Synthetic Data Generation Market – Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Data Type (Tabular Data, Text Data, Image & Video Data, Others), By Modeling Type (Direct Modeling, Agent-based Modeling), By Offering (Fully Synthetic Data, Partially Synthetic Data, Hybrid Synthetic Data), By Application (Data Protection, Data Sharing, Predictive Analytics, Natural Language Processing, Computer Vision Algorithms, Others), By End-use (BFSI, Healthcare & Life sciences, Transportation & Logistics, IT & Telecommunication, Retail & E-commerce, Manufacturing, Consumer Electronics, Others), By Region & Competition, 2021-2031F

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

Market Overview

The Global Synthetic Data Generation Market will grow from USD 443.27 Million in 2025 to USD 2261.88 Million by 2031 at a 31.21% CAGR. The Global Synthetic Data Generation Market is defined as the industry focused on the algorithmic creation of artificial datasets that replicate the statistical properties and correlations of real-world information without comprising personally identifiable details. The market is primarily driven by the urgent demand for vast, high-quality datasets required to train generative artificial intelligence models, coupled with the necessity to reduce data collection costs and navigate stringent global privacy regulations which restrict the use of sensitive real-world records. According to the CFA Institute, in 2025, synthetic data is predicted to account for more than 60% of all training data for generative AI models by 2030, underscoring the critical reliance on this technology for future development.

However, a significant challenge impeding broader market expansion is the difficulty of ensuring data fidelity and preventing bias propagation. If the generating algorithms are trained on flawed base data or fail to capture complex outliers, the resulting synthetic datasets may produce inaccurate analytical outcomes, thereby limiting their utility in high-stakes sectors such as healthcare and finance where precision is paramount.

Key Market Drivers

The escalating demand for high-quality AI and machine learning training datasets is the primary catalyst propelling the market, as developers face an impending shortage of real-world information required to scale Large Language Models. As models grow exponentially in complexity, the finite stock of human-generated public text is rapidly becoming insufficient, necessitating the mass production of synthetic alternatives to sustain innovation. According to Epoch AI, May 2024, in 'The Looming Data Scarcity Crisis in AI', tech companies are on track to exhaust the supply of publicly available training data between 2026 and 2032. This urgent scarcity has triggered massive capital inflows into the sector as investors recognize the necessity of artificial data; according to Scale AI, in 2024, the company secured $1 billion in Series F funding to reach a valuation of $13.8 billion, reflecting the critical commercial value placed on data generation infrastructure.

Concurrently, stringent data privacy regulations and global compliance mandates are forcing enterprises to adopt synthetic data as a strategic risk-mitigation tool. With frameworks like GDPR imposing severe penalties for mishandling sensitive information, organizations are increasingly utilizing artificial datasets that retain statistical utility while completely anonymizing Personally Identifiable Information. This operational shift is further accelerated by evolving consumer sentiment regarding data ethics and security. According to TELUS International, October 2024, in the '2024 Data & Trust Survey', 82% of respondents believe data privacy matters more to them now than ever before, compelling corporations to leverage synthetic generation methods to maintain analytical capabilities without compromising user trust or regulatory standing.

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Key Market Challenges

The difficulty of ensuring data fidelity and preventing bias propagation serves as a substantial barrier to the Global Synthetic Data Generation Market. As this technology is increasingly utilized to train generative artificial intelligence models for critical sectors like finance and healthcare, the accuracy and neutrality of the output are non-negotiable. When synthetic datasets fail to capture complex outliers or inadvertently amplify historical prejudices found in source data, the resulting AI models become unreliable and potentially discriminatory. This lack of fidelity erodes organizational trust and delays widespread enterprise adoption, as companies cannot risk deploying flawed algorithms in high-stakes environments.

The market’s struggle with these quality assurance issues is reflected in recent industry sentiment regarding AI ethics and reliability. According to ISACA, in 2025, only 41% of digital trust professionals believed their organizations were adequately addressing ethical concerns in AI deployment, such as bias and accountability. This statistic highlights a pervasive lack of confidence in managing data-related risks. Until synthetic data vendors can demonstrably guarantee high-fidelity, bias-free outputs, this trust gap will continue to restrain the market's expansion into regulated industries where precision is mandatory.

Key Market Trends

The convergence of synthetic data with digital twin and simulation technologies is reshaping how physical AI systems are trained and validated. By creating high-fidelity virtual environments, developers can generate vast quantities of perfectly labeled data for scenarios that are difficult, dangerous, or expensive to capture in the real world, such as autonomous driving accidents or industrial robot malfunctions. This approach allows for the precise control of environmental variables like lighting, weather, and object placement, ensuring robust model performance across diverse operating conditions. According to NVIDIA, June 2024, in the 'NVIDIA Advances Physical AI at CVPR With Largest Indoor Synthetic Dataset' announcement, the company released a massive synthetic dataset comprising 212 hours of video across 90 virtual scenes to accelerate the development of smart city and industrial automation solutions.

