Building outcome-based pricing for Fin for Sales

Our outcome-based pricing model hinges on one principle: you pay when Fin delivers value.

As Fin takes on new roles, that principle doesn’t change, but the definition of value does.

Fin for Sales qualifies leads, engages prospects, and routes high-intent buyers to your sales team. The value it creates isn’t a resolved query, but a pipeline of qualified opportunities. So we price accordingly: $10 per qualified lead. And you, the customer, define what “qualified” means, not Fin.

This is the first outcome-based pricing model for an AI Agent for sales. Here’s why we think it’s the right approach.

What outcome-based pricing taught us

A year ago, I wrote about what value-based pricing really means for AI. The core finding from our research was unambiguous: zero buyers preferred paying for activity. They wanted to pay for results.

That insight shaped how we priced Fin for its service role, $0.99 per resolution, where a resolution means the customer’s issue is fully solved without human intervention. More recently, we evolved that model to outcomes, reflecting the broader ways Fin delivers value across complex workflows.

We believe pricing should be aligned with value delivery, and the vendor should carry risk when the product doesn’t perform. In sales, the best unit of value is pipeline.

Pricing on the right signal

Most sales teams today are overwhelmed by leads. But the quality of these prospects can be hard to discern from online forms and generic chatbots, leaving reps tied up in conversations leading nowhere.

Ensuring the right opportunities promptly reach your sales team is what makes a difference. When a prospect visits your site, engages with Fin, answers qualifying questions, and is directed to a sales rep, Fin is identifying whether the opportunity is worth your team’s time and delivering value.

Charging per conversation would penalize businesses for every curious visitor who asks a question but isn’t a buyer. And charging per token, well, that’s always been a model that protects the vendor, not the customer.

We needed a metric that captures the actual value Fin creates in a sales context: qualified leads.

Why we rejected revenue as an outcome

The purest version of outcome-based pricing for Fin’s sales role would be a percentage of closed revenue. Fin qualifies the lead, a rep closes the deal, and we take a cut.

We considered it. On paper, it’s the most value-aligned model possible. In practice, it breaks down for two reasons.

First, attribution. Between the moment Fin qualifies a lead and the moment a deal closes, dozens of things can impact the final result. The quality of human-led demos can differ, products can have outages, prospects’ budgets can get cut. Tying Fin’s price to the final outcome holds it accountable for variables entirely outside its control.

Second, measurement. To track closed revenue, we’d need deep integration into every customer’s CRM, tracking each opportunity from qualification through to close. That’s a significant implementation burden that slows time to value, which is the opposite of what we want.

So we asked: what’s the most honest proxy for the value Fin delivers, where Fin is clearly the one creating it?

A qualified lead is the right proxy

A qualified lead represents the moment Fin has done its job. It has engaged the prospect, gathered the relevant information, evaluated them against your criteria, and determined they’re qualified. Everything up to that point is Fin’s work. Everything after it is the rep’s.

At $10 per qualified lead, the pricing reflects this boundary.

There are two key components to how this pricing model works:

1. The customer defines success

With Fin’s sales role, the customer sets their own qualification criteria based on their business context. A company with high average contract values might set a lower bar because they can’t afford to miss anyone. A company where rep time is scarce and deal sizes are smaller might set a much higher bar, filtering aggressively to only surface the most promising prospects. The criteria flex to match the business.

2. The economics are different by design

As a Customer Agent, Fin can switch between roles like sales and service. So if you’ve deployed Fin for Sales, it can still handle support queries like prospects asking a product question. Those queries are charged at $1 per resolution, consistent with our service pricing.

Disqualifications, where Fin determines a prospect doesn’t meet the criteria, are also $1. The $10 price point for qualified leads reflects the higher value of pipeline creation compared to issue resolution.

The ROI speaks for itself

Early customers are reporting significant returns using Fin for Sales. In our customer research, one participant shared:

“I would say it’s at least 10 times the value. You’re now giving the business exactly what it needs as opposed to just activity. We say this expression in sales leadership all the time – ‘I don’t pay my sales team for activity. I pay them for results.’ I want my AI engine to be the same way.”

When you compare the cost of a qualified lead from Fin against the fully loaded cost of an SDR, such as salary, benefits, tooling, and ramp time, the economics are compelling. For many businesses, particularly those that never had SDRs in the first place, Fin for Sales isn’t just replacing headcount, but creating an entirely new capability that wasn’t economically viable before.

Transparent pricing that’s grounded in research

This pricing model came from extensive customer research, both qualitative interviews and quantitative studies, exploring how buyers want to pay for AI in a sales context. We tested multiple concepts: per-conversation, per-token, per-seat, revenue share, and per-qualified-lead. The research consistently pointed to outcome-aligned pricing as the preferred model, with the qualified lead emerging as the metric that best balances value alignment, measurability, and practical implementation.

We’ve published our thinking on pricing at every stage, from why resolutions are the right metric for service, to how we evolved to outcomes, and now to how we’ve extended outcome-based pricing to sales.

Outcome-based pricing is still rare in AI, but we think that will change. For Sales Agents, we’re the first to do it.

Transparency is part of the model. If you understand why we price the way we do, you can evaluate whether it works for your business.