Three walkthroughs of how a team goes from "we just use the biggest model for everything" to a defensible, monitored, best-value choice — across cloud and on-premise.
Illustrative scenarios. Companies, people and figures below are composite examples built to show how Modeljury is used — not real customers.
Every incoming invoice was run through a frontier model to pull vendor, date and total. It worked — but at ~180k invoices a month, the bill was eye-watering, and nobody could say whether a cheaper model would do just as well. Switching felt risky without proof.
NorthLedger uploaded 300 hand-labelled invoices and set a 95% field-accuracy bar. The bake-off ran the full roster head-to-head: a mid-tier model cleared 96% at a fraction of the cost. Monitoring stayed on — six weeks later a newly-launched model cleared the same bar even cheaper, and the pick auto-switched.
Support ticket triage routed every message to the right team. But Helmsly's enterprise customers had contractual data-residency clauses — ticket content couldn't leave their environment. That ruled out most cloud APIs before cost was even on the table.
They flipped on the "data can't leave our environment" rule, which pruned the cloud candidates automatically. The bake-off then compared the self-hosted open models that survived. One cleared their 90% bar at a tiny per-call cost — and, crucially, ran entirely on their own hardware.
Content moderation ran at huge volume, and at 2M items a month, cost and latency both mattered. Policy also forbade routing user content through certain non-domestic providers — a rule that was easy to violate by accident when teams picked models ad hoc.
The provider-origin rule excluded the disallowed models up front — they were never even tested. Among the rest, a self-hosted model cleared the bar at scale. Monitoring kept running: when a flagged model later launched, it was skipped on compliance grounds, with the reason logged, before anyone wasted time evaluating it.
Pick a task, set your rules, and see the verdict — the same flow these scenarios followed.
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