Welcome to Salesforce Data 360 Credit Estimator
Estimate credits in 8 steps - no deep technical expertise needed.
This wizard uses the unified 2026 Salesforce credit model to provide accurate sizing, complete with safety buffers and self-service transparency.
Start from Scratch
Build a new estimate manually using interactive guides.
Load Customer Input
Search the database for a customer input by company name, ID, or link.
Commercial Pricing Model
Select the foundational structure for this estimate.
Data 360 Prep / Step 1
Step 1: Define your Landscape
Select your primary data sources and assess their data quality to estimate ingestion complexity.
Defaults are set for quick starts - adjust as needed.
Why this step matters: Source selection matters for data quality and transformation costs. In the 2026 model, ingestion from any source is completely free — costs only arise from transformation and unification.
- Common Tip: Zero-Copy integration for Snowflake/GCP is still recommended for performance and compliance — but ingestion is free regardless.
- Data Quality Impact: Choosing 'Messy' doubles transformation costs, while 'Clean' significantly reduces transformation credits.
- Retail: High web traffic with Web SDK — ingestion is free, focus on data quality for transformation.
- Mid-size B2B: ~1M CRM records, usually 'Governed' quality — transformation credits apply based on quality.
Data Foundation (Step 1 Sources)
Primary Data Sources & Quality
Tip: If data exists across multiple disjointed orgs with overlapping fields, select 'Messy'.
Use Cases
Activate Data 360 Value
Selecting use cases automatically provisions necessary compute resources.
Agentforce & AI Agents
Agentic digital labor — credits sized automatically
Custom Goals
Define your own goals beyond the standards above
Landscape Summary
Ingestion is free. Data quality is the main cost driver — messy sources double transformation credits.
Live Impact Preview
Estimates are approximate; based on standard Salesforce Data 360 2026 credit models.
Development Strategy / Step 2
Step 2: Set up your testing plan Use toggles to add sandboxes or buffers if needed. This accounts for safe development and avoids surprises in credit usage. Defaults are set for typical projects.
Why this step matters: Planning sandboxes and buffers protects your implementation from unexpected overages during UAT. Cost scales directly with the complexity calculated from Step 1.
- Common Tip: Don't skip sandboxes for multi-source projects; buffers absorb first-year learning curves and reduce AE friction.
- Sandboxes apply a standard 0.8x multiplier to all non-base compute operations.
- Complex Integration (4+ sources): Keep buffers at 20% and expect 3+ UAT cycles.
- Simple CRM-only rollout: Buffers can be minimized to 10% and UAT cycles to 1.
Development
Why: Protects your live customer profiles from accidental breaks.
Impact: Exactly 0.8x multiplier on non-base compute credits.
These are estimates; sandboxes consume credits like production - plan accordingly.
Risk Buffers
Why: Prevents shortfalls when teams experiment with new segments.
Impact: Exactly +10% to your total consumption estimate.
Why: Initial data imports often pull more history than expected.
Impact: Applies exactly % ONLY if native data quality is low.
Why: Extensive testing burns compute credits before you even launch.
Example: Launching 5 data sources normally requires 1-2 major UAT cycles.
UAT Testing Cycles — each adds ~50,000 credits.
Consumption Impact
Data Inventory / Step 3
Step 3: Input your data inventory Use rough estimates from your systems - no exact numbers needed. This auto-calculates unified profiles and impacts credit estimates. Defaults are conservative.
Why this step matters: This defines your profile baseline for identity resolution and unification. In the 2026 model, ingestion is free — profile counts here are used only for unification and transformation cost estimates.
- Common Tip: Higher profile counts increase identity resolution credits. Accurate estimates here improve the precision of your overall quote.
- These inputs dynamically update the Unify logic in the sidebar.
- Financial Services: 5M CRM updates, high governance requirement. Transforms are minimal.
- B2C Ecommerce: Millions of streaming events — ingestion is free, profile uniqueness drives unification costs.
Source Data Estimation
Why: Used to estimate your initial identity resolution costs.
Example: 5 million Salesforce contacts + 2 million Marketing Cloud subscribers.
Unstructured Data Types
Select document types present in your data sources to estimate AI processing credits.
Data Storage
Historical data migrated at go-live
Estimated yearly data volume increase
Inventory Impact
Ingestion from any source — Salesforce native, streaming, S3, Snowflake, etc. — is completely free in the 2026 model.
Data Sharing & Data Export/ Step 4
Step 4: Enter your estimated sharing volume Base it on how much data you'll export for analytics or external teams. Defaults to 0 if no sharing is planned.
Why this step matters: Moving data OUT of Data 360 to external data lakes costs Data Export / Sharing credits. It relies on the raw data volumes defined in Step 3.
- Common Tip: Switching your export schedule from Daily to Weekly reduces egress costs by ~70%.
