Synthetic Data Generation Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Data Type, By Modeling Type, By Offering, By Application, By End-use, By Region & Competition, 2021-2031F

Description

The Global Synthetic Data Generation Market is projected to expand from USD 443.27 Million in 2025 to USD 2261.88 Million by 2031, reflecting a CAGR of 31.21%. This industry is defined by the algorithmic production of artificial datasets that mimic the correlations and statistical properties of real-world information while excluding personally identifiable details. The market's growth is primarily fueled by the critical need for extensive, high-quality datasets to train generative artificial intelligence models, the drive to lower data collection costs, and the necessity to comply with strict global privacy laws that limit the use of sensitive real-world records. As noted by the CFA Institute, synthetic data is expected to comprise over 60% of all training material for generative AI by 2030, highlighting the sector's dependence on this technology for future progress.

Market Overview
Forecast Period	2027-2031
Market Size 2025	USD 443.27 Million
Market Size 2031	USD 2261.88 Million
CAGR 2026-2031	31.21%
Fastest Growing Segment	Hybrid Synthetic Data
Largest Market	North America

However, the market faces a substantial obstacle in maintaining data fidelity and mitigating bias propagation. If the algorithms used for generation are based on defective data or miss complex outliers, the resulting synthetic datasets may yield inaccurate analytical results. This limitation significantly hinders the utility of synthetic data in precision-critical sectors, such as finance and healthcare, where accuracy is essential.

Market Driver

The surging demand for superior machine learning and AI training datasets acts as the main catalyst for market growth, as developers encounter a looming shortage of real-world information needed to scale Large Language Models. As the complexity of models increases exponentially, the finite supply of human-generated public text is proving insufficient, requiring the mass creation of synthetic alternatives to support continued innovation. A May 2024 report by Epoch AI, 'The Looming Data Scarcity Crisis in AI', indicates that tech companies may deplete the stock of publicly available training data between 2026 and 2032. This urgent scarcity has prompted significant capital investment; for example, Scale AI raised $1 billion in Series F funding in 2024, achieving a $13.8 billion valuation, which underscores the high commercial value assigned to data generation infrastructure.

Simultaneously, rigorous global compliance mandates and data privacy regulations are compelling enterprises to adopt synthetic data as a key strategy for risk mitigation. With frameworks like GDPR enforcing heavy penalties for mishandling sensitive data, organizations are increasingly turning to artificial datasets that maintain statistical utility while completely anonymizing Personally Identifiable Information. This operational transition is further driven by shifting consumer attitudes regarding data ethics; the '2024 Data & Trust Survey' by TELUS International in October 2024 revealed that 82% of respondents prioritize data privacy now more than ever. Consequently, corporations are leveraging synthetic generation to uphold analytical capabilities without jeopardizing regulatory standing or user trust.

Market Challenge

A major barrier confronting the Global Synthetic Data Generation Market is the difficulty of guaranteeing data fidelity and preventing the spread of bias. As this technology becomes integral to training generative AI models for critical industries like healthcare and finance, the neutrality and accuracy of the output are essential. If synthetic datasets fail to reflect complex outliers or inadvertently reinforce historical prejudices present in source data, the resulting AI models may become unreliable and potentially discriminatory. This fidelity gap damages organizational trust and stalls widespread enterprise adoption, as companies cannot afford to deploy flawed algorithms in high-stakes scenarios.

The industry's struggle with these quality assurance challenges is mirrored in recent sentiment regarding AI reliability and ethics. According to 2025 data from ISACA, only 41% of digital trust professionals felt their organizations were effectively addressing ethical concerns in AI deployment, such as accountability and bias. This statistic underscores a significant lack of confidence in managing data-related risks. Until synthetic data vendors can effectively guarantee high-fidelity, bias-free outputs, this trust deficit will continue to impede the market's expansion into regulated sectors where precision is mandatory.

