There’s a clear shift in buyer attitudes when it comes to software. We increasingly expect to pay for value delivered, rather than a set of seats or features pre-determined by the vendor.
This is why outcome-based pricing, where customers pay only for successful results, is gaining traction. With companies like Intercom setting the bar high with its customer service agent, Fin, many others have been busy exploring how they can implement outcome based pricing.
While the rise of AI and automation is accelerating this trend, the complexity around defining value and implementing outcome-based pricing that (actually) makes sense for both the vendor and the buyer continues to be one of the biggest hurdles to adoption—requiring clear metrics, trust in data, and a shared understanding of what success really looks like.
In this guide, you’ll learn what outcome-based pricing is, how it works, how leading companies are putting it into action, and what people really think about the model—just a fad or here to stay?
Let’s dive in.
What is outcome-based pricing?
Outcome-based pricing (sometimes called performance- or results-based pricing) ties the price of a product or service directly to the results a customer achieves. This means customers pay only when the promised outcome occurs.
For example, Intercom’s Fin, an AI-powered support chatbot, uses an outcome-based pricing model where customers are charged $0.99 only when the bot successfully resolves a conversation—meaning no human agent needs to step in.
In contrast, traditional flat-fee and seat-license models charge for access, while usage- and credit-based models bill for consumption.
Outcome-based pricing shifts the focus entirely to measurable results, aligning cost with value delivered.
Why is outcome-based pricing gaining traction now?
The key driver for increase in popularity is changing customer expectations. Clients want to see tangible ROI from day one. Another factor is the evolution of software delivery models. We’ve moved from the era of one-time licenses to SaaS subscriptions, and then to consumption-based billing, where you pay for what you use.
Outcome-based pricing is the next evolution, enabled by better data tracking and automation. It promises to eliminate “shelfware” and wasted spend by charging only for real, measurable value.
That said, while outcome based pricing models sound great in theory, they’ve proven hard to implement (we’ll get to that later in the article).
Outcome-based pricing models in the age of AI
With an outcome based pricing model, instead of charging for access or usage, AI companies can bill for completed tasks, successful automations, or verified outputs. That means customers only pay when the AI actually delivers and not just when it’s used.
It’s a model that de-risks adoption for buyers and incentivizes continuous improvement for vendors.
This shift has opened the door for new AI-native monetization software. Platforms like Alguna are building systems specifically designed to meter AI outcomes, tracking events such as completed sessions, resolved tasks, or successful conversations, and turning them into billable units. By bridging data instrumentation with billing logic, they make AI monetization and outcome-based billing scalable, automated, and audit-ready.
We’ll take a closer look at billing platforms for outcome-based pricing models later in the article, but first, let’s get a better understanding of how outcome-based pricing models work.
How outcome-based pricing models work
The outcome-based pricing model links performance to payment. Instead of billing for access or activity, vendors and customers agree on measurable outcomes, like:
- Tickets resolved
- Leads generated
- Sales closed
If the outcome doesn’t happen, the customer doesn’t get charged.
These outcomes serve as the foundation for the pricing model. Once defined, each successful outcome triggers a payment, turning what was once a vague promise of value into a clearly measurable transaction.
Let’s take a closer look at how the logic is structured.
The logic behind outcome based pricing models
The table below summarizes the core steps of the outcome based pricing model, illustrating each with an example from Intercom’s Fin AI support agent.
Note how Intercom defines a resolved conversation, how it measures success and how it structures billing to avoid disputes.
| Step | Key Action | Example: Intercom Fin AI |
|---|---|---|
| 1. Define outcome | Agree on a specific result that the customer cares about. The outcome must be clearly defined to avoid disputes and should reflect a business impact rather than usage. | Intercom asked customers what would make them comfortable trying the product. The answer: “we’ll pay when it works.” A resolved conversation became the billing unit. |
| 2. Specify measurement metrics | Establish objective signals that show the outcome has occurred. Clear metrics prevent under- or over-counting and increase trust. | Intercom uses "confirmed" and "assumed" resolutions: confirmed when a user expresses satisfaction, assumed when the customer ends the conversation or doesn’t respond for 24 hours. |
| 3. Tie billing to the outcome | Build billing rules that charge only when the agreed outcome occurs. Contracts include safeguards like minimums or revision windows. | Fin bills $0.99 per resolved conversation. Multiple resolutions count as one, and customers can dispute resolution status within 24 hours. No resolution = no charge. |
| 4. Ensure data transparency and trust | Outcomes must be verifiable by both vendor and customer. Transparent instrumentation and shared data systems are crucial. | Resolution counts are visible in Intercom dashboards, providing traceability for billing. |
| 5. Align incentives and iterate | Tie pricing to outcomes to align vendor incentives with customer success. Monitor performance and iterate on the product and metrics. | Intercom quadrupled Fin's revenue year over year. Because revenue depends on successful outcomes, Intercom actively improves AI performance. |
Outcome based pricing model examples
Leaders like Intercom, Salesforce, and Zendesk have rolled out AI-driven support products that charge per successful resolution, not per ticket opened.
