4 AI pricing models: In-depth comparison and common mistakes

Old pricing playbooks are being thrown out the window as AI pricing models demand more flexibility to deliver value for customers and profitability for businesses. 

In the AI economy, pricing has quickly proven itself to be the most critical differentiator, fundamentally reshaping how we monetize AI products and features. 

That said, 95% of AI startups get pricing wrong.

That’s why this guide explores the evolving landscape of AI pricing models, examining how leading companies structure their AI pricing strategies, and what to think about when choosing your AI pricing model.

What’s an AI pricing model?

An AI pricing model is a strategic framework that determines how businesses charge for their AI products and features, based on the capabilities of their AI technology and the value delivered. 

The difference between (traditional) SaaS pricing models, AI pricing models, and AI agent pricing models

Traditional SaaS pricing models center around predictable, recurring payments for feature-based packages. These models prioritize simplicity, budget predictability, and broad accessibility, with billing often disconnected from actual user activity or direct value delivered.

AI pricing models typically employ usage-based charges (such as per token, per API call, or compute hour), or hybrid strategies blending subscriptions with granular usage overages. These models reflect the real-time costs of AI operation (like infrastructure/GPU use) and enable pricing power when direct ROI can be demonstrated.

AI agent pricing models charge based on tangible results attributed to an agent’s actions, enabling outcome-oriented, often variable and performance-based, revenue capture. This model leverages precise attribution unique to AI agent workflows.

The challenge with AI pricing models

The fundamental challenge with AI pricing lies in aligning costs with value delivery. AI systems don't just provide static functionality—they learn, adapt, and often produce results that were previously impossible or time-consuming to achieve.

This creates a complex pricing environment where the value delivered can vary significantly based on usage patterns, data quality, and specific customer contexts.

What does effective AI pricing look like? 

Effective AI pricing models must balance several key factors:

• Computational costs that scale with usage
• Unpredictable outputs
• Demonstrating clear ROI to customers

This requires moving beyond simple cost-plus pricing to models that reflect the true business value AI solutions provide, whether that's time savings, improved accuracy, or automated processes that replace human labor.

4 common AI pricing models

Per-user pricing (or seat-based pricing)

Per-user pricing remains popular for AI features that are integrated into existing SaaS platforms, charging fixed monthly fees per user. This model provides predictable revenue and simplifies billing, making it attractive for businesses with stable user bases. However, it struggles to capture the true value of AI, especially when AI solutions reduce the need for human users or when usage varies significantly across customers.

Best use case: Your AI product is designed for collaborative use by individual professionals or teams, where each user derives independent value from direct access to the tool.

Pitfall: Misalignment with resource consumption. Flat fees per user fail to account for large variations in AI usage, potentially leading to unprofitable power users or customers who feel overcharged if they use AI features sparingly.

Example: GitHub

GitHub Copilot charges a flat fee of $19-$39 per user per month for AI coding assistance. This model works well for developer tools where usage patterns are relatively predictable and the value proposition is clear, though it may leave money on the table for high-value use cases.

Usage-based pricing

Usage-based (or consumption based) pricing has emerged as the dominant model for AI services, with companies charging based on actual consumption metrics like tokens processed, API calls made, or compute time used.

Usage-based pricing often employs a credit or token-based approach to simplify billing and usage tracking. In practice, customers purchase a pool of credits or tokens upfront or as needed, and each AI action, API call, or feature usage consumes a specific number of these credits.

This model offers fairness and flexibility, allowing customers to pay only for what they use while helping providers manage variable infrastructure costs.

Best use case: Usage-based pricing works best for API-driven AI services with variable consumption patterns. Developer tools, AI APIs, and infrastructure services benefit from this model because it allows customers to experiment with low initial costs while scaling pricing with actual usage. Companies like OpenAI and Google Cloud AI have built successful businesses using consumption-based pricing using a token or credit-based approach.

Pitfall: Disconnect between cost and customer value. Charging by tokens, API calls, or compute hours often aligns with the vendor’s infrastructure costs but doesn’t reflect the business value delivered to the customer. This can confuse non-technical buyers and make it harder for them to understand the ROI.

Example: OpenAI

OpenAI employs a usage-based pricing model via the API (charging per token, image, or other generative operation). It’s primarily based on token usage, with different costs for input and output tokens.

Tiered pricing

Tiered pricing models combine features and usage limits at different price points, creating clear upgrade paths for customers with varying needs. This model helps businesses capture value across different customer segments while providing predictable pricing options.

Tiered pricing comes in different forms:

• Standard tiered pricing: Most common form of tiered pricing, where customers are offered different service levels or feature sets at corresponding price points. Applies a good-better-best approach. 
• Graduated tiered pricing: Also called tiered usage pricing, in this model the cost per unit decreases as quantity increases, with pricing calculated cumulatively across tiers.
• Bulk tiered pricing: Often called volume pricing, applies a single rate to the entire purchase once quantity thresholds are met.

Best use case: You have an AI product or platform with clearly segmented user types, scalable feature sets, and a need to balance predictable revenue with growth flexibility.

Pitfall: Strictly defined tiers may not reflect how customers actually use AI, causing mismatches and dissatisfaction if users are forced to upgrade for only one needed feature or minor increases in usage.

