Which pricing models are the most effective for AI agents in today's technology landscape?

Moriana Sigel, Raul

4. Semester

Economics and Business Administration (Marketing and Sales), Master

2025

2025-06-02

102 pages

The rapid emergence of agentic AI presents significant monetization challenges, with many vendors defaulting to legacy SaaS pricing models, which prove ill-suited for AI's unique cost structures and value propositions. This thesis investigates which pricing models are most effective, defined through the lens of consumer acceptance (Frohmann, 2018), for AI agents and assesses alignment with current market offerings. Grounded in the Technology Acceptance Model (TAM) (Davis, 1989) and Flat-Rate Bias theory (Lambrecht & Skiera, 2006), this study posits that pricing models delivering greater cost predictability, enhancing price transparency, reducing perceived financial risk and resonating with users’ perceived value will achieve higher acceptance. A quantitative approach was employed, combining a within-subject survey experiment with 53 business professionals and a structured market audit of 101 AI agent pricing pages. Statistical analyses included Cochran’s Q and McNemar tests to evaluate preference differences and binary logistic regression followed by Fisher's test and Cramer's V to determine a correlation between pricing attribute prioritization and pricing model preference. Results reveal a strong flat-rate bias: 43.4% of respondents preferred flat-rate subscriptions (net utility +14), significantly more than usage-based (-17) and license-plus-overage (-12) models (Cochran’s Q(4) = 45.75, p < .001). Outcome-based (24.5%, net utility 0) and credit-based plans (22.6%, net utility +7) were the next most favored. Cost predictability was the most important attribute (net utility +23). A logistic regression, however, demonstrates no significant predictors of flat preference: coefficients ranged from β = 1.31 (OR = 3.7, p = 0.78) for predictability, β = 6.0 (OR ≈ 400, p = 0.21) for simplicity, β = 0.45 (OR ≈ 1,57, p = 0.47) for simplicity each with wide confidence intervals arising from quasi-separation and low power. Fisher’s tests confirmed the null result (p = 0.78-1.00) with only moderate effect sizes for transparency and simplicity (V ≈ 0.23-0.24). A strong market misalignment was identified: 66% of audited vendors use credit-based models, and 22% use usage/overage, while only 2% offer flat-rate and <1% outcome-based pricing. This study concludes that flat-rate and outcome-based pricing models are most effective for AI agents based on consumer acceptance, with 43.4% of participants preferring flat-rate models for their cost predictability. The research reveals a significant market misalignment, as only 2% of AI agent vendors currently offer flat-rate subscriptions while 66% use credit-based systems, despite strong consumer preference for predictable pricing structures. The findings support Flat-Rate Bias theory in the AI agent domain and demonstrate that freemium models would positively influence sign-ups for 86.8% of users. This research underscores the necessity for an acceptance-driven approach to pricing models, suggesting vendors should strategically integrate flat-rate and outcome-based plans to better meet customer demands and achieve more effective monetization in the rapidly evolving AI agent landscape.

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