The emergence of industry-specific synthetic data platforms is rapidly advancing, particularly within regulated sectors that require highly specialized training environments. Unlike generic data generation, these vertical solutions leverage generative AI to replicate complex, domain-specific patterns—such as financial transaction flows—to enhance analytical precision while strictly adhering to data residency and privacy mandates. This shift allows enterprises to simulate rare fraud scenarios and improve decision-making accuracy without relying solely on finite historical records. According to Mastercard, February 2024, in the 'Mastercard supercharges consumer protection with gen AI' press release, the integration of advanced generative AI techniques into their fraud detection network has reduced false positive rates by more than 85%, highlighting the tangible operational improvements driven by synthetic data technologies.

Segmental Insights

The Hybrid Synthetic Data segment is currently recognized as the fastest-growing category within the Global Synthetic Data Generation Market. This rapid expansion is primarily driven by the segment's ability to strike an optimal balance between data utility and privacy preservation. By retaining the statistical properties of real-world datasets while masking sensitive identifiers, hybrid data enables organizations to train accurate artificial intelligence models without compromising security. This approach is particularly critical for enterprises navigating complex regulatory landscapes, such as the General Data Protection Regulation, allowing them to leverage high-quality data for innovation while strictly adhering to compliance standards.

Regional Insights

North America leads the Global Synthetic Data Generation Market, primarily due to the robust presence of key technology providers and the widespread adoption of artificial intelligence across the finance and healthcare sectors. Organizations in this region increasingly leverage synthetic data to train machine learning models, ensuring compliance with rigorous privacy standards without compromising sensitive information. Furthermore, initiatives by the National Institute of Standards and Technology (NIST) to advance differential privacy and data security standards actively foster market growth. This convergence of technological infrastructure and regulatory alignment establishes North America as the dominant market leader.

Recent Developments

• In December 2024, Databricks introduced a new synthetic data generation application programming interface (API) within its Mosaic AI Agent Evaluation module. This strategic launch in the Global Synthetic Data Generation Market addresses the critical need for high-quality evaluation datasets for artificial intelligence agents. The new tool allows enterprises to leverage their proprietary data to automatically generate realistic test cases, significantly reducing the time and cost associated with manual data labeling. By enabling developers to assess the quality, latency, and safety of their AI applications more efficiently, Databricks aims to accelerate the deployment of reliable agentic systems across various industries.
• In November 2024, SAS completed the acquisition of the principal software assets of Hazy, a UK-based pioneer in the Global Synthetic Data Generation Market known for its privacy-preserving data solutions. This strategic consolidation enables SAS to integrate Hazy’s advanced synthetic data capabilities into its own Data Maker platform, which is set to be available across major cloud infrastructure providers. The move is designed to empower organizations to overcome data scarcity and privacy compliance challenges by generating statistically significant synthetic datasets. This acquisition underscores the growing importance of synthetic data in training robust artificial intelligence models while safeguarding sensitive information.
• In June 2024, NVIDIA unveiled the Nemotron-4 340B family of open models, marking a significant technological breakthrough in the Global Synthetic Data Generation Market. This suite includes specialized base, instruction, and reward models designed to generate high-fidelity synthetic data for training large language models (LLMs). By providing a scalable pipeline optimized with the company's NeMo framework, NVIDIA enables developers to create diverse and accurate training datasets for commercial applications in sectors like healthcare and finance. This release addresses the industry-wide bottleneck of expensive and limited human-labeled data, facilitating the development of more capable and specialized generative AI systems.
• In June 2024, Gretel.ai announced the general availability of Gretel Navigator, an agent-based compound generative AI system that advances the capabilities of the Global Synthetic Data Generation Market. This innovative product allows users to generate, edit, and augment complex tabular datasets using simple natural language or SQL prompts. By automating the data creation and curation process, the tool enables developers to rapidly produce high-quality, privacy-preserving datasets for training and fine-tuning AI models. The launch represents a shift towards more accessible and automated data engineering workflows, helping enterprises bypass the limitations of real-world data collection such as privacy risks and scarcity.

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.

By Data Type	By Modeling Type	By Offering	By Application	By End-use	By Region
Tabular Data Text Data Image & Video Data Others	Direct Modeling Agent-based Modeling	Fully Synthetic Data Partially Synthetic Data Hybrid Synthetic Data	Data Protection Data Sharing Predictive Analytics Natural Language Processing Computer Vision Algorithms Others	BFSI Healthcare & Life sciences Transportation & Logistics IT & Telecommunication Retail & E-commerce Manufacturing Consumer Electronics Others	North America Europe Asia Pacific South America Middle East & Africa

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