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Synthetic data solutions for software and AI development
Accelerate engineering velocity and ensure compliance with AI-powered data synthesis
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Generate secure and scalable synthetic data, when you need it, where you need it
Tonic Fabricate
Generate data from scratch
No production data? No problem. Turbocharge new product development by generating fully relational synthetic databases with unlimited tables, as well as mock APIs, on demand.
Tonic Structural
Mimic your production data
Bring an end to critical bugs in production and accelerate your release cycles by fueling your staging and QA environments with synthetic data that mirrors the complexity of production.
Tonic Textual
Unlock your free-text data for AI
Safely leverage your unstructured data in AI development while safeguarding against leaks and ensuring regulatory compliance through industry-leading free-text redaction and synthesis.
Trusted by engineering teams throughout the world
Senthil Padmanabhan
Technical Fellow, VP of Eng
“Tonic has an intuitive, powerful platform for generating realistic, safe data for development and testing. Tonic has helped eBay streamline the very challenging problem of representing the complexities contained within Petabytes of data distributed across many environments."
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8PB -> 1GB
Subset size reduction
Sebastian Kowalczyk
Senior DevOps Engineer
“With Tonic, we’ve shortened our build process from 60 minutes down to 20. Their subsetting and de-identification tools are a critical part of Everlywell’s development cycle, making it easy for us to get data down to a useful size and giving me confidence it’s protected throughout."
3x
Faster release cycles
Jordan Stone
VP of Engineering
"If I think about what it would cost for us to build something even remotely viable for us to solve our test data problem in the way that Tonic has solved it for us, it's orders of magnitude more than what it costs us to run Tonic Cloud."
600 hrs
Development hours saved
Kevin Paige
Chief Information Security Officer
“Our security team loves it because it solves a complex problem crucial to reducing risk for our company. Infrastructure loves it because it’s on-prem and easily deployed in a container. And our engineers love it because it’s easy to use and integrates seamlessly into our software development lifecycle without asking them to do any extra work.”
SOC2
Certification achieved
Matty Woznick
Enablement Programs Manager
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“You can’t tell that our demo environment runs on Tonic data. It is so close to a mirrored experience for what our partners deal with, and that helps us empower them and guide them better. End of story.”
10x
Faster onboarding
Jason Lock
Senior Software Engineer and Tech Lead
“Tonic drastically reduces the amount of time it takes for a full regression test for all of our core features. Before it was somewhere within a two-week time span for QA to get the data set up; now they are ready to go and have tested all of the core features manually within a half a day.”
20x faster
Faster regression testing
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Deliver the value of realistic synthetic data across your organization
1
Deploy Tonic
Deploy a self-hosted instance of Tonic, or work with your generated data in Tonic Cloud.
2
Connect to your data
Tonic integrates with all the leading relational and NoSQL databases, data warehouses, and file types.
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3
Transform your data via realistic masking and synthesis
Automatically identify sensitive data types and realistically mask or synthesis net new values that maintain consistency and preserve relationships across your database
4
Distribute safe, realistic synthetic data to your team, refreshed on demand.
Provision your synthetic test data via container repos or by spinning up a net new database, as often as you need.
Find the plan that works for you.
Most recent
Guides
Explore the world of data masking and discover how it plays a crucial role in safeguarding sensitive information while maintaining data utility.
How to develop AI training datasets for compliance and performance
AI model training
Data synthesis for AI: A privacy-first approach
Data synthesis
Secure data generation for AI model training
AI model training
Preventing data breaches in AI systems
Data privacy in AI
How to prepare machine learning data responsibly
AI model training
Accelerating QA & development with Tonic Ephemeral
Tonic Structural how-tos
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Data masking and artificial intelligence: Protecting data
Data privacy in AI
Data masking in agile development environments
Developer productivity
Masking and subsetting data to optimize test data pipelines
Test data management
Data synthesis vs data masking
Data synthesis
Data synthesis techniques: a comparison for developers
Data synthesis
Generating mock APIs for testing and development
Tonic Fabricate how-tos
How to improve data accessibility for software and AI development
Developer productivity
Deterministic masking, explained
Data de-identification
Managing access to Tonic Fabricate accounts and workspaces
Tonic Fabricate how-tos
PII compliance checklist: how to protect private data
Data privacy in AI
Real-world applications of format preserving encryption
Data de-identification
Data masking for the insurance industry: a guide
Data de-identification
What is a rule-based test data generator?
Data synthesis
Data masking’s role in leveraging production data for testing and development
Test data management
Uploading and referencing production data in a synthetic dataset, with Tonic Fabricate
Tonic Fabricate how-tos
Customizing foreign key distribution in synthetic data with Tonic Fabricate
Tonic Fabricate how-tos
Data masking for government agencies: a guide
Data de-identification
How to mask data in Snowflake: a step-by-step guide
Tonic Structural how-tos
Build vs buy: your guide to finding scalable, efficient test data solutions
Developer productivity
Questions to ask when selecting a Test Data Management service
Test data management
Data masking: DIY internal scripts or time to buy?
