AI monetization: The complete guide

AI has moved beyond the hype and into the revenue phase. From generative content tools to autonomous agents and AI copilots, as of 2025, there are around 70,000 AI companies racing to turn their AI investments into monetizable products.

For SaaS and AI-native companies, this presents both opportunity and complexity. Traditional SaaS pricing models don’t always map perfectly to the variable costs and value dynamics of AI features. So how do you choose the right AI monetization strategy?

This guide explores monetization strategies, pricing models, and success stories along with how to approach building your billing engine to effectively capture revenue.

What’s AI monetization?

AI monetization, or generative AI monetization, refers to the process of converting AI capabilities into revenue-generating offerings. It’s the process of generating revenue from artificial intelligence capabilities, features, or products.

This involves strategically designing, pricing, and delivering AI-powered solutions in a way that captures their business value for your company.

This can take several forms:

• AI-enhanced SaaS features (e.g. AI search, smart recommendations)
• API-based AI services (e.g. LLM endpoints, vector search)
• Outcome-based tools (e.g. automated customer support, fraud detection)
• Autonomous AI agents (e.g. sales agents, coding copilots)

How is the monetization of AI different from traditional software monetization?

• Variable cost structures (e.g. GPU compute, API token usage)
• Differentiated value delivery (e.g. automated outcomes vs. manual work)
• Rapid commoditization ("AI-powered" is no longer a USP on its own)

What’s AI agent monetization?

AI agent monetization is the process of generating revenue from autonomous, AI-driven software agents performing tasks or delivering outcomes on behalf of users or businesses. 

Unlike traditional software monetization, where payment is often for access or usage, AI agent monetization focuses on the economic value created by agents actively doing work, such as handling customer service tickets, booking meetings, processing transactions, or solving business problems.

6 challenges of monetizing AI

1. AI is in its infancy: Many AI pricing models are still experimental. There’s no consensus on what customers will consistently pay for or how they prefer to pay. Plus, most users don’t fully understand how AI works—or what they’re paying for. Output is probabilistic (not deterministic), so users can get confused, frustrated, or surprised.
2. Vibe pricing: “Vibe pricing” happens when pricing decisions are made based on gut feel, market hype, or what “seems fair” rather than a clear alignment with customer value and business objectives. It’s especially dangerous when it comes to gen AI monetization, as you might end up leaving significant money on the table, stall adoption, or struggle to justify increases later.
3. Rapid evolution: The pace of advancements in AI models and products demands constant adaptation in pricing strategies. Teams struggle to communicate long-term value and cost curves aren’t always clear to customers.
4. Pressure on margins: AI technologies can significantly impact operating costs. Margins can be razor-thin unless you tightly align pricing with usage—and even then, you might need to subsidize lower-tier customers.
5. Complex billing: Monetizing AI often requires real-time metering, dynamic pricing, and flexible invoicing, all of which go beyond what traditional billing systems handle.
6. Proving value: With ongoing experimentation and advancement, customers are eager to see a return on investment from AI technologies. Just being "AI-native" or “AI-powered” isn’t enough anymore, customers want to know exactly how AI improves outcomes.

How to monetize AI

When it comes to monetizing AI, the first question to ask isn’t how to monetize AI, but should we monetize AI?

Start by asking these five questions:

1. Is the value creation clear for customers?
2. Does it drive adoption or retention?
3. Does it differentiate us from our competitors?
4. Will it build trust with our customers?
5. Can we confidently and accurately bill for AI usage? 

If the answer to all of those questions is “yes,” then let the fun begin.

AI monetization strategies: Direct and indirect monetization

According to Palle Broe, who’s led pricing strategy at companies like at Uber (B2C) and Templafy (B2B SaaS), there are two distinct approaches to the monetization of AI.

1. Direct monetization
2. Indirect monetization

1. Direct monetization

Direct monetization means you’re charging customers explicitly for AI capabilities, such as via subscriptions, one-time purchases, pay-per-use/API calls, or premium feature upgrades.AI-native companies offer AI as a core capability, not as an add-on. They typically monetize large language models, agents, and automation as the primary product.

Example: OpenAI

OpenAI’s core product is the suite of large language models and AI services delivered via the ChatGPT family (now built on GPT-4 and successor models), and its associated APIs. This includes the OpenAI API that powers text, code (Codex), image (DALL-E), and speech (Whisper) generation for end-users and developers. 

Customers pay OpenAI directly, either through subscription plans (like ChatGPT Plus, Team, and Enterprise) or on a usage-based basis via the API (charging per token, image, or other generative operation).

The price jump between their Pro and Plus plans has been a hot topic since they introduced it. If you want to go from Plus to Pro and access their more powerful models, you’re paying 10x more.

To this, Open AI responded saying “As AI becomes more advanced, it will solve increasingly complex and critical problems. It also takes significantly more compute to power these capabilities.”

