Tech companies are reassessing whether every AI workload needs a top-tier frontier model. For many tasks, cheaper models may be good enough if they preserve quality, latency, and reliability.
That tradeoff matters because inference costs can scale quickly once AI features move from prototypes into daily product use.
The next phase of AI adoption may depend less on raw model rankings and more on matching each job to the cheapest model that performs well enough.