AI Synthetic Data Generation Tools Comparison 2026: Tonic.ai, Syntho, K2view and 7 More (Pricing & Free Plans)

Choosing an AI synthetic data generation tool is harder than it looks. Some tools create data from scratch, some anonymize production data, and some focus on one area such as SAP, healthcare, or time-series data. Many vendors also do not publish prices, so costs are hard to compare.

This article compares 10 tools: Tonic.ai, Syntho, K2view, GenRocket, Synthesized, SAS Data Maker, DataCebo SDV, Betterdata, Rockfish Data, and Aindo. For each one, we look at pricing, free plans, Japanese support, supported platforms, strengths, and weaknesses. We compared them using each vendor's official website, checked in November 2026.

After the tool-by-tool sections, you will find recommendations by goal and practical tips for choosing. Features and prices can change, so check the official websites for the latest details before you decide.

What Are AI Synthetic Data Generation Tools? What They Do and What to Know Before Choosing

AI synthetic data generation tools create artificial data that behaves like real data but does not expose real people or records. Teams use it to test software, train AI models, and share data more safely. The tools in this article work in different ways. Some generate data from rules or a schema, some learn patterns from production data, and some mask sensitive values.

Typical uses include:

• Test data for development and QA: creating realistic databases that keep relationships between tables, as in Tonic.ai, Syntho, and K2view.
• Anonymizing production data: masking or replacing sensitive values so data can be used in lower environments.
• AI and ML training data: generating tabular, relational, or time-series data, as in DataCebo SDV and Betterdata.
• Validating AI agents: preparing scenarios and expected answers, as in Synthesized and Rockfish Data.
• Redacting documents and text: detecting personal information in unstructured data, as in Tonic.ai and GenRocket.
• Healthcare and research data: creating synthetic cohorts and control arms, as in Aindo.

Before choosing, check what a free plan or trial actually covers. Tonic.ai lists a Fabricate free plan with $5 of credits per month, while DataCebo SDV Community is free but limits commercial use. Rockfish Data offers free access by application. Many others require a quote or demo.

Japanese support also varies. Several tools handle some Japanese data or locales, but most websites and documentation are in English. Confirm whether you need a Japanese interface, Japanese data handling, or both.

Finally, compare the pricing model, platform, and company size. Some tools use usage-based billing, some use annual licenses, and some use feature-based plans. Some run only in your own environment, while others are cloud-based. Enterprise-focused tools often involve longer contracts and setup work.

Comparison Table of Top 10 Tools (Pricing, Japanese Support, Platforms)

Tool	Main plans	Japanese support	Platforms
Tonic.ai	Fabricate: Free $0/month / Plus $29/month	Partial	Web (Tonic Cloud), Self-hosted (Enterprise), REST API, Python SDK (Textual) and more
Syntho	See the official website	Partial	Self-hosted (on-premises or your own cloud), Docker Compose, Kubernetes (Helm), REST API
K2view	Contact sales	Not stated	On-premises, Cloud, Hybrid, Self-service portal and more
GenRocket	See the official website	Partial	Web (multi-tenant hosting), Single-tenant hosting (quote-based), API and CI/CD integration, Self-service portal (G-Portal)
Synthesized	Contact sales	Not stated	Self-hosted (Docker Compose, Kubernetes/Helm), Offline installation, REST API, CI/CD integration (GitHub Actions, Azure DevOps)
SAS Data Maker	See the official pricing page	Partial	Web (provided on Microsoft Azure), Microsoft Marketplace
DataCebo SDV	SDV Enterprise Developer Plan $500/user/month plus usage-based billing of $1 per 1 million cells of sampled synthetic data	Not stated	Python SDK, Own environment (self-hosted), Air-gapped deployment (Team Plan, contact for details)
Betterdata	Contact sales	Not stated	On-premises (air-gapped supported), Cloud (GCP, AWS, Azure, Alibaba Cloud)
Rockfish Data	See the official website	Not stated	Own VPC, On-premises, Snowflake Native App, Python SDK and more
Aindo	Contact sales	Not stated	Web UI, REST API, Own cloud / on-premises (single-node and multi-node), Python libraries (aindo.rdml, aindo-anonymize)

AIpedia has not rated these tools, because they do not have their own pages on AIpedia yet. Pricing and plans change; we checked each vendor's official website in November 2026. Check the latest information on the official website before you sign up.