Chargeflow and Riskified tie their revenue to financial outcomes like recovered chargebacks and fraud-free transactions. Meanwhile, enterprise platforms such as ServiceNow and HubSpot have started experimenting with performance-based pricing for workflow and marketing tools.
The table below provides an overview of how outcome based pricing pioneers are implementing outcome-based pricing models in real-world scenarios—each with a clear link between the product’s success and the invoice.
| Company | Model | Outcome |
|---|---|---|
| Salesforce – Agentforce | $2 per AI-handled conversation | AI successfully completes support/sales conversation |
| Intercom – Fin | $0.99 per successful resolution | Issue resolved by AI chatbot |
| Zendesk – Answer Bot | Charged only on resolved tickets | Support ticket fully resolved by bot |
| Chargeflow | ~25% of recovered chargeback | Chargeback successfully recovered |
| Riskified | Fee per approved, fraud-free order | Fraud-free order approved and completed |
| ServiceNow | Full fee only if efficiency targets met | Agreed efficiency improvements achieved |
| HubSpot | Discount tiers tied to performance | Marketing/sales KPIs met |
| InsideSales (XANT) | Fees tied to sales metric improvement | Improved conversion rate or deal velocity |
| Sierra AI | $ per qualified sales conversation | AI initiates and qualifies a sales conversation |
Best practices for implementing outcome-based pricing
Adopting outcome-based pricing takes more than a pricing update. It requires shared metrics, transparent data, and a culture of accountability. Here’s how to make it work in practice:
1. Define measurable outcomes
Start with metrics that clearly reflect customer value, like “tickets resolved,” “hours saved,” or “revenue generated.” Both sides should agree on what qualifies as a successful outcome and how it will be tracked. For example, an AI support vendor might define success as a ticket that remains closed for 72 hours. The clearer the definition, the less room for dispute.
2. Test with a pilot
Run a small-scale rollout first. A pilot helps you confirm that your tracking systems, outcome definitions, and billing logic actually hold up.
3. Prioritize transparency
Trust is everything. Use shared dashboards or monthly reports to show outcomes achieved, like “200 issues resolved” or “$50K in verified savings.” This turns billing into a conversation about value delivered rather than cost incurred.
4. Align your team
Sales, finance, and customer success all need to be on the same page. Sales teams should focus on ROI instead of features, finance teams should model variable revenue, and product teams must ensure outcomes are reliably measured. Many companies adopt a hybrid model (base fee + outcome fee) to balance predictability with performance.
5. Communicate value
Be upfront about pricing scenarios and show projections. If your solution performs at a certain level, outline what the customer pays and what they gain. That clarity reduces friction and positions you as a true partner, not just a vendor.
When done right, outcome-based pricing strengthens alignment and accountability on both sides. Start small, measure accurately, and scale what works.
Once you’ve defined outcomes and aligned your teams, the next challenge is operational: how do you actually track and bill for those outcomes? That’s where modern outcome-based billing platforms come in.
Outcome-based billing platforms: Overview
As outcome-based pricing gains traction, a new class of billing platforms is emerging to support it. Because when companies explore outcome‑based pricing, one of the first conversations should be whether their billing infrastructure can handle it.
Traditional systems were built for static subscriptions or simple usage metering (think Stripe and Chargebee) and not for dynamic, event-based models where every “successful outcome” might trigger a charge.
New platforms like Alguna and Paid.ai bring together usage data and outcome tracking enabling businesses to automate complex billing tied to value delivered.
Below is a quick overview of leading outcome-based billing platforms, each offering different levels of flexibility, precision, AI-readiness, and quote-to-cash capabilities.