Example: AutoDesk

Autodesk's Flex pricing model is a token-based, pay-as-you-go system designed for flexible software licensing, particularly suited for occasional or variable users. The more tokens you buy, the less they cost. 

Commitment + usage

A commitment + usage pricing model combines a baseline commitment with variable charges based on actual usage beyond that committed amount. In practice, this means customers agree to pay for a minimum level of usage or spend in a subscription period.

This model combines the predictability of subscription pricing with the flexibility and fairness of usage-based billing.

Best use case: AI products where customers benefit from a known base cost but may have fluctuating usage, such as data processing, API consumption, or AI compute credits.

Pitfall: Total revenue can still fluctuate with usage, making precise forecasting challenging, while customers may get unexpected bills.

Example: Apollo

Apollo’s pricing model combines a commitment-based monthly subscription fee per user with usage-based credits that are consumed as you access the platform’s data and features. Each subscription plan (Free, Basic, Professional, Organization) includes an allocated number of credits per user each month, which are used for actions like revealing contact details, accessing mobile numbers, exporting data, and making calls.

AI pricing models: Comparison overview

AI service pricing models: Bardeen’s pivot

Bardeen, initially a horizontal AI-automation tool for everyone, quickly realized the strongest fit was within GTM teams. So they pivoted. Today, they’re the “The AI Copilot for
GTM teams,” helping automate your GTM workflows.

But Bardeen isn’t competing solely on features. They understood that the most common bottleneck, turning product capabilities into implementation and value delivery for customers, needed to be addressed. 

That’s why they introduced services in their offering. Bardeen’s AIgency is part of their tiered pricing, giving customers a set number of hours per month. The goal? Increasing adoption and unlocking value realization.

Shaking up the traditional SaaS trial

In a highly strategic move, Bardeen includes those ​​service hours during the free trial, redefining the approach to self-service completely. And the results are speaking for themselves as Colby Morgan, VP, Revenue at Bardeen told Pricing SaaS that “We've lost zero of those trials so far. We've won all of them.”

Plus, for larger contracts, the service offering acts as a strategic lever, providing more flexibility during negotiations.

"It's the first variable we negotiate on deals that need negotiation. We don't touch the MRR. If somebody wants something more, we'll add a couple service hours to your plan."

Common mistakes in AI pricing models

Underestimating the importance of data quality represents a critical pricing mistake. AI systems trained on poor-quality data deliver suboptimal results, undermining value-based pricing strategies. Companies must invest in data cleaning, validation, and ongoing quality monitoring to support premium pricing models.

Over-automation without human oversight can lead to pricing failures. AI pricing systems require human judgment to account for market changes, customer relationships, and exceptional circumstances. Companies that rely too heavily on automated pricing risk customer frustration and margin erosion.

Pricing AI like infrastructure rather than outcomes misses value capture opportunities. Many AI companies default to cost-plus pricing based on computational resources rather than the business value delivered. This approach leaves money on the table and fails to communicate the true value proposition to customers.

Ignoring the variable cost structure of AI leads to margin compression. Unlike traditional software with near-zero marginal costs, AI solutions face significant variable expenses for compute, storage, and model inference. Pricing models must account for these costs to maintain profitability as usage scales.

4 key criteria for choosing the right AI pricing model

Cost structure

Understanding your cost structure is fundamental to selecting an appropriate AI pricing model. AI businesses with high variable costs tied to compute resources and API calls benefit from usage-based pricing that passes these costs to customers proportionally. Companies with more predictable infrastructure costs can consider subscription models that provide revenue stability while managing operational expenses.

Customer ROI

Customer ROI measurement capabilities strongly influence pricing model selection. If you can clearly demonstrate and measure the business value your AI delivers—such as time saved, revenue increased, or costs reduced—value-based or outcome-based pricing becomes viable. However, if value measurement is complex or disputed, simpler usage-based or subscription models may be more appropriate.

Sales process

Sales process complexity affects pricing model feasibility. Enterprise sales teams capable of conducting value assessments and ROI discussions can support sophisticated pricing models like value-based or outcome-based approaches. Self-service or product-led growth models typically require simpler, more transparent pricing structures like usage-based or tiered subscription models.

Revenue predictability

Revenue predictability needs vary by business stage and investor expectations. Early-stage companies may prioritize usage-based models to demonstrate product-market fit and growth potential, while mature businesses might prefer hybrid models that combine predictable subscription revenue with usage-based upside.

Pricing flexibility is your competitive advantage

The AI pricing landscape is constantly changing. We’re moving toward more dynamic approaches that align costs with value delivery. The only problem is this: value (or perceived value) changes at the same speed AI products are entering the market.

And while the key to successful AI pricing lies in understanding your specific cost structure, customer value delivery, and market positioning, you want to make sure your AI pricing model is rooted in flexibility, and as such, so is your billing engine.

Building a culture of pricing experimentation and iteration is what will create the foundation for long-term success (and revenue). Companies that invest in pricing experimentation capabilities and measurement systems will be best positioned to capture the full value of their AI products.

The most successful AI companies will be those that view pricing not as a static decision, but as a dynamic strategy that evolves with their technology, customer needs, and market conditions.