跳到主要内容
版本：最新版

Choose a Deployment

Semantic Router and its inference backends are separate services. Choose how to run the Router first, then connect model endpoints that it can reach.

The deployment guides are organized into three paths: Docker for one host, Kubernetes for cluster-managed deployments, and Hardware for preparing GPU-backed model servers or accelerating Router-side models.

At a glance​

DeploymentChoose it whenStart here
DockerYou are evaluating the Router, developing locally, or deploying on one host.Deploy with Docker
KubernetesYou need replicas, declarative rollouts, an operator, or an existing gateway or inference platform.Kubernetes
HardwareYou need to start a vLLM backend on AMD or NVIDIA GPUs, or accelerate supported Router-side models.Hardware

Docker​

vllm-sr serve starts the local Router, Envoy, Dashboard, and supporting services. It is the fastest path for configuration work, evaluation, and a single-host deployment.

The command does not normally provision the provider models referenced by a custom config or built-in virtual model. Start those endpoints first, or bind the config to endpoints that already exist.

Follow Deploy with Docker for the stack lifecycle and backend networking. If you want a small local model server, add Ollama. Both guides keep the Router and model-server responsibilities separate.

Kubernetes​

Choose the Kubernetes path that matches who should own the Router lifecycle:

  • Use the CLI and Helm workflow when your team already owns a complete canonical config and deploys releases through CLI or GitOps automation.
  • Use the Kubernetes Operator when Kubernetes should manage SemanticRouter resources, discovery, and lifecycle.
  • Choose a supported gateway when Semantic Router must join an existing traffic data plane.
  • Choose an inference-platform integration when another platform owns model deployment and replica scheduling.

Gateway and inference-platform integrations do not replace Router policy. They connect semantic model selection to infrastructure that already owns traffic or model-serving lifecycle.

Hardware​

The Hardware guides prepare GPU-backed vLLM endpoints and explain when to run supported Router-side signal models on a GPU:

  • AMD ROCm for vLLM on AMD Instinct GPUs;
  • NVIDIA CUDA for vLLM on NVIDIA GPUs and optional CUDA acceleration in the Router.

Hardware is not a separate Router topology. You can connect these model servers to either Docker or Kubernetes. Keep the Router on CPU unless measurements show that its local embeddings or classifiers benefit from sharing GPU capacity.

Whichever hardware path you choose, test the model endpoint directly before testing it through the Router. Confirm that it supports the context, modality, tool, and protocol requirements declared by the recipe.

Before production​

Review these areas before exposing a deployment: