Choosing your AI pricing strategy: 6 proven models for revenue growth

AI is rewriting the rules of pricing—fast. We can no longer just set a price and hope for the best when we check in six months from now. With AI, every pricing decision becomes a real-time lever for revenue, growth, and competitive advantage. That’s why today’s AI pricing strategies are as much about innovation as they are about profit.

According to Andreessen Horowitz, 73% of AI companies are still experimenting with their pricing models. As average gross margins for AI companies range between 50-60% compared to 80-90% for traditional SaaS, getting your AI pricing strategy right has never been more critical.

In this article we’ll clear up the confusion between pricing strategies and pricing models, provide an overview of the six most common AI pricing strategies, along with what to think about when choosing yours.

What’s an AI pricing strategy?

AI monetization vs. AI pricing strategies vs. AI pricing models

AI monetization refers to your go-to-market and business model strategy for turning AI into revenue. For example, is AI your core product, an add-on, or a growth lever?

AI pricing strategies represent the overarching framework that defines how you position and package your AI products to achieve your business objectives. It determines the best way to capture value.

This includes:

• Choosing the right pricing metric (per agent, per API call, per seat, per output)
• Defining tier structures or usage thresholds
• Deciding what’s free vs. paid, bundled vs. modular
• Running experiments to test willingness to pay

Your AI pricing strategy answers the strategic "why" behind your pricing decisions.

AI pricing models are the tactical mechanisms through which you collect payment from customers. These include specific structures like usage-based pricing, outcome-based pricing, or hybrid approaches.

Pricing models represent the operational "how" of your revenue collection.

💡 The strategy guides the model selection, while the model executes the strategy in practical terms.

6 AI pricing strategies and when to use them 

As companies increasingly integrate AI capabilities into their offerings, selecting the right AI pricing strategy becomes critical for sustainable growth and competitive positioning.

Today, we see six common AI pricing strategies being used by both SaaS companies offering AI capabilities and AI-native companies. Each strategy offers unique advantages and challenges, requiring careful consideration of your target market, cost structure, and business objectives.

1. Freemium AI pricing strategy

The freemium model offers basic AI functionality at no cost while charging for advanced features, higher usage limits, or premium capabilities. This approach allows users to experience AI value before committing to paid plans, creating a natural progression from free trial to paying customer.

When to use it

Freemium works best for AI companies focused on rapid user acquisition and market penetration. It's particularly effective when:

• Your AI capabilities can demonstrate clear value quickly
• Network effects improve as more users join the platform
• You need to build trust in new or emerging AI technologies

Advantages

• Lower barrier to entry: Users can test AI capabilities without financial commitment, reducing adoption friction.
• Viral growth potential: Free users often share experiences, driving organic user acquisition and reducing marketing costs.
• Data collection: Free users provide valuable usage data that improves AI models and informs product development as well as conversion optimization pathways to paid plans.

Disadvantages

• High infrastructure costs: AI inference costs can be substantial for free users, with LLM processing expenses adding up quickly.
• Low conversion rates: Typical freemium conversion rates range from 3-10%, meaning most users never pay.
• Feature limitation challenges: Restricting AI capabilities without frustrating users requires careful balance.

Billing setup implications

• Usage tracking systems: Implement robust metering to monitor token consumption, API calls, and feature usage across free and paid tiers.
• Automated tier management: Build systems that automatically enforce usage limits and prompt upgrades when thresholds are reached.
• Real-time cost monitoring: Essential for managing AI inference costs and identifying when free users approach profitability thresholds.

Example of a Freemium AI pricing strategy: Descript

Descript offers limited access to their AI features on their Free plan. For example, you only get 3 uses of Basic AI actions_just enough to demonstrate the value of the platform and push users towards a paid plan. 

2. Good-Better-Best (GBB) AI pricing strategy

The good-better-best (GBB) model structures AI offerings into three distinct tiers, each with progressively more features, capabilities, or usage allowances. This tiered approach caters to different customer segments while encouraging users to select higher-value options.

When to use it

This strategy works effectively when:

• Your AI solution serves diverse customer segments with varying needs
• You can clearly differentiate features and capabilities across tiers
• Customers have different willingness-to-pay levels

Advantages

• Market segmentation: Captures value from price-sensitive and premium customers simultaneously.
• Revenue optimization: The middle tier often becomes the "sweet spot" for conversions, driving higher average revenue per user.
• Clear value progression: Makes upgrade paths obvious and compelling for customers as their needs grow.

Disadvantages

• Complexity management: Maintaining feature differentiation across tiers requires ongoing product and technical coordination.
• Pricing optimization challenges: Determining optimal price points and feature allocation requires extensive testing and analysis.
• Customer confusion: Too many options or unclear differentiation can lead to decision paralysis.

Billing setup implications

• Feature entitlement systems: Implement granular controls that can enable/disable specific AI capabilities based on subscription tier.
• Usage allowance management: Track and enforce different usage limits (tokens, API calls, requests) for each tier.
• Automatic tier transitions: Enable seamless upgrades when customers exceed current tier limitations.

