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Salesforce Data Cloud Credits Guide

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    Szymon Lewandowski

Data Cloud Credits Guide

Salesforce Data Cloud offers a lot of functionalities in terms of data aggregation, transformation, unification, segmentation, and activation. But, using these features, we should be careful regarding service data usage billed with so-called Credits. How it works?

Table of Contents

  1. Data Cloud Licenses
  2. What are Credits?
  3. Credits usage per each activity
  4. Profile Unification Credits usage
  5. How do I check my Credit volume?
  6. How to save Credits?

Data Cloud billing could be tricky, especially with credit-burning features like data streaming and profile unification. With a higher amount of data (hundreds of thousands of records and more), you should be more careful when using Data Cloud capabilities. Especially if there is no easy way to check how much of them you still have.

Data Cloud Licenses

Data Cloud Licensing is not so clear in pricing and features as other tools e.g. Sales/Service/Marketing Cloud. From the official information available on the site we can evaluate that there are two common licenses - free Data Cloud Provisioning and paid Data Cloud Starter license. The second one appears in different types – Data Cloud for Marketing Starter, Data Cloud for Tableau Starter, and Data Cloud for Health Starter

Salesforce Data Cloud is officially free to use with its Data Cloud Provisioning package, containing 250,000 Data Services credits per year and 1 TB of storage for users with Salesforce Enterprise and Unlimited editions. Unlimited Plus edition gets 2,500,000 credits per year. This package is a nice entry to the Data Cloud world, especially with 1 TB of storage that we can use. It is also a good opportunity to check what Data Cloud could do. Remember that if you have a longer contract, for example for 3 years, the number of credits will be multiplied (e.g. 250,000 x 3 = 750,000) and you will have access to the entire pool immediately.

Data Cloud Starter for Marketing is probably the most common type of license. Usually sold with Marketing Cloud, contains 10M credits, 5TB of storage, 1 Data Cloud Admin Account, and 100 Data Cloud User Accounts. Note that despite being called “for Marketing”, all marketing-like features like segmentation, activation, and ad audiences are sold separately as add-ons.

Data Cloud Starter for Tableau looks similar to the Marketing one. 10M credits, 5 TB of storage, but there are no possible add-ons such as segmentation, data spaces, or ad audiences.

Data Cloud Starter for Health looks different from the two previous licenses. It is treated not as a separate Cloud license, but as an add-on to the Health Cloud. There are also no starter credits or storage - you have to buy it separately, the same as Unified Health Scoring, which is a Unified Profiles variation for your patients.

Marketing Cloud Growth is a new addition to the Salesforce portfolio. The license provides Data Cloud instance with 1 TB of space, limited 10,000 unified profiles, 240,000 Data Services credits and 10,000 Segment and Activation credits. The fun fact is that to use Marketing Cloud Growth license you should have Service/Sales or other Salesforce license (which provides free Data Cloud), so there could be an option to merge the free tier and Growth Edition credits pool (I hope so...).

In terms of the licenses, all topics like data storage, admin and user account number, and so on look quite clear. But what about credits? There is not so much information about credits in the documentation or pricing details, nor in the Data Cloud demos. But it turns out that they will be crucial in your implementation and maintenance. Why?

Free Data Cloud

What are Credits?

Credits in Data Cloud (a.k.a. CDP, a.k.a. Genie) are like coupons, that you spend to cover your Data Cloud usage bill. In other words – you buy Data Cloud Credits for your money and then you can use them to pay for importing data, exporting data, transforming data, profile unification, or segmentation and activation.

Salesforce Data Cloud is hosted on Amazon Web Services, where credits have the same role. So basically you pay Salesforce, who pays Amazon for operations, that you make in your Data Cloud instance. 💁‍♂️

Notable mention of the two types of Credits, that exist in Salesforce Data Cloud. Data Services Credits is a name for credits that are used for data processing (e.g. importing, exporting, transforming, unification). Segment and Activation Credits are used for data segmentation and activation (remember, available as an add-on, not included in the basic package).

Credits usage per each activity

Now we dive into the more interesting stuff. Surfing through the official Data Cloud documentation it won’t be so easy to find the proper Credits usage calculation for each feature in the system. The two core sources for us will be the Data Cloud Billable Usage Types from the documentation and Multipliers for Data Cloud, which are not so easy to find on the website.

