Salesforce Flex Credit estimation for Agentforce and Data Cloud workloads. TRIGGER when: user needs cost projections, scenario planning, budget sizing, or architecture tradeoff analysis for Agentforce prompts/actions, Data Cloud meters, or monthly Flex Credit usage. DO NOT TRIGGER when: user is buil

sf flex estimator: Agentforce & Data Cloud Flex Credit Estimation Use this skill when the user needs a public price estimate for: Agentforce prompt + action consumption Data Cloud monthly usage meters Flex Credit scenario planning cost optimization recommendations before build or rollout This skill is for planning and estimation , not implementation. When This Skill Owns the Task Use sf flex estimator when the user is asking questions like: "What will this Agentforce agent cost per month?" "Estimate Flex Credits for 5 prompts, 8 actions, and Data Cloud grounding" "Compare low / medium / high usage scenarios" "How much does Private Connect add?" "What Flex Credit savings do we get if we reduce streaming or action count?" Delegate elsewhere when the user is: building Builder metadata, Prompt Builder templates, or action wiring → [sf ai agentforce](../sf ai agentforce/SKILL.md) authoring or fixing .agent files → [sf ai agentscript](../sf ai agentscript/SKILL.md) implementing Data Cloud connections, streams, DMOs, segments, or activations → [sf datacloud](../sf datacloud/SKILL.md) and the phase specific sf datacloud skills creating test data or operational data imports → [sf data](../sf data/SKILL.md) deploying metadata or runtime assets → [sf deploy](../sf deploy/SKILL.md) Required Context to Gather First Ask for or infer: agent prompt count by tier: starter , basic , standard , advanced action count by type: standard , custom , voice , sandbox whether token overages are expected for prompts or actions monthly Data Cloud meter volumes, if Data Cloud is in scope whether Private Connect is required whether the estimate should model a pilot, small production, enterprise, or multiple scenarios whether the user wants public list price guidance or is trying to reconcile contract specific commercial numbers If the user does not know exact monthly volumes, start with a baseline template and generate multiple scenarios. Core Pricing Model Agentforce Agentforce billing is linear — no volume tiers. Component FC per invocation : Starter prompt 2 Basic prompt 2 Standard prompt 4 Advanced prompt 16 Standard / custom action 20 Voice action 30 Sandbox action 16 Data Cloud Data Cloud uses monthly cumulative tiering . Tier Monthly FC range Multiplier : : Tier 1 0 300K 1.0x Tier 2 300K 1.5M 0.8x Tier 3 1.5M 12.5M 0.4x Tier 4 12.5M+ 0.2x Other rules Flex Credits are priced at $0.004 per FC in this skill. Private Connect adds 20% of Data Cloud spend after tiering . Agentforce and Data Cloud are estimated separately, then combined. Estimates in this skill use publicly documented list pricing only . For the full meter table and examples, read: [references/agentforce pricing.md](references/agentforce pricing.md) [references/data cloud pricing.md](references/data cloud pricing.md) Recommended Workflow 1. Baseline the structure Model the agent and Data Cloud footprint first. Useful starting templates: [assets/templates/basic agent template.json](assets/templates/basic agent template.json) [assets/templates/hybrid agent template.json](assets/templates/hybrid agent template.json) [assets/templates/data cloud template.json](assets/templates/data cloud template.json) 2. Calculate the per invocation cost For Agentforce, estimate: 3. Calculate Data Cloud base FC Map each monthly meter volume to the current public rate card, then apply cumulative tiering. 4. Generate scenarios Use the standard scenario set unless the user provides a better one: Low: 1K invocations / month Medium: 10K / month High: 100K / month Enterprise: 500K / month 5. Validate assumptions and recommend optimizations Check for: too many prompts or actions unnecessary streaming usage likely token overages missing Private Connect handling unrealistic volume assumptions Scripts and Templates Calculator [assets/calculators/flex calculator.py](assets/calculators/flex calculator.py) [assets/calculators/tier multiplier.py](assets/calculators/tier multiplier.py) Validation helper [hooks/scripts/validate estimate.py](hooks/scripts/validate estimate.py) This validator is a manual helper . It is intentionally not wired into the shared auto validation dispatcher because generic .json or .md file patterns would create too much noise. Example commands High Signal Estimation Rules Prefer standard prompts for most production reasoning workloads. Use basic prompts only for simple routing/classification. Action count often dominates cost faster than prompt count. Data Cloud streaming is materially more expensive than prep/query/segment meters. Tiering matters only for Data Cloud , not Agentforce. Private Connect applies only to Data Cloud spend in this model. If the user has contract specific pricing, treat this skill as a public baseline and note that commercial terms may differ. Output Format When the estimate is complete, present: 1. workload summary 2. per invocation Agentforce cost 3. monthly scenario table 4. Data Cloud tiering impact 5. top optimization recommendations 6. confidence / validation notes Suggested shape: Cross Skill Integration Need Delegate to Why build the actual agent metadata [sf ai agentforce](../sf ai agentforce/SKILL.md) implementation of Builder assets build a deterministic .agent bundle [sf ai agentscript](../sf ai agentscript/SKILL.md) authoring and validation of Agent Script implement Data Cloud pipeline assets [sf datacloud](../sf datacloud/SKILL.md) and sf datacloud live Data Cloud setup package or deploy the solution [sf deploy](../sf deploy/SKILL.md) deployment workflow generate supporting test or sample data [sf data](../sf data/SKILL.md) data preparation A common chain is: Reference Map Start here [README.md](README.md) [references/calculation methodology.md](references/calculation methodology.md) [references/common use cases.md](references/common use cases.md) [references/edge cases.md](references/edge cases.md) Pricing references [references/agentforce pricing.md](references/agentforce pricing.md) [references/data cloud pricing.md](references/data cloud pricing.md) Validation and scoring [references/scoring rubric.md](references/scoring rubric.md) [hooks/scripts/validate estimate.py](hooks/scripts/validate estimate.py)

sf-flex-estimator