OpenAI Introduces Outcome-Based Pricing: Major Clients Pay Only When AI Completes Tasks

OpenAI has started letting some customers pay only when the AI works

Outcome pricing has been the norm in customer support for a year. The largest model vendor moving to it is a different matter. OpenAI has begun letting some of its largest customers pay only when its AI actually completes the job. Kevin McLaughlin and Amir Efrati reported the change to The Information, giving the example of a customer support interaction handled end-to-end. The arrangement is limited to select major accounts rather than offered generally, and OpenAI has not announced it. TNW has not independently verified the report, and the terms, the customers, and the prices are all unknown. The industry name for this is outcome-based pricing, and the appeal to a finance director is not subtle. A bill that arrives only when something worked is considerably easier to defend than one that arrives regardless. Token billing has made that a live problem. One developer running a hundred agents in parallel accumulated $1.3m in OpenAI tokens across thirty days, which is an extreme case of a general pattern where cost scales with attempts rather than results. Customer support is where the model has already settled, because a resolution is one of the few AI outputs anyone can define. Intercom charges $0.99 for each conversation its Fin agent resolves, and nothing at all for the ones it does not. Zendesk went further in May, restricting billing to what it calls Verified Resolutions, confirmed by an LLM evaluation within 72 hours of the conversation. Assisted escalations and contained resolutions became free, and the billable rate sits at roughly $1.20 to $1.50 on committed volume. Salesforce has been working through the same question in public and more awkwardly. Agentforce launched at $2 per conversation, charged for every 24-hour session whether or not anything was resolved, which customers found both expensive and impossible to forecast. Flex Credits arrived as the answer, moving the meter from conversations to individual actions at about 10 cents each, starting at $500 for 100,000 credits. That is consumption pricing rather than outcome pricing, and the distinction matters, because an action that fails still bills. Buyers appear to want both, in that order. Futurum Group found in May that 43% of them prefer consumption-based models and 27% prefer outcome-based ones, with fewer than one in five still preferring to pay per user. "Outcome-based pricing is becoming a market standard," wrote Keith Kirkpatrick, the firm's research director for enterprise software, whose sharper finding is that vendors offering seats alone are now being disqualified before the evaluation starts. For OpenAI, the move is a change of position rather than a new product. It has sold capacity by the token, priced per model and per call, and letting an enterprise pay for completed work instead means accepting the risk that the work does not complete. That risk has to be priced somewhere, and the interesting question is where. A vendor confident in its success rate can afford the arrangement, while one that is not has to load the per-success price until the economics match, which is why per-resolution rates cluster around a dollar rather than a cent. It also changes who carries the cost of a bad answer. Under token billing, the customer pays for every failed attempt, whereas under outcome billing the vendor absorbs them, which is a meaningful transfer of risk from the buyer to the company that built the model. The harder part is agreeing what success means. A resolution is definable, but the agentic work OpenAI has been pushing towards, with 10 million users on its agents, involves multi-step tasks where completion is a matter of judgement rather than a field in a database. There is a commercial reason to want it settled quickly. An enterprise that cannot forecast a bill tends to run a pilot indefinitely rather than sign, and outcome pricing removes the objection at exactly the point in the sales cycle where it usually stalls. None of that is settled, and none of it is public. What is on the record is that the largest model vendor has started, quietly and selectively, to sell results instead of capacity.

OpenAI Lets Some Customers Pay Only When AI Performs | PYMNTS.com

Under this system, these clients pay only when the company's artificial intelligence (AI) does its job, The Information reported Sunday (Aug. 30), citing a source familiar with the matter. The report notes that this previously unreported change comes as software firms change their pricing practices for AI. As these companies sell more, they are moving from subscription fees to charging customers based on usage and whether the AI helps their company. According to The Information, customer relationship management software company Salesforce is among the firms adopting this practice, letting businesses choose how they want to pay for its Agentforce AI. That includes negotiating custom contracts that charge businesses based on how much the AI either grows revenue by helping salespeople close more deals or cuts costs by automating more customer service interactions. "Customers want to buy and want to price in different ways. This is something I've learned really aggressively recently," CEO Marc Benioff said in a call with investors last week. The report contends that these comments demonstrate the uncertainty around software pricing amid the rise of AI. Salesforce and companies like it are now facing competition from startups like OpenAI who have launched "outcome-based" pricing. This practice, the report said, could make it tough to determine when cost savings come from the enterprise customer's efforts or from the software. To that end, Stripe has issued guidelines for outcome-based pricing, The Information added. A sales conversion of other successful outcomes "could stem from product tweaks, marketing campaigns or seasonality" rather than the software, the company said. "Unless attribution rules are explicit, customers could argue over whether the outcome belongs to you," referring to the software provider. PYMNTS wrote about this trend earlier this year, saying the shift marks a "structural recalibration" of Software-as-a-Service (SaaS) economics, not their demise. The per-seat model of charging for AI use worked because it aligned the incentives of customers, vendors and investors. AI agents make the picture more complicated. "Software companies are reconsidering pricing as AI systems perform work that would previously have required multiple employees," that report said. "A customer support platform powered by AI, for example, may resolve a growing share of tickets autonomously. Charging per human support representative becomes less intuitive when much of the work is automated."