# Scaling Seldon Services Seldon Core 2 runs with several control and dataplane components. The scaling of these resources is discussed below: * **Pipeline gateway**: The pipeline gateway handles REST and gRPC synchronous requests to Pipelines. It is stateless and can be scaled based on traffic demand. * **Model gateway**: This component pulls model requests from Kafka and sends them to inference servers. It can be scaled up to the partition factor of your Kafka topics. At present we set a uniform partition factor for all topics in one installation of Seldon. * **Dataflow engine**: The dataflow engine runs KStream topologies to manage Pipelines. It can run as multiple replicas and the scheduler will balance Pipelines to run across it with a consistent hashing load balancer. Each Pipeline is managed up to the partition factor of Kafka (presently hardwired to one). We recommend using as many replicas of dataflow-engine as you have Kafka partitions in order to leverage the balanced distribution of inference traffic using hashing * **Scheduler**: The scheduler manages the control plane operations. It is presently required to be one replica as it maintains internal state within a BadgerDB held on local persistent storage (stateful set in Kubernetes). Performance tests have shown this not to be a bottleneck at present. * **Kubernetes Controller**: The Kubernetes controller manages resources updates on the cluster which it passes on to the Scheduler. It is by default one replica but has the ability to scale. * **Envoy**: Envoy replicas get their state from the scheduler for routing information and can be scaled as needed. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.seldon.ai/seldon-core-2/user-guide/scaling/scaling-core-services.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.