Monetising AI Features Without Killing Adoption

The Compute Cost Problem

AI features, particularly those powered by large language models, have a fundamentally different cost structure to traditional software. Each inference has a marginal cost that scales with usage, unlike conventional SaaS where marginal costs approach zero.

This creates a monetisation challenge that Andreessen Horowitz has described extensively: how do you price a feature that costs more to deliver every time a customer uses it?

The Engagement Paradox

The features that drive the most user engagement are often the most expensive to run. An AI coding assistant that developers love using ten times per hour may be economically unsustainable at a flat subscription rate.

Companies face a difficult choice:

• Cap usage: Limit AI feature access to control costs, but risk undermining the core value proposition.
• Charge per use: Align revenue with costs, but create friction that reduces adoption.
• Bundle and absorb: Include AI features in existing pricing and accept lower margins, hoping that retention gains offset compute costs.

Framework for AI Monetisation

Drawing on principles from Simon-Kucher, we recommend a structured approach:

Step 1: Segment by value sensitivity

Not all users derive the same value from AI features. Power users who rely on AI for critical workflows will tolerate usage-based pricing. Casual users who experiment occasionally need a generous free tier to maintain engagement.

Step 2: Design tiered access

Create a structure where basic AI capabilities are included in existing plans (driving adoption and stickiness) while advanced capabilities (more powerful models, higher throughput, custom training) sit in premium tiers.

Step 3: Implement usage guardrails

Rather than hard caps, implement soft limits with clear communication. Notion's approach to AI credits demonstrates this well: generous allocation with transparent upgrade paths.

The Margin Stack

Understanding your AI margin stack is critical:

• Inference cost per request: Varies dramatically by model size and provider. Track this at the feature level, not the product level.
• Batch optimisation opportunity: Can you cache, pre-compute, or batch requests to reduce per-unit costs?
• Value delivered per request: What is the customer outcome worth? This determines pricing ceiling.

Pricing Models That Work

The most successful AI monetisation models share common traits:

1. Credits-based systems: As popularised by GitHub Copilot, users receive a monthly allocation that refreshes, with the option to purchase additional credits.
2. Outcome-based pricing: Charge for the result (document generated, analysis completed) rather than the input (tokens processed).
3. Hybrid models: Base subscription for platform access plus usage-based pricing for AI features, ensuring predictable revenue while capturing upside from power users.

Looking Forward

As model costs continue to decline and open-source alternatives mature, the economics of AI features will shift. Companies that design flexible monetisation frameworks now will be better positioned to adjust pricing as their cost structure evolves.