Data de-identification
Data in action: how quality data can revolutionize the financial industry
Developer productivity
AI in healthcare: data privacy and ethics concerns
Data privacy in AI
How to gather test data for testing purposes: a guide
Test data management
Data in action: how quality data can transform the healthcare industry
Developer productivity
How data quality issues can slow down product development
Developer productivity
How better data helps you do more
Developer productivity
The importance of using reliable datasets for testing, QA, and development
Test data management
A comprehensive guide to ethical fine-tuning of Large Language Models
AI model training
Privacy by Design in generative AI: building secure and trustworthy AI systems
Data privacy in AI
How secure, high-quality data can accelerate your time to market
Developer productivity
Advanced techniques for generating synthetic test data
Data synthesis
Creating an enterprise test data strategy with Tonic Structural
Tonic Structural how-tos
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Balancing compliance and data utility in AI model training
AI model training
Integrating Tonic Structural with your existing tech stack
Tonic Structural how-tos
AI compliance tools for your business
Data privacy in AI
How to overcome common data provisioning challenges
Test data management
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AI & data privacy: what every organization needs to know
Data privacy in AI
Use cases for de-identified datasets
Data de-identification
Synthesizing healthcare data for AI model training, with HIPAA Expert Determination
AI model training
Data privacy vs security: understanding the difference
Data privacy in AI
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Unstructured data management: what it is and how to manage it
Test data management
What is a RAG chatbot? Benefits, challenges, and how to build one
AI model training
Best LLM security tools: features & more
Data privacy in AI
Understanding LLM security risks (with solutions)
Data privacy in AI
Understanding data redaction: methods, use cases, and benefits
Data privacy in AI
What is retrieval augmented generation? The benefits of implementing RAG in using LLMs
AI model training
The hidden value of test data: a case study on tech debt & business value
Test data management
Data anonymization vs data masking: is there a difference?
Data de-identification
Data de-identification in the healthcare industry
Data de-identification
Static vs dynamic data masking
Data de-identification
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Data anonymization: a guide for developers
Data de-identification
De-identifying your unstructured data in Databricks with Tonic Textual
Tonic Textual how-tos
Quickly building training datasets for NLP applications
AI model training
How to generate synthetic data: a comprehensive guide
Data synthesis
Data de-identification in the finance industry
Data de-identification
Custom sensitivity rules to automate sensitive data detection
Tonic Structural how-tos
Optimizing Oracle database management, part 3: benefits & best practices of Ephemeral data environments
Test data management
Optimizing Oracle database management, part 2: CDB, PDB, & key features
Test data management
Optimizing Oracle database management, part 1: common challenges & innovative solutions
Test data management
Ensuring data privacy with privacy rankings in Tonic Structural
Tonic Structural how-tos
The role of Ephemeral environments in QA
Test data management
Understanding automated data redaction
Data de-identification
Guide to data privacy compliance for financial institutions
Data synthesis
The role of NER in GDPR compliance and beyond
Data privacy in AI
Security for Tonic.ai cloud products
Tonic Structural how-tos
Top 5 trends in enterprise RAG
AI model training
What is model hallucination?
AI model training
What is Named Entity Recognition (NER)?
AI model training
What is a database on demand?
Test data management
Safeguarding data privacy while using LLMs
Data privacy in AI
What is data de-identification?
Data de-identification
How to create database subsets for Ephemeral environments
Tonic Structural how-tos
Understanding model memorization in machine learning
Data privacy in AI
Using Tonic Structural and the Safe Harbor method to de-identify PHI
Tonic Structural how-tos
Maintaining data relationships in Structural generation output
Tonic Structural how-tos
7 test data pitfalls in software development
Test data management
Integrating Tonic Structural into your data refresh and CI/CD pipelines
Tonic Structural how-tos
Guide to test data automation
Test data management
Guide to synthetic test data generation
Data synthesis
How to prevent data leakage in your AI applications with Tonic Textual and Snowpark Container Services
Tonic Textual how-tos
How to automatically redact sensitive text data In JSON format
Tonic Textual how-tos
De-identifying free-text data in Snowflake using Tonic Textual
Tonic Textual how-tos
Tonic vs Delphix vs K2View vs IBM Optim. A full comparison.
Test data management
Using custom models in Tonic Textual to redact sensitive values in free-text files
Tonic Textual how-tos
What is test data management? A guide to TDM solutions
Test data management
What is data masking?
Data de-identification
Data masking vs data tokenization: differences and use cases
Data de-identification
What is data obfuscation?
Data de-identification
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We're proud recipients of glowing reviews from our customers.
Build better and faster with quality test data today.
Unblock data access, turbocharge development, and respect data privacy as a human right.
