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Prometheus Metrics

Nautobot supports optionally exposing native Prometheus metrics from the application. Prometheus is a popular time series metric platform used for monitoring.


Metrics are not exposed by default. Metric exposition can be toggled with the METRICS_ENABLED configuration setting which exposes metrics at the /metrics HTTP endpoint, e.g. https://nautobot.local/metrics.

In addition to the METRICS_ENABLED setting, database and/or caching metrics can also be enabled by changing the database engine and/or caching backends from django.db.backends / django_redis.cache to django_prometheus.db.backends / django_prometheus.cache.backends.redis:

    "default": {
        # Other settings...
        "ENGINE": "django_prometheus.db.backends.postgresql",  # use "django_prometheus.db.backends.mysql" with MySQL

# Other settings...
    "default": {
        # Other settings...
        "BACKEND": "django_prometheus.cache.backends.redis.RedisCache",
Added in version 2.2.1

In case the /metrics endpoint is not performant or not required, you can disable specific apps with the METRICS_DISABLED_APPS configuration setting.

For more information see the django-prometheus docs.


Added in version 2.1.5

Metrics by default do not require authentication to view. Authentication can be toggled with the METRICS_AUTHENTICATION configuration setting. If set to True, this will require the user to be logged in or to use an API token. See Rest API Authentication for more details on API authentication.

Sample Telegraf configuration

urls = ["http://localhost/metrics"]
http_headers = {"Authorization" = "Token 0123456789abcdef0123456789abcdef01234567"}

Metric Types

Nautobot makes use of the django-prometheus library to export a number of different types of metrics, including:

  • Per model insert, update, and delete counters
  • Per view request counters
  • Per view request latency histograms
  • Request body size histograms
  • Response body size histograms
  • Response code counters
  • Database connection, execution, and error counters
  • Cache hit, miss, and invalidation counters
  • Django middleware latency histograms
  • Other Django related metadata metrics

For the exhaustive list of exposed metrics, visit the /metrics endpoint on your Nautobot instance.

Multi Processing Notes

When deploying Nautobot in a multi-process manner (e.g. running multiple uWSGI workers) the Prometheus client library requires the use of a shared directory to collect metrics from all worker processes. To configure this, first create or designate a local directory to which the worker processes have read and write access, and then configure your WSGI service (e.g. uWSGI) to define this path as the prometheus_multiproc_dir environment variable.


If having accurate long-term metrics in a multi-process environment is crucial to your deployment, it's recommended you use the uwsgi library instead of gunicorn. The issue lies in the way gunicorn tracks worker processes (vs uwsgi) which helps manage the metrics files created by the above configurations. If you're using Nautobot with gunicorn in a containerized environment following the one-process-per-container methodology, then you will likely not need to change to uwsgi. More details can be found in issue #3779.


Metrics from the celery worker are not available from Nautobot at this time. However, additional tools such as flower can be used to monitor the celery workers until these metrics are exposed through Nautobot.