How should you monetize your AI features?
What we can learn from the monetization strategies of 44 leading tech companies, including OpenAI, GitHub, Zapier, Adobe, and Microsoft
Hey everyone,
I just wrote a piece on how tech companies can best monetize their AI features for Lenny Rachitsky’s Lenny's Newsletter and I’m really excited to share a bit of it with you here.
✨ Here’s the intro (full article is here):
Q: What trends are you seeing in how incumbents are pricing AI features and products? Have you seen any innovation in how companies charge?
AI features and products present brand-new pricing challenges for companies. I’ve spent the past decade working on monetization strategies for places like Uber and Templafy, as well as advising more than 20 tech companies on their approaches, and what I’m seeing around AI products is very different from older technologies. Unlike with traditional SaaS products, companies looking to integrate AI products and features need to consider the real, underlying costs of generative AI compute and the intense competitive pressure in the AI market now, while also focusing on adoption and new business models. To ensure long-term ROI, companies have always needed to think carefully about how and when to monetize, but AI requires even deeper analysis.
During Alphabet’s 2024 Q2 earnings call, many questions from investors addressed the ROI of the company’s $12 billion AI investments—a real shift from the previous quarter’s call. It’s clear that investor focus is changing from pure adoption to how big tech is going to be monetizing their innovations.
This left me wondering: How do tech companies monetize their new AI features today? And what can we learn from that data?
To answer, I investigated the pricing and bundling strategies of 44 leading tech incumbents. I focused on the “application layer”—companies that are building end-user products (e.g. Figma)—rather than base models (OpenAI’s LLM) or infrastructure (e.g. Azure). We reviewed public data for pricing models, value metrics, bundling and free versions to identify current trends. Based on that data and my own experience in pricing, I’ve put together a framework for you to help make strategic decisions for your own AI products and features.
A simplified perspective on the three AI layers. The focus of this article is on the top layer—the large tech incumbents in the application layer.
Click to see higher-resolution image. We added AI pricing for the companies that had publicly available data.
1. Direct and indirect monetization strategies
Broadly speaking, there are two methods that companies can use to monetize AI features: direct and indirect monetization. Direct monetization involves charging for the AI feature directly, or increasing the price of your product after adding the new AI feature. Conversely, indirect monetization integrates the AI feature into an existing bundle without altering the price, or offers the feature on its own at no additional cost.
Below is an overview of the five high-level monetization strategies we are seeing right now for tech companies launching AI features and products.
Ready to dive in? Here is the full AI pricing break down:
