Synthetic Evaluation Data Generation Market Report 2026

The synthetic evaluation data generation market size has grown exponentially in recent years. It will grow from $1.82 billion in 2025 to $2.39 billion in 2026 at a compound annual growth rate (CAGR) of 31.5%. The growth in the historic period can be attributed to increasing demand for safe testing data, rising need for scalable test datasets, growing emphasis on data privacy preservation, increasing requirement for diverse evaluation scenarios, and rising adoption of automated testing processes.

The synthetic evaluation data generation market size is expected to see exponential growth in the next few years. It will grow to $7.09 billion in 2030 at a compound annual growth rate (CAGR) of 31.2%. The growth in the forecast period can be attributed to increasing enterprise investment in quality assurance, rising demand for high-fidelity evaluation datasets, a growing requirement for scalable and repeatable testing, increasing adoption of continuous delivery workflows, and a rising preference for vendor-managed data services. Major trends in the forecast period include advancements in artificial intelligence-based synthetic data synthesis, innovations in machine learning-driven pattern and anomaly replication, developments in natural language processing-enabled text data synthesis, advancements in robotic process automation-assisted test data workflows, and innovations in application programming interface-based data provisioning.

The increasing adoption of AI and machine learning (ML) across industries is expected to drive the growth of the synthetic evaluation data generation market. AI and ML are technologies that enable computers to learn from data, identify patterns, and make intelligent decisions with minimal human intervention. The adoption of these technologies is rising as they improve operational efficiency, automate repetitive tasks, and enable smarter, data-driven decisions that enhance productivity and competitiveness. Synthetic evaluation data generation supports this trend by providing artificial evaluation datasets that organizations can use to test, validate, and benchmark ML models when real-world data is limited, expensive, or sensitive. For example, in March 2025, the Office for National Statistics reported that AI adoption grew from 9% in 2023 to 22% in 2024. As a result, the increasing adoption of AI and machine learning across industries is driving the growth of the synthetic evaluation data generation market.

Major companies in the synthetic evaluation data generation market are focusing on developing synthetic data generation pipelines to test, validate, and compare the performance of machine learning models under controlled, repeatable conditions. A synthetic data generation pipeline refers to an automated workflow that uses real data as a reference to create realistic, privacy-safe artificial datasets for testing, analytics, and machine learning. For example, in June 2024, NVIDIA Corporation, a US-based technology company, launched the Nemotron-4 340B family of open models. This family includes base, instruct, and reward variants, serving as an open synthetic data generation pipeline to train large language models (LLMs) more effectively, particularly in data-scarce domains like healthcare, finance, and manufacturing. The instruct model generates diverse, high-quality synthetic data that mirrors real-world distributions to improve LLM robustness, while the reward model evaluates and filters outputs based on criteria such as helpfulness, correctness, coherence, complexity, and verbosity, achieving top performance on the Hugging Face RewardBench leaderboard.

In November 2024, SAS, a US-based provider of analytics and AI software, acquired the principal software assets of Hazy for an undisclosed amount. This acquisition allowed SAS to integrate Hazy’s synthetic data capabilities, enabling the provision of privacy-preserving synthetic datasets for analytics, testing, and model evaluation. Hazy, a UK-based provider of synthetic data generation software, specializes in creating privacy-preserving synthetic records for analytics, testing, and model evaluation, particularly for tabular enterprise datasets.

Major companies operating in the synthetic evaluation data generation market are Amazon Web Services Inc., Microsoft Corporation, Synthesized Ltd., Snorkel AI Inc., Kognic AB, TonicAI Inc., Parallel Domain Inc., Gretel.ai Inc., Synthesis AI Inc., MDClone Ltd., MOSTLY AI Solutions MP GmbH, Rendered.ai Corporation, Anyverse S.L., YData Technologies S.L., Cognata Ltd., DataCebo Inc., Synthex Labs, OneView AI, DiffuseDrive Inc., syntheracorp.

North America was the largest region in the synthetic evaluation data generation market in 2025. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the synthetic evaluation data generation market report are Asia-Pacific, South East Asia, Western Europe, Eastern Europe, North America, South America, Middle East, Africa. The countries covered in the synthetic evaluation data generation market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Taiwan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

Note that the outlook for this market is being affected by rapid changes in trade relations and tariffs globally. The report will be updated prior to delivery to reflect the latest status, including revised forecasts and quantified impact analysis. The report’s Recommendations and Conclusions sections will be updated to give strategies for entities dealing with the fast-moving international environment.

Tariffs have had a limited but noticeable impact on the synthetic evaluation data generation market by increasing costs for imported IT hardware, storage systems, and on-premises infrastructure components used in data generation and validation environments. These impacts are more evident in on-premises deployment segments and in regions reliant on cross-border technology imports, including parts of Asia-Pacific and Europe. Cloud-based and software-centric offerings remain less affected, encouraging vendors to shift toward cloud delivery models. In some cases, tariffs have positively driven regional cloud adoption and localized service provisioning strategies.

Synthetic evaluation data generation is the process of creating artificial datasets that replicate the statistical properties and structure of real-world data without relying on actual sensitive or proprietary records. The goal is to facilitate safe, scalable, and cost-effective testing, validation, training, and evaluation of software systems and machine learning models, while safeguarding privacy and reducing dependence on limited real data.

The main components of synthetic evaluation data generation include software and services. The software segment consists of proprietary and open-source platforms and tools that leverage advanced algorithms, such as generative adversarial networks and large language models, to automatically generate synthetic data. The data types include text, image, audio, and others, and are deployed through cloud-based and on-premises solutions. Key applications include model training, model testing and validation, data augmentation, security and privacy, among others, with end-users across industries such as BFSI, healthcare, automotive, retail and e-commerce, IT and telecommunications, government, and more.

The synthetic evaluation data generation market consists of revenues earned by entities by providing services such as synthetic data as a service, data anonymization and de-identification services, data labeling and annotation for synthetic datasets, test dataset provisioning and management, and managed synthetic data validation and quality assurance. The market value includes the value of related goods sold by the service provider or included within the service offering. The synthetic evaluation data generation market also includes sales of synthetic data generation platforms, data augmentation toolkits, privacy-preserving synthetic data libraries, application programming interface connectors for data provisioning, and pre-built test dataset repositories. Values in this market are ‘factory gate’ values, that is, the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors, and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD unless otherwise specified).

The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.

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