# AI pricing Human Guide

## What This Is For
Provides AI product pricing strategist. It gives the agent a clearer input/output frame for go-to-market work: what context to ask for, what decisions to make, and what usable artifact to return.

Use this as a human-readable version of the AI pricing agent skill. It is meant for marketers, operators, founders, and other non-coders who want the workflow without reading agent-specific implementation instructions.

## When To Use This
- Use this when you need a repeatable process for AI pricing.
- Use this when the task needs judgment, examples, constraints, or a clear output format rather than a one-off prompt.
- Use this when you want to hand an AI assistant enough context to produce a usable marketing artifact.

## When Not To Use This
- Do not use this when you only need a quick factual answer.
- Do not use this when the work depends on private data you cannot share with the assistant.
- Do not use this as a replacement for legal, compliance, financial, or medical review.

## What You Need Before Starting
- The goal or business outcome you want.
- The audience, customer segment, or market context.
- Any source material the assistant should respect, such as notes, briefs, examples, URLs, or brand guidance.
- Constraints such as tone, length, channel, deadline, region, or approval requirements.
- A clear definition of what a good final answer should look like.

## Step-By-Step Workflow
1. State the job clearly: "Use the AI pricing guide to help me with..."
2. Add context: audience, goal, offer, channel, source material, and constraints.
3. Ask the assistant to identify missing inputs before producing the final output.
4. Have the assistant follow the skill-specific guidance below.
5. Review the result against the final checklist and ask for revisions where needed.

## Skill-Specific Guidance
- Ask what type of AI product is being priced (copilot, agent, AI-enabled service, API/platform)
- Clarify the target buyer persona (developer, business user, enterprise procurement, SMB founder)
- Understand current pricing if migrating from an existing model (per-seat, flat-rate, free)
- Ask about the underlying AI cost structure (which models, average tokens per task, hosting setup)
- Determine the primary value metric the customer cares about (time saved, tasks completed, revenue generated)
- Ask about competitive landscape and what alternatives cost the buyer today
- Understand the sales motion (self-serve, sales-assisted, enterprise) as it constrains pricing design
- Check if there are existing contracts or commitments that limit pricing changes
- Case Management: 3 actions = 60 credits = $0.30 per case
- Field Service Scheduling: 6 actions = 120 credits = $0.60 per appointment
- Credits mask underlying model costs and let Salesforce adjust compute allocation without repricing
- Free tier: 2,000 code completions + 50 chat messages/month

## Decision Points And Nuance
The original skill emphasizes: Before Starting, The Three Charge Metrics, Decision Framework: Picking Your Charge Metric, Credit Systems: The Abstraction Layer, Three Product Archetypes and Their Pricing, Archetype Comparison, Copilot Pricing Deep Dive, Agent Pricing Deep Dive, AI-Enabled Service Pricing Deep Dive, Hybrid Pricing Model Design.

Use these questions to steer the work:
- What is the intended audience or buyer?
- What source material must be preserved?
- What should the assistant optimize for: clarity, persuasion, accuracy, speed, creativity, or conversion?
- What examples represent the desired quality bar?
- What should the assistant avoid?

## Common Mistakes
- | You want to avoid exposing token economics | Open-source or API-first positioning |
- | gtm-engineering | Billing infrastructure must support the chosen pricing model (metering, credits, invoicing) |

## Copy-And-Paste Prompt
```text
Use the AI pricing human guide.

My goal:
[Describe the business outcome]

Audience:
[Describe who this is for]

Context and source material:
[Paste notes, examples, links, or existing copy]

Constraints:
[Tone, length, channel, timeline, must-include items, must-avoid items]

Before producing the final output, ask me for any missing information that would materially improve the result.
```

## Final Checklist
- [ ] The output matches the original goal.
- [ ] The audience and context are reflected in the answer.
- [ ] Important constraints and source material were preserved.
- [ ] The assistant made the relevant decisions explicit.
- [ ] The final artifact is ready to use, review, or hand to the next person.

## Source
This guide was generated from the tech-leads-club/agent-skills skill entry for `ai-pricing`.

## Source Skill Notes
These notes preserve the nuance from the original skill. Use them as supporting reference when the workflow above feels too generic.

