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Billing Considerations for Predictive AI
Predictive models in AI Models (formerly Einstein Studio) use Data 360 to ingest, query, and store data, and to create inferences. Using Data 360 services can impact the consumption of credits used for billing in these usage types.
This feature has access to Digital Wallet, a free account management tool that offers near real-time consumption data for enabled products across your active contracts. Access Digital Wallet and start tracking your org's usage. To learn more, see About Digital Wallet.
| Digital Wallet Card | Usage Type | usage type Description | Notes |
|---|---|---|---|
| Data Services | Inferences | Usage is calculated based on the number of unique inferences produced by a predictive model. A single inference includes one prediction and, optionally, one or more prescriptions and one or more top predictors. Inferences are consumed for any predictive AI model used, whether internal (built in Einstein Studio AI) or external (Bring Your Own Model). | Credits aren't consumed for inferences in orgs that use Flex Credits for Data 360 services. |
| Data Services | Batch Data Pipeline (External Data Pipeline) | Usage is calculated based on the number of rows batch data processed by Data 360 data streams across all connectors, with the exception of structured data ingested via the Internal Data Pipeline. | This category is billed when training data for predictive models is ingested into Data 360 via a batch pipeline. |
| Data Services | Streaming Data Pipeline (External Data Pipeline) | Usage is calculated based on the number of rows of streaming data processed by Data 360 across all data streams with stream processing, with the exception of structured data ingested via the Internal Data Pipeline. Data streams that report usage with this usage type include streams created by the Website and Mobile App connector and streaming ingestion API. |
This category is billed when training data for predictive models is ingested into Data 360 via a streaming pipeline. |
| Data Services | Batch Data Transforms | Usage is calculated based on the higher of the number of rows read or number of rows written. For incremental batch data transforms, after the first time a transform runs, usage is based on the number of rows that have changed since the previous run. |
Running a batch data transform that generates predictions also consumes credits in the Inferences usage type. |
| Data Services | Data Federation or Sharing Rows Accessed | For data federation, usage is calculated based on the number of records retrieved from the source. For a data share, usage is calculated based on the number of rows returned to fulfill an external data lake’s request. For data sharing between Data 360 orgs, this usage type applies to the target org for all queries. For external data shares, this usage type applies only to cross-cloud and cross-region queries. There’s no credit consumption if the query originates from the same region and the same cloud. |
This category is billed if a federated data source is queried to enhance a training model or generate a prediction. |
| Data Services | Data Queries | Usage is calculated based on the number of records processed. The count of records processed depends on the structure of a query as well as other related factors such as the total number of records in the objects being queried. |
Data queries are used to construct a data profile for model building. |
| Data Services | Storage Beyond Allocation | Usage is calculated based on the amount of storage used above the amount allocated. |
To estimate the number of Data 360 credits, see Multipliers for Data 360.
For more information on how Data 360 usage is billed, refer to your contract or contact your account executive.
Reduce Predictive AI Credit Consumption
You can lower consumption rates for predictive AI by evaluating what data you need to generate predictions and how up to date that data needs to be.
