Open-Source Observability · django-commons

django-prometheus

django-prometheus is a Python library that integrates Django web applications with Prometheus monitoring by exporting metrics for HTTP requests, database queries, cache operations, and model changes. It provides out-of-the-box instrumentation via middleware and optional mixins, enabling real-time performance monitoring without extensive code changes.

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Source: GitHub — github.com/django-commons/django-prometheus

1.7k

GitHub stars

262

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	django-commons/django-prometheus
Owner	django-commons
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	1.7k
Forks	262
Open issues	103
Latest release	v2.5.0 (2026-05-26)
Last updated	2026-06-09
Source	https://github.com/django-commons/django-prometheus

What django-prometheus is

The library wraps Django's request/response cycle, database backends (SQLite, MySQL, PostgreSQL), and cache implementations to emit Prometheus-compatible metrics via the prometheus_client library. It includes histogram-based latency buckets (configurable), counters for model operations, and migration tracking, with metrics exposed via a dedicated `/metrics` endpoint.

Quickstart

Get the django-prometheus source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/django-commons/django-prometheus.git2cd django-prometheus3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Production Django monitoring at scale

Organizations running Django in Kubernetes or on-premise infrastructure benefit from native Prometheus integration, enabling alert rules, dashboards, and correlation with infrastructure metrics from the same scraping endpoint.

Performance regression detection

Track request latency, database query duration, and cache hit/miss ratios over time to identify bottlenecks early; histogram buckets support SLO-based alerting on p95/p99 latencies.

Multi-tenant or microservice Django deployments

Custom metric namespacing and per-model operation tracking allow fine-grained visibility across multiple Django services feeding a shared Prometheus instance.

Implementation considerations

Middleware ordering is critical: PrometheusBeforeMiddleware must run first, PrometheusAfterMiddleware last; validate in local/staging before production rollout.
Custom latency buckets trade memory/cardinality for accuracy; follow Prometheus documentation on histogram sizing to avoid cardinality explosion on high-traffic endpoints.
Database and cache monitoring require explicit ENGINE/BACKEND replacement; verify compatibility with your ORM patterns (e.g., raw SQL, async ORM, connection pooling).
Model mixin adoption is opt-in; identify high-value models for tracking but be cautious of overhead on bulk operations (migrations, management commands).
Metrics endpoint (`/metrics`) must be protected from unauthorized scraping; ensure Prometheus server authentication and network isolation.

When to avoid it — and what to weigh

Monitoring non-Prometheus observability stacks — If your organization uses Datadog, New Relic, or CloudWatch natively, you may incur redundant instrumentation costs; consider vendor-specific APM agents instead.
Real-time sub-second latency requirements on high-traffic sites — Histogram bucketing and middleware interception add measurable overhead; validate performance at production scale before deploying to traffic-critical paths.
Legacy Django < 4.2 or Python < 3.9 codebases — The library requires recent versions; upgrading legacy deployments solely for monitoring may not justify the effort without broader modernization plans.
Fully serverless/FaaS architectures — Prometheus expects persistent scraping targets; Django on AWS Lambda or similar ephemeral compute requires custom exporter routing and state management not addressed by this library.

License & commercial use

Licensed under Apache License 2.0 (Apache-2.0), a permissive OSI-approved license allowing commercial use, modification, and distribution with attribution and liability disclaimers.

Apache-2.0 is a well-established permissive license compatible with commercial products and closed-source integrations. No license review is required for standard commercial use. Ensure compliance with attribution requirements in documentation or notices.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Low
DEV.co fit	Strong
Assessment confidence	High

Security considerations

The `/metrics` endpoint exposes operational metrics (request counts, latencies, database query patterns) which may reveal system internals; configure network access controls and authentication. Middleware adds minor request processing overhead; validate no sensitive data appears in metric labels. No known CVEs documented; monitor prometheus_client dependency for security updates.

Alternatives to consider

django-statsd-tags / statsd client

Lightweight StatsD integration for minimal overhead; suitable if you already use Graphite/InfluxDB instead of Prometheus, but requires separate scrape configuration.

Sentry + Performance Monitoring

Vendor-hosted APM with error tracking; eliminates self-hosted Prometheus infrastructure but introduces SaaS costs and data residency constraints.

OpenTelemetry Python instrumentation

Standards-based instrumentation abstraction supporting multiple exporters (Prometheus, Jaeger, commercial APM); more flexible but requires deeper integration and operator expertise.

Software development agency

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Integrate django-prometheus to gain visibility into request latency, database performance, and system health. Start with `pip install django-prometheus` and configure monitoring in minutes.

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django-prometheus FAQ

Does django-prometheus support async Django views?

Not clearly stated in provided documentation. Requires review of source code or maintainer confirmation for async view middleware behavior.

Can I use django-prometheus in a CI/CD pipeline without a persistent Prometheus server?

Possible with custom exporters, but not the intended use case. The library assumes a scraping Prometheus endpoint; for ephemeral environments, consider in-memory metrics collection or log-based metrics.

What is the performance impact of enabling all metrics (requests, DB, cache, models)?

Not quantified in documentation. Depends on traffic volume and histogram bucket count. Recommendations suggest validating at production scale in staging; overhead is generally low but should be benchmarked for latency-sensitive applications.

How does django-prometheus handle multi-process Django deployments (Gunicorn workers)?

prometheus_client supports multi-process mode via environment variables (PROMETHEUS_MULTIPROC_DIR); ensure each worker registers metrics to a shared registry. See prometheus_client documentation for multi-process setup.

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