How to Price and Package Your Gen AI Feature: Revisited
How some of the best SaaS companies are updating their pricing to compete in today's market.
As we head into the holidays, we asked some of our partners to reflect on their greatest hits from the archives. What’s changed about their thinking since the original post? How has the argument they outlined evolved?
Today, here’s Tugce Erten’s post from last year on pricing and packaging in the bold new world of AI. The world has moved so quickly in the past 18 months, and we’re excited to update it here.
Last spring, some of my a16z Growth colleagues and I published a framework for pricing and packaging your B2B or prosumer gen AI feature. I checked in on how some of the SaaS companies we referenced in the original post updated their pricing and packaging over the past ~20 months, below.
Some quick takeaways:
AI features that were originally premium add-ons have become table stakes. Major SaaS players like Notion and Salesforce have shifted from charging extra for AI to bundling it into core offerings, which tells me that AI features are now a requirement to play in a very competitive market.
Companies that shifted their AI feature packaging from add-on to core generally shifted their pricing from subscription to hybrid for the AI functionality, presumably to better account for the cost of inference. Despite the chatter around outcome-based pricing, we’re not seeing many SaaS companies adopt it right now. Companies that offer AI agents generally have better success with that pricing model.
While Microsoft and Adobe still monetize AI separately, the broader trend I’m seeing is pretty clear: AI has gone from appealing mostly to early adopters to appealing to all companies, which means that AI features are now a baseline expectation from customers.
As we said below, pricing is an iterative process, and in a market as dynamic as AI and software, companies should still be ready to adapt quickly to the fluctuating costs of inference.
Original post, March 22, 2024
Effectively monetizing any new technology is a race to capture market share while still giving yourself room to grow your business. But the stakes are much higher with generative AI: though it promises to deliver unprecedented value to businesses, it can also be very expensive to serve to each incremental customer. At the growth stages in particular, founders need to be mindful of their unit economics and margins. We often hear these founders ask: how can I capture the value created by gen AI? Should I eat the cost of my gen AI features, or pass it along to customers? Will my customers actually be willing to pay for gen AI and if so, how much?
We’re in the very early days of gen AI and until adoption curves and costs stabilize, there won’t be any tried-and-true pricing or packaging frameworks. That said, we’ve outlined how we’re thinking about pricing and packaging in a part of the market that’s debating how to monetize their new gen AI feature—B2B SaaS and prosumer companies—and how we’re seeing other companies approach the same question so you can better understand where your strategy fits in today.
Triangulate early usage, customer personas, and product mission
As with any pricing and packaging exercise, the best place to start is to understand:
How much value your feature delivers and to whom, and
How much serving that feature costs
Then, square those findings with how core you think gen AI will be to your overall product offering.
Some of this will be more art than science, since founders are still figuring out what value gen AI delivers to customers and how much it costs. That said, your early usage and customer personas can give you insight into both of these vectors.
Beta and early usage
For beta and early usage, you want to understand which customers are using your product, how often, how much it costs you to serve them, and how much they’re willing to pay for a gen AI feature. For instance, it’s important to dig into the following:
Does it increase your TAM? (e.g., were you previously serving 10 customers and can now serve 100?)
Does your gen AI feature increase conversion from a free to a paid tier, or a higher paid tier?
Does your product have a base of power users who account for the majority of the feature’s use? If so, how are they impacting your COGS?
Customer personas
For customer personas, you want to figure out which personas are willing to pay and which aren’t. Do all your customers realize value from the gen AI feature, or just some?
A good way to learn more about your customer personas and segments is through interviews, surveys, and sales team data.
Interviews. If you have a small number of customers, interviewing them can give you a better signal into who’s interested in buying your product and what products they might be interested in in the future.
Surveys. If you have a larger number, you can survey them about what potential new features are most important to them, then tie that information back to their company industry or function.
Sales team data. Your sales team talks to customers day in and day out, which means they usually have a great read on what features different types of customers need in order to be successful with your product.
One note here: be wary of “AI tourists”—or customers who sign up for your product because they either have a company-wide mandate to experiment with gen AI (B2B) or because they’re excited to try a new gen AI feature themselves (prosumer). These users can be difficult to retain, even in cases where they’re willing to pay to try your product. (That said, we are seeing more enterprise companies reallocate gen AI spend from innovation lines to standard software lines, which indicates that gen AI is evolving into a key part of many enterprises.)
Product vision
This is where your vision as a founder comes in. Maybe only a subset of your customers are excited about generative AI right now, but you believe that generative AI will eventually reshape the customer experience of your product and present a much richer value prop. Or maybe you’re still determining how generative AI will benefit your customers, and right now it’s a nice-to-have for certain users. This part of the exercise is qualitative and vision-oriented, and it’s up to you to decide how central gen AI will be to your product roadmap and value prop going forward.
Once you have both a good sense of the value and cost of your gen AI feature, and a working hypothesis for how generative AI figures into your current product offering and future roadmap, you can start thinking about packaging and pricing concretely.
Packaging: core, upgrade, or add-on?
We generally see B2B and prosumer gen AI features fall into 3 buckets: as a core offering, as part of an upgrade tier, or as an add-on. Many of the best practices for packaging non-gen AI prosumer and B2B SaaS still apply in the gen AI era at this point, so it can also be helpful to reference how companies have packaged their new non-gen AI features in the past.
Core
If all your customers are excited and willing to pay for your gen AI feature, early usage data shows that it significantly increases adoption and conversion, and gen AI is mission-critical to your value prop, it can be smart to include it as part of your core offering. In this case, you might not need to directly monetize your gen AI feature because it has significant downstream effects on your TAM and conversion. The example we cited earlier applies here: maybe you could serve 10 customers with your existing product and now you can serve 100 thanks to your gen AI feature.