Market Trends

The intersection of synthetic data with simulation and digital twin technologies is transforming the training and validation of physical AI systems. By constructing high-fidelity virtual environments, developers can produce immense volumes of perfectly labeled data for scenarios that are costly, dangerous, or difficult to capture in reality, such as industrial robot malfunctions or autonomous driving accidents. This method enables precise control over environmental variables like weather, lighting, and object placement, ensuring robust model performance across varied conditions. For instance, NVIDIA announced in June 2024 the release of a massive synthetic dataset containing 212 hours of video across 90 virtual scenes to accelerate the development of industrial automation and smart city solutions.

Furthermore, the rise of industry-specific synthetic data platforms is accelerating, particularly within regulated sectors that demand highly specialized training environments. Unlike generic data generation, these vertical-specific solutions utilize generative AI to replicate complex, domain-unique patterns-such as financial transaction flows-to improve analytical precision while strictly adhering to privacy and data residency mandates. This evolution allows enterprises to simulate rare fraud scenarios and enhance decision-making accuracy without depending solely on finite historical records. Highlighting this impact, Mastercard reported in February 2024 that integrating advanced generative AI into its fraud detection network reduced false positive rates by over 85%, demonstrating the tangible operational benefits of synthetic data technologies.

Key Market Players

Datagen Inc.
MOSTLY AI Solutions MP GmbH
TonicAI, Inc.
Synthesis AI
GenRocket, Inc.
Gretel Labs, Inc.
K2view Ltd.
Hazy Limited.
Replica Analytics Ltd.
YData Labs Inc.

Report Scope

In this report, the Global Synthetic Data Generation Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Synthetic Data Generation Market, By Data Type

Tabular Data
Text Data
Image & Video Data
Others

Synthetic Data Generation Market, By Modeling Type

Direct Modeling
Agent-based Modeling

Synthetic Data Generation Market, By Offering

Fully Synthetic Data
Partially Synthetic Data
Hybrid Synthetic Data

Synthetic Data Generation Market, By Application

Data Protection
Data Sharing
Predictive Analytics
Natural Language Processing
Computer Vision Algorithms
Others

Synthetic Data Generation Market, By End-use

BFSI
Healthcare & Life sciences
Transportation & Logistics
IT & Telecommunication
Retail & E-commerce
Manufacturing
Consumer Electronics
Others

Synthetic Data Generation Market, By Region

North America
- United States
- Canada
- Mexico
Europe
- France
- United Kingdom
- Italy
- Germany
- Spain
Asia Pacific
- China
- India
- Japan
- Australia
- South Korea
South America
- Brazil
- Argentina
- Colombia
Middle East & Africa
- South Africa
- Saudi Arabia
- UAE

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Synthetic Data Generation Market.

Available Customizations:

Global Synthetic Data Generation Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

1. Product Overview

1.1. Market Definition
1.2. Scope of the Market
- 1.2.1. Markets Covered
- 1.2.2. Years Considered for Study
- 1.2.3. Key Market Segmentations

2. Research Methodology

2.1. Objective of the Study
2.2. Baseline Methodology
2.3. Key Industry Partners
2.4. Major Association and Secondary Sources
2.5. Forecasting Methodology
2.6. Data Triangulation & Validation
2.7. Assumptions and Limitations

3. Executive Summary

3.1. Overview of the Market
3.2. Overview of Key Market Segmentations
3.3. Overview of Key Market Players
3.4. Overview of Key Regions/Countries
3.5. Overview of Market Drivers, Challenges, Trends

4. Voice of Customer

5. Global Synthetic Data Generation Market Outlook

5.1. Market Size & Forecast
- 5.1.1. By Value
5.2. Market Share & Forecast
- 5.2.1. By Data Type (Tabular Data, Text Data, Image & Video Data, Others)
- 5.2.2. By Modeling Type (Direct Modeling, Agent-based Modeling)
- 5.2.3. By Offering (Fully Synthetic Data, Partially Synthetic Data, Hybrid Synthetic Data)
- 5.2.4. By Application (Data Protection, Data Sharing, Predictive Analytics, Natural Language Processing, Computer Vision Algorithms, Others)
- 5.2.5. By End-use (BFSI, Healthcare & Life sciences, Transportation & Logistics, IT & Telecommunication, Retail & E-commerce, Manufacturing, Consumer Electronics, Others)
- 5.2.6. By Region
- 5.2.7. By Company (2025)
5.3. Market Map

