Executive Summary
The Illusion of "Data Points" and the Reality of "Value Services"
Pricing in the global synthetic data market has rapidly converged away from the initial simplistic metric of "cost per data point" toward a highly sophisticated 'Three-Part Tariff' structure. This report demonstrates that this Three-Part Tariff model serves as a universal framework explaining the revenue models of existing enterprise-grade synthetic data vendors.
Three-Part Tariff Model
A three-layer structure of Platform Floor + Variable Meter + Value-Add Services has been established as the industry standard
Modality Determines Price
The composition ratio of the Three-Part Tariff fundamentally varies based on the characteristics of tabular/text/image data
Solution-Centric Sales
Enterprise customers purchase 'problem-solving solutions' and 'infrastructure access,' not 'data'
I. Universal Framework for Synthetic Data Pricing: The Three-Part Tariff Model
The most common error when analyzing synthetic data pricing is mistaking simple "cost per data point" as the market standard. Actual enterprise-level project quotes far exceed platform usage fees, and this is explained by a Three-Part Tariff structure combining three elements.
A. The Platform Floor: Fixed Cost for Entry
The 'Platform Floor' is the minimum fixed cost (MRC or ARC) that customers must pay to maintain vendor software licenses, basic support, and security/compliance (e.g., SOC 2, HIPAA). This is a 'base fee' incurred regardless of usage ($0).
Tabular Data (Low-Mid Tier)
- • Tonic.ai: $199/mo
- • Gretel.ai: $295/mo
- • Hazy: $500/mo
Enterprise Tier
- • MOSTLY AI: $3,000/mo
- • Synthesis AI: $3,000/mo
- • Rendered.ai (Teams): $5,000/mo
- • Rendered.ai (Organizations): $15,000/mo
From $199 for tabular data to $15,000 for computer vision, the 'Platform Floor' cost shows approximately 75x variation depending on modality. This difference directly reflects the initial capital investment (CapEx) and infrastructure maintenance costs required to generate each modality.
B. The Variable Meter: Differentiation of Usage Measurement Metrics
The 'Variable Meter' is the pay-as-you-go cost based on actual usage. What a vendor measures is the most critical indicator revealing their business model and cost structure.
Compute-Based
Example: MOSTLY AI. Credits are consumed based on "total virtual CPU and GPU time."
Credits = A x Total Virtual CPU Time + B x Total Virtual GPU Time
Data Volume-Based
Example: YData SDK (1 credit = 1M data points = $1), Gretel.ai, Datagen.in
Source Volume-Based
Example: Tonic.ai (Structural). Priced based on "source data volume" (e.g., 2TB, 10TB)
Token/Word-Based
Example: Tonic Textual ("processed word count"), YData SDK (1 credit = 10,000 tokens), Gretel.ai
Image Count-Based
Example: Datagen.in (30,000 credits = 30,000 text rows or 3,000 images)
-> 1 image = 10 text rows exchange value
C. The Value-Add: Not an "Option" but a "Required" Cost
'Value-Add Services' are professional consulting and managed services for solving specific domain problems, quality assurance, scenario design, and privacy guarantees beyond the platform's basic features. In the enterprise market, this is effectively a 'mandatory core cost,' not an option.
Tabular/Time-Series Data
Domain constraint application, rare event control, physics law reflection, etc. ($10k-$40k)
Image/CV Data
Custom scenarios, 3D asset creation, TAM support (minimum $10k+)
II. Modality Analysis I: Tabular & Time-Series Data
Tabular and time-series data are the most widely used modalities across key industries including finance, healthcare, and manufacturing (e.g., BMS). Pricing models in this market show various combinations of 'Platform Floor' and 'Variable Meter,' and this is particularly the domain where the value of 'Professional Services' is maximized.
Key Table 1: Tabular/Time-Series Vendor Pricing Model Comparison
| Vendor | Core Product | Platform Floor | Variable Meter | BMS Project Cost Impact |
|---|---|---|---|---|
| MOSTLY AI | Platform (VPC) | $3,000/mo | vCPU/vGPU hours (credits) | More complex physics constraint models directly increase 'Variable Meter' costs (credits) |
| YData (SDK) | SDK (API) | $0 (PAYG) | $1 / 1M data points | 'Variable Meter' cost is fixed at $172.80. 'Professional Services' ($18k) charged separately |
| YData (Fabric) | Platform (VPC) | Undisclosed (Enterprise) | AWS infra costs (CPU/GPU) | Platform license + AWS costs + Professional Services. Most complex TCO |
| Gretel.ai | Platform (SaaS) | $295/mo | $2.20/credit (runtime/token) | Similar to MOSTLY AI, complex tasks (runtime) consume more 'Variable Meter' costs |
| Tonic (Structural) | Platform (SaaS) | $199/mo | Source DB size (e.g., 2TB) | Charges based on 5-day original data size. 4x augmentation (output) is cost-irrelevant |
Strategic Implications
When performing a BMS project on the YData (volume-based) platform, the platform cost ($172.80) is negligibly fixed. This can clearly demonstrate to clients that "the $18,000 they pay is purely for Pebblous's BMS domain expertise," making it most advantageous for value communication.
