> This page is for NeMo Relay, version dev. > For other versions, use one of these documentation indexes: > - Latest (v0.10.4) (default): https://docs.nvidia.com/nemo/relay/latest/llms.txt > - dev: https://docs.nvidia.com/nemo/relay/dev/llms.txt > - v0.10.4: https://docs.nvidia.com/nemo/relay/v0.10.4/llms.txt > - v0.9.4: https://docs.nvidia.com/nemo/relay/v0.9.4/llms.txt > - v0.8.4: https://docs.nvidia.com/nemo/relay/v0.8.4/llms.txt > - v0.7.3: https://docs.nvidia.com/nemo/relay/v0.7.3/llms.txt > For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.nvidia.com/nemo/relay/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/relay/_mcp/server. # Kubernetes Deployment Guidance > Decide whether you need a Kubernetes daemon and plan its setup. Prefer running the managed daemon on the host where users run their coding harnesses. The daemon helps sessions recover from worker failures and serves multiple users on one host. Kubernetes adds network and service management work. Avoid it unless you need it to address port conflicts or limits on user capacity. This page explains when to use Kubernetes and how to plan the setup. NeMo Relay does not provide a reference Helm chart. If you need Kubernetes, your organization must maintain its deployment manifests or Helm chart. ## Decide Whether Kubernetes Is Necessary Consider a separate daemon deployment only when a local daemon cannot solve one of these problems: * **Port sharing:** Pods using shared host networking cannot each bind the same daemon address and port on a node. First consider one shared host daemon or separate ports with matching managed bundles. Pods with their own network namespaces can each use the same port. They don't need a separate daemon deployment just to avoid port conflicts. * **Concurrent users:** One shared daemon can serve only a limited number of users at the same time. If it cannot serve enough users, measure the workload. Then plan separate daemon deployments for groups of clients. Moving the same daemon into a pod does not increase its capacity on its own. There is no published user limit that applies to every deployment. Capacity depends on the number of active requests, stream length, and response size. CPU, memory, and network bandwidth also affect capacity. Follow [Measure Daemon Transport](/daemon/reference#measure-daemon-transport) to plan capacity. Then test with the workload you expect from your clients. ## Keep Workers and Plugins With the Clients The daemon pod runs the route broker, which assigns workers to clients. Each coding harness runs its Model Context Protocol (MCP) process and worker on the **client computer**. If the harness runs in a pod, these processes run in that client pod. The worker runs plugins and sends model requests to the provider. The daemon also needs provider access for pass-through requests. Refer to [Architecture and Startup](/daemon/architecture). Install `config.toml` and `plugins.toml` on every client. Keep these files under administrator control. Follow [Configuration and Managed Clients](/daemon/configuration). Workers do not download these files from the daemon. A ConfigMap mounted only in the daemon pod does not configure clients. Configure the daemon's provider routing separately so pass-through reaches the intended providers. For native plugins such as Switchyard, install the approved plugin manifest, native library, and dependencies on each client that runs a worker. Match the library to that client's operating system, architecture, and Relay version. Follow [Configure Discoverable Plugins](/configure-plugins/discoverable-plugins) for installation and trust requirements. A pod that runs only the daemon does not load plugins and does not need their libraries. Provide plugin API keys through the method that the plugin supports. Place them in the client worker's environment or credential store. A Kubernetes Secret available only in the daemon pod does not reach client workers. Do not put secrets in `plugins.toml` files that all users can read. Each user on each client needs a route credential. Keep it separate from plugin and model-provider keys. ## Plan Network Access and TLS Use a stable private HTTPS origin for the daemon, such as `https://relay.example.com:8443`. Generate the managed client bundle for that origin. Follow the address and certificate requirements in [Remote Linux: Deploy the Daemon](/daemon/remote-server) and the worker address and port setup in [Remote Linux: Deploy Clients](/daemon/remote-clients). The deployment must allow all of these connections: * Clients to the daemon: HTTPS model and hook requests, plus secure WebSocket (WSS) control sessions from MCP and workers. Keep these sessions open. * Daemon to each assigned client worker: new HTTPS connections for readiness checks and routed requests. This is not a reverse tunnel. An ingress that accepts client traffic alone does not provide this return path. * Client workers and the daemon to configured model providers over HTTPS. * Client workers to any plugin services and telemetry collectors they use. Allow these connections in Kubernetes NetworkPolicies, host firewalls, VPN routes, and service routing. Set each worker's advertised address and port so the daemon pod can reach it. A client loopback address cannot serve a remote daemon. If workers share a host network namespace, assign a different port to each user on that host. Limit daemon and worker access to approved networks. Upstream URLs outside loopback must use HTTPS. This includes private Kubernetes Service addresses. Configure the daemon and every client to trust the upstream certificate authority (CA). Clients must also trust the daemon's HTTPS certificate. Relay sends worker Transport Layer Security (TLS) trust details over the authenticated daemon connection. Do not disable certificate checks to work around a private CA. Configure the ingress or reverse proxy to meet these requirements: * Allow WebSocket upgrades under `/_nemo-relay/control/`. * Keep long-lived sessions open and allow traffic in both directions. * Preserve ping and pong frames, which check whether the connection is alive. * Disable response buffering, compression, and other changes to response data. * Disable cache coalescing, which combines requests for the same resource. * Preserve streaming and response trailers. Follow [Reverse Proxy Requirements](/daemon/reference#configure-a-trusted-reverse-proxy) for keepalive timing. Advertise the external HTTPS origin and preserve request paths. If the proxy handles TLS, limit access to the daemon's backend listener. ## Plan Capacity, State, and Restarts Start with one active daemon per endpoint. The daemon keeps worker assignments and route credential bindings in memory. Independent replicas do not share this state. Do not assume that one load-balanced Service makes replicas work as one daemon. To serve more users, assign groups of clients to separate daemon origins. Use matching managed bundles and test each deployment's capacity. Use `XDG_CONFIG_HOME` to set where the daemon stores its identity state. Keep this state when you replace the pod. Set file ownership and permissions for the daemon account. If you replace the signing identity, clients can reject the daemon even if its HTTPS certificate stays the same. Do not let active daemons share a writable identity directory. Refer to [Keep Identity State](/daemon/operations#keep-identity-state). Before a planned upgrade or pod replacement, ask users to close affected harnesses and wait for workers to exit. Workers can take up to two minutes to drain. Set the pod's termination grace period long enough for accepted requests to finish. Stopping the daemon can interrupt active streams. Follow [Upgrade and Roll Back](/daemon/operations#upgrade-and-roll-back). A daemon restart clears route credential bindings. By default, requests bypass worker plugins until MCP registers again and a worker is ready. Use `--require-worker` to reject requests while worker processing is unavailable. Before adding users, complete [Verify Worker-Backed Operation](/daemon/operations#verify-worker-backed-operation) through the actual Service and ingress. Confirm a worker becomes ready, a model stream completes, and an approved plugin produces its expected result. Test pod replacement and control-session recovery. `/healthz` alone does not prove that the daemon can reach workers or that plugins are active.