6. North America Synthetic Data Generation Market Outlook

6.1. Market Size & Forecast
- 6.1.1. By Value
6.2. Market Share & Forecast
- 6.2.1. By Data Type
- 6.2.2. By Modeling Type
- 6.2.3. By Offering
- 6.2.4. By Application
- 6.2.5. By End-use
- 6.2.6. By Country
6.3. North America: Country Analysis
- 6.3.1. United States Synthetic Data Generation Market Outlook
  - 6.3.1.1. Market Size & Forecast
    - 6.3.1.1.1. By Value
  - 6.3.1.2. Market Share & Forecast
    - 6.3.1.2.1. By Data Type
    - 6.3.1.2.2. By Modeling Type
    - 6.3.1.2.3. By Offering
    - 6.3.1.2.4. By Application
    - 6.3.1.2.5. By End-use
- 6.3.2. Canada Synthetic Data Generation Market Outlook
  - 6.3.2.1. Market Size & Forecast
    - 6.3.2.1.1. By Value
  - 6.3.2.2. Market Share & Forecast
    - 6.3.2.2.1. By Data Type
    - 6.3.2.2.2. By Modeling Type
    - 6.3.2.2.3. By Offering
    - 6.3.2.2.4. By Application
    - 6.3.2.2.5. By End-use
- 6.3.3. Mexico Synthetic Data Generation Market Outlook
  - 6.3.3.1. Market Size & Forecast
    - 6.3.3.1.1. By Value
  - 6.3.3.2. Market Share & Forecast
    - 6.3.3.2.1. By Data Type
    - 6.3.3.2.2. By Modeling Type
    - 6.3.3.2.3. By Offering
    - 6.3.3.2.4. By Application
    - 6.3.3.2.5. By End-use

7. Europe Synthetic Data Generation Market Outlook

7.1. Market Size & Forecast
- 7.1.1. By Value
7.2. Market Share & Forecast
- 7.2.1. By Data Type
- 7.2.2. By Modeling Type
- 7.2.3. By Offering
- 7.2.4. By Application
- 7.2.5. By End-use
- 7.2.6. By Country
7.3. Europe: Country Analysis
- 7.3.1. Germany Synthetic Data Generation Market Outlook
  - 7.3.1.1. Market Size & Forecast
    - 7.3.1.1.1. By Value
  - 7.3.1.2. Market Share & Forecast
    - 7.3.1.2.1. By Data Type
    - 7.3.1.2.2. By Modeling Type
    - 7.3.1.2.3. By Offering
    - 7.3.1.2.4. By Application
    - 7.3.1.2.5. By End-use
- 7.3.2. France Synthetic Data Generation Market Outlook
  - 7.3.2.1. Market Size & Forecast
    - 7.3.2.1.1. By Value
  - 7.3.2.2. Market Share & Forecast
    - 7.3.2.2.1. By Data Type
    - 7.3.2.2.2. By Modeling Type
    - 7.3.2.2.3. By Offering
    - 7.3.2.2.4. By Application
    - 7.3.2.2.5. By End-use
- 7.3.3. United Kingdom Synthetic Data Generation Market Outlook
  - 7.3.3.1. Market Size & Forecast
    - 7.3.3.1.1. By Value
  - 7.3.3.2. Market Share & Forecast
    - 7.3.3.2.1. By Data Type
    - 7.3.3.2.2. By Modeling Type
    - 7.3.3.2.3. By Offering
    - 7.3.3.2.4. By Application
    - 7.3.3.2.5. By End-use
- 7.3.4. Italy Synthetic Data Generation Market Outlook
  - 7.3.4.1. Market Size & Forecast
    - 7.3.4.1.1. By Value
  - 7.3.4.2. Market Share & Forecast
    - 7.3.4.2.1. By Data Type
    - 7.3.4.2.2. By Modeling Type
    - 7.3.4.2.3. By Offering
    - 7.3.4.2.4. By Application
    - 7.3.4.2.5. By End-use
- 7.3.5. Spain Synthetic Data Generation Market Outlook
  - 7.3.5.1. Market Size & Forecast
    - 7.3.5.1.1. By Value
  - 7.3.5.2. Market Share & Forecast
    - 7.3.5.2.1. By Data Type
    - 7.3.5.2.2. By Modeling Type
    - 7.3.5.2.3. By Offering
    - 7.3.5.2.4. By Application
    - 7.3.5.2.5. By End-use

8. Asia Pacific Synthetic Data Generation Market Outlook

8.1. Market Size & Forecast
- 8.1.1. By Value
8.2. Market Share & Forecast
- 8.2.1. By Data Type
- 8.2.2. By Modeling Type
- 8.2.3. By Offering
- 8.2.4. By Application
- 8.2.5. By End-use
- 8.2.6. By Country
8.3. Asia Pacific: Country Analysis
- 8.3.1. China Synthetic Data Generation Market Outlook
  - 8.3.1.1. Market Size & Forecast
    - 8.3.1.1.1. By Value
  - 8.3.1.2. Market Share & Forecast
    - 8.3.1.2.1. By Data Type
    - 8.3.1.2.2. By Modeling Type
    - 8.3.1.2.3. By Offering
    - 8.3.1.2.4. By Application
    - 8.3.1.2.5. By End-use
- 8.3.2. India Synthetic Data Generation Market Outlook
  - 8.3.2.1. Market Size & Forecast
    - 8.3.2.1.1. By Value
  - 8.3.2.2. Market Share & Forecast
    - 8.3.2.2.1. By Data Type
    - 8.3.2.2.2. By Modeling Type
    - 8.3.2.2.3. By Offering
    - 8.3.2.2.4. By Application
    - 8.3.2.2.5. By End-use
- 8.3.3. Japan Synthetic Data Generation Market Outlook
  - 8.3.3.1. Market Size & Forecast
    - 8.3.3.1.1. By Value
  - 8.3.3.2. Market Share & Forecast
    - 8.3.3.2.1. By Data Type
    - 8.3.3.2.2. By Modeling Type
    - 8.3.3.2.3. By Offering
    - 8.3.3.2.4. By Application
    - 8.3.3.2.5. By End-use
- 8.3.4. South Korea Synthetic Data Generation Market Outlook
  - 8.3.4.1. Market Size & Forecast
    - 8.3.4.1.1. By Value
  - 8.3.4.2. Market Share & Forecast
    - 8.3.4.2.1. By Data Type
    - 8.3.4.2.2. By Modeling Type
    - 8.3.4.2.3. By Offering
    - 8.3.4.2.4. By Application
    - 8.3.4.2.5. By End-use
- 8.3.5. Australia Synthetic Data Generation Market Outlook
  - 8.3.5.1. Market Size & Forecast
    - 8.3.5.1.1. By Value
  - 8.3.5.2. Market Share & Forecast
    - 8.3.5.2.1. By Data Type
    - 8.3.5.2.2. By Modeling Type
    - 8.3.5.2.3. By Offering
    - 8.3.5.2.4. By Application
    - 8.3.5.2.5. By End-use

