👋 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.

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.

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.

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:

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.

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:

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.

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:

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).

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

Key characteristics:

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.

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