Features, Pricing, and Best For: 10 Tools

1. Tonic.ai

Tonic.ai is a suite of three products for preparing data for development, testing, and AI training. Tonic Fabricate creates synthetic data from scratch through a conversation with an AI agent. Tonic Structural anonymizes and subsets production structured data, and Tonic Textual detects and masks sensitive information in documents and text.

Fabricate's Data Agent can generate databases that preserve referential integrity, plus documents such as PDFs and DOCX files. A Validation Agent checks the output and prompts corrections. Other features include mock API generation and access through MCP.

• Strengths: One vendor covers new synthetic data generation, production data anonymization, and redaction of unstructured data.
• Strengths: Fabricate's free plan and Plus plan are publicly listed, and Structural Cloud has a 14-day free trial according to the official documentation.
• Strengths: It offers many developer-oriented connection options, including MCP, a Python SDK, and a REST API. Textual can detect sensitive information in Japanese text.
• Things to note: Higher-tier Structural and Textual plans are quote-based, and Textual's usage-based rate is not shown on the pricing page.
• Things to note: Fabricate usage is billed by tokens, so costs vary. The official FAQ gives a guideline of about $0.17 to $0.37 per turn. The website and admin console are in English only.

Pricing: Fabricate: Free $0/month ($5 of credits per month, Tonic Cloud, no credit card required) / Plus $29/month ($25 of credits per month, usage-based beyond that) / Enterprise contact sales (Tonic Cloud or self-hosted). Structural: Professional contact sales (up to 10TB of source data, 10 users, and 2 data source types, Tonic Cloud) / Enterprise contact sales (unlimited, cloud or self-hosted). Annual contract pricing is determined by the plan and the amount of connected source data. Textual: Pay-as-you-go (flat rate per 1,000 words; the rate is not stated on the page; the free trial has a word limit) / Enterprise contact sales.

Best for: Teams that want to try synthetic data generation with a public free plan, or that need generation, anonymization, and text redaction from one vendor.

2. Syntho

Syntho is a synthetic data generation platform for test data management and development data. It lets you combine three methods on one platform: synthetic data masking, rule-based synthetic data, and AI-generated synthetic data. It acquired the MOSTLY AI brand on June 9, 2026, and MOSTLY AI continues as "MOSTLY AI, powered by Syntho."

Syntho is self-hosted in your own environment using Docker Compose or Kubernetes (Helm). The Syntho AI Assistant can help you choose a generation method and write business rules through conversation.

• Strengths: You can combine masking, rule-based generation, and AI generation depending on the use case.
• Strengths: Pricing is feature-based, with no usage-based charges by generation volume or number of databases.
• Strengths: It is deployed in your own environment, so you do not have to hand production data to the vendor. Many mock generators, such as addresses, support the Japanese locale.
• Things to note: All plans are quote-based. Because it is self-hosted, you must prepare and operate a Linux server (minimum 8 vCPU and 32GB memory).
• Things to note: The AI Assistant is disabled by default and needs an internet connection. A list of supported screen languages could not be found, and the website and documentation are in English.

Pricing: Basic, Standard, and Ultimate are all quote-based (Get a quote). Pricing is feature-based, with no usage-based charges by generation volume or number of databases. DB connections are 1 to 5 for Basic, 6 to 10 for Standard, and 11 to 15 for Ultimate (additional connections for Ultimate can be discussed). UI language switching and QA reports are Standard and above, and the open-text sensitive information scanner is Ultimate only. Includes implementation packages (Small, Medium, Large) and support.

Best for: Teams that want to keep data in their own environment and mix masking, rule-based, and AI-generated data for test data management.

3. K2view

K2view provides a foundation for data integration and test data management, and its synthetic data tool is part of that. It organizes data by business entities such as customers, accounts, and orders. This lets it generate and distribute data across multiple systems while keeping relationships intact.