When to choose direct monetization

• Clear customer value: Your AI feature delivers a distinct, quantifiable benefit that customers recognize and are willing to pay for (e.g., GitHub Copilot, Intercom’s AI bot Fin).
• High variable costs: AI operations generate significant, usage-dependent costs such as compute, data storage, security, and ongoing maintenance which must be covered transparently.
• Distinct AI offerings: The AI capability can be isolated as an add-on, standalone product, or bundled with a price increase, allowing you to track adoption and measure willingness to pay precisely.
• Need for revenue attribution: You want clean data on AI monetization effectiveness to inform product development and market strategy.

Direct monetization often begins with AI add-ons or usage-based billing and is preferred if you want immediate revenue generation linked to AI consumption.

2. Indirect monetization

Leveraging AI to enhance user experience, optimize business operations, improve retention, or support other revenue sources (e.g., advertising, analytics, strategic partnerships) without charging separately for the AI itself.

B2B SaaS companies will typically monetize AI in the form of add-on AI features or bundled in tiered pricing options. 

Example: Miro

Miro includes a generous monthly allotment of AI credits across both Enterprise and Self-Serve plans, giving every user the opportunity to fully leverage AI capabilities as part of their experience.

We spoke with Vasilis Araitzoglou, Deal Desk Lead EMEA, who gave us a glimpse into how the company is shaping its AI strategy and contemplating pricing models. Araitzoglou emphasized the importance of maintaining ongoing feedback sessions with early enterprise adopters to help shape a more effective monetization strategy.

Araitzoglou shares that, “it’s critical to understand how other SaaS companies are approaching AI monetization. At Miro, we have a dedicated team focused on staying up to date with the latest best practices in AI monetization to ensure we remain competitive and aligned with market expectations.”

He adds, “we’re taking a phased approach, working closely with a group of existing customers to understand what resonates, where they see realized value, and where gaps remain. This feedback is directly shaping both our pricing model and go-to-market strategy.”

As they’re gearing up for a big launch at their Canvas event in New York in October, we’re 

bound to see some exciting new product offerings along with customer-driven pricing models.

When to choose indirect monetization

• AI as a value-add: AI improves user experience, engagement, retention, or operational efficiency but is not the primary product or feature justifying a direct charge.
• Cost absorption possible: The gain from improved growth or retention outweighs the AI’s incremental operating costs, allowing you to include AI without raising prices.
• Focus on long-term growth: Indirect monetization supports customer acquisition, differentiation, and loyalty rather than immediate revenue increases.
• Immature market or AI adoption: Early-stage AI features integrate quietly as part of the overall experience, minimizing customer friction around new costs.

Indirect monetization is often used to quickly launch AI features bundled in existing plans or offered free to enhance product stickiness without complicating purchase decisions.

Pricing models for monetizing AI

The pricing models for monetizing AI can be grouped into two main camps:

1. AI pricing models
2. AI agent pricing models

AI pricing models refer to broad pricing structures used for a wide range of AI products and services, while AI agent pricing models are more specialized and relate specifically to autonomous AI agents that perform tasks without direct human input, often replacing “users” or employees performing work.

4 AI pricing models

AI pricing models focus broadly on AI capabilities and usage metrics, suitable for platforms and software where AI supports or enhances user workflows.

Seat-based pricing

Customers pay a recurring fee per user or “seat” with access to AI capabilities. This model is familiar in SaaS but is declining in AI monetization because AI often replaces manual user effort, making per-seat less aligned with value delivery.

Usage-based pricing

Charges customers based on actual AI consumption metrics like API calls, tokens processed, or compute resources used. This granular model scales directly with usage and costs, e.g., OpenAI's per-million-token pricing.

Tiered pricing

Offers multiple subscription plans at different price points with increasing features, limits, or AI capacities. This model nudges buyers to upgrade for advanced AI features, common in SaaS and cloud AI services.

Commitment + usage based

You pay a fixed fee for a guaranteed minimum monthly/annual usage. This could be a minimum seat/subscription, spend, or resource allocation (e.g., number of AI credits included). In addition, you’ll pay variable charges for consumption beyond the committed baseline, billed according to actual usage (e.g., extra tokens processed, additional API calls, or AI credits used).

5 AI agent pricing models

AI agents are autonomous software entities designed to perform tasks independently of human users. Traditional user- or seat-based pricing is often inadequate, leading to distinct models adapted to agent workflows.

Per agent (FTE replacement model)Charges a fixed monthly fee per deployed AI agent, treating agents like fractional digital employees. This is common when AI replaces or supplements human headcount. For example, Intercom's FinAI charges approximately $29 per agent per month.

Outcome-based pricingFees based on successful completion of meaningful tasks, such as conversation resolutions or transactions processed. Salesforce Agentforce and Zendesk AI often use “per successful outcome” pricing (e.g., $2 per conversation or resolution). This aligns payment with business results.

Activity-based pricingBilling based on quantifiable agent activities like conversation count, API calls, minutes of engagement, or tokens processed. Examples include Microsoft Copilot charging per hour of use or OpenAI’s token pricing.

Credit/token-basedCustomers purchase credits that are consumed as AI agents perform specific actions (image generation, document processing, research). Kittl and Devin Cognition use credit-based pricing that abstracts from direct usage metrics to packaged units.

HybridCombine multiple approaches (e.g., a base subscription per agent plus per-action fees) to balance predictable costs with scalability. Salesforce and HubSpot blend traditional user seats with agent fees in a hybrid structure.