9. Middle East & Africa Synthetic Data Generation Market Outlook

9.1. Market Size & Forecast
- 9.1.1. By Value
9.2. Market Share & Forecast
- 9.2.1. By Data Type
- 9.2.2. By Modeling Type
- 9.2.3. By Offering
- 9.2.4. By Application
- 9.2.5. By End-use
- 9.2.6. By Country
9.3. Middle East & Africa: Country Analysis
- 9.3.1. Saudi Arabia Synthetic Data Generation Market Outlook
  - 9.3.1.1. Market Size & Forecast
    - 9.3.1.1.1. By Value
  - 9.3.1.2. Market Share & Forecast
    - 9.3.1.2.1. By Data Type
    - 9.3.1.2.2. By Modeling Type
    - 9.3.1.2.3. By Offering
    - 9.3.1.2.4. By Application
    - 9.3.1.2.5. By End-use
- 9.3.2. UAE Synthetic Data Generation Market Outlook
  - 9.3.2.1. Market Size & Forecast
    - 9.3.2.1.1. By Value
  - 9.3.2.2. Market Share & Forecast
    - 9.3.2.2.1. By Data Type
    - 9.3.2.2.2. By Modeling Type
    - 9.3.2.2.3. By Offering
    - 9.3.2.2.4. By Application
    - 9.3.2.2.5. By End-use
- 9.3.3. South Africa Synthetic Data Generation Market Outlook
  - 9.3.3.1. Market Size & Forecast
    - 9.3.3.1.1. By Value
  - 9.3.3.2. Market Share & Forecast
    - 9.3.3.2.1. By Data Type
    - 9.3.3.2.2. By Modeling Type
    - 9.3.3.2.3. By Offering
    - 9.3.3.2.4. By Application
    - 9.3.3.2.5. By End-use

10. South America Synthetic Data Generation Market Outlook

10.1. Market Size & Forecast
- 10.1.1. By Value
10.2. Market Share & Forecast
- 10.2.1. By Data Type
- 10.2.2. By Modeling Type
- 10.2.3. By Offering
- 10.2.4. By Application
- 10.2.5. By End-use
- 10.2.6. By Country
10.3. South America: Country Analysis
- 10.3.1. Brazil Synthetic Data Generation Market Outlook
  - 10.3.1.1. Market Size & Forecast
    - 10.3.1.1.1. By Value
  - 10.3.1.2. Market Share & Forecast
    - 10.3.1.2.1. By Data Type
    - 10.3.1.2.2. By Modeling Type
    - 10.3.1.2.3. By Offering
    - 10.3.1.2.4. By Application
    - 10.3.1.2.5. By End-use
- 10.3.2. Colombia Synthetic Data Generation Market Outlook
  - 10.3.2.1. Market Size & Forecast
    - 10.3.2.1.1. By Value
  - 10.3.2.2. Market Share & Forecast
    - 10.3.2.2.1. By Data Type
    - 10.3.2.2.2. By Modeling Type
    - 10.3.2.2.3. By Offering
    - 10.3.2.2.4. By Application
    - 10.3.2.2.5. By End-use
- 10.3.3. Argentina Synthetic Data Generation Market Outlook
  - 10.3.3.1. Market Size & Forecast
    - 10.3.3.1.1. By Value
  - 10.3.3.2. Market Share & Forecast
    - 10.3.3.2.1. By Data Type
    - 10.3.3.2.2. By Modeling Type
    - 10.3.3.2.3. By Offering
    - 10.3.3.2.4. By Application
    - 10.3.3.2.5. By End-use

11. Market Dynamics

11.1. Drivers
11.2. Challenges

12. Market Trends & Developments

12.1. Merger & Acquisition (If Any)
12.2. Product Launches (If Any)
12.3. Recent Developments

13. Global Synthetic Data Generation Market: SWOT Analysis

14. Porter's Five Forces Analysis

14.1. Competition in the Industry
14.2. Potential of New Entrants
14.3. Power of Suppliers
14.4. Power of Customers
14.5. Threat of Substitute Products

15. Competitive Landscape

15.1. Datagen Inc.
- 15.1.1. Business Overview
- 15.1.2. Products & Services
- 15.1.3. Recent Developments
- 15.1.4. Key Personnel
- 15.1.5. SWOT Analysis
15.2. MOSTLY AI Solutions MP GmbH
15.3. TonicAI, Inc.
15.4. Synthesis AI
15.5. GenRocket, Inc.
15.6. Gretel Labs, Inc.
15.7. K2view Ltd.
15.8. Hazy Limited.
15.9. Replica Analytics Ltd.
15.10. YData Labs Inc.