Pricing 101 for AI Apps: The Top Three Monetization Levers to Get Right

Part 2 of our Show Me the Money: How to Monetize AI series.

As an early-stage growth advisor for startups, one of the most common questions I am asked the most: how do I figure out how much to charge my customers, particularly in AI? Ask a founder and it's likely to be one of the top things keeping them up at night -- they're either afraid of charging too much for their early AI product that will stunt growth or scared of undercharging that will limit their addressable market.

Out of the top variables you can pull to impact growth, it doesn't get more table stakes than monetization -- for every 1% improvement in monetization, you can expect a 12.7% improvement to your bottom line. In comparison, a 1% improvement in acquisition and retention, respectively, only gives you a 3.3% and 6.7% return [1]. It's also the most underutilized lever for startups. Founders often tell me they're not sure where to start or are afraid of charging their customers as they iterate towards product-market fit as the AI market evolves.

But, getting your pricing right for your AI product is actually key to determining true product-market fit early. I break down the top three levers for startup founders to consider when charging for your AI application products below.

1. Figure Out the Value Equation

AI doesn't change the fundamentals of pricing. Of the three types of pricing strategies (value-based, cost-plus, competitor), I'd argue value-based is the most important to get right long-term. This becomes especially important for AI apps where buyers have high hopes but also don't know yet how to quantify the ROI. The other two strategies are important to consider in the short-term, but undercutting your closest competitor or pricing based on your ideal margins will ultimately leave money on the table in the future.

The first step of any pricing journey is to determine your value metric by deconstructing the value that you are building for the world. I like to ask founders I work with what success looks like in ten years when their companies have made their impact. What will you have done to change the world and how can you best measure this?

Here are some examples from a few of the most talked-about AI-applied software companies today:

What's notable? The pre-existing functional value metrics that tie value to how much or how often customers use a product are rapidly evolving. These are the per seat, per active user, average session length, and feature usage. As we saw with Part 1 of our series, there are more and more companies experimenting with hybrid pricing metrics or throwing out the per seat metric out altogether in favor of outcome-based value metrics. More companies are starting to realize that tying outcomes to pricing may be the better way to overcome ROI conversations during sales calls.

2. Align Pricing Model With GTM Strategy

With a pricing metric in hand, the next step is to figure out what pricing model to best use. "Pricing model" sounds intimidating for most founders in AI that are in their early innings and not sure they can develop something repeatable and scalable yet. But, it's a chicken and egg problem. You have to start somewhere for you to start learning fast -- get to V1 and then iterate.

Pricing models are simple. When you break it down, a pricing model has two components:

When are you going to charge them?
How are you going to charge your customers?

For the first, simple is best. Most AI-enabled software companies invoice on either a monthly or annual basis. Annual contracts typically have a 10-20% discount to monthly.

The second question is where it can get crazy and founders overthink. The most common pricing models for value-based pricing are:

Subscription: The majority of companies still prefer to price via subscription. Not only does it keep things simple and easy to pitch, it also creates predictable recurring revenue for startups to plan for and measure against.
Usage-based: Pay-as-you-go models align incentives between software providers and users. After all, you only pay for what you use and you don't get much closer than this model to aligning value and price.
Platform: Some companies choose to charge a base fee to access a platform, and leverage it in conjunction with other pricing models. Add-ons solving more specific use cases are typically offered on top of this. This is often common for companies that have bespoke implementation or onboarding where customers have specific needs.
Transactional: Take rate models are common for platforms that facilitate transactions or for products that scale directly with transaction volume. This is common for companies where customers have consistent, high transaction volumes and success is directly tied to transactions.
Outcome-based: More recently, I've seen companies start tying pricing with specific outcomes achieved (e.g., per lead, per conversion). This is the closest you can get to aligning price with value delivered and incentivizes quality. But, it's also not the easiest thing to define "successful" outcomes. It also creates risk for startups who can't deliver high accuracy yet.

This doesn't change if you are working in the AI space, but there are considerations for how scalable and costly your model training or LLM fine-tuning may cost. My advice is always to match your GTM strategy and value equation to one pricing model first, and then calibrate as you gather additional customer data.

3. Set Prices and Experiment!

Lastly, how much should you charge for your AI product? This is the single most overanalyzed question in pricing. Let's assume you're a great student of pricing and you've figured out your value equation and pricing model. Do you then charge less than this and overdeliver? Do you charge more than your competitors for the same buyers? What if you undercharge or overcharge?

Your GTM strategy will naturally create boundaries for pricing. Your pricing needs to be consistent with your product and distribution channels as it will have a direct impact on what channels you can afford (direct impact on LTV/CAC). It also affects your perception in the market. If you are leading with product-led growth, then you can't have a prohibitively expensive product that won't scale with freemium or trials. If you are squarely an enterprise-focused product with a large sales force, you can't be the affordable solution that always undercuts -- buyers are going to question the quality of your product and your sales team won't be as efficient.

I recommend leveraging a combination of customer data, competitive market dynamics, and customer surveys or conversations to understand willingness-to-pay and product ROI. Then, experiment! When you get to 20-40% loss rate, you're in the right ballpark in pricing level and can start optimizing with more data.

That's it for now. In Part 3 of our series, I'll explore a few things startup founders need to be on the look out for in their GTM strategy for as AI apps and end buyers become more sophisticated.

Notes:

[1] Pricing Intelligently, https://www.paddle.com/resources/subscription-revenue-model