AI Business Models 2026: Pricing That Works

Traditional SaaS metrics don't apply to AI. Here's what every AI founder needs to know about building sustainable businesses.

The Problem

Traditional SaaS

• 70-80% gross margin
• 10-20% COGS
• Scalable revenue

AI SaaS

• 40-50% COGS (or higher!)
• High variable costs
• Margin pressure

"Instead of 10-20% of revenue going to COGS, an AI SaaS might see 40-50%"

Pricing Models

1. Per-User (Traditional)

• Still works for some products
• Simple to understand
• Doesn't align with AI costs

2. Usage-Based (Recommended)

• Pay per token
• Pay per API call
• Pay per task completed
• Aligns cost with value

3. Outcome-Based (Premium)

• Pay per result
• Revenue share
• Performance fees
• Highest LTV potential

The Shift

"As autonomous agents do work without tying activity to a named employee, the link between headcount and software revenue is weakening."

Old Model

• Per seat = per employee
• Tied to headcount

New Model

• Per task/transaction
• Tied to value delivered

How To Price

Questions to Ask

1. What's your COGS per unit?
2. What's the customer's value per use?
3. How predictable is usage?
4. What's the competitive baseline?

Pricing Strategy

Model	Best For	Risk
Per seat	Low-usage tools	Underpriced
Usage-based	High-variance usage	Revenue volatility
Outcome-based	High-value tasks	Hard to measure

Real Examples

Company	Model	Notes
OpenAI	Usage (tokens)	Industry standard
Anthropic	Usage (tokens)	Context matters
Zapier	Per-task + seat	Hybrid
Harvey	Outcome-based	High-value legal

The Key Insight

Price for value, not cost.

If AI saves a customer $100K/year, charging $100/month is underpriced. Charge for impact.

Building Sustainable AI Businesses

1. Know your unit economics: Every model call costs money
2. Monitor gross margin: Target 60%+
3. Price for value: Don't leave money on the table
4. Watch usage patterns: Understand your customer

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Build better AI businesses. tldl summarizes podcasts from founders and investors.