Data Services Usage Types

Usage TypeUnitMultiplier (Credits)
Batch Data Pipeline1 Million Rows Processed2,000
Batch Profile Unification1M Rows Processed100,000
Batch Calculated Insights1M Rows Processed15
Data Queries1M Rows Processed2
Accelerated Data Queries1M Rows Processed2
Streaming Data Pipeline1M Rows Processed5,000
Streaming Calculated Insights1M Rows Processed800
Streaming Actions (including lookups)1M Rows Processed800
Real-Time Profile API1M Rows Processed900
Inferences1M Inferences3500
Data Share Rows Shared (Data Out)1M Rows Shared800
Data Federation or Sharing Rows1M Rows Accessed70

Segmentation and Activation Usage Types

Usage TypeUnitMultiplier (Credits)
Segment Rows Processed1M Rows Processed20
Batch activation1M Rows Processed10

Let’s start with a Multiplier explanation. This is nothing more than the amount of credits used per 1 million processed records. For example, if we have processed 500,000 records in Calculated Insights, we have used 15/2 = ~8 credits. Now, let’s talk about each of the types, following the billable usage types documentation.

The Batch Data Pipeline represents the ingested data through the Data Stream to the Data Cloud. With the multiplier set as 2000 credits per 1M rows, you should estimate how much data you will bring. There could be scenarios when starting ingested data won’t be a problem, but their rapid growth may be pushing usage to your license limit day by day.

The Batch Profile Unification represents the unified source profile data. It is the most “expensive” task in the Data Cloud. It is also the most tricky one, with a not-so-clear definition of what 1 million processed rows mean and how many rows takes to unify one Profile. We will cover it in the next paragraph.

The Batch Calculated Insights represent all records processed during building the CI (Calculated Insights). Note that we count records in all objects that we use for our query and it counts every time the CI runs. For example, with a multiplier of 15 credits, if we build Calculated Insight joining the first DMO (Data Model Object) with 2M records and the second DMO with 1M records, no matter how many records we finally got, we processed 3M records, so we used 45 credits. If we use this CI in the segment that is updated every day, we spend 45 credits every day.

The Data Queries and Accelerated Data Queries represent the usage in the Data Transforms tool and are calculated on records processed. Accelerated Data Queries are used with Tableau or CRM Analytics. They are the cheapest operations in Data Cloud, so check if you can use them in your case.

The Streaming Data Pipeline represents the Streaming Data Streams and Streaming Data Transforms operations. Calculated in the same way as Batch Data, they are 2.5x more expensive, but they could process the data in real-time.

The Streaming Calculated Insights are similar to basic Calculated Insights but are used for real-time data processing. Pretty expensive.

Streaming Actions represent the Data Actions activity in the Streaming Pipeline and Streaming Calculated Insights flow (real-time). Also not so cheap, but usually you will use them in pretty important use cases.

The Real-Time Profile API represents the data queried through Profile API, if you want to retrieve the Profile data with third-party custom solutions, you should look at this type of billable usage type.

Inferences are defined as any data output produced by AI model in Einstein Studio. For example: using AI in flow action (1 flow triggered = 1 inference), prediction job on data model (100 records = 100 inferences), generate text with LLM (1 text generated = 1 inference).

Data Share Rows Shared and Accessed represents the data processed with Data Share functionality. The Shared type represents the new or changed records in the data share and the Accessed type represents the data that was used to fulfill the external system request. Since March 2024, Data Share Rows Accessed are Credits-free.

Data Federation or Sharing Rows Accessed represents the data processed with Data Federation functionality, regarding the data from BYOL models (Google BigQuery and Snowflake). It is counted per 1M rows accessed with BYOL Data Federation (Data In) and 1M rows accessed with BYOL Data Shares (Data Out).

The Segment Rows Processed represents the number of rows processed to create the Segment.

The Batch activation represents the number of rows processed through the segment activation process. Note that this is counted in the same way with or without related attributes.

Free Data Cloud

Profile Unification Credits usage

Talking about Profile Unification, there are a lot of questions about how many of the Profiles can be processed with some amount of credits, especially in the free Data Cloud Instance. Some of the promoting materials were talking about 10,000 profiles in the Data Cloud Provisioning Package, which contains 250,000 Credits. With this information, Salesforce Ben estimated, that the one processed Unified Profile takes 25 credits.

25 credits is a very, very huge number. With official Credits add-on pricing set as 1000 USD per 100,000 credits, we can calculate that in this theory one Unified Profile will cost us 0.25 USD. Pretty much, right? With 10M credits in the Data Cloud Starter package, we would use all our credits if we unify only 400k Profiles. But this calculation is a sort of generalization. Why?

To go through this, we need to take a deep dive into the documentation and Salesforce nomenclature. Citing from official Docs:

“Batch Profile Unification usage is calculated based on the number of source profiles processed by an identity resolution ruleset. After the first time a ruleset runs, only new or modified source profiles are counted. A source profile is an individual and their related records, such as contact points and party identifiers, which are included in the identity ruleset. For example, modified means deleted profiles, or profiles marked as suppressed via Consent API preferences.”

