Which pricing models are the most effective for AI agents in today's technology landscape?
Author
Moriana Sigel, Raul
Term
4. Semester
Publication year
2025
Submitted on
2025-06-02
Pages
102
Abstract
The rapid rise of AI agents (autonomous, task‑performing AI systems) has made pricing difficult. Many vendors still use legacy SaaS pricing that does not fit AI’s cost structures or the value customers perceive. This thesis examines which pricing models are most acceptable to customers for AI agents and whether that aligns with what the market offers. It draws on the Technology Acceptance Model (TAM) and Flat‑Rate Bias theory, expecting that predictable, transparent prices that lower perceived financial risk and match perceived value will be preferred. The study used a within‑subject survey experiment with 53 business professionals and a structured audit of 101 AI‑agent pricing pages. Participants compared several models: flat‑rate subscription (fixed fee), usage‑based (pay per use), license plus overage (base license with extra fees when limits are exceeded), credit‑based (prepaid credits), and outcome‑based (pay for achieved results). Findings show a clear flat‑rate preference: 43.4% chose flat‑rate subscriptions, significantly more than usage‑based and license‑plus‑overage options (statistically significant, p < .001). Outcome‑based (24.5%) and credit‑based plans (22.6%) were the next most favored. Cost predictability was the most important attribute. However, a follow‑up logistic regression found no statistically significant predictors of preferring flat‑rate; estimates were imprecise due to low statistical power and model issues, and additional tests reached the same conclusion, with only moderate associations for transparency and simplicity. The market review revealed a strong mismatch: 66% of vendors use credit‑based pricing and 22% use usage/overage, while only 2% offer flat‑rate and less than 1% offer outcome‑based pricing. In addition, 86.8% of respondents said a freemium model (a free basic tier with optional upgrades) would increase their likelihood of signing up. Overall, flat‑rate and, to a lesser extent, outcome‑based pricing appear most acceptable for AI agents, largely because they provide predictability. Yet most vendors offer credit‑ or usage‑based plans, creating a misalignment between supply and demand. Vendors should consider adding flat‑rate and outcome‑based options to better meet customer needs and monetize more effectively in a fast‑moving market.
Den hurtige udbredelse af AI‑agenter (selvstændige, handlekraftige AI‑systemer) skaber nye prisudfordringer. Mange udbydere bruger stadig klassiske SaaS‑modeller, som ofte ikke passer til AI’s omkostninger og den værdi, kunder oplever. Denne afhandling undersøger, hvilke prismodeller kunder accepterer bedst for AI‑agenter, og om det stemmer overens med, hvad markedet faktisk tilbyder. Arbejdet bygger på Teknologiacceptmodellen (TAM) og teorien om præference for fast pris (Flat‑Rate Bias) og forventer, at kunder foretrækker priser, der er forudsigelige, gennemsigtige og reducerer den økonomiske usikkerhed. Metoden kombinerer et inden‑for‑personer spørgeskema‑eksperiment med 53 erhvervsprofessionelle og en struktureret gennemgang af 101 prissider for AI‑agenter. Deltagerne sammenlignede forskellige prismodeller: flatrate‑abonnement (fast pris), forbrugsbaseret (betaling per brug), licens med overforbrugstillæg (basislicens plus ekstra gebyrer ved overskridelse), kreditbaseret (forudkøbte kreditter) og resultatbaseret (betaling for opnåede resultater). Resultaterne viser en tydelig flatrate‑præference: 43,4 % foretrak flatrate‑abonnementer, markant mere end forbrugsbaserede og licens‑plus‑overforbrug modeller (statistisk signifikant, p < 0,001). Resultatbaserede (24,5 %) og kreditbaserede planer (22,6 %) var de næstmest foretrukne. Prisforudsigelighed var den vigtigste egenskab. En efterfølgende regressionsanalyse fandt dog ingen statistisk signifikante forudsigere for at vælge flatrate; estimaterne var upræcise på grund af lav statistisk styrke og modelproblemer, og supplerende tests pegede på samme konklusion med kun moderate sammenhænge for gennemsigtighed og enkelhed. Markedsanalysen viste en markant skævhed: 66 % af udbyderne bruger kreditbaserede modeller, 22 % bruger forbrugs-/overforbrugsmodeller, kun 2 % tilbyder flatrate, og under 1 % tilbyder resultatbaseret prissætning. Desuden angav 86,8 % af deltagerne, at en freemium‑model (gratis basisversion med mulighed for opgradering) ville øge sandsynligheden for tilmelding. Konklusionen er, at flatrate‑ og i nogen grad resultatbaserede prismodeller er mest acceptable for kunder af AI‑agenter, især på grund af prisforudsigelighed. Markedet tilbyder imidlertid overvejende kredit‑ og forbrugsbaserede planer, hvilket skaber en misalignment mellem udbud og efterspørgsel. Udbydere bør derfor overveje strategisk at integrere flatrate‑ og resultatbaserede muligheder for bedre at imødekomme kundebehov og opnå mere effektiv indtjening i et hurtigt udviklende marked.
[This apstract has been rewritten with the help of AI based on the project's original abstract]