AI & ML

The AI pricing and monetization playbook

An early primer on how founders and AI product leaders capture value in a world where every token has a cost, and every customer expects exponential outcomes.

Every new AI product typically aims to achieve three goals: reach new audiences, deepen engagement through new capabilities, and expand the total addressable market. But at the foundation of AI pricing lies a hard truth that changes everything: unlike traditional software, delivering AI isn't free.

Cost of goods sold (COGS)—specifically, compute and inference costs, plus customer support such as "humans in the loop"—weighs heavily on monetization strategy. Unlike classic SaaS, where serving one more customer costs virtually nothing, every AI query incurs a non-trivial expense. Your pricing must account for these material unit costs while capturing the value you create.

Here lies the challenge for founders and product leaders: AI is in its early innings, and reliable pricing benchmarks are scarce. Comparing AI companies is often apples-to-oranges, given how differently each model and industry operates. Yet through our work with dozens of AI teams across verticals, we've observed patterns and pitfalls that transcend any single niche.

Key AI pricing takeaways

Your charge metric is a strategic statement, not just a billing decision: Tokens work for technical buyers but confuse everyone else. Outcomes maximize value alignment but require absorbing cost variability. Choose based on what customers will pay for, then build operational discipline to make it profitable. Hint: Hybrid models (base subscription + usage/outcome tiers) win when you're uncertain. They provide customer predictability while capturing upside as they scale—the effective middle ground for early-stage startups.
AI economics are fundamentally different from SaaS—COGS matter again: Every AI query incurs real compute costs. Companies see 50-60% gross margins vs. 80-90% for SaaS. If the math doesn't work at 10 customers, it won't at 1,000. Track true costs from day one (including founder time) and design pricing that covers compute while capturing customer value.
Soft ROI positioning kills willingness-to-pay: Copilots offering advice without closing the loop live in dangerous soft ROI territory—customers question "are we really getting value?" As 2025 pilots hit 2026 renewals, pricing must reflect actual value, not promise.
Find your pricing sweet spot through friction, not spreadsheets: Start with a price. If customers say "sold" immediately, you're too cheap. Raise incrementally until you hear "we have to think about that." Stop before it becomes a blocker. This is how multi-billion dollar companies found their sweet spot. Most founders default to cost-plus (calculate costs, double it) because asking for more feels awkward. Lead with value.Pricing shapes your entire GTM motion and org structure
Pricing determines how sales, customer success, and product teams operate. Intercom's $0.99 per resolution aligns every team around one outcome: resolved tickets. Outcome-based contracts create new questions: How do AEs size scalable deals? How much upside should CS capture? Avoid the complexity trap—identify one model that works at both 10 and 1,000 customers.

How should I price my AI product?

As investors, we can't offer a one-size-fits-all formula. Business models are still emerging and vary dramatically across horizontal and vertical AI categories. But we can provide a framework—one built on a fundamental principle that's especially true in vertical AI: companies are no longer just selling access; they're selling outcomes.

That's why your pricing strategy must be bespoke: tailored to your industry, your customer's workflows, the concrete outcomes your product delivers, and the cost structure required to ensure profitable growth. Put simply, you must align the value your product creates with the value you capture through pricing to drive enduring revenue streams.

The first step? Convene a cross-functional team across product, sales, finance, and GTM operations to explore four critical questions:

What's our go-to-market motion?
What's the unit of value our pricing can scale with?
How do we determine a customer's willingness to pay?
How do we design a pricing model that's flexible and scalable—delivering value at each stage of customer growth without causing sticker shock?

This primer offers seven guiding principles and five founder best practices for determining willingness to pay and setting the right strategy. It's not a plug-and-play formula—it's a framework to equip you with the right considerations and questions as you design a pricing strategy that scales value for your customers and your company alike.

Three emerging AI business models shaping AI pricing today

Across our portfolio, we've observed three AI business models signaling a fundamental shift in software economics. Copilots, Agents, and AI-enabled Services are poised to define how AI businesses grow and monetize in the coming decade—and each comes with distinct "charge metrics."