In contrast, using MOSTLY AI (compute-based), complex BMS models consume more credits, potentially driving 'Variable Meter' costs much higher than $172, which could relatively dilute the perceived value of the partner's 'Professional Services.'
III. Modality Analysis II: Text & Language Data (Text/NLP/LLM)
The text modality's pricing model is being completely redefined due to the emergence of LLMs (Large Language Models). It is now possible to obtain high-quality synthetic data simply by sending inference requests to powerful SOTA LLMs.
This paradigm shift is converging text synthetic data pricing models toward "synthetic data generation cost = LLM inference cost."
Key Table 2: Text Modality Pricing Model Comparison
| Use Case | Key Vendor | Pricing Unit (Meter) | Cost Determinant |
|---|---|---|---|
| Anonymization / Masking | Tonic Textual | Processed word count | Total volume of original documents to protect |
| LLM Training Data (Specialized Models) |
Gretel.ai | Generated token count or job runtime | Volume of data to generate + privacy (DP) application |
| LLM Training Data (SOTA Utilization) |
AWS Bedrock | Teacher model input/output tokens | API pricing of the chosen teacher model (e.g., Claude 3) |
Paradigm Shift: Teacher Model Cost Linkage
AWS Bedrock's pricing policy clearly demonstrates a critical paradigm shift in the text synthetic data market. Bedrock defines synthetic data generation cost as "the on-demand pricing of the chosen teacher model."
This suggests that the role of "synthetic data vendors" is shifting from being "unique generative model providers" to "prompt orchestration and privacy layer providers" that leverage SOTA LLMs to generate data.
IV. Modality Analysis III: Image & Computer Vision Data
The computer vision (CV) modality has a fundamentally different economic structure from tabular or text data. This can be likened to the "Hollywood Model." The cost of data generation is determined not by algorithms, but by the expensive infrastructure of 3D assets, simulation engines, and rendering power.
Rendered.ai
Platform Floor
Teams: $5,000/mo
Organizations: $15,000/mo
Variable Meter
Max instances, storage (GB), number of users
Professional Services
TAM (Technical Account Manager) included in Organizations plan
Synthesis AI
Platform Subscription
Annual subscription: from $3,000/mo
Custom Projects
Minimum $10,000 one-time cost
Model
Clear separation of PaaS subscription and DaaS projects
Key Insight: 1 Image = 10 Text Rows
Datagen.in's credit model (30,000 credits = 30,000 text rows or 3,000 images) is quantitative evidence that the vendor itself acknowledges CV data generation has 10x the value or cost compared to tabular data generation.
The reason the CV market's 'Platform Floor' ($3,000 - $15,000) is overwhelmingly higher than tabular/text ($0 - $500) is clear. The CV market does not sell data; it sells access to highly specialized 3D simulation software and infrastructure.
V. Delivery Model Comparative Analysis (API, SaaS, On-Premise)
Synthetic data pricing is heavily influenced not only by 'what' you buy (modality) but also by 'how' it is delivered (delivery model).
A. API-Based (Public SaaS)
Pricing Model
Pure PAYG. Per token, API call, or record
Advantages
$0 initial cost, instant use
Disadvantages
Data leak risk - sensitive source data sent to vendor
B. Platform Subscription (VPC)
Pricing Model
Platform Floor + Variable Meter + cloud infrastructure costs (double billing)
Advantages
Maximum data security - source data never leaves the VPC
Disadvantages
Dual cost structure (license fee + infrastructure fee)
C. On-Premise
Pricing Model
Expensive annual license (typically $80,000 - $200,000/year)
Advantages
Highest security level, complete operational control
Disadvantages
Highest initial cost, self-maintained infrastructure burden
D. Project-Based (Managed Service)
Pricing Model
One-time project cost (NRE)
Advantages
Fixed cost, no platform learning curve, deliverable guaranteed
Disadvantages
Limited scalability (new contract needed per dataset)
Key Table 3: TCO & Security Impact Analysis by Delivery Model
| Delivery Model | Cost Structure | Security Level | Data Portability | BMS Project Strategy |
|---|---|---|---|---|
| API (Public SaaS) | PAYG (low initial cost) | Low (external data transfer) | High | For simple demos or non-sensitive data augmentation |
| VPC (Marketplace) | $3K+ MRC + infra costs (double billing) | High (processed within VPC) | None | When BMS source data security is critical (must explain 'double billing' to clients) |
| On-Premise (License) | $80K+ ARC (high initial cost) | Highest (Air-gapped) | None | For finance/defense clients requiring maximum security |
| Project (Managed) | $10K+ NRE (fixed cost) | High (vendor/partner handles) | Low (deliverables only) | Current PoC model. Most efficient for removing platform adoption barriers |
VI. Strategic Conclusions & Recommendations
A. Validation of Internal Analysis
The three-part pricing model of (Platform Floor) + (Usage) + (Professional Services) established for BMS time-series data augmentation PoC has been validated as the standard model in the global synthetic data market, particularly in the high-value enterprise segment.
