Pricing strategies for AI agents can vary; if the service is used by humans, it may follow a per-user pricing model, while machine-to-machine services might adopt usage-based pricing.

The learning curve associated with AI agents is a significant barrier for product managers, who often prefer tools that allow for natural language interactions over complex programming.

Most AI applications today, particularly those labeled as AI agents, often have low operational costs due to their reliance on software and LLMs, which are becoming cheaper over time.

The marketing narrative around agents often suggests they can replace human workers, which can lead to inflated pricing models for AI software.

Using AI prototyping tools for production apps can lead to maintenance issues, as they require ongoing attention to bugs and regressions, making them less reliable than dedicated AI agent platforms.

Many buyers of AI solutions are sophisticated and understand that the underlying technology is often simple, leading them to question the true costs of running these systems.

There are four pricing models for AI: activity-based, workflow-based, outcome-based, and agent-based pricing.

The pricing model for AI tools is shifting towards usage-based pricing, making it easier for companies to justify ROI and shorten sales cycles.

The pricing model for AI services is evolving; companies might pay for successful outcomes, qualified leads, or resource utilization, reflecting a shift in how value is perceived in AI-driven businesses.