What's the size of the synthetic data market?

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The synthetic data market is experiencing explosive growth as organizations across industries seek privacy-compliant, cost-effective alternatives to real-world data for AI training and analytics.

This comprehensive analysis provides actionable insights for entrepreneurs and investors looking to capitalize on this rapidly expanding market, covering everything from market size and regional dynamics to funding trends and competitive landscapes.

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What was the total market size of synthetic data globally in 2024 and how much has it grown in 2025?

The global synthetic data market reached between $310.5 million and $470 million in 2024, depending on the research methodology and scope of analysis.

Most industry reports converge around $310-350 million as the baseline 2024 market size, with projections for 2025 ranging from $690 million to $1.42 billion. This represents explosive year-over-year growth of 35-61%, making synthetic data one of the fastest-growing segments in the broader AI infrastructure market.

The variation in market size estimates reflects different approaches to market segmentation. Some analysts include only pure-play synthetic data providers, while others incorporate synthetic data capabilities from major cloud platforms like AWS, Google Cloud, and Microsoft Azure.

Early indicators for 2025 suggest the market is tracking toward the higher end of projections, with significant enterprise adoption accelerating in Q1 2025 and major platform announcements from tech giants driving increased market awareness and validation.

What's the projected CAGR of the synthetic data market over the next 5 and 10 years?

The synthetic data market exhibits remarkable growth projections across multiple time horizons, with 5-year and 10-year CAGRs varying significantly based on market maturity assumptions.

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Which regions are driving the most revenue in synthetic data today, and how will that shift by 2026?

North America currently dominates the synthetic data market with 34.5-38% market share in 2024, driven by advanced AI research ecosystems, stringent privacy regulations like CCPA, and early enterprise adoption.

Europe holds the second-largest market share at 27%, primarily fueled by GDPR compliance requirements that make synthetic data an attractive alternative for organizations needing to process personal data while maintaining privacy compliance. The region's strong automotive and manufacturing sectors also contribute significantly to demand.

Asia-Pacific represents 23% of the current market but is projected to exhibit the highest growth rates through 2026. China, Japan, and India are investing heavily in AI infrastructure and digital transformation initiatives, with government support accelerating adoption across multiple sectors.

By 2026, analysts expect Asia-Pacific to challenge North America's leadership position, potentially capturing 29% market share compared to North America's declining 35%. This shift reflects Asia-Pacific's massive digitalization efforts, growing AI capabilities, and increasing demand from automotive and manufacturing sectors pursuing Industry 4.0 initiatives.

Which industries are currently the biggest buyers of synthetic data and how are their spending patterns evolving?

Healthcare leads synthetic data adoption with 23% market share, spending approximately $71-108 million in 2024 on synthetic patient data for clinical trials, medical imaging, and drug discovery applications.

Financial services represents the second-largest vertical at 20% market share, with particularly strong growth in fraud detection and risk modeling applications. The sector's 7% year-over-year spending increase reflects increasing regulatory scrutiny and the need for real-time risk assessment capabilities.

Automotive shows the fastest spending growth at 10% year-over-year, driven by autonomous vehicle development and the need for diverse edge-case scenarios that would be impossible or dangerous to capture in real-world testing.

What are the key use cases fueling synthetic data adoption and which will dominate by 2026?

AI/ML model training represents the dominant use case, accounting for 31% of the synthetic data market in 2024 and projected to exceed 60% market share by 2026.

This growth is driven by the exponential increase in AI model complexity and the corresponding need for diverse, high-quality training datasets. Organizations are increasingly recognizing that synthetic data can provide the scale and diversity required for robust AI models while avoiding the privacy and bias issues inherent in real-world data.

Privacy protection and compliance use cases currently represent the second-largest segment, particularly in healthcare and finance where GDPR, HIPAA, and CCPA regulations make real data sharing challenging. Synthetic data enables organizations to maintain analytical capabilities while ensuring regulatory compliance.

Simulation and digital twin applications are emerging as high-growth use cases, particularly in automotive, manufacturing, and smart city applications. These use cases allow organizations to test scenarios that would be impractical, dangerous, or expensive to replicate in the real world.

Software testing and development represents a mature but stable use case, with organizations using synthetic data to populate development and testing environments without exposing sensitive production data.

How many startups and major players are active in the market and how has that changed since last year?

The synthetic data ecosystem includes an estimated 131 pure-play synthetic data companies globally, representing a 31% increase from approximately 100 companies in 2023.

This growth reflects both the creation of new startups and the pivot of existing data companies toward synthetic data capabilities. The market spans from early-stage startups focusing on specific verticals to well-funded companies like Mostly AI, Synthesis AI, and Gretel.ai that have raised significant venture funding.

Major technology platforms including Microsoft, Google Cloud, AWS, IBM, and NVIDIA have integrated synthetic data capabilities into their broader AI and cloud offerings. These platforms often partner with or acquire specialized synthetic data companies to enhance their capabilities.

The competitive landscape is becoming increasingly stratified, with infrastructure providers (focusing on the underlying generation technology), application-specific providers (tailored to specific industries or use cases), and platform integrators (embedding synthetic data into broader AI/ML workflows) emerging as distinct categories.

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What's the average deal size for synthetic data providers by segment and how is that trending?

