judoscale
Official Python package for the Judoscale autoscaler
Decision gist · record as of 2026-08-14
Yes, if you use Judoscale and run Django, Flask, FastAPI, Celery, Dramatiq, or RQ. The adapter is actively maintained, has no known vulnerabilities, and low install friction. Install only if you have a Judoscale subscription and are running one of the supported frameworks; it is not useful standalone.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Celery integration requires Redis 6.0+ as the broker.
- Low install friction with a single runtime dependency (requests).
License · maintenance · safety
MIT (permissive) — MIT license permits use, modification, and distribution with minimal restrictions—suitable for most commercial and open-source projects.
last release 2026-06-05 (70 days) · last repo commit 2026-06-16 · 8 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 121,946 downloads/mo, #11,966 on PyPI
Alternatives
Verify before relying
# For Django:
pip install 'judoscale[django]'
# In settings.py:
INSTALLED_APPS = ["judoscale.django", ...]
# For Flask:
pip install 'judoscale[flask]'
from judoscale.flask import Judoscale
app = Flask("MyApp")
judoscale = Judoscale(app)
# For Celery:
pip install 'judoscale[celery-redis]'
from judoscale.celery import judoscale_celery
judoscale_celery(celery_app)- Whether the package works with Python 3.12+ (classifiers list only 3.10 and 3.11)
- Performance overhead of queue time metric collection in high-throughput applications
- Compatibility with custom ASGI frameworks beyond FastAPI, Starlette, and Quart
What it is and what it does
Judoscale is the official Python adapter for the Judoscale autoscaler service. It instruments web frameworks (Django, Flask, FastAPI, Starlette, Quart) and background job processors (Celery with Redis, Dramatiq with Redis, RQ) to measure request queue times and job queue latencies, then reports these metrics back to Judoscale for autoscaling decisions.
The package works by installing framework-specific middleware or integrations that hook into the request/job lifecycle. You install it with extras matching your stack (e.g., `judoscale[django]`, `judoscale[celery-redis]`), configure it minimally in your application settings, and it runs in the background collecting and reporting metrics. It depends only on requests for HTTP communication and supports Python 3.10+.
Use it for
- Monitor request queue latency in Django, Flask, or FastAPI applications deployed on Heroku or similar platforms using Judoscale.
- Track Celery job queue depth and age with Redis broker to enable automatic worker scaling based on queue backlog.
- Collect Dramatiq or RQ job queue metrics to feed into Judoscale's autoscaling engine for background task processing.
- Instrument ASGI applications (FastAPI, Starlette, Quart) to capture request queue times without manual metric collection code.
- Customize queue monitoring for Celery by specifying which queues to track and setting a maximum number of queues to report on.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you use Judoscale and run Django, Flask, FastAPI, Celery, Dramatiq, or RQ.
The adapter is actively maintained, has no known vulnerabilities, and low install friction. Install only if you have a Judoscale subscription and are running one of the supported frameworks; it is not useful standalone.
Install
judoscale on PyPI
Before you install
Low install friction with a single runtime dependency (requests). Active maintenance with a recent release (70 days ago) and ongoing repository activity.
Requires Python 3.10 or later. Celery integration requires Redis 6.0+ as the broker.
License in practice
MIT license permits use, modification, and distribution with minimal restrictions—suitable for most commercial and open-source projects.
Quickstart
# For Django:
pip install 'judoscale[django]'
# In settings.py:
INSTALLED_APPS = ["judoscale.django", ...]
# For Flask:
pip install 'judoscale[flask]'
from judoscale.flask import Judoscale
app = Flask("MyApp")
judoscale = Judoscale(app)
# For Celery:
pip install 'judoscale[celery-redis]'
from judoscale.celery import judoscale_celery
judoscale_celery(celery_app)Verify before relying
- Whether the package works with Python 3.12+ (classifiers list only 3.10 and 3.11)
- Performance overhead of queue time metric collection in high-throughput applications
- Compatibility with custom ASGI frameworks beyond FastAPI, Starlette, and Quart
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagerequests |
| Maintenance | Actively maintained 70 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 121,946 / month, #11,966 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11 |
Evidence: judoscale-1.13.4-py3-none-any.whl
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