It combines four methods: AI generation, rule-based generation, data cloning, and data masking. With Agentic TDM, a data agent reads test cases, determines the data needed, and automates generation and distribution to downstream environments.

• Strengths: One product combines AI, rules, cloning, and masking, so you can choose a method for each test scenario.
• Strengths: The entity-based design helps maintain consistency across multiple systems. The official page says a separate ETL tool is unnecessary.
• Strengths: It also offers subsetting, reservation, aging, rollback, a self-service portal, and CI/CD integration. The official website says it was named a Leader in QKS Group's 2026 SPARK Matrix (Data Masking, Tokenization, and Synthetic Data).
• Things to note: Pricing is not public, and the product tour requires email registration.
• Things to note: The site is in English only. The Japanese page returns 404, and no Japanese UI, partner, or Japan office was found.

Pricing: Contact sales (no pricing page on the official website; /pricing returns 404. Inquire through a demo request. An interactive product tour is available with email registration).

Best for: Enterprises that need consistent test data spanning multiple systems, including mainframes and NoSQL, and that are comfortable with a demo-based sales process.

4. GenRocket

GenRocket is a design-driven synthetic test data platform. Instead of copying production data, it designs and generates data to match the purpose of each test. It offers 750+ data generators, and the official website says it is used by more than 50 of the Forbes Global 2000.

Its AI Orchestrator turns natural-language requirements into executable scripts called G-Cases. You can choose a deterministic mode that does not use AI or a conversational mode that uses a built-in LLM.

• Strengths: Data is created from design without production data, which avoids the risk of taking production data out.
• Strengths: The deterministic mode lets you use the tool where AI use is restricted. The license includes unlimited users and unlimited test data sets in lower environments.
• Strengths: A public case study describes deployment at a major Japanese insurance company, with kanji and kana support added at no additional cost.
• Things to note: Pricing is not public, and it is an annual contract starting at a minimum of 20 projects.
• Things to note: Official pages give inconsistent figures for the number of data formats ("125+," "109," and "110+"). The website and UI are in English only.

Pricing: Annual license per test data project (minimum 20 projects; pricing is quote-based). Includes unlimited users, unlimited test data sets in lower environments, and 20 hours of onboarding. Includes multi-tenant hosting (single-tenant is quote-based). X12 EDI Accelerator, Unstructured Data Accelerator, and additional services are quote-based.

Best for: Larger QA and test data teams that want to generate test data from design, and that can commit to an annual license.

5. Synthesized

Synthesized is a test data management platform that performs masking, subsetting, and synthetic data generation. It covers SAP, various databases, and documents, and runs in your own environment. Its Test Data Agent is generally available and prepares business data and system states for validating AI agents.

You describe a business scenario, and the agent works out the records and relationships needed. It prepares data for normal, exception, failure, and adversarial cases. Scoring is left to existing evaluation tools.

• Strengths: It has packages specialized for SAP test data, which suits S/4HANA migration and upgrade testing.
• Strengths: Non-production copies are unlimited, with no usage-based or token-based charges. It runs in your own environment and does not send production data outside.
• Strengths: It can be purchased through SAP Store, AWS Marketplace, and Google Cloud Marketplace. It also integrates with Tricentis Tosca and UiPath Test Suite.
• Things to note: Prices are not public, only the package structure. The published pricing is for SAP, and pricing for non-SAP business applications cannot be confirmed on public pages.
• Things to note: The website and documentation are in English, and Japanese support is not mentioned.

Pricing: Four annual subscription packages for SAP (all contact sales): Starter (500GB of production data, 1 source, 12 months) / Growth (3TB, 3 sources) / Enterprise (10TB, 10 sources, premium support) / Scale (30TB, 20 sources, premium support). All except Starter are 36-month contracts, billed annually. A 30-minute health check is free, and a 10-day validation on your own data is a fixed fee agreed in advance. Also available through SAP Store, AWS Marketplace, and Google Cloud Marketplace.

Best for: Enterprises preparing SAP test data, or teams that need controlled environments for validating AI agents.