# AI Pricing Skill

You are an AI product pricing strategist. You help founders, product leaders, and GTM teams choose the right charge metric, design pricing tiers, set margin targets, and build packaging that scales with customer value. You ground every recommendation in the economics unique to AI products - where compute costs are variable, margins start lower than traditional SaaS, and the pricing model you pick reshapes your entire GTM motion.

## Before Starting

- Ask what type of AI product is being priced (copilot, agent, AI-enabled service, API/platform)
- Clarify the target buyer persona (developer, business user, enterprise procurement, SMB founder)
- Understand current pricing if migrating from an existing model (per-seat, flat-rate, free)
- Ask about the underlying AI cost structure (which models, average tokens per task, hosting setup)
- Determine the primary value metric the customer cares about (time saved, tasks completed, revenue generated)
- Ask about competitive landscape and what alternatives cost the buyer today
- Understand the sales motion (self-serve, sales-assisted, enterprise) as it constrains pricing design
- Check if there are existing contracts or commitments that limit pricing changes

## The Three Charge Metrics

Every AI pricing decision starts with choosing your charge metric. This is the unit of value you bill for. Get this wrong and everything downstream breaks.

| Charge Metric | What You Bill For | Real Examples | Best When | Watch Out For |
|---|---|---|---|---|
| Consumption | Per token, per API call, per compute minute, per credit | OpenAI API ($0.01/1K tokens), AWS Bedrock (per-token), Anthropic API | Technical buyer wants granular control; platform/API play | Customers afraid to use product; unpredictable bills kill adoption |
| Workflow | Per automation run, per agent task, per document processed | n8n (per workflow run), Jasper (per content piece), DocuSign (per envelope) | Clear time-saving value per task; easy to define boundaries | Must define task boundaries precisely; scope creep erodes margins |
| Outcome | Per resolved ticket, per qualified lead, per successful match | Intercom Fin ($0.99/resolution), Sierra (per completed outcome), Salesforce Agentforce ($2/conversation) | Maximum value alignment; outcome is measurable and attributable | You absorb cost variability; must define "success" precisely |

### Decision Framework: Picking Your Charge Metric

```
START HERE
    |
    v
Can the customer measure a specific business outcome
from your product? (resolved ticket, qualified lead, closed deal)
    |
   YES --> Is the outcome clearly attributable to YOUR product
    |      (not shared with other tools)?
    |          |
    |         YES --> OUTCOME-BASED pricing
    |          |      Charge per resolved ticket, per qualified lead
    |         NO  --> WORKFLOW pricing
    |                 Charge per task/run (shared attribution = charge for the work)
    |
   NO --> Does the customer perform discrete, countable tasks?
    |      (document processed, image generated, report created)
    |          |
    |         YES --> WORKFLOW pricing
    |          |      Charge per task, per run, per document
    |         NO  --> CONSUMPTION pricing
                      Charge per token, per API call, per credit
```

### Credit Systems: The Abstraction Layer

Credits sit between raw consumption and the customer. They let you change underlying costs without repricing. 126% growth in credit-model adoption among SaaS companies from end of 2024 to end of 2025.

**How credits work in practice:**

| Component | Example |
|---|---|
| Credit unit | 1 credit = 1 standard task |
| Simple task | 1 credit (e.g., summarize email) |
| Medium task | 3 credits (e.g., draft response) |
| Complex task | 10 credits (e.g., full research report) |
| Monthly package | Starter: 500 credits, Pro: 2,000 credits, Enterprise: custom |

**When to use credits vs. direct metering:**

| Use Credits When | Use Direct Metering When |
|---|---|
| Multiple task types with different costs | Single task type (API calls, resolutions) |
| You need pricing flexibility as models change | Buyer expects transparent per-unit cost |
| Bundling features across product lines | Developer audience wants raw metrics |
| You want to avoid exposing token economics | Open-source or API-first positioning |

**Salesforce Agentforce credit example:**
- 20 Flex Credits = 1 action
- $500 buys 100,000 credits
- Case Management: 3 actions = 60 credits = $0.30 per case
- Field Service Scheduling: 6 actions = 120 credits = $0.60 per appointment
- Credits mask underlying model costs and let Salesforce adjust compute allocation without repricing