AI business model	What is it?	How is it priced?	Examples
Copilots	AI sidekicks that sit beside human users, enhancing productivity without replacing the person in the loop.	Copilots are typically priced per seat or consumption (much like SaaS) and are already driving major revenue expansions at companies like Microsoft, Google, and Salesforce.	From GitHub Copilot’s developer acceleration to Abridge’s clinical documentation assistant, copilots are proving that AI can double – even triple – employee productivity across text, code, image, and voice workflows.
Agents	Autonomous AI actors that represent the next leap in productivity. Rather than merely assisting a human, an Agent can execute entire workflows on its own, decoupling output from human headcount.	Pricing models for Agents are still evolving, but they’re often tied to tangible ROI (e.g. workflow-based, outcome-based, cost savings or output equivalent to a human’s work) instead of per-seat fees.	Early examples are cropping up in sales, recruiting, and customer support – such as Intercom’s Fin agent for customer service — functions where automation can substitute for incremental hires.
AI-enabled Services	Companies that blend automation with human oversight to deliver a service faster, cheaper, and more consistently than traditional providers.	This model shows how an AI-driven service can pass savings to customers while capturing more value for itself, also allowing for customers to scale spend up and down far more flexibly. Charge metrics could range from consumption- workflow- or outcome-based pricing, including cost of hiring an equivalent FTE or market rate for the service provider.	EvenUp combines AI and legal experts to generate personal injury demand letters; by charging per output (per completed letter) rather than by the hour and benefit from higher margins.

Within these emerging AI business models, founders face a fundamental choice: what unit of work should we charge for? The answer determines not just revenue structure, but how closely your pricing aligns with the value your customers actually experience.

Three charge metrics dominate the AI landscape, each representing a different trade-off between cost predictability and value alignment:

1. Consumption-based pricing (per API call, per LLM token)

This approach mirrors the underlying economics of AI infrastructure. Every call, every token, every inference has a known cost—which means your margins are predictable and your accounting is clean.

But here's the problem: customers don't wake up thinking about tokens. They think about problems solved and work completed. Consumption-based pricing works best when your customer is a technical buyer who wants granular control—developers building on your API, data scientists optimizing workflows. For everyone else, it's a translation problem: you're asking them to estimate their needs in units they don't naturally understand.

Leena AI learned this lesson early on. The company provides AI "colleagues" that automate employee support and back-office tasks across HR, IT, finance, and procurement. They initially charged on consumption, but customers became wary of using the product—the pricing model was counterproductive. Once Leena AI shifted to an outcomes-based model focused on solving real problems, customers gained clearer ROI and the business accelerated revenue. Enterprise buyers, especially CIOs and CFOs, want to allocate set budgets for technology. If teams can't determine a fixed number, adoption can wane. Zenskar, a provider of AI-powered billing solutions, reports that their customers' CIOs frequently ask for overage invoices to be adjusted into the following year's budget.

2. Workflow-based pricing (per completed task)

This metric moves closer to how work actually happens. Booking a meeting. Analyzing a spreadsheet. Drafting a contract. These are discrete, recognizable units of productivity—customers understand what they're buying, and they can calculate time saved or efficiency gained.

Cost variability increases (one spreadsheet analysis might require 10x the compute of another), but the value proposition becomes dramatically clearer. Workflow-based pricing works when the task itself is the outcome customers care about, and when the range of complexity is bounded enough to avoid margin erosion.

3. Outcome-based pricing (per successful outcome)

This is where pricing strategy becomes product strategy. When Intercom charges $0.99 per ticket Fin resolves—not per message sent, not per token consumed, but per problem solved—they've made a bet. They've accepted maximum cost variability in exchange for perfect value alignment.

If Fin resolves a ticket in three messages or thirty, the customer pays the same. The risk is real: a difficult customer issue could consume far more compute than anticipated. But the reward is equally real: customers know exactly what they're getting, and they can calculate ROI in their sleep. Outcome-based pricing works when you're confident in your AI's performance, when you can absorb cost variance, and when the outcome is unambiguous and measurable.

A clear pattern emerges: As you move from consumption to workflow to outcome-based pricing, you're making a deliberate trade. You're accepting more cost risk in exchange for tighter alignment with customer value. The best founders don't choose based on what's easier to implement—they choose based on what their customers will pay for, and they build the operational discipline to make that model profitable.

