A new framework for AI agent pricing

Analyzing patterns from 60+ AI agent companies

👋 Hi, it’s Kyle Poyar and welcome to Growth Unhinged, my weekly newsletter exploring the hidden playbooks behind the fastest-growing startups.

Y’all know I’m passionate about AI monetization. Turns out, I’m not alone. Manny Medina, the founder of Outreach (last valued at $4.4B), just took his new startup out of stealth — and announced €10M in pre-seed funding. It’s called Paid and it tackles what Manny calls the biggest problem not enough people talk about in AI: how to monetize and capture the value of AI agents.

After analyzing patterns from 60+ AI agent companies, Manny has put together a new framework for AI agent pricing. Keep reading for highly tactical advice with plenty of examples from early adopters.

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Struggling to price your AI agent? You're not alone.

Most founders I've worked with leave money on the table with the wrong pricing model. Following our recent launch of Paid.ai, 130 of 175 (that’s 75%!) of our signups said “I am not sure how to price my AI features”.

After analyzing 60+ AI agent companies, I've identified four pricing frameworks that actually work, and one that consistently outperforms the rest.

This guide will show you exactly which model to choose based on your specific agent capabilities, how to implement it without tanking your margins, and how to future-proof your pricing against inevitable LLM cost reductions.

By the end, you'll have a concrete pricing strategy that scales with your business instead of against it.

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The four AI Agent pricing models dominating the market

Many companies use just one of the four models, but some hybrid approaches exist too (e.g., Per seat pricing together with Per agent pricing). Read on to understand each of the models.

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Model 1: Price per agent, aka the FTE replacement model

Companies like 11x, Harvey, and Vivun have pioneered this approach, effectively positioning their AI agents as digital employees. This model treats each agent as a fractional replacement for a junior hire or a portion of a full-time employee (FTE); indicating the spend should come from the headcount budget vs. the IT or software tools budget.

Key characteristics:

• Fixed monthly fee per agent deployed

• Value proposition tied directly to headcount spend

• Predictable cost, similar to traditional platform or seat-based SaaS pricing

Tip: This model works particularly well when your agent performs a comprehensive set of tasks that would otherwise require hiring additional staff. Customers find it easy to understand the value proposition when you can demonstrate that your $2,000/month agent replaces a $60,000/year junior employee.

This model is often merged as a hybrid model together with per-seat, per-project, or other usage-based metric. Companies like Salesforce and HubSpot charge for traditional user seats plus an additional fee for agent functionality. Companies like ServiceNow include agent capabilities at the higher end of their tiered seat pricing.

Best for: AI agents handling broad responsibilities or entire job functions with consistent, predictable workloads. AI agents that clearly articulate the savings from taking over the function.

Advantages: You get to draw from the headcount budget which is at least 10x larger than the tech tools budget.

Disadvantages: Low competitive differentiation. This pricing leaves you exposed to “I-do-the-same-but-cheaper” competitors.

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Model 2: Price per agent action, aka the consumption model

Companies like Bland, Parloa, and HappyRobot have adopted this approach, which mirrors usage-based pricing seen from cloud infrastructure, Business Process Outsourcing shops (BPOs), and other kinds of call centers. Every time an agent performs a discrete action, the customer pays for that specific interaction.

Key characteristics:

• Often appears as token consumption with a margin on top

• Sometimes manifests as per-minute pricing

• Direct correlation between usage and cost

Tip: This model provides transparency and aligns costs with actual usage. Customers only pay for what they use, making it attractive for organizations with variable workloads or those testing the waters with AI.

Best for: Agents performing varied, discrete tasks with unpredictable frequency or volume.

Advantages: It is fairly easy to go after the BPO budget as well as other freelancing agencies with a higher performing offer with better SLAs and lower costs. BPO spend for large organizations will reach a staggering $152.8 billion in 2025 or $877 per employee.

Disadvantages: Lowest competitive differentiation. Pricing per activity essentially makes you a commodity and prices only go down.

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Model 3: Price per agent workflow, aka the process automation model

Companies like Rox, Salesforce, and Artisan have implemented this model, which charges for complete sequences of agent actions that deliver specific intermediate outcomes.

Key characteristics:

• Pricing based on completed workflows rather than individual actions

• Each workflow represents a chain of related and useful tasks (e.g., conducting research, composing and sending emails, or handling a conversation)

• Value tied to process automation rather than just task execution

Tip: This approach strikes a balance between consumption-based and outcome-based pricing, making it ideal for complex but standardized processes.

Best for: Agents that execute multi-step processes with clear intermediate deliverables.

Advantages: Easy to implement if the workflows are standard and you can easily measure the cost savings from having an AI vs a person do the work. Complex workflows are harder to price but they give you competitive protection.

Disadvantages: If the workflow is standard, like account research or email composing, this pricing leaves you exposed to price compression. If the workflow is complex, it will be hard to price and you may end up upside down with negative margin for a workflow that ran longer and you couldn’t charge for it, like in the case of parsing a set of long complex documents (like Icertis) or performing a security scan (like XBOW).

