Build freely with AI-powered synthetic data solutions

High-fidelity data generation and transformation to fuel development, unblock model training, and ship better products faster, all while safeguarding data privacy.

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App development

Accelerate velocity, unblock teams.

Rapidly generate realistic synthetic data—from scratch or modeled after production—to eliminate data dependencies and ship product innovations faster.

Testing & QA

Secure testing, zero regrets.

Transform sensitive production databases into high-fidelity, referentially intact test data to accelerate release cycles and drastically reduce critical defects escaping to production.

AI model training

Unlock sensitive data, safely.

Detect, redact, and synthesize sensitive data in your unstructured datasets to develop and fine-tune LLM and AI models without compromising privacy.

Compliance

Data governance at scale.

Build continuous data privacy compliance into every development and AI workflow to satisfy global regulations while maximizing data utility across the entire organization.

Trusted by engineering teams throughout the world

Data synthesis for every stage of development, testing, and AI model training

Generate synthetic data from scratch

Turbocharge new product development and AI model training with fully relational synthetic databases, realistic unstructured data, and mock APIs generated at scale and on demand.

How to generate synthetic data via agentic AI

Read this guide

Sanitize production data for testing

Accelerate your release cycles and eliminate critical bugs in production by fueling staging and QA environments with high-fidelity test data that mirrors the complexity of production.

Patterson uses Tonic Structural to generate test data 75% faster and increase developer productivity by 25%.

Read their case study

Unlock unstructured data for AI

Safely leverage your unstructured data in AI development while preventing data leaks and ensuring regulatory compliance through industry-leading data redaction and synthesis.

Wellthy uses Tonic Textual to unblock AI initiatives and reduce workflow inefficiencies by 50%.

Read their case study

Find the product and plan that works for you.

Synthetic data success stories

50% reduction in flagged care team actions
25x productivity
600 developer hours saved
20x faster regression testing
10x faster onboarding
3x faster release cycle
SOC 2 certification achieved
8PB subset down to 1 GB dataset

Deliver the value of AI-powered synthetic data across your organization

Technology tailored for data privacy compliance across regulated industries

GDPR compliance icon for sensitive data processing

Certified, secure solutions to ensure your company’s compliance.

We're proud recipients of glowing reviews from our customers

“Before implementing Tonic, our QA and development environments looked nothing like production. Tonic removed a major blocker for us by enabling our teams to test at scale with data that mirrors the size, shape, and feel of our production data. And by guaranteeing privacy for HIPAA compliance, Tonic allows us to share that data safely with our off-shore development teams, too.”
Nemo Nemeth
Head of Data Products
“We selected Tonic as our preferred vendor due to its plethora of advanced features, better UX, faster time to value, and lower total cost of ownership than any of the alternatives.”
Donal Mac An Ri
Architecture Team Lead
“Thanks to Tonic Structural, we’re always testing with the latest version of our production schema and masked data. I don’t have to do anything special to make it work.”
Youssuf Elkalay
Executive Engineer
"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."
Jordan Stone
VP of Engineering
“Tonic has been incredibly user-friendly, providing the features we needed to scale our performance testing. What once took nearly two and a half hours to generate the test data we need, now takes just 35 to 45 minutes, end-to-end.”
Debarati Mukhopadhyay
Principal Performance Engineer
“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. That’s a huge win for us, equipping us with the real security we need to meet compliance obligations and safeguard our customer's privacy.”
Kevin Paige
Chief Information Security Officer
“Without a solution for secure, realistic test data, many of our new AI features simply wouldn't have been possible. With Tonic Textual, we can now confidently build and test these features without exposing PII, all while maintaining the rigorous privacy standards we hold ourselves accountable to as a healthcare company serving millions of families.”
Kevin Roche
Co-founder and CTO of Wellthy
"The faster lead time that we have with QA, the more things we can test in a day, the faster we can release and at higher quality than before."
Felipe Talavera
Engineering Fellow
"We are very much a ‘Let’s deploy as quickly as possible’ company. Accessing data took so long that developers didn’t want to do it. Tonic changed that out of the gate. We used to have to load tables overnight sometimes. Now Tonic can kick off a database that anyone can pull down in an hour or less."
Stephen Wooten
Co-Founder & Director of Engineering
“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.”
Senthil Padmanabhan
VP of Engineering
“Before we had Tonic and the availability of production-quality data for our engineers and QA, we would see critical issues at least once a week that were tied to not being able to accurately test our features under real-world scenarios. Now, we haven't had a critical issue since we fully operationalized Tonic into our software development life cycle. That was nine months ago."
Jason Lock
Senior Software Engineer and Tech Lead
"With Tonic, you get all the benefits of a smart, hungry startup—being super flexible, working hand-in-hand, iterating quickly to deploy new features—without any of the drawbacks some people fear. They have a superstar engineering team, willing to engage on every level with a white-glove approach. I can’t say enough good things about them."
Nemo Nemeth
Head of Data Products
“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.”
Matty Woznick
Enablement Programs Manager
“Tonic Textual unblocked all of our AI initiatives. We’ve been able to build the infrastructure that will power them all, abstracting the Textual SDK and creating a wrapper that integrates well with our systems. So now we have a button that we can press to activate data privacy, allowing us to move forward with AI. It is quite transformative.”
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[OCCUPATION]
“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.”
Sebastian Kowalczyk
Senior DevOps Engineer

Data synthesis guides

Explore the world of data synthesis and discover how it plays a crucial role in safeguarding sensitive information while maintaining data utility in software and AI development.