AI entrepreneurs must remember: the charge metric you pick isn't just a billing decision. It's a statement about what you believe your AI is worth, and what you're willing to stake your margins on to prove it.

At Bessemer, we've been tracking the emerging trends shaping AI monetization and have distilled seven principles to help product teams and early-stage founders find the right pricing strategy for their specific business context.

The seven guiding principles for AI pricing

1. Pricing in the AI era ties directly to value delivered—not access granted

By and large, AI-native companies are abandoning seat-based SaaS pricing in favor of usage-, output-, and outcome-based models that directly align revenue with measurable results.

Usage-based pricing: Customers pay per token, API call, or inference.
Workflow- or outcome-based pricing: Customers pay when the AI completes a defined task (e.g., ticket resolved, document drafted, lead generated).
Hybrid pricing: A base subscription ensures predictability, while usage tiers capture upside as customer value grows.

“When you receive $10 from the customer, you can’t just spend 10 cents on AWS,” said Jacob Jackson, co-founder of Supermaven and ML leader at Cursor. “GPUs are expensive, and they have a real footprint in electricity and heat. The right way to price is relative to the value being delivered.”

By tying pricing directly to measurable output, founders align value creation with value capture—giving customers clear ROI benchmarks while retaining upside as performance improves.

2. Hybrid and tiered models create predictability with upside

Many vertical AI companies are adopting hybrid models that blend a base subscription with usage or outcome-based tiers. This approach provides:

Predictability for revenue forecasting and customer budgeting.
Elasticity for expansion as usage scales or AI results improve.

Hybrid models are especially effective in quantifiable verticals—like legal (EvenUp and Legora)—where AI output can be measured and tied to specific outcomes. But the approach is applied across horizontal enterprise solutions, too, as seen in customer support (Intercom) and back-office automation (Leena).

Sett.ai offers another compelling example. The agentic AI platform creates and tests mobile game ad creatives, generating campaign "winners" for customers. Some contracts scale Sett's payment with the customer's ad spend—allowing Sett to tap into a much larger budget as campaigns succeed.

3. Pricing must account for inference costs

Unlike traditional SaaS, where additional users cost almost nothing to serve, AI products face real marginal costs per inference.

That makes pricing design both financial and strategic—founders must balance customer value, compute cost, and model efficiency. Leading strategies include:

Usage-based monetization that scales naturally with inference costs.
Bundled or embedded AI features inside seat-based products for predictability.
Workflow- or outcome-based pricing that charges for completed business processes.

As Gorkem Yurtseven, co-founder of fal.ai, explained: "Running the same model might get cheaper—but everyone wants the best model, and those are more expensive to run."

AI pricing is, at its core, a reflection of the physics of compute—and founders who design around that reality will own the economics of the next era.

4. AI tools reimagine traditional budgets

Enterprise budgets are restructuring around AI. Dedicated AI and automation spend now sits alongside traditional IT budgets—a shift we're seeing especially in AI developer tools and healthcare solutions.

Buyers include CIOs, engineering leaders, and product teams, requiring multi-threaded sales and pricing strategies. In many cases, AI tools replace headcount or augment workflows, reframing the spend narrative from cost reduction to capability expansion. The justification has shifted from saving money to unlocking new capacity.

AI pricing must articulate that transformation clearly, reflecting how your product fits this new spend narrative.

5. New success metrics redefine "value"

Legacy SaaS metrics—ARR, CAC, and gross margin—don't fully capture AI's impact. Founders and customers are evaluating success through new lenses:

Time from idea to prototype

% of work completed autonomously
AI resolution rate or accuracy
Developer acceptance rate (e.g., % of AI-generated code accepted in Cursor)

These "magical experience" metrics blend productivity, reliability, and delight—reflecting AI's shift from software tool to creative partner. When pricing your AI product, consider the number and intensity of "magical" moments created for your customers.

