In the big-data era, the amount of traffic is rapidly increasing. Therefore, scaling methods are commonly used. For instance, an appliance composed of several instances (scaled-out method), and a load-balancer that distributes incoming traffic among them. While the most common way of load balancing is based on round robin, some approaches optimize the load across instances according to the appliance-specific functionality. For instance, load-balancing for scaled-out proxy-server that increases the cache hit ratio. In this paper, we present a novel load-balancing approach for machine-learning based security appliances. Our proposed loadbalancer uses clustering method while keeping balanced load across all of the network security appliance's instances. We demonstrate that our approach is scalable and improves the machine-learning performance of the instances, as compared to traditional loadbalancers.