研究60家AI代理公司，我总结了AI代理的4大定价模式

Group 1 - The core viewpoint of the article is that AI agents are rapidly emerging and commercializing, leading to the need for effective pricing strategies [1] - The article identifies four main pricing models for AI agents: seat-based pricing, action-based pricing, workflow-based pricing, and outcome-based pricing [1][21] Group 2 - Model 1: Seat-Based Pricing: This model treats each AI agent as a digital employee, with costs linked to employee budgets rather than IT budgets. It includes a fixed monthly fee per agent and is effective when the agent can perform tasks that would otherwise require hiring additional staff [5][6] - Model 2: Action-Based Pricing: Companies like Bland and Parloa use this model, charging clients based on the number of actions performed by the AI agent. This model offers transparency and aligns costs with actual usage, making it attractive for organizations trying AI [9][10] - Model 3: Workflow-Based Pricing: This model charges for a complete sequence of operations to deliver specific results. It balances consumption and outcome-based pricing, making it suitable for complex but standardized processes [12][13] - Model 4: Outcome-Based Pricing: Companies like Zendesk and Intercom utilize this model, linking pricing directly to completed goals. It provides a clear value proposition as clients only pay for tangible results [16][18] Group 3 - The article suggests that as the costs of large models decrease, new pricing models for AI agents will emerge, putting pressure on traditional pricing strategies [21] - Recommendations for optimizing pricing models include maintaining seat-based pricing for the foreseeable future, while action-based pricing may struggle due to technological cost reductions and price wars [22] - The article emphasizes that the ideal pricing model should align with how clients perceive and measure value, allowing for adjustments based on customer feedback and understanding [27]