Redefining SLAs unlocks premium pricing

Some AI-first companies are using this shift to redefine service-level agreements (SLAs) that outperform traditional vendors—creating opportunities for premium pricing. Where legacy SaaS solutions relied on human labor to complete tasks, AI-native vendors can now commit to differentiated value metrics:

Turnaround time: Faster processing than human-powered services
Task completion rate: Higher throughput versus standard service providers
Accuracy of output: Measurable quality improvements

Consider healthcare payment services: traditional vendors often write off low-value claims because the unit economics don't justify manual processing. With AI, companies can profitably process these previously abandoned claims—getting compensated by insurance companies while maintaining healthy margins. The cost structure has fundamentally changed, and claims are processed far more quickly.

6. Pricing strategy shapes GTM and customer success

Intercom's AI product, Fin, charges $0.99 per AI resolution, aligning revenue, sales, and success teams around a single outcome: resolved tickets.

This approach turns pricing into a unifying North Star. It forces every internal team—sales, product, engineering—to work toward realized customer value, not just adoption.

AI deployments also demand consultative onboarding, not transactional selling. The best teams embed forward-deployed engineers to ensure that pricing outcomes match delivered outcomes.

The takeaway? Pricing is no longer a back-office decision—it drives the entire go-to-market motion, including team incentive structures. Outcome-based contracts create new questions: How should AEs size deals when revenue scales with results? How much expansion upside should Customer Success and post-sales teams capture? These answers will shape your entire organizational model.

7. AI is more akin to adding co-workers, not just tools

The biggest evolution in pricing strategy isn't structural—it's philosophical. AI is no longer just a tool that extends human capacity; it's a productive teammate that completes work autonomously.

When your AI resolves a ticket, drafts a brief, or ships a line of code—it's doing real work. Products should get paid for outcomes, not access.

This shift reframes pricing as a partnership model between human and machine productivity. Founders who internalize that will define how AI monetization—and value—evolves. Common threads among AI pricing leaders show that solutions are priced on delivering measurable work:

Company	Model type	Pricing mechanism	Value focus
DeepL	Hybrid	Per user + per editable file	Accuracy & customization
EvenUp	Outcome-based	Per AI-generated demand package	Legal time saved
Graph AI	Outcome-based	Per case processed	Regulatory compliance & efficiency
Intercom (Fin)	Outcome-based	$0.99 per AI resolution	Support efficiency
Leena AI	Outcome-based	Prices using ROI basis number of tickets automatically closed by agents, often offering a minimum threshold of tickets to be closed	Back office automation and outcomes delivered for teams
Pepper Content	Outcome-based	Charges per word - graphic - content piece delivered	Assets created
Resolve AI	Outcome-based	Pay when AI ensures uptime	Reliability & engineering outcomes
Sett.ai	Hybrid	Per generative module, plus share of ad spend on winning campaigns	Assets and campaign created
Zenskar	Hybrid	Annual subscription (tiered plans) with flexible fees that scale by usage and complexity	Flexible billing automation and reduced engineering/finance effort

Across all models, one truth holds: AI earns its keep by delivering measurable work.

As product builders and GTM leaders shape willingness-to-pay and design new monetization strategies, waiting for perfect data or polished frameworks is a mistake—playbooks are still being written (this article included).

The most effective founders move fast: they test value early, price boldly, and build monetization muscle alongside product-market fit. The five practices that follow aren't checklists—they're instincts sharpened by real-world execution.

Five founder best practices to hone the right AI pricing strategy

1. Lead with a value-first pricing test

When founders describe their product without mentioning LLM costs or infrastructure—just pure value proposition hardened through customer conversations—they understand their market.

They can immediately articulate why customers value their product and frame it as a no-brainer: "We're creating $X in new revenue and only charging Y%."

Most founders understand value intellectually but default to "cost-plus" pricing when it's time to ask for money. They calculate delivery costs, double it, and undercharge because asking for more feels awkward. Even good founders fall into this trap.

A better approach: hybrid pricing as a bridge

Early-stage startups often use hybrid pricing structures as a middle ground between pure usage and pure value-based models. Here's one formula that works:

Platform fee (2X calculated delivery costs) + outcome credits

For example:

$12K annual platform fee covers your infrastructure costs
Includes 100 ticket resolutions
Additional resolutions: $5K per 100 tickets

As outcomes scale, your pricing per outcome falls—but total revenue from the customer increases. This creates natural expansion while maintaining healthy margins.