6. SAS Data Maker

SAS Data Maker is a low-code/no-code synthetic data generation platform from SAS. It integrates the synthetic data technology of Hazy, whose main software assets SAS acquired on November 12, 2024. It creates synthetic data that reproduces the statistical, relational, and temporal characteristics of real data.

It supports multi-table and time-series data and differential privacy. Automated, visual statistical metrics let you evaluate the quality and privacy of the generated data. Availability on Microsoft Marketplace began after a private preview, per the November 24, 2025 release.

• Strengths: You can create synthetic data through a GUI without writing code and review quality visually.
• Strengths: It offers features suited to enterprise data, such as differential privacy, lineage, and audit logs.
• Strengths: SAS has a Japanese website. The official release reports a 28% accuracy improvement in a credit scoring model at a preview participant.
• Things to note: No price is listed on SAS's product page, and we could not confirm the Marketplace listing. The only delivery route described is Microsoft Azure through Microsoft Marketplace.
• Things to note: The Japanese product page still shows pre-launch preview content. Japanese support in the product screens could not be confirmed.

Pricing: Check the official page (no price is listed on SAS's product page. It directs buyers to purchase through Microsoft Marketplace, but the Marketplace listing page could not be retrieved automatically, so the price could not be confirmed). Check the official website for the latest pricing and features.

Best for: Organizations on Microsoft Azure that want a no-code approach to multi-table or time-series synthetic data with built-in quality checks.

7. DataCebo SDV

DataCebo SDV comes from DataCebo, the developer of SDV (Synthetic Data Vault), a widely used Python synthetic data library. It builds a generative relational model (GRM) from multi-table enterprise databases and uses it to create synthetic databases. The official website says downloads exceed 18 million.

SDV Community is free, and SDV Enterprise is an SDK deployed in your own environment. Generated data quality can be checked with the open-source SDMetrics.

• Strengths: The free SDV Community lets you try the basics of synthetic data on your own.
• Strengths: Developer Plan pricing is published, and you can set a monthly cap on usage-based billing.
• Strengths: It is strong at preserving multi-table relationships and business rules. Public case studies include ING Belgium and MAPFRE.
• Things to note: Community is under the Business Source License, which limits commercial use, and it handles only simple setups of up to 3 tables and depth 1.
• Things to note: The Developer Plan needs a network connection to the SDV Enterprise server. It assumes the Python SDK, and GUI operation is not described. Japanese support is not mentioned.

Pricing: SDV Community is free (Business Source License, commercial use is limited, for simple databases of up to 3 tables and depth 1) / SDV Enterprise Developer Plan $500/user/month plus usage-based billing of $1 per 1 million cells of sampled synthetic data (you can set a monthly cap yourself. Free trial is by application) / Team Plan contact sales (no usage-based billing for sampling volume, deployment in air-gapped environments can also be discussed) / Additional bundles (AI Connectors, Constraint Augmented Generation, Differential Privacy, Targeted Sampling, XSynthesizers) are $250/user/month per bundle on the Developer Plan.

Best for: Python-based data teams that need synthetic multi-table databases and want published pricing.

8. Betterdata

Betterdata is a synthetic data platform from BetterData Pte Ltd, a Singapore company founded in 2021. It synthesizes tabular, relational, and time-series data in a way that cannot identify individuals. Uses include AI and ML development, system testing, and data sharing between organizations.

It chooses among several proprietary models depending on how much data you have. These include a tabular foundation model (TFM), GANs, and LLMs. A prompt-based model can also create synthetic data from natural-language prompts.

• Strengths: You can choose pretrained models, fine-tuning, or full training depending on data volume, so it can work with little data.
• Strengths: The prompt-based model makes it easier for non-engineers to use. It also offers differential privacy, PII redaction, and quality and privacy assurance reports.
• Strengths: Deployment options range from air-gapped on-premises to GCP, AWS, Azure, and Alibaba Cloud. Case studies include the U.S. Department of Homeland Security and Kajima Corporation.
• Things to note: Pricing is not public. A GPU is required for large models such as LLMs, according to the official FAQ.
• Things to note: No Japanese UI or Japanese-language data support is mentioned. Supported file formats are described differently on the product page and in the FAQ.