Furthermore, the estimated PoC cost range of $10,000 - $40,000 and annual enterprise license cost of $80,000 - $200,000 are highly realistic and aligned with market standards.
B. Core Conclusion: Modality Determines Price Structure
Customers buy 'solutions,' not 'data'
What enterprise customers actually purchase is not simply '1TB of data' or '1 million records.' For tabular data, it is domain expertise; for text data, it is SOTA LLM access; for image data, it is 3D simulation infrastructure. This is why 'cost per data point' cannot explain actual market pricing.
Tabular/Time-Series (BMS)
Variable Meter: $172 (negligible)
99% of cost is Professional Services ($18,000)
-> Domain constraints (Physics/Rules) are the core value
Text (LLM)
Variable Meter is a significant portion of cost
Directly linked to teacher LLM inference cost
-> Based on API token costs
Image/Vision (CV)
Platform Floor: $5,000 - $15,000
Majority of cost is Platform Floor
-> 3D simulation infrastructure + TAM costs
Pricing Breakdown: Comparison by Modality
The chart below visualizes the proportion of the Three-Part Tariff components (Platform Floor, Variable Meter, Value-Add) in the total cost for each data modality. You can see that cost structures fundamentally differ based on modality characteristics.
C. Strategic Recommendations
Strategic Selection of 'Variable Meter'
When using YData (volume-based): Variable Meter cost fixed at $172.80 -> Clearly communicates the value of the partner's BMS domain expertise to clients
Strengthen 'Professional Services' Packaging
The $18,000 "Pro" package should be sold as 'BMS Engineering Consulting,' not data generation.
Customer Segmentation via 'Delivery Model'
'Project-based (Managed Service)' approach is optimal for PoC and new customer acquisition. For long-term customers, prepare 'VPC deployment + annual license' model.
D. Reference: Pebblous DataClinic Pricing
Similar to the global synthetic data vendors analyzed in this report, Pebblous DataClinic also provides a transparent pricing structure based on data modality and usage. From data quality diagnostics to Data Diet (removing unnecessary data), and Data Bulk-up (augmenting insufficient data with synthetic data), we offer various data improvement options tailored to customer needs.
Free
$0
/mo
Public dataset quality diagnosis
- Free public dataset diagnostics
- Basic quality report
- Community support
Basic
~$8
/mo
10,000 images/mo diagnostic credits
- 10,000 images/mo diagnostics
- Detailed quality report
- Email support
Pro
~$400
/mo
20,000 images/mo, custom data support
- 20,000 images/mo diagnostics
- Custom data upload support
- Custom quality criteria
- Priority tech support
Enterprise
~$4,000
/mo
200,000 images/mo, data improvement services
- 200,000 images/mo diagnostics
- Data quality improvement services
- Dedicated TAM
- SLA guarantee & custom solutions
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References
- [1] Amazon Bedrock pricing. https://aws.amazon.com/bedrock/pricing/
- [2] Solutions Pricing for AI Synthetic Data Generation Needs. https://rendered.ai/pricing/
- [3] Human Faces Synthetic Dataset - AWS Marketplace. https://aws.amazon.com/marketplace/pp/prodview-hkxlb5jtkrics
- [4] YData data quality for Data Science | Synthetic data Data-Centric AI. https://ydata.ai/
- [5] Pricing - Tonic.ai. https://www.tonic.ai/pricing
- [6] Pay-As-You-Go Cloud Solution from Tonic. https://www.tonic.ai/blog/
- [7] Gretel.ai Reviews 2025: Pricing & Features. https://tekpon.com/software/gretel-ai/reviews/
- [8] Gretel.ai | BrXnd.ai Landscape. https://landscape.brxnd.ai/companies/gretelai
- [9] Hazy: Set your data free with synthetic data solutions. https://dynamicbusiness.com/ai-tools/
- [10] Pricing - MOSTLY AI. https://mostly.ai/pricing
- [11] AWS Marketplace: MOSTLY AI Data Intelligence Platform. https://aws.amazon.com/marketplace/pp/prodview-clqfgzfzznfoc
- [12] synthetic data platform as a service (paas) - Rendered.ai. https://rendered.ai/platform/
- [13] Usage and credits - Docs - Mostly AI. https://docs.mostly.ai/usage
- [14] What's new in MOSTLY AI. https://mostly.ai/docs/whats-new
- [15] Gretel.ai Pricing 2025. https://www.g2.com/products/gretel-ai/pricing
- [16] DataGen - AI Synthetic Data Solutions. https://datagen.in/
- [17] Billing and Usage | Gretel.ai. https://docs.gretel.ai/
- [18] What Is Synthetic Data? - Salesforce. https://www.salesforce.com/data/synthetic-data/
- [19] What is the ROI of synthetic data? - Syntho. https://www.syntho.ai/
- [20] Synthetic data tools: Open source or commercial? - Medium. https://medium.com/statice/