2. Find your sweet spot through friction

The best startups have zero-friction pricing in their sales cycle, built on strong product-market fit.

The process is simple:

Start with a price (say, $12K/year)
If customers immediately say "sold," you're probably too cheap
Raise prices incrementally until you hear "we have to think about that"
Stop just before pricing becomes a real blocker

This intuitive approach—finding where friction begins but hasn't yet killed deals—is how many multi-billion dollar companies found their sweet spot in years five to ten.

3. Map your product to the value framework: Revenue vs. efficiency, hard vs. soft ROI

Map your product along two dimensions:

Dimension 1: Value type

Revenue uplift: Are you creating clear new workflows with undeniable revenue impact? Or just helping salespeople perform "a little better next month"?
Cost savings/efficiency: Including potential headcount reduction

Dimension 2: ROI clarity

Hard ROI: Measurable, undeniable, clear metrics
Soft ROI: Incremental improvements that are harder to quantify

Product implications by category:

Copilots = Softer ROI

Chatbots offering advice and answers don't close the loop. Customers question "are we really getting value?" This kills risk appetite and willingness to pay.

Agentic products = Harder ROI

Close the loop entirely themselves, delivering stronger pricing power through measurable outcomes.

Service replacement = Clear cost reduction

Sold purely on alternative cost comparison. For founders in this category, mastering the total cost of ownership (TCO) comparison is critical—enterprises routinely underestimate the true cost of their legacy approaches when evaluating AI-first alternatives.

The renewal cliff ahead: Much of the "sexy" AI products today live in soft ROI territory, which is dangerous for monetization. In 2025, most companies operated in "AI adoption at all costs" mode with minimal price sensitivity. As many enter renewal cycles for the first time in 2026, pricing will need to reflect actual value, not merely potential or promise.

4. Build “unit economics discipline” from day one

It can feel premature to do full product-line profitability studies on day one when everyone's rowing in the same direction. But you should do it anyway. Have brutal self-awareness as early as practical:

If your CEO spends half their time selling, allocate that to sales & marketing costs
If your CTO answers support tickets half-time, account for that drag
Track variable costs to support revenue and investment costs to grow revenue

Why it matters: Eventually, you'll need to hire full-time customer support, and it becomes a drag on margins. Companies that ignore true costs early—doing back-of-envelope LLM consumption math without full accounting—can scale to negative margins without realizing it.

The bottom line: Is this math even worth it? If the unit economics don't add up in the early days, they'll surprise you (badly) in later days.

5. Steer away from the “pricing complexity trap” at scale

One of the most dangerous pitfalls is letting pricing complexity proliferate—nine different pricing approaches across different contracts, with sales running wild on custom deals. This becomes unmanageable and painful at scale.

What works at the seed stage can become a liability at Series B. The informal, inconsistent pricing that helped you close early deals becomes an operational nightmare as you scale.

Start simple, stay disciplined: As early as possible, identify the pricing model and formula that works both at 10 customers and 1,000 customers—without adding unnecessary complexity.

Next steps: Setting your pricing exercise up for success

For any team launching a new AI product, the journey to a sustainable pricing model should begin with a cross-functional huddle between product, finance, sales, and GTM operations. As new pricing strategies create more complexity for CFOs, platforms like Zenskar can help monetize tokens, API calls, and LLM models with ease. But in the early stages of monetization, the preliminary goal is to explore all the strategic questions that will shape your model—and to align your entire organization around a shared definition of value.

Ten crucial questions to explore during your AI pricing journey

As you convene your cross-functional team, use these questions as a structured framework to pressure-test your assumptions:

