integrations
Gretel provides a synthetic data platform designed for generative AI, enabling developers and data scientists to create artificial datasets that mirror the statistical properties of real-world data. This allows for the training and improvement of AI models without compromising data privacy. Gretel’s core capabilities include generating anonymized and safe synthetic data through its APIs, training generative AI models to learn data’s statistical properties, and validating models with quality and privacy scores. The platform supports various data types, including tabular, time-series, and unstructured text. Gretel’s ability to produce high-quality, privacy-preserving synthetic data gives it a competitive edge, particularly for organizations dealing with sensitive information in sectors like healthcare and finance. This focus on data privacy and utility enhances Gretel’s reputation as a provider of tools for responsible AI development. Gretel integrates with major cloud providers such as Amazon AWS, Google Cloud Platform (GCP), and Microsoft Azure, as well as platforms like Databricks. This allows users to incorporate synthetic data generation directly into their existing MLOps workflows.
Products and Services
- Gretel Cloud: A fully managed service for synthetic data generation, operating within Gretel’s cloud infrastructure. It handles compute, automation, and scalability, allowing developers to train and generate synthetic data using Gretel’s GPUs. Users can collaborate on cloud projects and share data across teams.
- Gretel Hybrid: A deployment option that operates within a customer’s own cloud tenant (GCP, Azure, or AWS) on Kubernetes. Data remains in the customer’s environment, making it suitable for sensitive or regulated data. It interfaces with the Gretel Control Plane API for job scheduling.
- Gretel Navigator: A compound AI system designed to create high-quality tabular datasets from scratch using natural language or code. It allows users to iteratively create, edit, and augment tabular datasets with simple prompts. Gretel Navigator is available on the Azure AI Foundry Model Catalog.
- Gretel Tabular Fine-Tuning: An AI system (formerly known as Navigator Fine Tuning) that combines a Large Language Model pre-trained on tabular datasets with schema-based rules to generate synthetic datasets. It supports various tabular modalities, including numeric, categorical, free text, JSON, and time series values, and can incorporate differential privacy.
- Gretel APIs (Synthetics, Transform, Classify): These allow developers to generate, anonymize, balance, and share data with privacy guarantees.
- Synthetics API: Leverages Large Language Models (LLMs), Generative Adversarial Networks (GANs), and Diffusion Models to generate synthetic data for various data types. It includes built-in privacy filters. Gretel’s flagship synthetic model is a neural network-based language model with features like differentially-private training.
- Transform API: Applies discrete mutations to data for de-identification, tokenization, and anonymization. Gretel Transform v2 offers a fast and flexible de-identification and rule-based transformation solution, for example, for HIPAA compliance.
- Classify API: Identifies and tracks sensitive information within datasets using Named Entity Recognition (NER) and Natural Language Processing (NLP).
- Gretel Workflows: Automate and operationalize synthetic data generation by connecting directly to data sources and destinations.
- Gretel Evaluate: Generates a Synthetic Data Quality Score (SQS) report by comparing synthetic datasets to real-world data.
- Gretel Amplify: A general-purpose copula model designed to take existing synthetic data and significantly increase its size quickly.
- Gretel GPT: A framework that allows plugging in open-source GPT models and adds capabilities like differentially private training, tuning, and enterprise-grade reporting.
- Gretel Relational: Enables the generation of high-quality synthetic databases while preserving cross-table relationships.
- Gretel Benchmark: A toolkit to evaluate any synthetic data algorithm on any production dataset.
- Gretel Connectors: Facilitate integration with various data services, including object storage (Amazon S3, Google Cloud Storage, Azure Blob Storage), relational databases (MySQL, PostgreSQL, Microsoft SQL Server, Oracle Database), and data warehouses (Snowflake, Google BigQuery, Databricks).
Gretel Navigator, launched in June 2024, is highlighted as a new product. Gretel’s flagship models include its LSTM-based general-purpose synthetic data model (Gretel Synthetics) and its LLM-based system for tabular data (Gretel Tabular Fine-Tuning). Gretel was reportedly acquired by NVIDIA in March 2025.
Target Customers
Gretel’s target customers include developers, data scientists, data engineers, and analysts who need to work with data while ensuring privacy and compliance. Gretel aims to solve the “cold start” problem of data access, which can consume a significant portion of project timelines. Companies in sectors such as finance, healthcare, and the public sector, which handle sensitive data and have stringent privacy requirements, are key market segments for Gretel. These customers benefit from Gretel’s products and services by being able to:
- Accelerate AI development by providing quick access to safe, high-quality synthetic data for training and testing AI/ML models.
