Gretel
Gretel.ai is a multimodal synthetic data platform designed for developers. The platform's foremost attribute is its ability to generate artificial, synthetic da...
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What is Gretel?
Gretel is a multi-modal synthetic data platform purpose-built for AI that generates high-quality, safe synthetic data on demand. The platform uses proprietary generative AI models that learn the patterns and distributions of ground truth datasets to recreate artificial datasets with the same statistical characteristics but without any sensitive information, mitigating data privacy concerns. Companies across finance, healthcare, and technology use Gretel to safely access and share data, train generative AI models, and improve ML model performance.
Gretel provides four core APIs: Synthetics, Transform, Classify, and Evaluate. Gretel Synthetics produces statistically accurate, privacy-protected training data for AI models with differentially private datasets. Transform anonymizes, encrypts, or removes PII and sensitive data. Classify uses Named Entity Recognition to identify and label PII. Evaluate validates data accuracy to avoid hallucinations. The platform also includes Gretel Navigator, the first compound AI system designed to create, edit, and augment tabular data using natural language or SQL prompts.
The platform is designed for developers and ML/AI engineers who need to unlock data bottlenecks. Users can access flexible and scalable APIs through Gretel's online console, CLI, SDK, or connectors. Gretel services run in Gretel's managed cloud service or within users' own private cloud environments. The platform supports text, tabular, and time-series data with automated data validation, synthetic data quality reports, and privacy filters with optional differential privacy support.
Gretel enables multimodal synthetic data generation with enterprise-grade features including balance datasets or boost minority classes using Conditional Data Generation. The platform generates synthetic data for conversational AI, evaluation and benchmarks, low-resource adaptation, private and compliant data meeting HIPAA and GDPR regulations, and synthetic documents for tax form validation, legal documents, and mortgage approvals.
Gretel pricing
Pricing model: Free
Gretel offers 3 subscription plans: Developer (free), Team ($295/month), and Enterprise (customizable). The Developer plan is free monthly with 15 free credits (1.5 million free characters) per month, 2 concurrent jobs, 1 hour runtime limit, unlimited maximum dataset size, best effort SLA, email and OAuth authentication, and community support. Pay-as-you-go is $2 per credit after free credits are used. The Team plan is $295/month plus $2.20 per credit with 10 concurrent jobs, 12 hours runtime limit, 99.5% API availability SLA, custom SSO setup, weekday email support, and private 60 minute onboarding. One Gretel credit equals 100,000 characters including both input and output. All inference APIs including Navigator are billed by characters.
Gretel pros
- Generates unlimited amounts of synthetic data on demand
- Supports text, tabular, and time-series data types
- Provides differential privacy support for enhanced privacy guarantees
- Works in managed cloud or private cloud environments
- Simple APIs accessible via console, CLI, SDK, and connectors
- Gretel Navigator enables natural language and SQL prompts for tabular data
- Automated data validation prevents hallucinations
- Synthetic data quality reports provided automatically
- Privacy filters allow tuning data privacy versus utility
- Conditional Data Generation balances datasets or boosts minority classes
- Open source core with reference examples available
- Gets developers started in minutes
- Supports CSV, JSON, and Parquet input formats
- Meet HIPAA and GDPR compliance requirements
- Unlimited maximum dataset size on all plans
- Generate data from scratch without seed datasets
- PII detection and labeling capabilities included
- Anonymize and remove biases from data
Gretel cons
- Free tier has only 2 concurrent jobs limit
- Free tier has 1 hour runtime limit per job
- Free tier has best effort SLA not guaranteed availability
- Free tier has community support only no email support
- Team plan costs $295/month plus $2.20 per credit
- Pricing billed by characters which can be expensive at scale
- No 24/7 support on any plan except Enterprise
- No dedicated success engineer on Developer or Team plans
Frequently asked questions about Gretel
What is Gretel?
Gretel is a multi-modal synthetic data platform for generating high-quality, safe data at scale. Our proprietary generative AI models learn the patterns and distributions of your ground truth datasets and recreate artificial datasets with the same statistical characteristics but without any of the sensitive information, mitigating data privacy concerns. Companies across industries use Gretel to safely access and share data, train generative AI models, or improve their ML models performance.
What is Gretel Navigator?
Gretel Navigator is the first compound AI system designed to create, edit, and augment tabular data using natural language or SQL prompts. With simple natural language or SQL prompts, it enables users to create, edit, and augment tabular data and design realistic, high-quality test and training datasets from scratch. It is built to automate data creation and curation processes for AI development.
What is synthetic data?
Synthetic data is artificial data created by generative AI models that learn from real ground truth datasets. It has the same statistical characteristics and patterns as the original data but contains no sensitive information. Synthetic data eliminates bottlenecks like data scarcity, security concerns, and cost of manual data collection while enabling safe data sharing compliant with HIPAA and GDPR.
What is differential privacy?
Differential privacy is a privacy technique that Gretel applies to create highly accurate synthetic data with enhanced privacy guarantees. It adds controlled noise to the data during generation to ensure that individual data points cannot be identified while maintaining overall statistical accuracy. Gretel offers optional differential privacy support for users who need stronger privacy protections.
How do I start generating synthetic data with Gretel?
To start synthesizing data with Gretel, first sign up for a free account at console.gretel.ai and retrieve your API key. You can get started in just a few clicks with the free account. Set up your environment and connect to the SDK. Install the Gretel CLI and use the Gretel client to store your API key. Then you can train models and generate synthetic data using simple API calls.
What problem does Gretel solve?
Gretel solves data bottlenecks including data scarcity where domain-specific datasets are limited or unavailable, security concerns where internal data is too sensitive to share externally, and the cost and time of manual data collection and labeling. It also addresses complex requirements for reasoning LLMs, multi-agent systems, and multimodal AI assistants that require ample training data to be useful and autonomous.
What are the benefits and drawbacks of synthetic data?
Benefits include eliminating data privacy concerns since no sensitive information is included, enabling infinite scalability of datasets, providing statistically accurate data for AI training, and allowing safe data sharing compliant with regulations. Drawbacks include potential for synthetic data to not capture all nuances of real data, the need for validation to ensure quality, and the computational cost of generating large synthetic datasets.
How does Gretel define synthetic data quality?
Gretel defines synthetic data quality through automated data validation that verifies data accuracy to avoid hallucinations. The platform provides synthetic data quality reports and uses enterprise-grade features to validate that generated data maintains the patterns, distributions, and characteristics of actual data while ensuring privacy protection and statistical accuracy.
Does Gretel have a responsible AI policy?
Yes, Gretel has a responsible AI policy that guides their development and deployment of synthetic data technologies. The policy ensures that their generative AI models are developed ethically and that synthetic data generation meets privacy, security, and compliance standards including HIPAA and GDPR requirements.
What are the pros and cons of building versus buying synthetic data tools?
Building your own synthetic data tools requires significant investment in development time, expertise in generative AI, and ongoing maintenance but offers full customization. Buying Gretel provides immediate access to a complete platform with proprietary models, APIs for multiple use cases, cloud or private deployment options, and community support. Gretel's open source core and reference examples also help reduce the gap between building and buying.