AI Agent Pricing Cheat Sheet

Pricing may feel simple, but for AI Agents, it hides real financial risk.

We built the AI Agent Pricing Cheat Sheet to help teams think clearly about monetizing agents.

It breaks down four foundational pricing models:
- Per Agent: simple, predictable, but detached from value.
- Per Action: aligns with LLM costs, but hard to forecast.
- Per Workflow: structured, easy to explain to ops buyers.
- Per Outcome: aligned with ROI, but complex to attribute.

But the real unlock is in the edge cases, the products that intentionally blur the lines:
- Cursor charges a flat fee but caps usage behind the scenes.
- Intercom Fin quietly introduced per-resolution billing ($0.99).
- Agentforce blends seats with Flex Credit-based usage.
- Regie.ai markets outcome pricing, but requires a base commitment.

These are not mistakes. They’re adaptations. The takeaway isn’t “pick the right model.” It’s that in agentic systems: Cost varies per user. Value varies per task. The best pricing strategies model usage, then abstract it away.

But they come with risk. If you cap usage too loosely or misattribute outcomes, you end up with the worst of both worlds: buyers think they’re paying for value, while LLM costs quietly stack up behind the scenes. And that's why it's critical to measure your Margins early.

Good pricing doesn’t just protect margin, it aligns the buyer’s mental model with what they’re actually getting.

Curious to hear from teams shipping AI agents, how are you thinking about this?

If you’re an AI Agentic company figuring out pricing strategy or billing, write to me at vibhanshu.karn@stykite.com or Join the waitlist at https://stykite.com