The Pricing Maturity Curve for Agentic AI Companies

AI application companies can progress from basic activity-based pricing to outcome-based pricing as their products get closer to providing the equivalent value of fulltime employees.

Post methodology: Claude 3.7 via Dust with prompt: please write a concise essay based on the text of this podcast transcript that describes the different types of business/pricing models that medina discusses (activity, workflow, outcome and agent-based) and how these are an evolution from traditional SaaS models. v2 prompt: can you please reframe this essay from the perspective of "The pricing maturity curve for agentic AI companies"? Light editing and reformatting for the Substack editor.

Agentic AI companies face a critical evolution in how they monetize their value. The old per-seat pricing paradigm of SaaS is irrelevant in the era of AI agents. As AI agent companies move from experimentation to production, their pricing models must mature to capture their fair share of the value they create. As Paid founder Manny Medina shared on a recent episode of Training Data, a "pricing maturity curve" is emerging that represents how AI application companies can progress from basic activity-based pricing to outcome-based pricing as their products get closer to providing the equivalent value of fulltime employees.

Stage 1: Activity-Based Pricing - The Starting Point

At the beginning of the maturity curve, most agentic AI companies default to activity-based pricing. This is the easiest to sell because it’s the easiest mental model for buyers who just want to test the product and “see what it does.”

• Characteristics: Charging per API call, token, or discrete action performed by the AI

• Appeal: Easy to implement, understand, and sell, especially for early adopters

• Limitations: Creates a direct correlation between usage and cost, leading to commoditization

• Market Position: Positions the company as a utility rather than a strategic solution

Medina warns about the dangers of staying at this level: "If you stay there, somebody will come along and say, 'I'll do the same thing for cheaper.' And then you are in a nightmare scenario in which there are tons of others who look just like you."

This stage is appropriate for initial market entry and proof-of-concept deployments but becomes increasingly problematic as competition intensifies.

Stage 2: Workflow-Based Pricing - The First Evolution

As companies gain confidence and customer validation, they can advance to workflow-based pricing:

• Characteristics: Charging for a sequence of activities that accomplish a meaningful task

• Appeal: Begins to align pricing with business processes rather than technical metrics

• Advantages: Allows differentiation based on the complexity and value of workflows

• Market Position: Positions the company as a process enhancer rather than just a technology provider

This represents a critical first step in value-based pricing: "Moving to a workflow allows you to move out of the treadmill of charging for pure work to charging for work that is worth something to somebody," explains Medina.

At this stage, companies begin deeper conversations with customers about the business problems they're solving, rather than just the technology they're providing.

Stage 3: Outcome-Based Pricing - Value Alignment

As the agentic AI solution proves its reliability and impact, companies can progress to outcome-based pricing:

• Characteristics: Tying pricing directly to measurable business outcomes

• Implementation: Often begins with a base fee plus "outcome bonuses" for achieving specific results

• Advantages: Creates strong alignment between vendor success and customer success

• Market Position: Positions the company as a strategic partner in achieving business goals

This stage represents a significant maturity milestone. As Medina notes: "If an outcome happens of a particular quality that is measurable, charge for it. That way it opens the door to a conversation of value alignment."

Outcome-based pricing requires:

1. Clear definition of what success looks like for each customer

2. Reliable measurement of outcomes

3. Confidence in the AI's ability to consistently deliver results

Stage 4: Agent-Based Pricing - The Advanced Approach

Once outcomes have a proven track record and product capabilities mature, the most mature pricing model positions AI agents as direct replacements for human labor:

• Characteristics: Charging per AI agent deployed, with pricing benchmarked against human labor costs

• Appeal: Directly addresses labor replacement value proposition

• Advantages: Targets headcount budgets (typically larger than software budgets)

• Market Position: Positions the company as a workforce transformation solution

Medina provides a clear example: "I'm going to deploy X many agents, the agents are going to do this amount of work that is equivalent to a $90,000 a year SDR. I'm going to charge you $20,000 per agent."

This approach requires:

1. Clear definition of the human role being augmented or replaced

2. Comprehensive coverage of that role's responsibilities

3. Reliable performance at or above human-level quality

The Ultimate Maturity: Bespoke Value-Based Contracts

The pinnacle of pricing maturity is developing customized contracts based on each customer's unique definition of value:

• Characteristics: Highly customized pricing aligned to specific customer success metrics

• Implementation: Requires deep understanding of each customer's business

• Advantages: Creates "sticky" relationships that are difficult to displace

• Market Position: Positions the company as an indispensable business partner

As Medina explains: "Sierra doesn't have pricing on their main page. Because they're sitting down with each customer, and they're finding out what's important to them. Is it time to resolution? Is it that the customer resolves a ticket and then buys something? Is it CSAT? Is it NPS? What is it? And then you build a box around and you say that that's your outcome function."

Navigating the Maturity Curve

When to Evolve Your Pricing

Companies should consider advancing their pricing model when:

1. Market Saturation: Multiple competitors offer similar capabilities at similar price points

2. Value Demonstration: Customers consistently report outcomes that far exceed what they're paying for

3. Customer Sophistication: Buyers begin to focus on business outcomes rather than technical capabilities

4. Margin Pressure: Token/compute costs threaten profitability under activity-based models

Challenges in Advancing the Curve

Moving up the maturity curve isn't without challenges:

1. Measurement Complexity: More sophisticated models require reliable tracking of outcomes

2. Sales Complexity: Sales teams must be equipped to sell value rather than features

3. Customer Education: Buyers may be conditioned to purchase AI like traditional SaaS

4. Internal Resistance: Finance and product teams may prefer the predictability of simpler pricing models

Market-Specific Considerations

Different markets may support different levels of pricing maturity:

1. BPO Replacement: Markets where AI replaces business process outsourcing are particularly receptive to agent-based pricing

2. Specialized Professional Services: Areas like legal, healthcare, and financial services often support outcome-based approaches

3. High-Volume Transactions: Markets with high-volume, low-complexity tasks may remain at workflow pricing longer

Conclusion: The Imperative to Evolve

The pricing maturity curve isn't just about maximizing revenue—it's about business sustainability in the age of agentic AI. As Medina emphasizes: "The problem we're seeing is that the value is accruing to the customer, not to the agent business. They're capturing all the value of all the savings, and that needs to change."

Companies that fail to progress up the pricing maturity curve risk:

1. Commoditization as competitors undercut activity-based pricing

2. Margin erosion as token/compute costs remain significant

3. Valuation challenges as investors look for sustainable business models

The most successful agentic AI companies will be those that deliberately advance their pricing models in lockstep with their technology and market position. As Medina advises: "How you differentiate in your market is [that] your story has to be different—and if your story is different and your pricing is the same, your story ends up being the same."

By understanding and navigating this pricing maturity curve, agentic AI companies can build sustainable businesses that capture their fair share of the tremendous value they create.