The key term here is a source profile. A source profile is a record with personal data that is processed by the Identity Resolution ruleset.

Let’s take an example. We have 500,000 user records ingested from Amazon S3 Connector, 700,000 user records from Salesforce CRM connector, and another 200,000 user records from Marketing Cloud connector, all ingested 1:1 to Individual and related Data Model Objects, e.g. Contact Point Email. Starting the unification process, we have 1,400,000 source profiles. After Unification, no matter if we finally got 1,400,000 Unified Profiles, 400,000 Unified Profiles, or 100,000 Unified Profiles, we used 140,000 credits.

Looks better, right? In this scenario, with the result of 400,000 Unified Profiles, we used only 0.35 credits (0.0035 USD) per Profile (and we used about 60% of our credits from the Data Cloud free license). Even if we add the cost of Data Stream ingestion (2800 credits), it is a cost of 0.357 credits (0.00357 USD) per Profile.

What is more, a source profile is a record with information from the Individual DMO and all related records, which includes Contact Point DMOs, Party identifiers, or related custom DMOs. Good information is that when we do the Profile Unification, we count only one processed record as a whole source profile instead of for example 3 records (when we have Individual, Contact Point Email, and Contact Point Address DMOs).

There is another side of the coin. We also count modifications, deletions, or suppression as processed records in Profile Unification. It means that every small change, no matter if we modify 1, 5, or 50 attributes in one record, even in the related DMO, will process the source profile once again in the next Identity Resolution run and will cost us 0.1 credit per modified source profile.

Unified Profile

How do I check my Credit volume?

To check how many Credits you got with your license, you should first find out what license you bought (of course if you don’t remember or if you were not responsible for buying it). If you have not got access to the contract, you can check it in the Your Contracts section. Look for the Data Cloud named product.

If you want to check how many credits you have left, you must contact your Account Executive. Currently, there is no other way. Note that the summary of used Credits generated by your AE could be not up to date due to the processing delays from AWS.

How to save Credits?

There are many ways to reduce Credits usage. After reading this article you probably have some ideas about how you could do this. But we can collect the most obvious and effective ways.

Make a good Discovery Phase

The first and probably most important tip. It can not only reduce used Credits but also speed up your implementation. Gather the functional requirements for the Data Cloud, get to know the data architecture in your company, and localize the necessary data sources that you need to fulfill your use cases.

Reduce unused data

Use only the data that are necessary for your implementation. Check if you can get all data from less amount of data sources. Remember that more normalized data means more imported tables, which means more records processed.

Get every needed attribute

Sometimes it is good to reduce the amount of attributes that you ingest from data sources to Data Lake Objects (because if you import it, you will be able only to disable it, but not hide or delete it). But remember, that after the first ingestion, if you would like to add another attribute, you will have to process all the records again.

Reduce the frequency

Data Cloud can work in near real-time with Streaming Data solutions and regular data updates from Connectors. But every new data import, no matter if with an upsert or full refresh method, will use your credits. So check if your use case needs highly frequent data ingestion. The same tip can be used with segment and activation. Check how frequently the data should be updated in your scenario.

Note that Salesforce CRM Connector uses only automatic, 15-minute frequency, so if you have a lot of data processed many times on the same records throughout the day, the import could cost you additional credits.

Check if you need the Profile Unification

Profile Unification is the most expensive feature in the Salesforce Data Cloud. But you won’t use it in every implementation. If you know that the data is normalized, unique, and well-arranged, you can build a fully custom solution and/or skip the Unification process and work on defined relationships. It will be a more complicated, more technical, and not so easily scalable solution, but can save you a huge amount of credits, which is crucial, especially in the free tier.

Be careful

Note that Data Cloud hasn’t got the Sandbox environment and that every action in the Data Cloud costs you. Processing data in every ingestion, every Data Transform, every Calculated Insight, and every Segment will be paid. Remember that you can add the Data Streams and map the Data Lake Objects without starting the data ingestion. You should be careful, especially if there is still no easy way to find out how many Credits you spent for which operation.

Data Cloud is a powerful tool, with powerful features. Although it is in development (just check how many new options and fixes it got through the year), it can be used in a lot of use cases, especially focused on marketing automation solutions. With a nearly no-code approach, it seems to be an easy and friendly option to process and segment your data. Especially if there are a lot of rumors about moving the Marketing Cloud options into the Data Cloud.

But there are some things that we should be careful about. I hope that this article will make it clear what Data Cloud billing looks like, how to calculate Credit usage, and how to become more aware while working with this tool.