{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T18:20:21Z","timestamp":1787509221585,"version":"build-2736575974"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":58,"publisher":"Elsevier BV","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>We study AI as a Service, in which a monopolistic provider sells downstream users access to AI agents\/models. Motivated by prevalent contracting practices, we focus on conversation-based pricing (CBP), which charges a fixed fee per invocation, and outcome-based pricing (OBP), which charges only upon success. Buyers have private information about valuation and task difficulty, which determines the AI's success probability. We start with a binary-type model: CBP reduces to a standard screening problem, whereas OBP induces two-dimensional screening because success-contingent payments interact with type-dependent success probabilities. Under an OBP-specific increasing-differences condition, we characterise the optimal OBP menu and provide a \"virtual type\" interpretation that adjusts value by success probabilities. We show that OBP outperforms CBP when per-success value (value normalised by success probability) is aligned with success probability across segments; that is, buyers who are willing to pay more per success are also those for whom success occurs sufficiently often. When this alignment fails, CBP dominates. The underlying driver is a distinctive feature of OBP: a buyer may accept a high per-success price yet generate low, or even negative, expected profit for the provider. The same force yields an asymmetric robustness result: CBP guarantees at least half of second-best revenue, while OBP can be arbitrarily poor in the worst case. We apply the theory to a customer-service queueing setting in which a buyer deploys an AI service to handle incoming customer requests, and only unresolved requests are escalated to downstream human agents. We show that misalignment between AI capability and human service rate favours OBP, whereas aligned systems favour CBP. Finally, we extend the analysis to many types and show that the alignment logic continues to govern the comparison.<\/jats:p>","DOI":"10.2139\/ssrn.6254459","type":"posted-content","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T09:42:06Z","timestamp":1773135726000},"source":"Crossref","is-referenced-by-count":0,"title":["Pricing AI as a Service"],"prefix":"10.2139","author":[{"given":"Ilan","family":"Lobel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3682-1137","authenticated-orcid":true,"given":"Yanwei","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahua","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"6","key":"ref1","doi-asserted-by":"crossref","first-page":"2413","DOI":"10.1287\/opre.2020.0651","article-title":"Optimal auction design with deferred inspection and reward","volume":"72","author":"Saeed Alaei","year":"2024","journal-title":"Operations Research"},{"key":"ref2","article-title":"Optimal mechanism design with postallocation signal","author":"Saeed Alaei","year":"2026","journal-title":"SSRN Electronic Journal"},{"issue":"4","key":"ref3","first-page":"50","article-title":"Ariel Rabkin, Ion Stoica, and Matei Zaharia. 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