- Data Export / Sharing rate is roughly 800 credits per 1M rows exported.
- Data Science Team: Exporting 500k unified profiles weekly to Databricks for modeling.
- Enterprise BI: Pushing 1M aggregated records daily to Tableau for leadership dashboards.
Select use cases — configure each independently
cr / yr
Export Type
Method
Frequency
Sharing Impact
Data Share: 800 cr/1M rows. Zero Copy: 70 cr/1M rows. Delta exports use 10% of base volume.
Identity Resolution Logic / Step 5
Step 5: Set up profile unification This step can use significant credits - start simple. Base on your data from Step 3; defaults minimize costs.
Why this step matters: Identity Resolution is highly compute-intensive. It merges the source records from Step 3 into master profiles.
- Common Tip: Every extra ruleset multiplies cost. Start with 1-2 rulesets. Sub-second merging is premium - only use for live personalization.
- Data Spaces segregate data for regional compliance but add 20% to identity costs per extra space.
- B2C Retail: 10M raw profiles, 5:1 consolidation ratio, 1 ruleset. Yields 2M unified profiles.
- B2B SaaS: 500k profiles, 1:1 consolidation, 2 data spaces for regional compliance (adds 20% cost).
Customer Identity Resolution
Configure unification logic and spaces across your sources.
Unified Households
Groups individual profiles into family or corporate units. Requires additional resolution rules and processing.
Strict Brand / Region Separation
Keeps identities entirely separate by brand or country using Data Spaces.
Real-Time Data Layer & Personalization
Optional Add-On · Requires Streaming Ingestion
Which real-time use cases apply? Select all that fit and enter the approximate audience size for each.
Website Personalization
Adapt content, banners, or offers in real time
Example: Show a returning visitor their loyalty tier and a tailored product on the homepage.
Mobile App Personalization
Serve dynamic content inside native apps
Example: Greet a user by name, surface their in-progress order, and show a coupon on app open.
Next Best Action for Agents
Live customer context in call center / service tools
Example: When a customer calls, the agent instantly sees churn risk, recent purchases, and a recommended retention offer.
Event-Triggered Journeys
Launch journeys the moment a behavior occurs
Example: Fire a cart-abandonment journey within 5 minutes of a customer leaving the checkout page.
Manual Cache Size
Optimization Tips
- Fewer rulesets save credits drastically. Combine rules where possible.
- Use filters to reduce data scope before unifying profiles.
- This is a high-credit area - review Salesforce guides for optimization.
Identity Impact
Identity Credit Stack
Trust Foundation
(eff. src / 1M) × 100k ×
Household Intelligence
Second IR pass (same records)
Separation Overhead
RT Platform Fee
100,000 flat ×
Live Cache Charge
Website API Calls
1 cr/100 calls ×
Mobile API Calls
1 cr/100 calls ×
NBA API Calls
1 cr/100 calls ×
Event-Triggered Journeys
5k cr/1M events ×
Activation Wizard / Step 6
Step 6: Plan data activation Estimate insights, queries, and ad integrations - key for credit usage. Defaults are low; adjust with examples provided.
Why this step matters: Calculates the ROI-driving actions (Segments, Insights, & Predictive AI). Depends directly on unified profiles from Step 5.
- Common Tip: Fewer activations save credits. Predictive AI automatically provisions 3,500 credits per million inferences.
- Weekly schedules reduce segment costs by 70%.
- Marketing Heavy: 100 segments published weekly to 3 ad targets (Meta, Google, SFMC).
- Service Focused: Daily Calculated Insights on 1M profiles for live churn risk scoring.
Step 1 of 2: Reporting & Dashboards
Determine how often your teams and systems will query Data Cloud for dashboards and calculated metrics.
Step 2 of 2: Data 360 Zero Copy Sharing Out & Segmentation
Determine segment batches (Data 360 Segmentation) and data sharing (Data 360 Zero Copy Sharing Out).
Activation Impact
Optimization Tips
- Fewer activations save credits; tie to unified profiles from Step 5.
- Consolidate downstream BI requests to minimize query costs.
- Credits unified as of 2026; monitor with Digital Wallet.
Unstructured Data & AI / Step 7
Step 7: Add Notebook AI & Unstructured Data Estimate credits for file ingestion (PDFs, emails, call transcripts), vector database indexing for RAG pipelines, and Notebook AI / Agentforce inference queries.
Agentforce Use Cases
Select all AI agent patterns for this customer — each billed independently
Expected Monthly Interactions
(queries × 2,000 tokens / 1k) × 25 cr × 12 mo ×
Which Predictive AI Use Cases do you want to power?
Optional Add-On · Auto-enabled from Step 1 selections
Churn Prediction
Example: Flag VIP subscribers with 80% churn probability 30 days before renewal.
Spot at-risk customers before they cancel
Trigger Type
Model Source
BYOM: 3,500 cr/1M (vs Einstein 5,000/1M). 30% savings on compute.