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Model 4: Price per agent outcome, aka the results-based model

Companies like Zendesk, Intercom, Airhelp, and Chargeflow (featured earlier) have pioneered this approach, which ties pricing directly to completed objectives.

Key characteristics:

• Charges based on completed jobs or achieved results

• Can be standardized outcomes (Intercom) or bespoke per customer (Sierra)

• Directly aligns price with business value delivered

Tip: This model creates the clearest value proposition for customers, as they only pay when they receive tangible results. However, it requires confidence in your agent's ability to consistently deliver those outcomes.

Best for: AI applications with predictable performance and clearly defined success metrics in markets that already expect it.

Advantages: Highest customer alignment with the lowest risk of competitive displacement and price compression.

Disadvantages: Outcomes may be highly customized which may lead to proliferation of bespoke contracts. If your contracts are large and long-term (>1 year) this should not be a problem. Outcomes may also be subject to attribution, like with AI SDRs. If you don’t have a clear path to attribute results to your agent either via A/B testing or with a POC, this may not be the best route for you.

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Future proofing the models

As LLM costs continue to decline dramatically, you should be prepared for significant shifts in AI agent pricing models by your competitors.

While core technology costs are likely to drop by 10-100x over the next 3-5 years, newer and better models will show up. We believe this combination will create substantial pressure on traditional pricing models.

Price per agent (FTE replacement model)

We believe this model will likely last for a while, but in order to future-proof it, we recommend that you:

• Shift value proposition from "cheaper than human" to "vastly more capable than human"

• Bundle more capabilities and integrations into the fixed price

• Create tiered agent levels with clear capability differentiators

Price per agent action (consumption model)

We don’t believe this model will last the test of time. It has a too-direct correlation to dropping technology costs and a race-to-the-bottom pressure.

In order to future proof it, we recommend that you:

• Transition to workflow or outcome-based models quickly

• Add proprietary capabilities not available in commodity offerings

• Focus on specialized domains where your expertise could command premiums

Price per agent workflow (process automation model)

This model is pretty robust, but you can still future-proof it. We recommend that you:

• Focus on complex, multi-step workflows with clear ROI you can demonstrate

• Develop proprietary workflow components that resist commoditization

• Bundle business-critical bits like analytics and optimization into the workflow pricing

Price per agent outcome (results-based model)

We strongly believe this is the model that will win in the long run. It is still nascent, and it is harder to get the attribution correct, but it needs the least future-proofing. To implement it correctly, we recommend that you:

• Develop robust attribution methodologies

• Create shared risk/reward models with performance guarantees or bonuses for success

• Focus on the high-value business outcomes you can measure.

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Decision framework: Choosing the right pricing model for your AI agent

When determining which pricing model works best for your specific AI agent, ask yourself these questions.

Tip: As you go through the decision framework, understand why you say “yes” or “no” to these junctions. Is it a business reason, or a technical reason? Should this answer be changed in the long run?

Am I replacing a headcount directly?

If your agent's value proposition centers on saving time but doesn't deliver clearly identifiable outcomes (document reviews, security scans, etc.):

• Price per agent: Position as a fractional FTE replacement if you replace predictable tasks

• Price per workflow: Use time saved × hourly rate as your baseline if your agent completes multi-step workflows that have step value.

Can I measure the outcome?

If you are confident of your agent’s ability to consistently deliver identifiable outcomes:

• Price per outcome: Direct alignment with value created

• Outcome-based bonuses: Supplement another pricing model with performance incentives

Does my agent have a varied set of tasks with unpredictable volumes?

If your agent's value proposition centers on performing varied tasks that can differ greatly:

• Price per action: Position as a consumption model (potentially hybrid with “per agent”) and charge per discrete actions, like Number of Actions × Action Rate.

Finally, understand that the ideal pricing model aligns with how your customers perceive and measure value. It’s somewhat fashion and somewhat art.

The most successful AI companies don't just pick a pricing model, they deliberately align their pricing with their customers' value perception and their own long-term strategy.

Analyzing why you chose a specific junction will help you understand where your areas for improvement could be.

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The TL;DR:

Take action now: map your AI agent to the right pricing model today.

If you're automating full job functions, use per-agent pricing to tap into headcount budgets. For variable workloads, price per action. For complex processes, charge per workflow. For measurable results, price per outcome to maximize value capture.

Start with a simple model, then evolve as you learn what customers truly value. Test with your best customers first, measure results, and adjust quickly. The AI pricing landscape will only get more competitive—position yourself correctly now before your margins disappear.

Don’t be afraid to get creative. In a competitive environment, differentiated pricing makes you stand out. Try things like “outcome bonus”, instead of outcome pricing get paid a bonus for each outcome. Remember, pricing isn't just about capturing value, it's about communicating it. The way you price your AI agent sends powerful signals about how it should be perceived and utilized.

As you refine your pricing strategy, continuously gather feedback from customers and monitor key metrics like conversion rates, expansion revenue, and churn. The most successful AI agent companies will be those that evolve their pricing models alongside their technology and their customers, creating sustainable business models that align costs with the true value their agents deliver.