Predictable Pricing forMeasurable Data Complexity
Structural. Measurable. Predictable.
DataPancake® pricing is based on the structural complexity of the data environment being operationalized. Complexity is measured from discovered structure and system-generated metadata, giving teams visibility into projected cost before activation.
Structural
Pricing reflects the complexity of the data being operationalized: nesting, polymorphism, arrays, escaped JSON, schema drift, and transformation requirements.
Measurable
Complexity is derived from deterministic schema analysis, configured transformations, and system-generated metadata, so projected cost can be reviewed before activation.
Predictable
Teams purchase annual capacity, activate intentionally, and avoid runtime pricing based on rows, executions, compute, users, or tokens.
Deterministic Complexity Scoring
DataPancake calculates structural complexity using deterministic schema analysis, configured transformations, and system-generated metadata maintained within the customer’s Snowflake environment.
Projected Complexity Scores can be evaluated before activation, giving teams visibility into the structural footprint of each data source before committing capacity.
The score reflects two dimensions of complexity:
Depth-Weighted Attributes
Measures the discovered structure of the data, including the number of attributes, nesting depth, and whether attributes are active, inactive, or previously included in code generation.
Schema Transformations
Measures the configuration required to normalize complex structures, including key consolidation, value expansion, and hierarchy reshaping.
Together, these factors reflect the work required to transform complex semi-structured data into secure, queryable relational tables and views.
Complexity Score is deterministic, customer-visible, and evaluated before activation.
Attributes
The fields and structures discovered in the source data.
Depth
The nesting level and hierarchy of those attributes.
Transformations
The configuration required to normalize complex structures into relational form.
Assurance Tiers
Each activated data source is designated as Enterprise or Regulatory based on the environment, sensitivity, and responsibility of the data. The tier determines the per-unit rate applied to the source’s Complexity Score.
Enterprise
For business-critical data sources that support production systems, enterprise analytics, AI workflows, and operational reporting without specific regulatory compliance requirements.
- Customer platforms
- Operational data systems
- Enterprise analytics and AI pipelines
- Large-scale application integrations
Regulatory
For data subject to legal, regulatory, audit, government, or operational exposure where structural accuracy carries greater responsibility and consequences of error.
- Healthcare and FHIR data
- HIPAA, GDPR, SOX, and similar compliance contexts
- Government and public sector systems
- Financial and statutory reporting
Evaluate First.Activate With Control.
DataPancake is licensed through annual Complexity Unit capacity. Teams can review projected complexity before activation, purchase capacity aligned to expected scope, and activate data sources only when they are ready to operationalize them.
Evaluate
Connect a data source and review its Projected Complexity Score before activation.
Plan Capacity
Purchase a pool of Complexity Units sized to the data sources and Assurance Tiers expected to be operationalized.
Activate
Activate approved data sources when ready. Complexity Units are consumed when a source enters the operationalized footprint.
Included within purchased annual capacity
Core DataPancake capabilities are available without separate feature gates, so teams can evaluate, configure, generate, document, and validate within the capacity they purchase.
Schema Discovery
Discover structural complexity before activation.
Code Generation
Generate SQL to materialize operationalized sources into relational tables and views.
Data Dictionary Generation
Create governed documentation for activated data.
Semantic Model Generation
Prepare semantic metadata for Snowflake-native AI workflows.
Validated Data Loading
Load and validate files to understand structure before flattening, normalization, and activation decisions.
Pricing Controls
No per-user fees.
No feature gates.
Annual capacity planning.
Complexity visibility before activation.
Built for Annual Planning and Control
DataPancake pricing is designed around visibility, activation control, and annual capacity planning, so teams can manage licensing scope before deployment decisions are made.
Plan Capacity Before Activation
Review projected consumption, choose which data sources and attributes to operationalize, and align purchased capacity to the planned scope.
Formula Locked During Term
The pricing formula stays fixed during the subscription term, supporting annual planning and procurement confidence.
Same-Rate Overage Treatment
If activated scope exceeds purchased capacity, overages follow the contracted per-unit rate rather than a punitive overage rate.
Unused Capacity May Roll Over
Unused Complexity Units can roll into the next term when renewal conditions are met, reducing pressure to estimate perfectly on day one.
Complexity Assessment Process
Pricing is determined during evaluation based on the projected structural complexity of each data source, its Assurance Tier, and the scope the customer chooses to activate.
Most organizations complete the assessment during a short pilot engagement inside their own Snowflake environment.
Connect
Connect a representative data source and run schema discovery.
Review
Review the Projected Complexity Score and structural footprint before activation.
Classify
Select the Enterprise or Regulatory Assurance Tier for each data source.
Activate
Purchase annual capacity and activate only the approved data sources, attributes, and transformations your team chooses to operationalize.
Annual Capacity, Clear Boundaries
DataPancake licenses are structured as annual capacity agreements based on the structural complexity your team chooses to operationalize.
Snowflake consumption costs, including DataPancake runtime, optional Cortex AI use with the Data Dictionary Builder, and deployed Dynamic Table execution, are billed separately by Snowflake under your existing Snowflake agreement.
See Your Projected Complexity Cost
Evaluate your structural footprint before activation and understand expected licensing scope before deployment.