How Are Companies Pricing Their AI Features?

In 2025, building AI features is the easy part. Pricing them? Not so easy.

Should it be per token?
Per seat?
Per outcome?
Per magical fairy sprinkle of GenAI?

I went deep into current pricing strategies and found 10 battle-tested models that product and GTM teams are using today, from OpenAI to Grammarly to Forethought.

Here's the breakdown, and where most teams still get it wrong.

1. Usage-Based Pricing

🔍 What it is: Pay per token, API call, or compute.
🏢 Used by: OpenAI API, Amazon Lex
✅ Why it works: Scales with usage. Feels “fair” to many buyers.

2. Value-Based, Performance-Based, Succes-Based Pricing

🔍 What it is: Charge based on the impact you deliver, not your infra cost.
🏢 Used by: Intercom, IBM Watson
✅ Why it works: Makes ROI obvious. Great for enterprise buyers.

3. Subscription or Tiered Pricing

🔍 What it is: Recurring payments tied to access levels.
🏢 Used by: Midjourney
✅ Why it works: Predictable revenue, familiar buyer mental model.

4. Freemium Model

🔍 What it is: Free core, paid power.
🏢 Used by: Clarifai, Grammarly
✅ Why it works: Great for adoption, long-term monetization.

5. License-Based/ By-Seat Pricing

🔍 What it is: Flat-rate access, usually for enterprise
🏢 Used by: Most B2B AI platforms
✅ Why it works: Stability. Negotiable. Legal & procurement-friendly.

6. Add-On AI Features

🔍 What it is: AI as a premium bolt-on.
🏢 Used by: Notion AI
✅ Why it works: Easy upsell path. Keeps base product clean.

So… Which Pricing Model Should You Pick?

You can’t just copy-paste another company's pricing. Well, I guess, technically, you can. But you should reverse-engineer your strengths and pick the right model to test. And don’t forget that you can mix the pricing models.

Start here:

• Is your cost structure predictable? → Go subscription.

• Is ROI clearly measurable? → Try value-based or success-based.

• Are you early and want adoption? → Use freemium + performance-based hybrid.

The Missing Piece: Pricing ≠ Value Without Story

Most pricing strategies fail not because they’re wrong but because they’re not communicated clearly.

Strong AI companies don’t just set a price. They:

✅ Break down infra cost transparently (compute, tokens)
✅ Quantify efficiency gains or cost savings
✅ Highlight outcomes with real customer examples

TL;DR

Pricing AI products is a blend of:

• Usage metrics

• Value storytelling

• Buyer psychology

• Strategic optionality

The best pricing model is the one that learns fast, adapts with your customer, and reinforces your product’s value.