Enterprise segment contracts average $330,000 annually, reflecting the complexity and scale of enterprise synthetic data deployments that often involve multiple use cases and significant data volumes.

Small and medium enterprise (SME) contracts average $75,000 annually, typically focusing on specific use cases like software testing or compliance requirements. This segment is growing rapidly as synthetic data tools become more accessible and self-service oriented.

Government contracts average $150,000 annually, often involving proof-of-concept projects or specific mission-critical applications. Government adoption tends to be more conservative but represents a significant growth opportunity as agencies seek to modernize data practices while maintaining security.

Deal sizes across all segments are increasing approximately 10% year-over-year as solutions mature and customers expand their synthetic data usage beyond initial pilot projects. This trend reflects growing confidence in synthetic data quality and expanding use case adoption within customer organizations.

What percentage of AI and ML budgets are allocated to synthetic data and how will that evolve?

Organizations currently allocate approximately 8% of their AI/ML budgets to synthetic data solutions, representing a significant increase from less than 3% in 2022.

This allocation is projected to reach 12% by 2030 as organizations recognize synthetic data as essential infrastructure for AI development rather than a optional enhancement. The increase reflects several factors: growing data privacy requirements, increasing model complexity requiring more diverse training data, and cost efficiencies compared to real data collection and labeling.

Early adopters in healthcare and finance are already allocating 12-15% of their AI budgets to synthetic data, suggesting the broader market will trend toward these higher allocation levels as synthetic data adoption matures.

The budget allocation varies significantly by use case, with organizations pursuing privacy-critical applications allocating 15-20% of relevant AI budgets to synthetic data, while those using synthetic data primarily for testing and development typically allocate 5-8%.

What are the main barriers to market growth and which will intensify by 2030?

Data quality and realism concerns represent the primary technical barrier, with 67% of enterprises citing synthetic data quality as their top concern when evaluating solutions.

Regulatory uncertainty, particularly around AI governance frameworks like the EU AI Act, creates hesitation among enterprises seeking clarity on synthetic data compliance requirements. This barrier is expected to diminish as regulatory frameworks mature and provide clearer guidance.

Trust and validation challenges persist, with organizations struggling to develop reliable methods for evaluating synthetic data quality and ensuring it maintains the statistical properties necessary for their specific use cases.

Skills and expertise gaps represent a growing barrier, as organizations lack the internal capabilities to effectively implement and manage synthetic data solutions. This barrier is intensifying as demand outpaces the supply of qualified professionals.

By 2030, technical barriers are expected to diminish significantly as generation quality improves, while regulatory and governance challenges are projected to intensify as governments implement more comprehensive AI oversight frameworks.

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How much VC funding has been injected into synthetic data startups and what does the 2026 pipeline look like?

Venture capital investment in synthetic data startups reached approximately $350 million across 45 deals in 2024, representing a 40% increase from 2023 levels.

H1 2025 has seen $150 million across 20 deals, suggesting a more selective investment environment where VCs are focusing on companies with proven traction and clear paths to revenue scale. The average deal size has increased significantly, indicating investor preference for later-stage companies with established market positions.

The 2026 investment pipeline includes an estimated 30+ active Series A and Series B rounds in various stages of due diligence, with total projected funding of $800 million to $1.2 billion. This represents a maturation of the funding landscape, with fewer seed rounds and more growth-stage investments.

Notable funding trends include increased participation from strategic investors (cloud platforms, enterprise software companies) seeking to integrate synthetic data capabilities, and growing interest from AI-focused funds that view synthetic data as critical AI infrastructure.

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What pricing models are most commonly used by synthetic data providers and how are they shifting?

Subscription-based pricing currently dominates at 45% of provider revenue models, typically offering unlimited data generation within specified volume tiers and use case categories.

Per-data-point pricing represents 30% of the market and is growing rapidly toward 35% by 2025, driven by enterprise demand for usage-based pricing that aligns costs with actual consumption. This model offers greater cost transparency and scalability for organizations with variable data needs.

Service-based pricing accounts for 25% of the market and remains stable, primarily serving large enterprises requiring custom data models, specialized consulting, or white-glove implementation support.

The shift toward usage-based pricing reflects enterprise customer preferences for cost alignment and scalability, while enabling providers to capture more value from high-usage customers. This trend is expected to accelerate as synthetic data becomes more commoditized and customers demand greater pricing flexibility.

What are the forecasted revenue and market share rankings of the top 5 synthetic data providers by 2026 and 2030?

The competitive landscape is expected to be dominated by major cloud platforms that integrate synthetic data into broader AI and analytics offerings, rather than pure-play synthetic data companies.

These projections assume continued platform consolidation and the ability of major cloud providers to leverage their existing customer relationships and AI infrastructure investments. Pure-play providers like Mostly AI and Synthesis AI are expected to maintain strong positions through specialization and technical innovation, but will likely face increasing competition from platform providers.

Conclusion

Sources

1. Global Market Insights
2. Technavio
3. Precedence Research
4. Credence Research
5. Grand View Research
6. Fortune Business Insights
7. Market Research Future
8. Next Move Strategy Consulting
9. Future Market Insights
10. Industry ARC
11. Reuters
12. EY
13. Crunchbase
14. Business Wire
15. Straits Research