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aiAxonIQ exposes a Prometheus-compatible query API, so Grafana and other Prometheus clients can read your metrics directly. There is no exporter, bridge or adapter to run. This is the answer to “we already have Grafana dashboards and we are not rebuilding them.”
Before you start. You need metrics in aiAxonIQ and a bearer token for the API. See Authentication.

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Supported endpoints

That is the subset Grafana needs to drive a dashboard and populate its query builder’s dropdowns.

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Configure the datasource

1

Add a Prometheus datasource in Grafana

Set the URL to your aiAxonIQ API origin. It is the same host you sign in to.
2

Add the credential as a custom HTTP header

Under Custom HTTP Headers, add:
Authentication is the standard bearer token used by the rest of the API — not a license key. License keys are for sending data; sessions are for reading it. See Authentication.
3

Save and test

Then build a panel with a metric name and confirm data appears.

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The limits — read this before relying on it

This is a simplified PromQL subset, not PromQL. The supported shapes are:
Aggregations avg, sum, max, min and count are supported.Rate functions are accepted and treated as an average. rate(metric[5m]) parses, runs, and returns an average rather than a per-second rate — so it returns a plausible number that is not a rate.This is the single most important thing on this page. A copied Grafana dashboard full of rate() expressions will render, will not error, and will show wrong values. Verify every panel against the same data in aiAxonIQ before trusting a migrated dashboard.
Other consequences of the subset worth knowing:
  • No binary operators between series — you cannot divide one metric by another to compute a ratio.
  • No by or without grouping on aggregations.
  • No sub-queries, no histogram_quantile, no recording rules.
  • Only equality label matchers. Regular-expression matchers are not part of the subset.

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Metric names

Metric names are matched in both common shapes, so a query written against a Prometheus-style name will match telemetry stored under its OpenTelemetry dotted equivalent and the reverse:
This is what lets an existing Prometheus dashboard find data that arrived over OTLP.

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When to use this, and when not to

A mixed setup is entirely reasonable. Keep your Grafana dashboards where they are, and use aiAxonIQ for the investigation surfaces — logs, traces, the service map and the AI features — that Grafana was never going to serve from a Prometheus datasource anyway.

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Other Prometheus clients

Anything speaking the Prometheus HTTP API can read from these endpoints under the same limits. The rate-function caveat applies equally: a client that assumes full PromQL will get answers rather than errors.

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The other direction

To send metrics from an existing Prometheus server into aiAxonIQ, use remote write — a configuration change on one side and nothing on your services. See Prometheus remote-write.

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Next

Prometheus remote-write

Getting metrics in.

Exploring metrics

The native metrics surface.

API reference

The full API, including these endpoints.