For years, the seat-based pricing model has been the go-to standard in the SaaS industry. This model charges companies based on the number of user licenses or "seats" they purchase.
However, the integration of AI into SaaS platforms is disrupting traditional approaches, necessitating adaptations and refinements to existing strategies.
In fact, AI's ability to automate tasks actually challenges the relevance of seat-based pricing. Let’s discuss this further today.
The decline of seats
As AI continues to evolve, it automates many tasks that once required human intervention. This shift reduces the need for multiple-user licenses, making the traditional seat-based model less practical.
For instance, AI-driven customer support systems can resolve issues without human involvement, reducing the number of seats needed. As a result, the value customers derive from the software is less about the number of users and more about the amount of work being accomplished by the AI.
And while we’re very much in the early innings, we’re starting to see a few examples of the pressure on seats already.
Anecdotally, sales engineers at Salesforce note that some large accounts are reducing seats by 10% due to productivity gains from the Einstein AI.
“I also spoke to a Salesforce sales engineer who handles 90 enterprise accounts on the Service Cloud side. He said across his customers, they’re seeing a 10% reduction in seats/headcount because Einstein is making their customer service agents that much more efficient. There is a slight offset from revenue coming in on Data Cloud, but otherwise they haven’t shifted the pricing model on service yet so they are slightly losing out.” – Portsea Capital, at the AWS Summit
As SaaS platforms incorporate AI, the link between user count and value creation weakens. In some cases, fewer users may generate more value, or user numbers may become irrelevant to the value produced. This shift necessitates changes in pricing models, ranging from minor tweaks to complete restructuring.
In terms of emerging pricing models, I think two interesting ones are worth discussing: work-based pricing and outcome-based pricing. Both of them share roughly the same philosophy, charging based on some proxy of the work that AI is actually doing.
For work-based pricing, companies can use softer “proof of work” metrics that demonstrate task completion such as credits/work processed or similar. Outcome-based pricing focuses on end results that directly matter to customers. This could involve charging based on measurable business impacts or specific performance metrics.
Work-based pricing
This model, also called usage-based pricing, charges customers based on the amount of “work” the software performs. In other words, the pricing is based on some measure of the usage of the software or AI agents.
Usage-based pricing is already the default in infrastructure services like cloud computing platforms (think AWS and Azure) and other independent databases or developer tools (as well as AI model providers themselves). It is usually some function of storage and compute services.
In SaaS, although the metrics may differ from those used in storage or compute services, a similar approach can be applied to measure the AI's workload. This strategy is particularly relevant as many SaaS providers are essentially "selling the work" performed by AI. It's logical to base pricing on the work sold rather than the number of users.
One thing to note is that this isn’t necessarily a completely new concept in SaaS. Many of the email marketing tools (Mailchimp), GTM data vendors (Apollo) and others did price on some proxy of usage. However, the concept will likely be relevant to many more companies than in the past, and the metrics may get closer to some proxy of “work performed.”
Here are some examples:
- Clay: Clay is an AI-powered workflow automation tool for sales. Since the number of seats is less relevant, the platform charges on a function of work or usage. Specifically, Clay has various subscription offerings based on the number of credits the customer expects to need, where every automation is associated with a certain number of credits.
- Heygen and Synthesia are both AI-powered avatar video creation platforms. Both of them have various subscription tiers that are largely tied to the number of minutes of videos produced, their core unit of work.
- AirOps: AirOps is an AI-powered workflow product for content and growth teams. The company charges in a tiered manner based on the number of tasks it performs (posts written, landing pages created, etc.)
However, work-based pricing models come with a significant drawback: unpredictability. While they offer flexibility, they also introduce a high degree of uncertainty for end customers, who may struggle to forecast their expenses accurately. Additionally, this model poses challenges for SaaS businesses, as it can lead to less stable, truly recurring revenue streams.
