AI model selection paralysis causing wasted credits and productivity loss for developers

Developers building web and mobile applications struggle to choose between AI models, often selecting expensive options (like multi-agent systems) that rapidly deplete their API credits while delivering diminishing returns compared to cheaper alternatives. Current solutions lack clear guidance on cost-to-performance ratios, forcing users to learn through expensive trial-and-error, with additional friction from session timeouts and cache expiration that compound wasted spending.