AI teams are hemorrhaging budget on expensive frontier models when cheaper alternatives could handle most tasks

Companies deploying AI agents and LLM applications are burning through their yearly AI budgets faster than expected because they route all requests to expensive frontier models like Claude or GPT-4, even for simple tasks that cheaper open-source models could handle. Teams lack visibility into which model is appropriate for each request, forcing them to choose between overspending on premium models or risking quality degradation by using only cheap models. Current solutions require manual model selection or expensive custom infrastructure, leaving no middle ground.