Synthetic data solutions for software and AI development

Accelerate engineering velocity and ensure compliance with AI-powered data synthesis

Book a demo
An arrow pointing up and right

Generate secure and scalable synthetic data, when you need it, where you need it

Generate data from scratch with Tonic Fabricate

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.

Bring an end to critical bugs in production and accelerate your release cycles by fueling your staging and QA environments with data that mirrors the complexity of production.

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.

Bring an end to critical bugs in production and accelerate your release cycles by fueling your staging and QA environments with 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."
Read the story
"8PB > 1GB" graphic
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."
Read the story
"3x" graphic
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."
Read the story
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.”
Read the story
EOC2 graphic
Certification achieved
Matty Woznick
Enablement Programs Manager
“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.”
Read the story
"10x" graphic
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.”
Read the story
"8PB > 1GB" graphic
Faster regression testing
Arrow Left
Arrow Right

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.

Self-Hosted or Cloud Based Synthetic Data Generation
2

Connect to your data

Tonic integrates with all the leading relational and NoSQL databases, data warehouses, and file types.

View connection documentation
Tonic Integrates With Relational, NoSQL Databases, Data Warehouses, and File Types for Seamless Data Connectivity
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

View generator documentation
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.

View Pricing
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.

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

Balancing compliance and data utility in AI model training

AI model training

Integrating Tonic Structural with your existing tech stack

Tonic Structural how-tos

The importance of AI compliance tools for your business

Data privacy in AI

How to overcome common data provisioning challenges

Test data management

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

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

Test data management

RAG chatbot: What it is, 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

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

Understanding named entity recognition (NER) models

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

Guide to test data management 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

We're proud recipients of glowing reviews from our customers.

Read our reviews on G2
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.