5 billing platforms that support outcome-based pricing
| Platform | Outcome-based pricing | Usage vs outcome granularity | Pricing |
|---|---|---|---|
| Alguna | Yes – Built for AI agents. Every agent action, session, or task is automatically captured. Supports outcome events alongside usage and hybrid models. | Highly flexible: can bill per token/API-call/seat or per outcome event (task, session, etc.). | Free starter tier. Starts at $399/month White glove migration and onboarding included Go live in weeks. |
| Stripe Billing | Via Usage-Metering: Can simulate outcome pricing by billing custom “usage” events (leads, tasks, conversions) | Primarily usage-based (per-token, API-call, etc.), but fully customizable – you define usage metrics (even outcomes) via API | 0.7% of revenue billed |
| Paid.ai | Yes – Supports outcome-based models (mixing seat-, activity-, and outcome-fees in contracts). E.g. charge per qualified meeting or pipeline influenced | Event-based: ingests “signals” from AI agents (token usage, actions) to meter cost and bill per outcome. In practice bills on business results (meetings booked, leads, etc.) | Free first-year for agent cost-tracking; paid tier (custom pricing) for automated billing/invoicing |
| Orb | Yes – Outcome-driven billing: “AI-native pricing” with token-, outcome- or hybrid plans without code | Very granular: captures high-volume AI events and custom outcomes. Supports usage-metering and outcome events (e.g. chat completions, data hits) in same framework | Starts at $749 - $3490 per month + additional usage and integrations |
| Zenskar | Yes – Customizable billing events: e.g. “successful automation completed” are treated as usage metrics to invoice | Fully flexible usage/metrics: Ingests raw or aggregated usage (via API, CSV, 100+ warehouses); can define any metric with SQL/no-code | Starts at $10k per year |
Outcome based pricing: Opinions and hot takes 🔥
As outcome-based pricing gains momentum across SaaS and AI markets, opinions are heating up. Is it the holy grail of value alignment—or just a shiny but complex pricing experiment? Founders, revenue leaders, and investors have strong (and sometimes conflicting) views.
In this section, we spotlight the boldest takes, the biggest debates, and the most provocative insights on why this model is either the future of SaaS—or a slippery slope without the right infrastructure.
I’ve spent the last few months digging into it, and I’ve come to the conclusion that the future isn’t outcome-based pricing.
It’s hybrid pricing, where usage becomes the verified proxy for outcomes.
For 99% of businesses, true outcome-based pricing breaks down fast.
• It’s nearly impossible to isolate your contribution to the result.
• Customers rarely have clean, real-time data to measure it.
• And most buyers don’t want to or know how to share that data anyway.
But hybrid pricing models, the kind used by companies like Fin, Sierra, and modern API platforms use, solve that.
They link pricing to measurable behavioral signals (usage, conversions, workflow completions, time saved) that correlate with outcomes, without pretending to control them.
It’s the best of both worlds:
• Aligned incentives
• Predictable revenue
• Measurable customer value
Outcome-based pricing isn’t dead, it’s just evolving into something more tangible.
The next generation of SaaS won’t price on outcomes.
They’ll design systems where outcomes are observable, and price accordingly."
- Todd Saunders, CEO at Broadlume
The good news is that unlike traditional SaaS where customers pay up front, outcome-based models usually bill in arrears.
It lowers the barrier to entry as a startup - it’s easier for prospects to use your product when they’re only paying for results.
The bad news?
It may seem counterintuitive but enterprise clients hate paying in arrears. CFOs care more about predictability than flexibility.
They want to forecast costs, not wait and see.
So while charging up front might feel bold (you haven’t proven the results yet), it could actually make it easier to close those bigger deals.
- Dani Penev, CEO at Slicker (YC S23)
Honestly? That’s plain bull****. At least for now. And referencing Clay or Notion doesn’t make it any more true.
The reality: For most businesses, credit- and outcome-based pricing just don’t make sense (yet).
This year alone, I’ve run five pricing projects for AI products – and not once did we end up implementing a credit- or outcome-based model. In every simulation, these models performed worse.
The truth is simple: every AI product is different – and the right pricing strategy depends entirely on how the product delivers value.
Before discussing price models, we should discuss what type of AI product we’re actually talking about. Over time, I’ve learned that answering these six questions is essential before choosing the right model:
1. Is the product suite AI-native, or are we talking about an AI add-on to an existing SaaS offering?
2. Does it work standalone, or only in combination with the core product?
3. What is the incremental customer value of the AI component?
4. Is the buying process transactional or consultative?
5. How significant are compute costs relative to ACV?
6. Will adoption be constrained by usage costs?
Answer these questions first — and you’ll see where subscription, bundled, standalone add-on, usage-based, outcome-based, or hybrid models truly make sense… and where they don’t.
- Timo Mueller, CEO at pqX
Putting outcome-based pricing into practice
Outcome-based pricing marks a clear shift in how software and services are sold. Instead of charging for access or usage and hoping customers find value, it ties revenue directly to tangible outcomes, closing the gap between performance and payment.
This shift is being fueled by rising expectations and better technology. Customers want visible ROI from day one, and advances in AI and automation now make outcomes easy to verify in real time.
Moving to this model isn’t simple. It demands transparent data systems, cultural buy-in, and billing tools that can handle variable, event-driven pricing. But the payoff is clear: predictable value delivery, deeper customer alignment, and continuous product improvement tied directly to outcomes.
As AI takes on more complex work, outcome-based pricing will become increasingly relevant, rewarding results over activity. Platforms like Alguna make this evolution practical, automating the heavy lifting of tracking, billing, and revenue recognition.
