A Guide to the Design of Credit-Based Pricing for AI Agents

The opportunity with credit-based pricing is to align value with actions and charge for the actions that create value.

TL:DR

What Are Credit-Based Pricing Models

• Definition: Users buy a bucket of credits that they consume as they execute actions or get results from AI agents
• Prevalence: 13% of AI agent companies use credit-based pricing as a primary metric, but momentum is growing rapidly
• Per-user pricing still dominates at 35%, but credit models are becoming the trend

Why Companies Are Switching to Credits

• Cost alignment: Better match pricing with actual token consumption and operational costs
• Flexible adoption: Buyers can commit to credits without knowing exact usage patterns upfront
• Granular pricing: Allows charging for specific valuable actions rather than broad access
• Predictability: When designed well, it provides predictable costs for both buyers and vendors

Key Design Principles

• Value: Ensure credits align with economic value delivered to customers
• Cost: Map credit consumption to actual delivery costs
• Transparency: Provide clear visibility into credit consumption
• Predictability: Help buyers understand credit requirements in advance
• Fungibility: Allow credits to be moved between users, actions, and time periods

Basic Structure of Credit-Based Pricing

The basic structure of a credit model is simple:

• The user takes an action
• The action has a value and a cost
• The user pays for the action with credits

These models are often combined with other pricing models to create hybrid credit models.

Why Credit-Based Models Are Being Adopted

Per user pricing metrics may be the most common, but the momentum is with credit-based pricing. All of the APIs for foundation models use credit-based pricing, and all of the hugely popular vibe coding apps have credit-based pricing (Bolt, Cursor, Lovable, Replit, V0).

Four Reasons:

1. Align price with cost - Token consumption is growing exponentially
2. Flexible adoption - Buyers can commit without knowing exact usage
3. More granular pricing - Charge for specific valuable actions
4. Predictability - When designed well, predictable for both parties

Design Goals for Credit-Based Models

A good design integrates five key aspects:

1. Value - the economic value of the solution
2. Cost - the cost of delivering the system
3. Transparency - visibility into credits consumed and remaining
4. Predictability - credits required can be understood in advance
5. Fungibility - credits can be moved between actions, users, time periods

The Design of Credit-Based Pricing Models

Things to consider:

1. Unit design - What does a credit let you do?
2. Entitlement management - Who can use credits for what actions?
3. Credit pooling and rollover - Can credits be shared and carried over?
4. Credit gifting - Can users transfer credits?
5. Credit scaling - How do credits scale with volume?
6. Hybrid pricing - How do credits combine with other models?

Common Hybrid Models

• Tiered subscriptions + credits: Like Cursor, Perplexity AI, Lovable AI
• Per-feature + credits: Like Netlify, combining infrastructure credits with feature pricing
• Output-based credits: Like Copy.ai, charging based on content generation

Design Gotchas

• Scaling: Generally avoid volume discounts since AI costs don't decrease with scale
• Expiration: All credits must eventually expire for proper revenue recognition
• Cost management: Internal token consumption can be 50-90% of total usage
• Billing complexity: Ensure billing systems can handle credit assignment and tracking

Step-by-Step Implementation

1. Create a granular value model for different agent actions
2. Develop a cost model tracking token consumption per action
3. Find the lowest common denominator for the credit unit
4. Assign credit costs to all actions
5. Design packages for common use cases (include enough for 3+ attempts)
6. Enable additional credit purchases
7. Consider hybrid pricing combinations
8. Define policies for scaling, rollover, pooling, and change management

Hybrid credit-based pricing is becoming the default approach across AI companies of all sizes, from early-stage startups to unicorns.