Scaling your ML model horizontally can help handle high traffic, but it's not just about adding more replicas and relying on autoscaling to manage the load. There are key considerations to keep in mind, such as handling variable demand, managing infrastructure costs, and ensuring even utilization of resources. By understanding these limitations and using a combination of techniques like response caching and model optimization, you can optimize your ML model's performance and reduce waste.