This study introduces FaaS-Utility, a novel approach aimed at optimizing Function-as-a-Service (FaaS) systems by addressing the critical issue of cold starts, which significantly impede system performance. By introducing a utility function informed by customer preferences and pricing goals, our methodology prioritizes resource allocation to enhance service quality effectively. We implement this strategy within Apache OpenWhisk, demonstrating its integration into a real-world FaaS platform. Our evaluation reveals that the proposed approach notably improves system performance, particularly in over-provisioned states, by reducing latency up to 2.37 times with a maximum additional cost of only 30%. While our method performs best in cold environments, it also maintains performance when applied in warm settings, offering a balanced solution between client and provider through adaptive pricing.