To counteract this unpredictability, startups might consider implementing tiered pricing structures, particularly in the early stages of transitioning to work-based pricing. These tiers would offer customers relatively stable monthly fees, but the tier levels would be determined by the volume of work performed. Some tiers might also incorporate a maximum number of seats as an additional limiting factor.
Clay’s pricing structure below is a good example.
Outcome-based pricing
This model takes the concept of work-based pricing a step further. It focuses on charging customers based on the specific results that matter most to them. This approach aims to measure the price directly according to the actual value the customer receives from the service.
Often, one of the challenges is identifying the right metric, in terms of both being willing to charge on it (since some factors may be out of the vendor’s control) and the customer willing to pay on it (since they may feel like they’re paying too much in some cases or the pricing is too uncertain).
Outcome-based metrics can vary widely depending on the service and industry, and might include things like:
- Customer support: Number of successfully resolved tickets.
- Sales: Quantity of meetings booked.
- Marketing: Volume of qualified leads generated.
- Finance: Cost savings achieved.
It should be noted that a strong form of outcome-based pricing (very specific outcome metrics) is unlikely to make sense for many categories of software. While I don’t expect to see very many AI companies charge on outcomes immediately, we may see it soon, in a few select areas of software.
Look for this model in areas like:
- Customer support AI agents: Customer support SaaS platforms are starting to implement outcome-based pricing. AI agents such as Fin from Intercom and startups in this space seem to be landing on a pricing structure of charging in the $1-2 range per successful ticket resolution. In these cases, AI agents try to handle every incoming ticket. They succeed on some and “fail” on others by deflecting them to humans, and only charge for the ones they resolve on their own.
- Vendr and Chargeflow are a few other examples of companies that have used outcome-based pricing prior to this AI era. Vendr is a SaaS management platform that charges customers typically around 20% of the savings it is able to generate for them by negotiating their SaaS subscriptions. Chargeflow charges companies 25% of the chargebacks they are able to successfully reverse for their customers.
A middle-ground approach to pricing AI services is to benchmark them against equivalent human labor costs, offering a discount compared to hiring actual people. This method provides a familiar reference point for customers while demonstrating the cost-effectiveness of AI.
For example, 11x is building AI agents for sales, specifically AI-based SDRs. The company sells its products in units of its AI SDR, “Alice.” Each unit of Alice costs a certain amount per month and does some specific amount of work (number of accounts researched, number of emails sent, etc.).
In 11x marketing materials, the company also references the notion of “hiring” Alice, a parallel to hiring a human SDR, and compares the work one Alice does to a human SDR. An approach like this can allow for value-based pricing where the comp of value is a human equivalent.
Devin is an AI software engineer similar to 11x. It allows companies to hire instances of Devin, which are capable of doing a certain amount of work. Its pricing can run into the thousands of dollars per Devin per month, a portion of what a junior engineer may cost to hire.
Managing the transition
While innovative pricing models offer significant advantages, most incumbent companies will still rely on seat-based pricing, particularly if the core of the software is still based on user seats (CRMs, Docs, etc.) but with added work-based components. Notion, Salesforce and other companies often price AI features as premium per-user add-ons.
This hybrid approach allows them to transition gradually while still leveraging AI's capabilities, and capture some of the potential benefits of the AI features they launch. In fact, if the AI they offer is largely the copilot type, as I discussed in my previous piece, that will likely continue to be the right form of pricing to offer.
In contrast, startups, especially those offering AI agents rather than copilots, can gain an edge by adopting work-based pricing models. In fact, the pricing model itself may help them counterposition themselves against the incumbents, who may find it harder to overturn their seat-based models given the revenue that is at risk.
The balancing act of SaaS AI pricing
Many companies, particularly established ones, may view AI and the potential loss of seats as a risk. But it also represents a big opportunity.
For incumbents, the challenge lies in balancing the transition to innovative pricing structures, while startups can differentiate themselves by adopting work-based or outcome-based models from the outset. Success will depend on effectively communicating and monetizing the unique benefits of AI-enhanced offerings, so they can find new avenues for growth and customer satisfaction.
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