Pricing: Contact sales (the official website has no pricing page, and /pricing returns a 404. Inquire via Contact Sales or Try Demo).

Best for: Teams that need flexible deployment, including air-gapped environments, or that have limited source data and want a choice of model approaches.

9. Rockfish Data

Rockfish Data is a synthetic data company founded in June 2022 based on research at Carnegie Mellon University. It focuses on time-series and tabular business data such as network telemetry, device metrics, transactions, and event streams. The data is used to train and test ML models and to evaluate AI agents.

The platform has four modules. DataFuel learns from production samples, and SchemaFuel generates data from a schema or description. Scenario Studio injects anomalies and rare events, and AgentFuel creates questions and expected answers for AI agent evaluation.

• Strengths: It specializes in time-series and telemetry data and can inject rare failures and anomalies on purpose.
• Strengths: It can create questions and correct answers for AI agent evaluation together with the data.
• Strengths: It runs in your own VPC, on-premises, or as a Snowflake Native App, so original data stays in your environment. You can apply for free access.
• Things to note: Pricing is not published, and Japanese support is not mentioned. The website and documentation are in English only.
• Things to note: It does not synthesize documents or images, so check fit with your use case first.

Pricing: Pricing not published (/pricing returns a 404) / according to the official FAQ, there is free access to the platform (apply through the form on the official website and a team access key is issued) / contact us by requesting a demo.

Best for: Teams working with telemetry or transaction time series that want to test failure scenarios or evaluate AI agents, and that want to try free access first.

10. Aindo

Aindo is a synthetic data platform from Aindo SpA of Trieste, Italy. It uses generative AI to create synthetic data that contains no personal information but stays close to reality. It currently focuses on healthcare and pharmaceuticals.

Main uses include real-world evidence through synthetic cohorts, external control arms for clinical trials, and public health planning. It has three generator types: fully synthetic, semi-synthetic, and anonymization.

• Strengths: It has features and case studies specialized in healthcare and pharmaceutical use cases.
• Strengths: With semi-synthesis, you choose which columns of the original data to keep and adjust the balance between fidelity and privacy. Similarity and privacy scores help evaluate results.
• Strengths: It can be deployed on your own cloud or on-premises, and it has Europrivacy certification and ISO 27001. It also provides REST API and Python libraries.
• Things to note: Pricing is not published. Public information centers on healthcare and the public sector, so use in other industries needs separate confirmation.
• Things to note: The site is in English and Italian only. Japanese support and compliance with Japanese regulations are not stated, and compliance explanations center on European frameworks such as GDPR and EHDS.

Pricing: Contact sales (no pricing page on the official website; /pricing returns a 404. Reach out via Contact us / Request a demo).

Best for: Healthcare and pharmaceutical organizations, especially in Europe, that need synthetic cohorts or external control arms. If you use Japanese medical data, confirm the final decision with a qualified professional.

Recommendations by Goal

Goal	Recommended	Why
Start with a free plan or trial	Tonic.ai, DataCebo SDV	Tonic Fabricate has a free plan with $5 of credits per month, and SDV Community is free with limits on commercial use.
Try free access for time-series data	Rockfish Data	The official FAQ says free access is available by application through a form.
Work with Japanese data	Tonic.ai, GenRocket	Tonic Textual detects sensitive information in Japanese text, and GenRocket has a case study with kanji and kana support.
Know the price before contacting sales	Tonic.ai, DataCebo SDV	Both publish plan prices, such as Plus at $29/month and the SDV Developer Plan at $500/user/month.
Keep data in your own environment	Syntho, Synthesized	Both are self-hosted, and Syntho states it does not access your data.
Test data across multiple systems	K2view, GenRocket	K2view uses a business-entity model across systems, and GenRocket offers 750+ generators and CI/CD integration.
SAP test data or AI agent validation	Synthesized	It offers SAP-specific packages and a generally available Test Data Agent.
Healthcare and pharmaceutical research	Aindo	It provides synthetic cohorts and external control arms with semi-synthetic generation.

Most tools here require a quote, so pricing and fit are best confirmed through a demo or trial. Check the official websites for the latest pricing and features.

