Claude
Skill
ai-billing-models
AI vendor billing-model fluency — auto-activates when normalizing, comparing, or forecasting AI spend across seats, usage, credits, and reserved capacity
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Download
alexclowe-awesome-copilot-cowork-plugins-finops-practitioner_skills_ai-billing-models-6662711.zip · 1 KB
Install
skills CLI
npx skills add https://github.com/alexclowe/awesome-copilot-cowork-plugins/tree/main/finops-practitioner/skills/ai-billing-models
Claude Code
claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install alexclowe-awesome-copilot-cowork-plugins@llmmart
Git
git clone https://github.com/alexclowe/awesome-copilot-cowork-plugins.git
The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole alexclowe/awesome-copilot-cowork-plugins collection as a plugin from our marketplace. Git is the plain clone.
Skill manifest
You have deep fluency in how AI products bill. When the user is working on AI spend tasks, apply this knowledge automatically.
The billing shapes
- Per-seat subscription — flat monthly/annual per user (chat assistants, copilots, embedded AI tiers). The waste mode is inactive seats; the control is seat-to-active-user reconciliation on a cadence
- Usage-based API — metered per token, request, image, or minute, usually with different input/output rates and per-model pricing. The waste modes are retries, oversized prompts/contexts, and defaulting to a bigger model than the task needs
- Prepaid credits — money in, credits out, often with expiry and sometimes with bonus tiers. The waste modes are expiry (breakage) and the false comfort of "already paid" masking a rising run rate
- Reserved / provisioned capacity — committed throughput (e.g., provisioned units) billed whether used or not. The waste mode is committing to peak instead of base load; the control is utilization tracking against the commitment
- Hybrid — seat + usage overage, platform fee + credits, or an enterprise minimum with drawdown. Read the invoice's fixed and variable parts separately or the normalization will be wrong
- Embedded AI — AI features priced inside a non-AI product (CRM tiers, office suites, support desks). Frequently invisible in an "AI spend" search because the line item doesn't say AI
Normalization rules
- Amortize annual prepay to monthly (÷12) and SAY it's amortized — cash timing and run rate are different questions
- Convert credit purchases to run rate from actual burn (credits used per month × price per credit), not from the purchase amount
- Count reserved capacity at commitment, with a utilization percentage alongside — 40% utilization of a committed pool is the finding, not a lower cost
- Keep one currency and one period; label estimates as estimates every time
Contract mechanics worth flagging
- Auto-renewal windows and notice periods (the negotiation clock starts at notice-minus-30, not at renewal)
- True-up clauses on seat growth; whether unused seats true DOWN
- Credit rollover and expiry terms
- Rate-lock or price-protection language, and what happens to grandfathered pricing at renewal
Always distinguish run-rate math (FinOps working numbers) from accounting treatment (the controller's domain) — never present the former as the latter.
Files (awesome-copilot-cowork-plugins)
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SKILL.md 2.6 KB
--- name: ai-billing-models description: AI vendor billing-model fluency — auto-activates when normalizing, comparing, or forecasting AI spend across seats, usage, credits, and reserved capacity --- You have deep fluency in how AI products bill. When the user is working on AI spend tasks, apply this knowledge automatically. ## The billing shapes - **Per-seat subscription** — flat monthly/annual per user (chat assistants, copilots, embedded AI tiers). The waste mode is inactive seats; the control is seat-to-active-user reconciliation on a cadence - **Usage-based API** — metered per token, request, image, or minute, usually with different input/output rates and per-model pricing. The waste modes are retries, oversized prompts/contexts, and defaulting to a bigger model than the task needs - **Prepaid credits** — money in, credits out, often with expiry and sometimes with bonus tiers. The waste modes are expiry (breakage) and the false comfort of "already paid" masking a rising run rate - **Reserved / provisioned capacity** — committed throughput (e.g., provisioned units) billed whether used or not. The waste mode is committing to peak instead of base load; the control is utilization tracking against the commitment - **Hybrid** — seat + usage overage, platform fee + credits, or an enterprise minimum with drawdown. Read the invoice's fixed and variable parts separately or the normalization will be wrong - **Embedded AI** — AI features priced inside a non-AI product (CRM tiers, office suites, support desks). Frequently invisible in an "AI spend" search because the line item doesn't say AI ## Normalization rules - Amortize annual prepay to monthly (÷12) and SAY it's amortized — cash timing and run rate are different questions - Convert credit purchases to run rate from actual burn (credits used per month × price per credit), not from the purchase amount - Count reserved capacity at commitment, with a utilization percentage alongside — 40% utilization of a committed pool is the finding, not a lower cost - Keep one currency and one period; label estimates as estimates every time ## Contract mechanics worth flagging - Auto-renewal windows and notice periods (the negotiation clock starts at notice-minus-30, not at renewal) - True-up clauses on seat growth; whether unused seats true DOWN - Credit rollover and expiry terms - Rate-lock or price-protection language, and what happens to grandfathered pricing at renewal Always distinguish run-rate math (FinOps working numbers) from accounting treatment (the controller's domain) — never present the former as the latter.
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