Examples of AI monetization: Success stories

Lovable: $100M in ARR in 8 months

Among all AI monetization success stories, Lovable stands out as the poster child of not just “vibe coding,” but how to monetize AI effectively.

Recently, they hit $100 in ARR—8 months into building the company, making them a centaur. They hit that number faster than other software companies in history, including OpenAI, Cursor, Wiz.

The numbers:

• 2.3 million active users
• 180,000 paying subscribers
• 45 full-time employees (+14 open positions on their careers page)
• $2.2+ million ARR per employee
• $100M ARR

Now, they’ve released their Lovable Agent, with the promise to yield 91% fewer errors and a more autonomous Lovable. “It should feel like you're now working with a senior developer,” said Anton Osika, Co-founder at Lovable.

So what does their pricing model look like? Well, it used to be a Freemium, credit-based model. Where each credit would get you a prompt/message.

The crux of the matter? If you’re on the free model, you’re building in public. If you’re taking vibe coding seriously, and working on an actual business, you’re immediately incentivized to pay, just for the privacy.

What Lovable managed to do—early and well—was incentivize the behavior that would eventually fuel their revenue.

Enable AI monetization with a flexible billing engine

When it comes to AI monetization, the only constant is change. Things are moving at a pace that demands the right infrastructure. That’s why the speed at which companies can make pricing adjustments is quickly becoming a competitive advantage.

A flexible billing engine gives you a major advantage when testing new AI pricing models because it lets you experiment with different structures. Whether it’s changing usage tiers, bundling options, or introducing new billing metrics, you shouldn’t have to wait for engineering resources to make any changes. 

Below, we walk you through the core steps of setting up your billing engine.

Note that in this context, we’re assuming you’ve already identified your value metric(s) and chosen a pricing model, and with that in mind can start setting up your billing engine. 

Let’s start by defining your goals.

Step 1: Define the goals for your billing engine

Your billing engine should never be an afterthought. That’s why it’s important to define specific goals for your billing system from the start.

• Target billing accuracy rate (aim for >97%)
• Invoice processing time reduction goals
• Revenue leakage prevention targets
• Customer experience improvements

Step 2: Design a flexible product catalog and pricing rules

• Lay out pricing tiers, volume discounts, overage fees, and prepaid credit models as needed.
• Ensure pricing can adapt to mixed models (e.g., base subscription plus pay-as-you-go usage).
• Plan for promotions, free trial limits, and dynamic usage caps if applicable.

Step 3: Implement real-time usage metering and data collection (if applicable)

• Integrate your AI platform with a usage metering system capable of ingesting data at scale, potentially thousands of events per second.
• Use tagging and filters to attribute usage to the correct customer and product plans.

Step 4: Map usage data to billing meters and pricing plans

• Configure billing meters in your billing system that aggregate usage metrics (e.g., total tokens, API calls) per customer and per billing period.
• Assign customers to their subscribed pricing plans reflecting allowed usage limits or tiers.
• Set up filters or triggers that define what counts toward billable consumption (e.g., calls to API route).

Step 5: Automate invoice generation and revenue recognition

• Link aggregated usage to automated invoice systems that reflect accurate charges based on metered usage and subscriptions.
• Implement revenue recognition processes consistent with accounting standards, managing deferred revenue for prepaid credits or subscriptions.
• Track expiration or rollover of unused credits programmatically.

Step 6: Connect to ERP and financial systems

• Ensure usage and billing data synchronizes with your enterprise resource planning (ERP) and accounting software for seamless invoicing, payment collection, and financial compliance.
• Automation here reduces manual intervention, errors, and helps with audit readiness.

Step 7: Provide customer transparency and self-service tools

• Build dashboards for customers to view real-time or near real-time usage and billing information to avoid surprises.
• Implement notifications or alerts when usage approaches limits or incurs overage charges.
• Support flexible payment options including prepaid credits, subscriptions, and pay-as-you-go.

Step 8: Monitor performance and iterate

• Continuously monitor key metrics such as usage patterns, billing accuracy, revenue leakage, and customer feedback.
• Iterate pricing rules, add new billing features, or refine usage attribution based on business insights.
• Test new pricing models or tiers with segmentation to optimize revenue and customer satisfaction.

By following these steps, your company can build an AI billing engine that scales with your usage patterns, supports complex pricing models, protects revenue, and delivers a transparent, customer-friendly experience.

Aligning price with value

AI monetization is not a “set it and forget it” type of thing. Pricing AI products demands constant evolution. Today, teams have to frequently experiment and adapt to fluctuations in model costs and capabilities, along with changes in value perception.

The key to success lies in designing a pricing experience that extends beyond the price tag. This includes everything from feature packaging to communication strategies. The focus should be on aligning price with customer-perceived value.

AI monetization is evolving quickly, and the right strategy can be a powerful differentiator. Whether you're selling access to a foundation model or embedding AI in your SaaS product, success depends on aligning cost, value, and customer experience. 

Choose the right model for your context. Test, iterate, and measure. And above all, make sure your pricing reflects the impact your AI delivers.