> For the complete documentation index, see [llms.txt](https://k-ai.gitbook.io/knowledge-ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://k-ai.gitbook.io/knowledge-ai/operate/monitoring.md).

# Monitoring & observability

K-AI exports operational signals in standard formats so you can integrate them with your existing observability stack. This page covers what is emitted, how to consume it, and what you can observe self-service.

## What K-AI emits

* **Per-pod metrics** — every K-AI service exposes a Prometheus-compatible scrape endpoint covering CPU, memory, request latency, error rate, and queue depth.
* **Queue depth** — the LLM service and the indexation pipeline expose pending counts and oldest-pending-request age as first-class signals.
* **Cost events** — every billable action is emitted as a cost event aggregated into KCU (K-AI Consumption Units). See [Cost events](#cost-events).
* **Auth events** — login attempts, token issuance, refresh-token rotation, revocation, Dynamic Client Registration, Microsoft SSO callbacks.
* **MCP invocations** — every MCP tool call is logged with tool name, target K-AI Instance, calling user, latency, and outcome.

All services log structured JSON to stdout. No extra configuration is required to ship logs to your collector.

## How to consume it

K-AI does not bundle an observability stack — you point your existing tooling at the metrics and logs the platform emits.

* **Datadog, Grafana, Splunk, Elastic, etc.** — scrape the Prometheus endpoints (standard `ServiceMonitor` pattern on Kubernetes) and collect stdout logs via your usual agent.
* **PagerDuty, Opsgenie, …** — wire alerts off your collector. Useful starter alerts: sustained LLM-queue backlog, abnormal failed-indexation rate, login-failure spikes from a single IP, per-instance KCU consumption beyond your expected envelope.
* **SIEM** — the auth-event stream is the highest-signal input. Ingest it as-is.

For on-premise deployments, the same metrics and logs are emitted inside your cluster — no new networking required.

## Cost events

The K-AI Consumption Monitoring System aggregates billable actions across the platform into KCU per K-AI Instance and per organisation. Cost dashboards are available in the pricing section of the K-AI Studio portal (`app.kai-studio.ai`); the same dashboards ship with on-premise deployments.

Eight cost types are tracked:

| Type              | What it covers                               |
| ----------------- | -------------------------------------------- |
| `LLM`             | LLM model calls (prompt + completion tokens) |
| `FILEPARSER`      | Per-document parsing and extraction          |
| `SEARCH_INDEX`    | Vector-index footprint per day               |
| `INSTANCE_ACCESS` | Per-instance availability                    |
| `INDEXING_JOB`    | Bulk re-indexation batches                   |
| `AUDIT_TASK`      | Audit AI crew runs                           |
| `CRAWL_URL_TASK`  | Web-crawler activity                         |
| `RETRIEVAL_TASK`  | MCP tool invocations                         |

Cost events are scoped either to a specific K-AI Instance or to an organisation (for org-level activity such as the web crawler). RBAC distinguishes `admin` (full configuration and invoice access) from `sales` (consumption dashboards only). K-AI platform operators see all orgs; client users see only their own.

## What you can observe self-service

* **Per-instance indexation status** — call `GET /documents/list?state=…` to see in-flight, indexed, or errored documents. See [Instance API — Documents](/knowledge-ai/sources-and-ingestion/instance-api/documents.md).
* **Cost reports** — consumption dashboards (pricing section of the K-AI Studio portal) with drill-down by cost type, instance, and day.
* **OAuth lifecycle** — the auth service exposes active-session listing and revocation endpoints. See [Authentication — OAuth 2.1](/knowledge-ai/authentication/oauth.md).
* **MCP tool calls** — visible inside your MCP client (Claude Desktop, Cursor, Le Chat, etc.) for end-user debugging.

For incidents that go beyond self-service observability, contact your K-AI support representative.


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