Since we’re in the “land grab” phase of generative AI adoption, including your generative AI feature in your core offering can also make your product more appealing than offerings from incumbents and other startups. Because there’s demand for this feature across all segments, however, we imagine that some companies will eventually increase the total price of their core package to better account for the cost of serving the feature.
Upgrade
Packaging your gen AI feature as an upgrade works when it’s a nice-to-have feature that can act as an upsell lever for the majority of your customers. The feature doesn’t radically change the way customers use your product, but it can help most of them unlock more value. Some gen AI companies might offer more data sets in an upgrade feature, for instance. Or take Mailchimp as an example. Most of their users might not need a gen AI feature in the core offering—they likely just want to be able to build and serve an email list—but gen AI-generated email copy, segmentation, and analytics could enhance most users’ experience of the product. We’ve also seen some companies use their gen AI feature as an upsell lever to increase conversion to a higher pricing tier or cover part of the cost of serving gen AI.
Add-on
Packaging a gen AI feature as an add-on is wise if your gen AI feature delivers significant value to a small set of customers willing to pay a premium and you want to directly manage your margins when serving the feature. Packaging as an add-on can:
Monetize your innovation directly, which leads to more sustainable margins over the short term. (If you believe AI will become a core differentiating factor for your product over the long term, however, you may need to transition to a different packaging model.)
Increase your TAM: you can charge more for the customers who are willing to pay more while also maintaining customers at your existing price point.
Offer a way to beta test your feature with an audience that’s excited about the product and willing to give feedback on it.
We think of the add-on as the “power user package”—companies can charge a premium for their products because a set of power users will disproportionately benefit from that feature. That said, we have seen some other companies who package their add-on gen AI features sell the add-on to the entire company when doing enterprise deals to prevent users from sharing logins, even if only a few individual users want it. This might cover costs in the short-term, but be aware that buyers might not always want to buy software packages with this mandate.
One note: we’ve seen some companies include basic gen AI features in a core or lower-paid tier and gate better-performing gen AI features or more gen AI usage in a higher tier. In these cases, the logic of segmenting for value remains the same. If a gen AI feature can expand the number of users you can serve, for instance, consider offering it as a core feature. If your other, more high-powered gen AI feature enables your power users, you could gate that feature and charge more for it.
Pricing: subscription or hybrid?
Because most B2B SaaS and prosumer companies sell software-to-human products, it makes sense to monetize through subscriptions instead of usage—no customer wants to estimate “how much” gen AI they’ll use. But subscription pricing in the gen AI era can exaggerate the gap between how much your customers use your product and how much revenue you actually bring in. In fact, selling seats for your gen AI feature can actually put you in the position of hoping your customers don’t use your products. Power users pay the same flat fee as customers who barely use your product, which means your most important customers can eat into your margins.
So how can you better align your incentives with your customers’? Because we’re still in the early phase of gen AI adoption, it remains an open question.
When we take a look at the current landscape, however, we see core and upgrade packaging priced by seat by default. Gen AI is either part of a base product or an added feature in an existing subscription tier. That said, some companies are experimenting with hybrid subscription–consumption models when pricing their add-on features in order to better cover costs and monetize power users. These hybrid approaches include credit drawdown approaches (like Box) or flat-rate seats with credits for incremental levels of consumption (like Adobe Creative Cloud). (When adding a usage-based element to an existing subscription motion, consider how to provide predictability to customers and handle overconsumption.)
What are others doing?
We examined 31 companies with new gen AI offerings to see how they’re pricing and packaging their new gen AI feature. Here’s what we see in the data.
How will gen AI pricing and packaging evolve?
Outcome-based pricing and other pricing metrics gain traction.
As gen AI starts to offer customers significant productivity gains, some companies are thinking ahead to implementing outcome-based pricing, in which vendors charge companies for the outcomes of their software instead of for the software itself.
Outcome-based pricing is harder to get right today, since founders are still figuring out how to quantify the value gen AI provides their customers. But if gen AI features make companies significantly more productive down the road, it won’t make economic sense to price your offerings on a contracting user base. So we see outcome-based pricing having a potentially significant impact on companies selling a software-to-human product, like a workflow or human resources tool.
The advantage of this pricing model is getting tightly aligned with your customers’ incentives, but making sure you and your customers agree on what defines an “outcome” or “resolution” can be difficult and you’d need to trust that gen AI could reliably resolve your customers’ questions. That said, we’re already seeing some companies experiment with this, like Intercom’s Fin Chat product, and we’re excited to see how this evolves.
Prepare to be nimble with your pricing.
The cost of inference is stabilizing, open source is booming, and different model providers are constantly driving down the price of their models in a bid to attract more users. Given this, companies should be ready to adapt their pricing models as model providers continue to lower the cost of APIs.
For now, it’s probably wise to price at a level that’s at least somewhat economical for your business in the short term while expecting the cost to service to decline over the long term (as it already has!) and drive future margin expansion. That said, if you’re banking on your gen AI feature to become a core part of your business and you’re not seeing results soon, don’t hesitate to revisit your pricing and packaging structure.
Generative AI stands to deliver an unprecedented amount of value to software end users, and the goal for growth-stage founders now is to figure out how to best capture that value while maintaining stable unit economics and solid margins. With no one-size-fits-all solution, successful founders will have to take past and real-time learnings to build a clear, nimble pricing and packaging structure that communicates the value of their product roadmap.
Though best practices are still emerging, we hope these frameworks help you better navigate the pricing and packaging process for your new gen AI feature.
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