- Protect sensitive data and ensure compliance with regulations like GDPR and CCPA by generating anonymized or differentially private synthetic datasets.
- Improve model performance by augmenting limited datasets, balancing underrepresented demographics, and creating domain-specific data.
- Enable data sharing for research and collaboration without exposing sensitive information.
- Reduce operational overhead associated with manual data labeling and structuring.
- Support software development by providing quality, production-grade data for testing and CI/CD pipelines without privacy risks.
Cloud Integrations and Marketplaces
Gretel offers several cloud integrations and has a presence on major cloud marketplaces, enabling users to incorporate its synthetic data generation and privacy-enhancing technologies into their existing cloud workflows.
- Amazon Web Services (AWS): Gretel’s synthetic data generation tools are available on the AWS Marketplace. This allows AWS customers to use Gretel to train custom AI models with their proprietary data while maintaining privacy and compliance. Users can utilize existing AWS credits and commitments for Gretel’s services. Gretel also integrates with Amazon SageMaker, allowing for automated secure workflows where Gretel jobs can run in the background when data is uploaded to an S3 bucket. Gretel supports Amazon S3 as a connector for data input and output. Gretel Hybrid can be deployed on AWS using its managed Kubernetes services.
- Google Cloud Platform (GCP): Gretel’s suite of privacy-enhancing tools and generative AI models are available on the Google Cloud Marketplace. This enables Google Cloud users to generate synthetic versions of sensitive datasets. Google Cloud customers can use their existing Google Cloud commitments for Gretel’s products. Gretel integrates with Vertex AI and BigQuery, allowing developers to use synthetic data within their MLOps workflows. Gretel has achieved Google Cloud Ready – BigQuery designation, and there are plans for a native integration to make Gretel synthetic data available within the BigQuery console. Gretel supports Google Cloud Storage and Google BigQuery as connectors for data input and output. Gretel Hybrid can be deployed on GCP using its managed Kubernetes services.
- Microsoft Azure: Gretel’s synthetic data generation platform and privacy-preserving technologies are available on the Microsoft Azure Marketplace. This allows Azure users to train custom AI models on proprietary data while meeting privacy compliance and leveraging existing Azure credits and commitments. Gretel is also part of the Microsoft for Startups Pegasus Program. Gretel on Azure allows enterprises to incorporate generative AI directly into their MLOps workflows. Gretel supports Azure Blob Storage as a connector for data input and output. Gretel Hybrid can be deployed on Azure using its managed Kubernetes services. Gretel Navigator, a chat model, can be deployed as a serverless API endpoint with pay-as-you-go billing through the Azure Marketplace and is integrated with Azure AI Foundry.
Gretel provides connectors for various cloud storage solutions including Amazon S3, Google Cloud Storage, and Azure Blob Storage, as well as relational databases and data warehouses. Gretel’s platform offers deployment models such as Gretel Cloud (a fully managed service) and Gretel Hybrid, which operates within a customer’s own cloud tenant (AWS, GCP, or Azure) for enhanced data control.
Key People
- CEO & Co-Founder: Ali Golshan
- CPO & Co-Founder: Alexander Watson
- CTO & Co-Founder: John Myers
- VP of Product: Yamini Kagal
- VP of People Ops: Derrick Murphy
- Chief Business Officer: Paul Smith
- VP of Finance: Conor Finan
- Head of Marketing: Rebecca Kao
- Head of Product Design: Arron Hunt
- Head of Applied Science: Maarten Van Segbroeck
- Head of Engineering: Stefan Gavrilovic
- Head of Security: Bryan Zimmer
Key Facts
- Headquarters Location: San Diego, CA, United States.
- Number of Employees: Approximately 80-94.
- Annual Revenue: Estimated $14.1M – $15M.
- Parent Company: Nvidia.
- Subsidiary Companies: None.
- Publicly Listed: No (Acquired by Nvidia).
Analyst Recognition
Gretel has been recognized by several major analyst groups for its work in synthetic data and AI.
- Gartner: Gretel was named a Gartner Cool Vendor in AI Core Technologies in 2022. This recognition was specifically for its role in Sourcing Data for AI.
- Forrester: Gretel was cited in Forrester’s reports, “The Synthetic Data Landscape, Q2 2023” and “The Data Security And Privacy Landscape, Q1 2023“. These inclusions highlight Gretel’s relevance in the synthetic data and data security markets.
- IDC: Gretel was named an IDC Innovator in the “Synthetic Data for AI and Analytics” report in 2023. This positions Gretel as a key innovator in the field of synthetic data generation for artificial intelligence and analytical purposes.
- Everest Group: There is no specific information available from the provided search results regarding recognition of Gretel by Everest Group.