Example of a Good-Better-Best AI pricing strategy: Slack

Slack offers increased access to AI features on Pro ($4.38/per user/month), Business+ ($9/user/month), and Enterprise+ (custom) tiers.

3. Standalone (all-you-can-eat) AI pricing strategy

A standalone AI pricing strategy offers AI functionality as a complete, independent product rather than as an add-on or integrated feature within a larger suite. Customers purchase access to the AI tool directly, often on a per-user or per-seat basis, where the value and pricing reflect the AI’s ability to deliver specific outcomes without reliance on other platforms or software. 

When to use it

• Mature AI capabilities: Best suited for proven AI solutions that deliver clear, measurable value without requiring additional products
• Specialized use cases: Ideal when AI addresses specific, well-defined problems that customers recognize as valuable
• Market leadership position: Works effectively when you have differentiated AI capabilities that can command premium pricing

Advantages

• Clear value proposition: Customers understand exactly what they're paying for without complexity of bundled offerings
• Premium pricing potential: Standalone positioning can support higher prices when AI delivers significant business value
• Simplified customer decision: Eliminates confusion about integrated features or complex package configurations

Disadvantages

• Higher customer acquisition costs: Requires convincing customers to adopt entirely new solutions rather than upgrading existing ones
• Market education requirements: May need significant investment in educating market about standalone AI value proposition
• Competition from integrated solutions: Risk of being displaced by platforms that bundle AI with existing products

Billing setup implications

• Simple billing architecture: Straightforward pricing structure focused on single product without complex integrations
• Value-based metrics: Billing systems should track metrics that align with customer outcomes rather than just usage
• Flexible pricing models: Support for various pricing approaches including subscription, usage-based, or outcome-based billing

Example of a standalone AI pricing strategy: OpenAI 

As an AI-native company, OpenAI offers multiple AI products users can access directly. The AI capabilities are sold as independent and fully functional tools. For its API specifically, customers pay based on actual usage, specifically calculated by the number of tokens processed both in input and output, making pricing highly granular and aligned with the value delivered. 

4. Add-on (use-case packages) AI pricing strategy

The add-on strategy offers AI capabilities as modular packages that customers can purchase alongside core products. Each package typically focuses on specific use cases or functionalities, allowing customers to pay only for the AI features they need.

When to use it

This approach works best when:

• AI features serve distinct, separable use cases
• Customers have varying AI needs and adoption timelines
• You want to test AI market demand without restructuring core pricing

Advantages

• Flexibility: Customers can choose specific AI capabilities without paying for unused features. This reduces the perceived risk for customers hesitant about AI adoption.
• Revenue expansion: Generates additional revenue from existing customers without changing core product pricing.
• Granular value alignment: Pricing can closely match the specific value delivered by each AI use case.

Disadvantages

• Pricing complexity: Multiple add-ons can create confusion and complicate purchasing decisions which can create friction in the user experience.
• Lower adoption rates: Optional nature may limit AI feature uptake compared to bundled approaches.
• Integration challenges: Separate pricing for AI features may not reflect integrated value propositions.

Billing setup implications

• Modular billing architecture: Support independent pricing and billing for multiple AI add-ons.
• Usage attribution: Track and bill for different AI features separately while maintaining unified customer accounts.
• Flexible packaging: Enables customers to combine different add-ons with custom configurations.

Example of an add-on AI pricing strategy: Microsoft Power Platform

Microsoft Power Platform combines a base subscription with AI Builder capacity add-ons sold separately, enabling scalable AI adoption based on specific use cases.

4. Platform + modules AI pricing strategy

This strategy combines a base platform fee with additional charges for specialized AI modules or capabilities. Customers pay for platform access and then add specific AI functionalities based on their requirements, creating a hybrid model that balances predictable revenue with usage-based expansion.

When to use it

Platform + modules pricing works well when:

• Your AI solution requires significant infrastructure investment
• Different customer segments need vastly different AI capabilities
• You want to maintain recurring revenue while allowing usage expansion

Advantages

• Predictable revenue base: Platform fees provide a stable recurring revenue foundation.
• Scalable expansion: Module pricing allows customers to grow usage without major pricing restructures.
• Competitive differentiation: Platform depth can create switching costs and competitive moats.

Disadvantages

• Higher initial costs: Platform fees may deter price-sensitive customers from initial adoption.
• Complexity in sales process: Requires education about both platform value and module benefits.
• Pricing optimization challenges: Must optimize pricing across the platform and multiple modules simultaneously.

Billing setup implications

• Hierarchical billing structure: Support base platform subscriptions with additional module-based charges. 
• Unified usage tracking: Monitor platform usage alongside individual module consumption patterns.
• Flexible module combinations: Allow customers to mix and match modules with different pricing models (flat-rate, usage-based, etc.).

Example of a platform + modules AI pricing strategy: Salesforce

Salesforce’s add-on AI pricing strategy is centered around its AI product suite, Agentforce, which offers AI capabilities as modular, optional packages that customers can purchase alongside their core Salesforce subscriptions.