+50,000 cr/yr Vector/Semantic Search overhead included.
Credit Preview — Compute Intensity: cr/1M
Propensity to Buy / Lead Scoring
Example: Rank 50k leads by purchase probability before a product launch campaign.
Find who is most ready to buy right now
Trigger Type
Model Source
BYOM: 3,500 cr/1M (vs Einstein 5,000/1M). 30% savings on compute.
+50,000 cr/yr Vector/Semantic Search overhead included.
Credit Preview — Compute Intensity: cr/1M
Customer Lifetime Value
Example: Automatically qualify top 10% of customers for a concierge loyalty program.
Identify and prioritize your highest-value customers
Model Source
BYOM: Inference at 3,500 credits/1M (vs Einstein 5,000/1M) — pipeline & query credits still apply.
Credit Preview
Next Best Offer Recommendations
Example: Show each customer a personalized cross-sell offer they're 3× more likely to click.
AI picks the right offer for every individual customer
Model Source
BYOM: Inference at 3,500 credits/1M (vs Einstein 5,000/1M) — pipeline & query credits still apply.
Credit Preview
Sentiment Analysis
Example: Instantly escalate any service ticket with negative sentiment to a senior agent.
Detect frustrated customers before they escalate
Model Source
BYOM: Inference at 3,500 credits/1M (vs Einstein 5,000/1M) — pipeline & query credits still apply.
Credit Preview
Other / Custom
Example: Demand forecasting, inventory optimization, fraud probability scoring.
Any other AI scoring use case not listed above
Model Source
BYOM: Inference at 3,500 credits/1M (vs Einstein 5,000/1M) — pipeline & query credits still apply.
Credit Preview
Knowledge Base Volume
PDFs · Wikis · Playbooks — ingested + vector-indexed for RAG
1 MB ≈ 5 PDF pages or ~1,000 plain-text emails. 160 cr/MB = 60 ingest + 100 vector index.
Unstructured AI Impact
Sizing Tips
- 1 MB ≈ 1,000 plain-text emails or ~5 PDF pages.
- Notebook AI queries include Einstein Copilot sessions and Agentforce runs.
- 2026 Fact: 1 MB = 60 (ingest) + 100 (vector index) = 160 cr/MB combined.
- 2026 Fact: Zero Copy (70 cr/1M) is the preferred architecture vs Data Share (800 cr/1M) — saves 91%.
Review & Export / Step 8
Step 8: Your estimate summary Review the credit breakdown and costs — based on the unified credit model (2026). Edit prior steps to refine.
Why this step matters: This is your final executive readout. Use it to validate sizing thresholds and share quotes with decision-makers.
- Tip: If Unify takes >50% of the pie, you may be over-processing records.
- Toggle between Executive and Architect view below for different levels of detail.
Data Health
AI Mix
Architecture
Targets
Consumption-Based (Flex)
All 10 Data 360 meters billed dynamically via Flex Credits. Rate: $0.005/credit.
Note: Total exceeds standard 10M bundle. Additional capacity needed.
Profile-Based Pricing
Includes Prep, Unification, Segmentation & Activation.
Added Flex Credits for extra meters.
Annual Storage Cost (Separate)
Workload Analysis
Total
Total Annual Investment
Cost per Unified Profile
AI Readiness Score
Investment Value Tree
Total Investment
/year
Business Impact
Trust & Identity — Executive Impact
| Category | Strategic Impact | Annual Cost |
|---|---|---|
| Trust Foundation | Duplicate removal and profile unification. | |
| Household Intelligence | Mapping individual behaviors to family/corporate units. | |
| Multi-Region Overhead | Strict brand/region data separation compliance cost. | |
| Real-Time Readiness | Ability to react to customer clicks in <200ms. |
Agentforce Digital Labor
Top 3 Use Case Costs
Strategic Cost Pillars
Step 4 Export Use Cases
| Use Case | Strategic Value | Method | Annual Credits | Annual Cost |
|---|---|---|---|---|
Technical Consumption Manifesto — The Truth Table
| Step / Operation | Input | Calculation String | Annual Credits | Annual USD |
|---|---|---|---|---|
| Configure data sources and use cases in Steps 1–7 to see the detailed breakdown. | ||||
| Total Annual Credits | ||||
Functional Breakdown — Architect View
Use Case Breakdown — Incremental Cost per Outcome
Activation Portfolio
Action Center
Annual Total —
+ storage (separate)
Native Salesforce Savings
Savings Tips
- Switch high-volume AI models to BYOM to save 30% on inference credits.
- Use Zero-Copy for Snowflake/S3/BigQuery — eliminates activation fees entirely.
- Reduce daily queries in Step 6 to drastically cut ANALYZE credits.
2026 Unified Credit Model — estimates approximate. Consult Digital Wallet for actuals.