Nearly every day, I’m asked about pricing challenges, most of them stemming from the complexities and constant evolution of AI. Pricing models are evolving nearly quickly as AI technologies.

AI Is Driving The Evolution Of B2B Pricing Models

Usage-based pricing is rapidly gaining momentum as an alternative to traditional flat-fee and subscription models. This shift reflects the reality that value doesn’t always scale with user count. For example, security tools often deliver value based on endpoints or data volume, not the number of users.

APIs and AI agents are accelerating the shift away from user-based pricing by delivering value through automation, integration, and scale rather than human interaction. APIs create value based on call volume or task automation, not user count. Similarly, AI agents now perform tasks autonomously, reducing the relevance of user-based metrics. As AI continues to decouple usage from value, buyers will increasingly favor outcome-based pricing. In light of these changes, organizations should reassess their pricing models to ensure they align with how value is delivered.

The Evolving Range Of B2B Pricing Models

In my new report, Rethinking Pricing: How To Choose Models That Reflect AI-Era Value, I provide an overview of different B2B pricing models, the pros and cons of each model for both customers and providers, as well as recommendations on when each model is best suited. Below is a recap of the pros and cons of each for suppliers.

Fixed/Subscription Pricing 

Definition: Charges a set amount monthly or annually, regardless of usage. Often seat-based.

Pros:

  • Predictable cash flow
  • Enables deeper user engagement
  • Allows for new paradigm or new concept offerings to demonstrate value

Cons:

  • Must help customers identify users who will realize value
  • Customers may not see the connection to value
  • AI automation will replace users and thus seats

Usage-Based Pricing 

Definition: Tracks a customer’s usage of the offering and bills accordingly. May also track events (e.g., API calls).

Pros:

  • Broaden market potential
  • Unlock revenue from heavy users
  • No revenue ceiling
  • Shortened procurement cycle

Cons:

  • Can generate viral adoption without the artificial barrier of users
  • Less predictable revenue
  • Susceptible to revenue loss during downturns
  • Billing systems and sales compensation must be more complex
  • Sales and success teams must drive adoption for “nice to have”/less established offerings
  • May promote “spend anxiety” among customers

Hybrid Pricing 

Definition: Base subscription plus usage-based charges. Could be over and above the base allocation. Can also combine subscription and outcome- based charges.

Pros:

  • Ensures a baseline of predictable revenue
  • Scales with heavy usage
  • Ease customers into usage-based spending
  • Encourages adoption and provides upsell opportunity

Cons:

  • Harder to communicate and manage
  • Billing systems must be more complex
  • Sales and success teams must track consumption to prevent surprises
  • More challenging for sales compensation
  • Adds complexity to the sales process

Outcome-Based Pricing 

Definition: Ties pricing to tangible success metrics. Can be structured as event-based (e.g., charging per resolved customer support call).

Pros:

  • Builds customer trust
  • Provides a strong incentive for increasing product quality
  • Competitive differentiation

Cons:

  • Must define and track customer KPIs
  • Can be complex to measure and monitor
  • Potentially higher risk
  • Longer sales cycle

For custom guidance on which pricing model will work best and implementation tips for AI offerings, set up an inquiry with analyst Lisa Singer.