5. Price bundling strategy

Price bundling combines multiple AI services, features, or usage allowances into packages sold at a single price, often at a discount compared to purchasing components separately. This strategy simplifies purchasing decisions while encouraging broader AI adoption.

When to use it

Bundling works effectively when:

• You offer complementary AI services that work better together
• Customer acquisition costs are high and bundling increases transaction value
• You want to encourage trial of multiple AI capabilities

Advantages

• Increased transaction value: Customers typically spend more on bundles than individual services.
• Simplified decision making: Reduces complexity of evaluating multiple AI services separately.
• Improved customer stickiness: Multiple services create higher switching costs, plus comprehensive bundles can be harder for competitors to match.

Disadvantages

• Value perception challenges: Customers may focus on price rather than individual component value.
• One-size-fits-all limitations: Bundles may include unused features, reducing perceived value.
• Pricing optimization complexity: Must optimize bundle pricing while maintaining individual component viability.

Billing setup implications

• Bundle management systems: Track and bill for multiple AI services as unified packages.
• Component usage tracking: Monitor individual service usage within bundles for optimization insights.
• Flexible bundle configuration: Enable different bundle combinations for different customer segments.

Example of a price bundling strategy: Adobe Creative Cloud

Adobe uses a sophisticated price bundling strategy for its AI capabilities within its Creative Cloud suite, positioning AI as a core feature rather than an optional add-on.

Adobe’s Creative Cloud Pro plan (formerly All Apps) bundles extensive AI-powered tools, including Firefly generative AI for image creation, video and audio generation, and content optimization, into a single subscription. Users get a set of generative credits for AI features powered by Firefly across the applications included in their subscription. 

7 steps to choosing your AI pricing strategy

1. Establish your AI monetization model

Start by defining AI’s role in your business:

• Core product: AI is the main offering.
• Add-on: AI enhances an existing product.
• Growth lever: AI drives usage/frequency or upsells.

This will determine whether you’re going for a direct or indirect monetization model. This choice shapes your AI pricing strategy. For example, AI as a core product may favor subscription or usage-based pricing, while an add-on may be bundled or modular.

2. Understand your pricing strategy foundations

Your AI pricing strategy is the overarching framework that answers why you price your AI offering a certain way. It shapes how you:

• Choose key pricing metrics (per agent, per API call, per seat, per output).
• Define tier structures or usage thresholds.
• Decide the free vs. paid feature balance or bundling vs. modular packaging.
• Plan experiments to test customer willingness to pay.

This strategic layer guides your tactical pricing model choices.

3. Evaluate pricing metrics based on your AI product

Common metrics include:

• Per agent or seat: Ideal if AI licenses per user or agent (e.g., AI assistants).
• Per API call or action: Fits AI with variable workloads (e.g., image generation, chatbot messages).
• Per output or outcome: Suits AI delivering measurable results (e.g., predictive analytics tied to sales uplift).
• Flat fee or subscription: When usage is predictable and customers value ongoing access.

Align your metric with how customers perceive and measure value from your AI.

4. Define tier structures and thresholds

Tiering helps capture different customer segments and usage patterns while increasing revenue predictability.

• Design tiers based on feature sets, number of users, usage volume, or service levels.
• Include free tiers or freemium models to lower adoption barriers.
• Consider thresholds within tiers to encourage upgrades (e.g., API call limits).
• Use modular add-ons for specialized features.

5. Balance free vs. paid offering and bundling vs. modularity

• Free vs. paid: Free tiers or trials increase user adoption but must be carefully sized to avoid cannibalizing paid plans.
• Bundling vs. modular: Bundled plans simplify sales and encourage upsell; modular options let customers pay only for what they need, increasing price transparency.

Choose balance based on your market, competitors, and AI value delivery.

6. Choose your AI pricing model (the tactical "How")

Common AI pricing models:

• Usage-based: Pay per API call, action, or compute time. Good for scalability and aligning revenue with consumption.
• Subscription: Fixed recurring fees with feature or usage tiers. Ensures predictable revenue and customer loyalty.
• Outcome-based: Prices linked directly to performance or results delivered (e.g., increased sales). Aligns cost with customer value.
• Hybrid: Combine models like commitment + usage (e.g., base subscription plus usage credits) to capture value from multiple customer types.

Selecting the right model(s) depends on your AI’s value drivers, operational costs, and customer preferences.

7. Test, measure, and iterate

Pricing is not static. It evolves as you learn more about your customers and AI’s market fit.

• Run experiments such as A/B pricing tests or targeted pilots to gauge willingness to pay.
• Collect customer feedback on value perception and pricing fairness.
• Adjust your strategy over time based on learnings and market changes.

Pricing is not a one-time decision 

AI pricing strategies aren’t set-it-and-forget-it decisions—they’re a dynamic tool kit. As markets shift and customer needs change, your AI pricing strategy should evolve too. The smartest companies blend models to maximize reach and revenue. 

This flexibility has to extend to your billing engine. Robust tracking, automated upgrades, support for bundles, add-ons, and real-time analytics are non-negotiable. With a flexible pricing playbook and a billing system built for anything, you can confidently adapt, experiment, and capture more value as AI and your business move forward.