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You can connect a local LLM to self-hosted monitoring without making Grafana or Prometheus public. The two documented approaches are to configure Grafana’s LLM app to use a local OpenAI-compatible model endpoint, or to let an MCP-capable client call Grafana or Prometheus tools through a protected MCP server. In either case, keep the services on a trusted network, limit the bridge’s permissions and reachable destinations, and protect credentials in transit and at rest.
Choose the integration that matches what the LLM needs to do
“Local LLM” can mean a model served on your own machine or network, while “monitoring tool” may mean Grafana’s dashboards and operations or Prometheus metrics queries. The integration choice depends on which system the model needs to use and which component should initiate requests.
| Pattern | Best fit | Key security boundary |
|---|---|---|
| Grafana LLM app with a custom provider | Grafana’s LLM features calling a local OpenAI-compatible model endpoint such as Ollama, vLLM, LM Studio, or LiteLLM. | The Grafana plugin proxies authenticated requests and stores API keys. Confirm provider compatibility and the exact Grafana and plugin versions. Grafana custom-provider configuration; Grafana LLM app documentation |
| MCP server | An MCP-capable LLM client invoking monitoring tools. Grafana’s server exposes Grafana functions; the Prometheus server focuses on Prometheus access and documents use with a local Ollama model. | The MCP process and its endpoint become a service boundary: clients that can reach it may exercise the authority of its configured credentials. Limit access and permissions. Grafana MCP server; Prometheus MCP server |
Use the Grafana LLM app when Grafana’s own LLM features need to call your model. Use MCP when a separate client should invoke monitoring tools. Neither approach requires exposing monitoring endpoints to the public internet.
Secure the connection before enabling model access
Apply these controls to the full route between the LLM client, integration layer, monitoring service, and local inference endpoint—not just to the model server.
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- Keep the services private. Place the model endpoint, Grafana, Prometheus, and any MCP server on a trusted network segment. Prometheus Authors advise that Prometheus component HTTP endpoints should not be exposed to publicly accessible networks without appropriate safeguards. Prometheus security model
- Restrict access to MCP endpoints. The Prometheus MCP server warns that anyone able to reach its endpoint may query Prometheus using at least the default client’s credentials. Limit network reachability or configure an appropriate web-access control layer. Do not treat an MCP endpoint as harmless just because the model runs locally. Prometheus MCP server security notes
- Constrain Grafana’s data-source proxy. Grafana warns that services reachable from its host or local network may be exposed through the data-source proxy. Use datasource URL allowlists, firewall rules, or a controlled proxy to restrict outbound destinations. Grafana security documentation
- Encrypt credential traffic. Prometheus documentation notes that Basic Authentication without TLS sends usernames and passwords in cleartext. Use TLS or a protected tunnel whenever credentials cross a network. Prometheus security model
- Put secrets in secret fields. Grafana provisioning supports encrypted secure JSON settings for API keys, passwords, and TLS material; custom header values belong in
secureJsonData, not ordinary configuration fields. Grafana provisioning documentation - Grant narrow monitoring permissions. For a self-run Grafana MCP server, use a service account token and review which monitoring permissions and tool actions the client can invoke. Check permission granularity for the deployed version rather than assuming every action can be separately scoped. Grafana MCP server
- Review data sent to and retained by the client. Metrics, labels, dashboards, and incident details can reveal sensitive operational information. Limit the data available to the model and check the selected client and provider’s logging and retention behavior; those controls vary by deployment.
Configure Grafana’s LLM app for a local model
This path is for self-managed Grafana deployments using the Grafana LLM app. The app supports a custom OpenAI-compatible API, with local providers including Ollama, vLLM, LM Studio, and LiteLLM given as examples in its documentation. Custom-provider support was added in plugin version 0.10.0; verify that your installed plugin and Grafana release are compatible before configuring it. Custom-provider setup; Plugin documentation
- Install the Grafana LLM app in your self-managed Grafana instance, following the installation instructions for the exact Grafana and plugin versions you run.
- Make the local inference endpoint reachable only where needed. Configure the model server on a private address or trusted network path that Grafana can reach. Do not publish it broadly to make setup easier.
- Configure the custom provider with the endpoint and model mapping required by your chosen OpenAI-compatible server. The exact field values depend on that server’s API compatibility and authentication configuration; use its own documentation alongside Grafana’s custom-provider guide.
- Store any API key or custom header as a secret. Use the plugin’s designated secret handling and Grafana secure provisioning fields where applicable; avoid putting credentials in plain configuration.
- Test with a restricted account and limited data. Confirm Grafana can reach the intended model and that the configured feature behaves as expected before broadening access. Verify logs and network rules do not expose secrets or unnecessary monitoring data.
Use MCP when the client should call monitoring tools
An MCP server gives an MCP-capable LLM client tools to invoke. Choose the server based on the system it should operate: Grafana’s open-source server provides Grafana-facing tools such as dashboards, data sources, alerting, and incidents; the Prometheus MCP server is more directly focused on Prometheus queries and documents a local Ollama connection. Grafana MCP server; Prometheus MCP server
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- Decide whether the client needs Grafana operations or Prometheus queries. Choose the corresponding MCP implementation rather than granting access to both by default.
- Run the MCP server on a restricted network path. Allow only the intended LLM client or trusted segment to connect. Treat reachability as access to the server’s configured monitoring credentials.
- Configure credentials deliberately. Grafana’s self-run MCP server uses a service account token. Grant only the permissions needed for the intended tasks and validate what the deployed version permits.
- Review available tools and actions. Expose only the functions the client needs, and consider whether a tool can make changes or only read information. Keep query and operational access no broader than necessary.
- Test the complete path. Check that the client can use the intended tools, cannot reach unrelated internal services, and does not receive more monitoring data or authority than required.
Common mistakes to avoid
- Making a private service public for convenience: keep Prometheus, Grafana, inference, and MCP endpoints private unless you have deliberately implemented appropriate protections.
- Assuming “local” means isolated: a local model does not prevent an MCP client, Grafana proxy, or other reachable service from accessing internal resources.
- Using Basic Authentication over an unencrypted network: credentials can be exposed in transit without TLS.
- Storing keys in ordinary configuration: use designated encrypted secret fields, including
secureJsonDatafor applicable Grafana provisioning settings. - Giving the bridge broad authority: an MCP server can extend its configured monitoring access to any client that can reach it. Scope credentials, tools, and network routes.
What depends on your deployment
The documented examples do not establish a universal hardware requirement, model-quality threshold, throughput expectation, or safe permission set. Those depend on the model, inference server, data volume, client, network topology, and monitoring configuration. Confirm the current documentation for your Grafana release, plugin release, MCP implementation, and model server before deploying; logging and retention behavior also depends on the specific client and provider.
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