Why “Per Seat” pricing is failing for AI agents (and what's replacing it)

Every week, we talk to founders and revenue leaders building AI agents.

They’re replacing analysts, SDRs, ops managers, even engineers with products that run continuously and get smarter over time.

But when it comes to pricing? Most teams are stuck using SaaS playbooks.

That means:

• 📉 Flat fees.
• 👥 Per-seat pricing.
• 🧾 Arbitrary implementation charges.

Our data shows that it’s not working.

We just had Rob Litterst on our podcast, and he said the same things:

I’ve been in + reviewed 178 recorded conversations with buyers across agent-powered products - from devtools to finance—and mapped where pricing friction shows up and what high-performing teams do differently.

Here’s what I found.

What our data says

I tracked conversation density across key themes like pricing, value attribution, and margin visibility. Here's what stood out:

Key spike themes:

• “What are we paying for?”
• “How do we prove the agent works?”
• “Can we scale pricing with usage?”

These patterns repeat across sectors and maturity stages, with almost even splits so these problems meet everyone similarly!

The pricing patterns that don’t work

Per-seat pricing confused nearly every buyer.

They’d ask things like:

“Is this per user or per agent?”

“What happens if it runs 24/7?”

“How do I know I’m not overpaying?”

My takeaway: Flat fees made early deals easier but blocked expansion.

In dozens of renewals, companies delivered $500K to $1M+ in agent output… and were still charging the same $25K/year contract.

That disconnect kills your pricing power!

The models that do work

The teams that consistently closed and expanded used hybrid pricing:

A good example looks like:

• $8K/month base (e.g., platform fee)
• $20 per strategy executed, $0.50 per task completed
• 9% of savings > $100K (revenue share)

Customers understood it. They trusted it. They paid more.

What actually works

From the best teams in our network, here’s what we recommend:

✅ Show the work
Track what the agent does. Log signals. Share it with customers.
I can’t overstate how important showing the work is.

✅ Anchor pricing to outcomes when possible
Frame the price in terms of cost avoided, time saved, or money made.

✅ Use a hybrid model
Blend base fees with metered usage and outcome-linked bonuses.

✅ Tie invoices to value
Your invoice should prove the agent delivered ROI. If it doesn't, pricing is guesswork.

💬 tl;dr

Most agent pricing doesn’t reflect the product. You're not selling seats. You're selling output, work, and value.

If you’re using SaaS-style pricing for agents, you’re leaving money on the table and confusing your customers in the process.

Need help instrumenting usage, tracking value, or rewriting your pricing model?

That’s what we do at Paid.

We’ve helped dozens of teams price and prove the value of their agents without adding complexity for their end customers.