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To monitor a Mitsuki API with Grafana and Prometheus, let Prometheus scrape Mitsuki’s Prometheus-format endpoint at /metrics/prometheus, verify the target is reachable and marked UP, then provision Mitsuki’s framework dashboard in Grafana. Keep business-specific metrics on a separate dashboard, and do not expose an anonymous Grafana administrator configuration to the public internet.
How the monitoring stack fits together
Mitsuki exposes metrics; Prometheus collects and stores them; Grafana queries Prometheus and presents the data in dashboards. In a Docker Compose network, Prometheus can address the API by its service name rather than a host-facing address. The Mitsuki example uses mitsuki:8000 as its scrape target.
This walkthrough follows the Compose configuration in David Landup’s October 3, 2026 Mitsuki observability tutorial. It uses latest image tags and does not establish compatibility for specific pinned releases. Verify the endpoint, configuration keys, and dashboard-generation command against the Mitsuki release you deploy.
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Prometheus needs Mitsuki’s Prometheus text format endpoint. The tutorial distinguishes this from /metrics, which returns a JSON summary that Prometheus cannot parse as a scrape response.
The example’s relevant Prometheus configuration is:
scrape_configs:
- job_name: 'mitsuki'
scrape_interval: 5s
metrics_path: '/metrics/prometheus'
static_configs:
- targets: ['mitsuki:8000']The five-second interval is the tutorial’s example setting, not a general requirement or a benchmark. Choose an interval appropriate to your monitoring needs and the load you are willing to place on the API and monitoring stack.
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Allow Prometheus to reach the metrics endpoint
Mitsuki’s metrics.allowed_ips setting must permit the address range from which Prometheus connects. In the tutorial’s Docker bridge example, that range is 172.16.0.0/12. Use the range appropriate to your own Docker network; do not assume that example range applies to every installation.
Once Prometheus is running, open Status → Target health in Prometheus and confirm that the mitsuki target is UP. A DOWN target points first to connectivity, the allowed-IP restriction, or a mismatched target address or metrics path. Resolve scrape health before troubleshooting empty Grafana panels.
Provision Mitsuki’s framework dashboard
The tutorial generates Mitsuki’s bundled dashboard with the CLI command mitsuki grafana-dashboard -o /dashboards. Its Compose setup writes the generated file to a shared volume, which Grafana reads through a dashboard provider. The initialization service is one-shot; Grafana should wait until it completes so the dashboard file exists before Grafana tries to load it.
The resulting framework dashboard is intended to work across Mitsuki applications and covers framework-emitted metrics. The tutorial’s example panels include:
- Overall request totals and requests per second.
- Average response time, error rate, and p95 and p99 latency.
- Route-level request rates, totals, and average, p95, and p99 latency.
- Status-code distribution.
- Component and method call rates, durations, and failures.
- Scheduler task rates, durations, and failures.
- CPU and memory usage.
These are the panels described for the tutorial’s configuration; they are not independently verified behavior for every Mitsuki release. If Grafana has more than one Prometheus data source, make the correct one the default or adjust the dashboard panels to query it.
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Add application-specific metrics separately
Framework metrics answer operational questions about requests and Mitsuki components. They do not necessarily capture the business events or database behavior important to a particular API. The tutorial’s users-and-orders example records custom metrics in an OrderService through InstrumentationProvider, then places them on a separate demo dashboard. Its panels track order creation, database writes, returned rows, full table scans, and expensive aggregations.
When adding custom metrics, choose a metric type that matches the measurement. Prometheus describes counters as values that accumulate events and can be analyzed with rate(); gauges represent values that can rise and fall, such as in-progress requests or memory. See the Prometheus Authors’ instrumentation guidance for these distinctions and label-cardinality recommendations.
Each distinct label set creates another time series, consuming resources. Prometheus Authors advise: “As a rule of thumb, try to keep the cardinality of your metrics below 10, and for metrics that exceed that, aim to limit them to a handful across your whole system.” Avoid labels with unbounded or rapidly growing values, such as user identifiers, when they would create a new series for each value.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep Grafana access private
The tutorial’s Compose example enables anonymous Grafana access with the Admin role. That is suitable only for a private local demonstration, not a publicly reachable Grafana instance: anyone able to reach it would have administrator-level access. Do not publish that configuration on an internet-facing server. Use an access-controlled Grafana deployment and configure authentication and permissions for the environment in which you run it.
Self-hosted Prometheus or a managed observability service?
The Compose approach keeps collection and storage in infrastructure you operate. A hosted observability option changes who runs those services and how dashboards and alerts are configured; confirm that it supports your data sources and deployment model before switching.
Grafana’s documentation describes Cloud integrations that bundle Alloy configuration, tailored dashboards, and alert defaults for supported targets. These integrations are optimized for Alloy and may not work with a local Prometheus, Graphite, or Loki deployment out of the box, so they should not be treated as a drop-in replacement for this tutorial’s Prometheus setup. See Grafana integrations documentation for details.
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