Test data management

Managing test data from multiple sources without losing consistency

Test data management

Test data subsetting strategies for targeted software testing

Tonic Fabricate how-tos

Creating unstructured files from Fabricate Data Agent generated data

Tonic Fabricate how-tos

Using real-world data for synthetic data generation with the Fabricate Data Agent

Data synthesis

Synthetic data for agentic workflows: A guide

Data privacy in AI

Named Entity Recognition for data compliance automation

Developer productivity

How to hydrate agile development environments with realistic test data

Developer productivity

Build vs buy: Your guide to scalable synthetic data via LLMs

Data synthesis

How to generate synthetic data via agentic AI

Data synthesis

What is Synthetic Data?

Tonic Structural how-tos

How to use Structural data and Claude Code for test automation

Test data management

How to ensure test coverage for edge cases with representative data

AI model training

How to develop AI training datasets for compliance and performance

Data synthesis

Data synthesis for AI: A privacy-first approach

AI model training

Secure data generation for AI model training

Data privacy in AI

Preventing data breaches in AI systems

AI model training

How to prepare machine learning data responsibly

Data privacy in AI

Data masking and artificial intelligence: Protecting data

Test data management

Masking and subsetting data to optimize test data pipelines

Data synthesis

Data synthesis vs data masking

Data synthesis

Data synthesis techniques: a comparison for developers

Developer productivity

How to improve data accessibility for software and AI development

Data de-identification

Deterministic masking, explained

Tonic Fabricate how-tos

Managing access to Tonic Fabricate accounts and workspaces

Data privacy in AI

PII compliance checklist: How to protect private data

Data de-identification

Real-world applications of format preserving encryption

Data de-identification

Data masking for the insurance industry: A guide

Data synthesis

What is a rule-based test data generator?

Test data management

Data masking’s role in leveraging production data for testing and development

Tonic Fabricate how-tos

Uploading and referencing production data in a rule-based dataset, with Tonic Fabricate

Data de-identification

Data masking for government agencies: A guide

Tonic Structural how-tos

How to mask data in Snowflake: A step-by-step guide

Developer productivity

Build vs buy: Your guide to finding scalable, efficient test data solutions

Test data management

Questions to ask when selecting a Test Data Management service

Developer productivity

Data in action: How quality data can revolutionize the financial industry

Data privacy in AI

AI in healthcare: Data privacy and ethics concerns

Test data management

How to gather test data for testing purposes: a guide

Developer productivity

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

AI model training

A comprehensive guide to ethical fine-tuning of Large Language Models

Data privacy in AI

Privacy by Design in generative AI: Building secure and trustworthy AI systems

Tonic Structural how-tos

Creating an enterprise test data strategy with Tonic Structural

AI model training

Balancing compliance and data utility in AI model training

Tonic Structural how-tos

Integrating Tonic Structural with your existing tech stack

Test data management

How to overcome common data provisioning challenges

Data privacy in AI

AI & data privacy: What every organization needs to know

Data de-identification

Use cases for de-identified datasets

AI model training

Synthesizing healthcare data for AI model training, with HIPAA Expert Determination

Data privacy in AI

Data privacy vs security: Understanding the difference

Data privacy in AI

AI compliance tools for your business

Test data management

Unstructured data management: What it is and how to manage it

AI model training

What is a RAG chatbot? Benefits, challenges, and how to build one

Data privacy in AI

Best LLM security tools: Features & more

Data privacy in AI

Understanding LLM data security risks (with solutions)

Data privacy in AI

Understanding data redaction: Methods, use cases, and benefits

Test data management

The hidden value of test data: a case study on tech debt & business value

Data de-identification

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

Data anonymization: a guide for developers

Tonic Textual how-tos

De-identifying your unstructured data in Databricks with Tonic Textual

Data synthesis

How to generate synthetic data: a comprehensive guide

Data de-identification

Data de-identification in the finance industry

Tonic Structural how-tos

Custom sensitivity rules to automate sensitive data detection

AI model training

What is retrieval augmented generation? The benefits of implementing RAG in using LLMs

Tonic Textual how-tos

Using custom models in Tonic Textual to redact sensitive values in free-text files

Tonic Textual how-tos

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

Data privacy in AI

Safeguarding data privacy while using LLMs

AI model training

Top 5 trends in enterprise RAG

Tonic Structural how-tos

Ensuring data privacy with privacy rankings in Tonic Structural

Data de-identification

Understanding automated data redaction

Data synthesis

Guide to data privacy compliance for financial institutions

Tonic Structural how-tos

Security for Tonic.ai cloud products

AI model training

What is model hallucination?

AI model training

What is Named Entity Recognition (NER)?

Data de-identification

What is data de-identification?

Data privacy in AI

Understanding model memorization in machine learning

Tonic Structural how-tos

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

Integrating Tonic Structural into your data refresh and CI/CD pipelines

Test data management

Guide to test data automation

Test data management

Tonic vs Delphix vs K2View vs IBM Optim. A full comparison.

Test data management

What is test data management? A guide to TDM solutions

Data de-identification

What is data masking?

Data de-identification

Data masking vs data tokenization: differences and use cases

Data de-identification

What is data obfuscation?

Accelerate development with high-quality, privacy-respecting synthetic test data from Tonic.ai.Boost development speed and maintain data privacy with Tonic.ai's synthetic data solutions, ensuring secure and efficient test environments.