Summation meters its $60 AI analyst in credits while quoting a per-user price

Summation says its near-zero hallucination claim rests on multiple agents checking numbers, calculations and assertions against the underlying data, and that the software recomputes outputs independently instead of asking another language model whether an answer looks plausible ^[8]. A second model asked whether an answer looks plausible is judging fluency.

One of the two failures in the August test matters more than the other. Visibly broken output announces itself. The balanced model that violated its own cash constraint does not, and it turned up in one run out of twenty ^[21]. Summation also said an independent mathematical check caught every error it had deliberately planted in a separate set of models ^[10]. According to runtimewire, that evidence is largely tests Summation designed and published, and it does not establish an independent hallucination rate across customers, data sources and business workflows ^[11].

The credit arithmetic is worth doing before the seat arithmetic. Pro is 12,000 credits for $60 and Max is 40,000 for $200 ^[6], which works out at 200 credits per dollar on both plans ^[20]. The extra $140 a month buys point-in-time data snapshots and priority support at the same credit rate ^[6].

Whatever the meter, the constraint sits upstream of the model. Metrics carry internal definitions and permissions differ by employee. Source systems frequently disagree, so a report can read cleanly while its calculations rest on the wrong interpretation of revenue, churn or inventory ^[14]. Summation describes its answer as a governed context layer holding the customer's definitions, business logic and access rules, combined with model routing and automated verification, with results that trace back to the underlying data ^[15]. A team encodes its data sources, analytical logic and checks into a workflow that then runs each week or month ^[16].

This shape of product did not appear this week. ThoughtSpot shipped industry-specific versions of its Spotter agent in March 2026 with semantic models intended to ground answers in business and industry rules ^[19], and Gartner warned in May that agents without semantic and structural context were more likely to produce inaccurate results and waste spending ^[18].

"Most businesses aren't short on valuable problems to solve. They're short on the analytical capacity to solve them," Wong said in the announcement ^[4]. In a 38-post thread on X he positioned Summation against general-purpose products such as ChatGPT and Claude, arguing that a finance leader can teach the system how revenue is recognized and colleagues can then reuse that definition instead of rebuilding it inside separate chatbot sessions ^[5]. The same thread carried Summation's $35 million financing figure, disclosed about eleven months before the self-serve release ^[27].

For the $60 plan to carry the trust the deployments carried, a customer's definitions have to be encodable through the product's own configuration, by the people who own them. So far both accounts point elsewhere. The named references are still the year-one accounts: Wong named Fanatics and Lineage, and Summation's website carries testimonials from Fanatics Commerce CEO Andrew Low Ah Kee and Lineage data science executive Elliott Wolf ^[26].