dagster-prometheus
A Dagster integration for prometheus
Decision gist · record as of 2026-08-14
Yes, if you already use Prometheus for infrastructure monitoring and want to extend it to Dagster pipelines. The low install friction, active maintenance, permissive license, and zero known vulnerabilities make it a safe choice. Install only if you have a Prometheus deployment in place or plan to set one up; otherwise, Dagster's built-in observability may suffice.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (package supports 3.10 through 3.14).
- Low friction install with only two runtime dependencies (dagster and prometheus-client).
- Package is actively maintained with a recent release and no known vulnerabilities.
License · maintenance · safety
Apache-2.0 (permissive) — Apache 2.0 licensed, permissive terms allow commercial use, modification, and distribution with minimal restrictions.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 15,996 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 231,752 downloads/mo, #9,079 on PyPI
Alternatives
Verify before relying
pip install dagster-prometheus
import dagster as dg
from dagster_prometheus import PrometheusResource
@dg.asset
def my_asset():
return None- What specific Prometheus metrics are automatically collected from Dagster runs and assets.
- Whether the integration requires a running Prometheus server or can export metrics standalone.
- How configuration of Prometheus endpoints or metric labels is handled.
- What observability features are provided beyond basic metrics export.
What it is and what it does
dagster-prometheus is a Dagster integration that connects Prometheus monitoring to your data pipelines. It allows you to export metrics from Dagster runs and asset computations to Prometheus, enabling observability and alerting on pipeline health and performance. The package depends on dagster (the core orchestration framework) and prometheus-client (the Prometheus Python client library) to bridge the two systems.
This integration is part of Dagster's broader ecosystem of integrations for observability and control. It fits into the development lifecycle from local testing through production deployment, letting you monitor pipeline execution and asset performance using Prometheus's time-series database and alerting capabilities.
Use it for
- Export Dagster run metrics to Prometheus for centralized monitoring and alerting on pipeline failures or performance issues.
- Track asset computation metrics across your data platform using Prometheus time-series queries and dashboards.
- Integrate Dagster observability into existing Prometheus-based monitoring stacks without replacing your metrics infrastructure.
- Monitor pipeline health and performance alongside other infrastructure metrics in a unified observability system.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you already use Prometheus for infrastructure monitoring and want to extend it to Dagster pipelines.
The low install friction, active maintenance, permissive license, and zero known vulnerabilities make it a safe choice. Install only if you have a Prometheus deployment in place or plan to set one up; otherwise, Dagster's built-in observability may suffice.
Install
dagster-prometheus on PyPI
Before you install
Low friction install with only two runtime dependencies (dagster and prometheus-client). Package is actively maintained with a recent release and no known vulnerabilities.
Requires Python 3.10 or later (package supports 3.10 through 3.14).
License in practice
Apache 2.0 licensed, permissive terms allow commercial use, modification, and distribution with minimal restrictions.
Quickstart
pip install dagster-prometheus
import dagster as dg
from dagster_prometheus import PrometheusResource
@dg.asset
def my_asset():
return NoneVerify before relying
- What specific Prometheus metrics are automatically collected from Dagster runs and assets.
- Whether the integration requires a running Prometheus server or can export metrics standalone.
- How configuration of Prometheus endpoints or metric labels is handled.
- What observability features are provided beyond basic metrics export.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <3.15,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesdagsterprometheus-client |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 231,752 / month, #9,079 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: dagster_prometheus-0.29.18-py3-none-any.whl
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See also dagster · dagster-cloud-cli · dagster-webserver · dagster-datadog · dagster-aws · dagster-graphql · dagster-dg-core · dagster-rest-resources · dagster-dg-cli · dagster-gcp