	Question	Consideration
1	How do AI economics differ from SaaS—and why do margins matter so much more?	Unlike traditional software, where serving one more customer costs virtually nothing, every AI query incurs real compute costs. Understanding this fundamental shift is critical to avoiding the trap of subsidized growth that never reaches profitability.
2	What are the dominant AI pricing models emerging in 2025—and which one best fits my product?	From consumption-based to outcome-based to hybrid models, each approach carries different trade-offs around predictability, adoption friction, and value alignment. Your choice should reflect both your product category (Copilot, Agent, or AI-enabled Service) and your customers' buying behavior.
3	How can I tie pricing to customer outcomes without confusing or overwhelming users?	The most powerful pricing models make the value equation instantly clear—but clarity shouldn't come at the cost of complexity. Test whether a first-time buyer can understand what they're paying for and why it's worth it.
4	How do I manage variable compute costs while maintaining predictable revenue?	This is the central tension in AI pricing. Hybrid models, usage caps, prepaid credits, and tiered subscriptions are all tools to balance this—but the right answer depends on your customer segment and their appetite for variability.
5	When should I shift from free usage to paid tiers in an AI product?	Consumption-based pricing lowers barriers to adoption, but free pilots can obscure true willingness to pay. The transition from free to paid is a critical inflection point that requires careful timing and clear communication about the value threshold.
6	How should I measure success—what replaces SaaS metrics like CAC, LTV, and the Rule of 40 in an AI business?	Traditional metrics still matter, but AI introduces new success indicators: AI resolution rate, developer acceptance rate, time from idea to prototype, and percentage of work completed autonomously. These "magical experience" metrics capture what truly differentiates AI products.
7	What early data do I need to model my AI unit economics accurately?	From day one, track the full cost stack—not just model inference costs, but also the human-in-the-loop expenses, customer success overhead, and sales allocation. Companies that don't build this discipline early scale to negative margins without realizing it.
8	How can pricing itself become a moat—differentiating my business model from competitors using similar AI models?	When everyone has access to the same foundation models, your pricing strategy can be a source of competitive advantage. Outcome-based pricing that deeply aligns with customer workflows is harder to replicate than feature sets.
9	What signals show I've found a sustainable, value-based pricing model (versus one propped up by hype and subsidies)?	Look for customers who renew without hesitation, expand usage naturally, and refer others based on clear ROI. Sustainable pricing feels like a no-brainer to customers while maintaining healthy margins for you.
10	What mistakes are leading AI startups making right now with pricing and monetization—and how can we avoid them?	The most common pitfalls include: pricing complexity that spirals out of control, pure consumption models that commoditize the product, cost-plus pricing that leaves money on the table, and soft ROI positioning that fails to justify premium pricing.

AI product leaders will monetize outcomes

Every major software revolution rewrites the rules of value capture. Client-server monetized licenses. SaaS monetized access. AI will monetize outcomes.

The shift is already underway. The most successful AI companies aren't charging for technology—they're charging for work completed, problems solved, and results delivered. They've aligned their pricing with the fundamental promise of AI: to act as a productive teammate, not just a productivity tool.

This transition demands more than new pricing models—it requires a new mindset. Founders must think like their customers, understanding not just what the product does, but what it accomplishes in the context of real business operations. They must build unit economics discipline from day one, resist the temptation of pricing complexity, and have the courage to charge for the value they create rather than defaulting to cost-plus formulas.

The founders who design pricing systems that reflect both real compute costs and real customer value will not only survive this transition—they'll define the next generation of category leaders.

AI pricing is not about how you charge. It's about what your product earns.

As you embark on your own pricing journey, remember: there's no universal template, no plug-and-play formula. The landscape is evolving rapidly—what works at 10 customers may need revision at 1,000; what closes deals today may face resistance at renewal. But with the right frameworks, the right questions, and a willingness to experiment and iterate, you can build a pricing strategy that scales with your customers' success—and your own.

The AI era rewards those who price with conviction, align with outcomes, and never lose sight of the value they're creating.

As AI pricing models mature, we’re tracking and researching several open questions:

How will companies manage the 2026 renewal cliff as pilots convert to production contracts?
What anti-patterns are emerging that look clever now but create problems at scale?
How do pricing strategies need to evolve from 10 customers to 1,000?

This AI pricing playbook was created in collaboration with Byron Deeter, Kent Bennett, Sameer Dholakia, Brian Feinstein, Adam Fisher, Talia Goldberg, Sofia Guerra, Nithin Kaimal, Amit Karp, Anant Vidur Puri, Ariel Sterman, Janelle Teng, Sam Bondy, Lindsey Li, Maha Malik, Caty Rea, and Alex Yuditski.