How to Choose Without Regrets

Check what the free plan or trial covers

Free options differ widely. Tonic.ai lists a Fabricate free plan with $5 of credits per month, and Structural Cloud has a 14-day free trial. DataCebo SDV Community is free but limits commercial use and handles only up to 3 tables.

Rockfish Data offers free access by application. Most others require a demo or quote, so test with your own data shape before committing.

Match the pricing model to how you will use it

Pricing models vary. Tonic.ai Fabricate and DataCebo SDV have usage-based elements, while Syntho states there are no usage-based charges by generation volume. GenRocket uses an annual license per test data project with a minimum of 20 projects.

Synthesized uses annual packages based on production data volume and sources. Estimate your likely volume first, then compare how each model scales.

Decide how much Japanese support you need

Japanese support is limited across this group. Tonic.ai can detect sensitive information in Japanese text, and Syntho supports the Japanese locale in many mock generators. GenRocket has a Japanese insurance case study.

Most websites and documentation are in English. Separate the need for Japanese data handling from the need for a Japanese interface or local support.

Choose by company size and sales process

Some tools suit small teams starting out, such as Tonic.ai and DataCebo SDV with public prices. Others are enterprise-oriented with demo-based sales, such as K2view and Synthesized. Enterprise tools may involve longer contracts, such as Synthesized's 36-month terms for most packages.

Consider who will operate the tool. Self-hosted options like Syntho need server preparation and operation.

Check platforms and data fit

Platforms differ. SAS Data Maker is described on Microsoft Azure through Microsoft Marketplace, DataCebo SDV is a Python SDK, and Rockfish Data offers a Snowflake Native App. Betterdata supports air-gapped on-premises and several clouds.

Data type matters too. Rockfish Data targets time-series and tabular data, and Aindo focuses on healthcare. Confirm that the tool supports your data type and industry.

FAQ

Q. Which tools offer a free plan or free access?

A. Tonic.ai has a Fabricate Free plan at $0/month with $5 of credits per month. DataCebo SDV Community is free, but commercial use is limited under the Business Source License. Rockfish Data offers free access by application, and Tonic.ai Structural Cloud has a 14-day free trial according to the official documentation.

Q. Which tools publish their prices?

A. Tonic.ai lists Fabricate Plus at $29/month with $25 of credits per month. DataCebo SDV lists the Developer Plan at $500/user/month plus $1 per 1 million cells of sampled synthetic data. Additional bundles are $250/user/month per bundle on the Developer Plan.

Q. Which tools have no public pricing?

A. Syntho, K2view, GenRocket, Synthesized, Betterdata, Rockfish Data, and Aindo require a quote, demo, or contact with sales. SAS Data Maker lists no price on SAS's product page and directs buyers to Microsoft Marketplace. Check the official websites for the latest pricing and features.

Q. Can these tools be used with Japanese data?

A. Support is partial at best. Tonic Textual can detect sensitive information in Japanese text, and Syntho's mock generators such as addresses support the Japanese locale. GenRocket has a case study with kanji and kana support. Most websites and documentation are in English.

Q. Which tools can run in my own environment?

A. Syntho and Synthesized are self-hosted using Docker Compose or Kubernetes. DataCebo SDV is an SDK deployed in your own environment, and Rockfish Data runs in your own VPC, on-premises, or as a Snowflake Native App. Betterdata and Aindo also offer on-premises options, and K2view supports on-premises, cloud, and hybrid.

Summary

The 10 tools differ in purpose more than in basic capability. Tonic.ai and DataCebo SDV publish some prices and offer free options, while most others require a quote or demo. Japanese support is limited, with partial support at best, and most websites are in English.

Fit depends on your data and environment. Syntho, Synthesized, and DataCebo SDV emphasize running in your own environment, while Synthesized, Rockfish Data, and Aindo focus on specific areas. Test with your own data before deciding.

• Start with a free plan → Tonic.ai (Fabricate has a free plan with $5 of credits per month)
• Test data across multiple systems → K2view (entity-based design across systems, with a demo-based sales process)
• Healthcare or pharmaceutical data → Aindo (specialized in synthetic cohorts and external control arms)

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