For a self-hosted view of Proxmox health, combine the Prometheus PVE Exporter for Proxmox API metrics with a separate uptime checker such as Uptime Kuma for the services people actually use. Prometheus can collect both kinds of data. A local LLM can be added as a custom summarization layer, but the documented tools do not establish a turnkey Proxmox-to-LLM integration.
What each monitoring layer tells you
| Layer | What it observes | Useful for | Important boundary |
|---|---|---|---|
| Prometheus PVE Exporter | Proxmox API data about nodes, guests, storage, cluster state, and other enabled collectors. | Infrastructure dashboards and alerts about resources managed by Proxmox VE. | It does not establish whether an application endpoint is responding to users. |
| Uptime Kuma | Configured endpoint checks, including HTTP(S), TCP, ping, DNS, push, and other documented monitor types. | Checking web apps, DNS, reverse proxies, and other reachable services; notifications and status pages. | A successful endpoint check does not by itself explain the underlying Proxmox resource state. |
| Local LLM (optional) | Only the selected monitoring information that you deliberately pass to it. | Custom summaries or explanations of alerts and metric context. | The documented projects do not verify a supported local LLM runtime, official connector, or safe autonomous Proxmox operator. |
Prometheus is the collection and query layer in this design: it can scrape the PVE Exporter and Uptime Kuma’s metrics endpoint. Keep alerting and operational decisions in systems with explicit rules and permissions; treat model-generated text as an interpretation, not a health check.
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Collect Proxmox-aware metrics with the PVE Exporter
The community Prometheus PVE Exporter queries Proxmox VE and exposes metrics for Prometheus. Its documentation describes collectors for cluster and node information, as well as guest and storage-related data. Which metrics appear depends on the collectors you enable, so select them for the operational questions you need rather than assuming every collector is necessary.
Use a dedicated read-only identity
Create a separate Proxmox user or API token for collection and grant it the PVEAuditor role with the required read scope at /, as specified in the exporter documentation. Do not give a metrics scraper write authority. For token-based authentication, protect the token as a secret and limit access to the exporter and Prometheus endpoints to trusted systems.
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Preserve TLS certificate verification when connecting to the Proxmox API by trusting the appropriate certificate. The exporter offers a way to disable verification for self-signed setups, but doing so removes the certificate check; it is a security trade-off, not an equivalent substitute for trust configuration.
Deploy and scrape it
The exporter documentation describes installation with pip or Docker and a Prometheus scrape using the /pve path with module and cluster or node parameters. Run it on a Proxmox VE node or another system that can reach the API. Follow the current project documentation for the configuration syntax and version-compatible deployment details; the available documentation summary does not establish a universal configuration file or command that fits every installation.
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Balance coverage against collection cost
- Cluster metrics can be collected from one node, according to the exporter guidance.
- To obtain a complete set of node metrics, scrape each node using the documented parameters.
- The config collector makes one API request per VM or container. The project recommends disabling that collector on large deployments, without defining a numeric threshold for “large.”
- Choose scrape frequency and collectors with your API load and monitoring needs in mind; the project documentation does not provide a universal performance guarantee.
Check service availability separately with Uptime Kuma
Add checks for endpoints that matter to users, such as a web application, DNS resolver, or reverse proxy. Uptime Kuma’s README lists HTTP(S), TCP, keyword and JSON checks, WebSocket, ping, DNS, push, Docker, and other monitor types, along with notifications and status pages. The README describes 20-second intervals as a feature; that is a project feature statement, not a guarantee that every monitor or configuration will run at that interval.
Uptime Kuma can expose monitor status and response-time metrics at /metrics for Prometheus to scrape. The documented metrics include labels such as monitor name and type, and URL or host and port. Configure access to this endpoint according to the deployed Kuma settings, and do not expose it publicly without appropriate authentication and network controls.
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Use the two layers together to distinguish failure classes: a Proxmox node or guest metric can indicate an infrastructure-side issue, while an endpoint check can show whether a service is reachable from the checker. Neither signal alone proves every part of an application is healthy.
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Keep collection and monitoring local
For an all-local approach, run the exporter, Prometheus, and Uptime Kuma on systems within your own environment, and keep their storage, dashboards, and alerting there. This keeps the telemetry flow under your control, subject to how your infrastructure, backups, network access, and notification providers are configured.
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Consider OneUptime only if its destination fits
OneUptime documents a Proxmox agent based on a preconfigured OpenTelemetry Collector. The guide describes scraping the PVE Exporter, or running the exporter in a bundled profile, and forwarding telemetry to a OneUptime instance. Its setup requires Docker Engine 20.10 or later, the Compose v2 plugin, Proxmox API access on port 8006, a read-only PVEAuditor API token, and a telemetry ingestion token. The guide’s ONEUPTIME_URL example can point to a self-hosted OneUptime host. Check the agent’s current behavior and the destination before deployment: the telemetry is sent to that configured instance, which may be outside your preferred trust boundary.
Add a local LLM as a controlled extension
The documented monitoring projects do not establish an official connector that lets a local model query Proxmox or consume these metrics, nor do they verify a particular local runtime or prompt. If you build this layer, treat it as your own integration and validate it independently.
- Choose a narrow input, such as selected alert text or a small set of metrics and recent events. Avoid passing credentials, tokens, or unrelated guest data.
- Send that context only to a model and runtime you have confirmed are local under your deployment’s data-handling requirements.
- Ask for a plain-language summary or explanation, and show the relevant source metrics or alert alongside it so an operator can check the model’s interpretation.
- Keep alert generation, access control, and any remediation in deterministic monitoring or automation systems. Do not provide the model broad Proxmox credentials or treat generated advice as authorization to make changes.
This separation lets a model help explain telemetry without confusing a summary with tested detection or safe autonomous repair.
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