Combining multiple AI models, known as model ensembling or the multi-model strategy, can significantly reduce errors, improve accuracy, and enhance the reliability of AI-powered Software as a Service (SaaS) applications. This approach leverages the strengths of different models to compensate for their individual weaknesses, thus addressing common issues such as data limitations, bias, and performance gaps. Techniques like model voting, routing, and fallback logic are employed to ensure better accuracy, reduce hallucinations, and achieve more consistent results at scale. Platforms like Eden AI simplify the integration of multiple models by providing access to numerous AI providers through a single API, offering built-in model comparison, fallback, and cost optimization, thus streamlining the deployment of multi-model strategies.