Python Hosting with WSGI Included, virtualenv Isolation, and Autoscaling
Run Django, Flask, or any WSGI (Web Server Gateway Interface) app on Apache + mod_wsgi (Apache’s native WSGI handler), with no server config required. virtualenv keeps packages isolated per project, and autoscaling stays within the limits you set.
Isolated environments with pip-driven dependency management. requirements.txt resolves on deploy.
Apache + mod_wsgi runs your Django or Flask app. Add a load balancer when you need horizontal scale-out for API or inference traffic.
Attach managed database clusters and cache nodes. Serve model inference endpoints with autoscaling behind a load balancer.
Switch Python versions, set environment variables, and view logs from the dashboard. Web SSH and a config file manager are available when you need them — routine operations stay in the UI.
From pip install to production in three steps
Deploy with repeatable builds
Deploy from Git or archive. Push a commit and the platform deploys automatically. The platform installs dependencies from requirements.txt during each build. Automation & CI/CD →
Run on managed WSGI serving
Apache + mod_wsgi serves your Python app. Include a wsgi.py entry point and the platform starts your app automatically. Package management →
Scale within the limits you set
Set reserved cloudlets and a scaling limit. The platform scales up under load and scales down when traffic drops, billing only for what you use.
Stateless (no local data kept between requests) and stateful (session data and uploaded files preserved across instances) horizontal scaling modes available. Autoscaling & cost controls →
Common patterns: Django/Flask behind LB • data APIs • ML model serving endpoints. Browse pre-configured stacks in the Marketplace.
Configure domains, autoscaling, and databases
Domain routing and cutover
Autoscaling triggers
Database connectivity
Deploy your first Python app in minutes
Model Django, Flask, or ML-serving traffic before you decide how much baseline and burst capacity to reserve.
A common Python starter uses 3 reserved cloudlets (~384 MiB RAM) with a scaling limit of 16. That is enough for many Django or Flask apps under moderate traffic. Use the calculator to model your configuration. See Database clusters for replication and failover options.
Operate a full Python stack from one screen.
Topology view shows load balancers, app nodes, databases, storage, and an active terminal.
Switch Python runtime versions without wiping persistent data.
Redeploy dialog shows a newer image tag with Keep volumes data turned on.
Scale Python without managing servers
Manage Python versions and scaling without managing the server yourself. Set reserved cloudlets for a predictable baseline and dynamic cloudlets for bursts, so you pay for extra capacity only when it is used. Pick a Python version when you create the environment and switch later with a container redeploy. Both stateless (no local data between requests) and stateful (sessions and files preserved) horizontal scaling modes are available, along with environment cloning for staging or pre-release testing. Built-in SSL via Let’s Encrypt is included.
Choosing a Python architecture pattern? Chat with an engineer.
Ship Python apps from Git to production
Web app
LB → Python nodes → DB
Scale Django/Flask nodes horizontally behind a load balancer. Keep sessions and cache in external stores.
Data API
REST/GraphQL endpoints
Serve data APIs with autoscaling Python nodes backed by managed database clusters.
ML serving
Model inference endpoints
Deploy trained models behind WSGI-served endpoints. The platform autoscales to meet inference traffic peaks.
Common Questions
Which Python web server is used?
Python environments use Apache with mod_wsgi. Include a wsgi.py entry point and the platform handles serving.
Is pip supported?
Yes. pip and virtualenv are available on all Python nodes. Dependencies install from requirements.txt automatically during deployment.
How do I deploy my Python app?
Deploy from archives (local upload or external URL) or remote Git repositories with optional auto-redeploy. Automation & CI/CD covers automation options.
Can I deploy Django and Flask apps?
Any WSGI-compatible Python framework works. Include a wsgi.py entry point and requirements.txt for your dependencies.
How does scaling work and how do I set baseline/max cost guardrails?
Define a reserved baseline for typical load and a max cap for spikes. Python workers scale within those limits automatically. Autoscaling & cost controls walks through the setup.
What is stateless vs stateful horizontal scaling?
Stateless mode creates new nodes from the base image template (fresh state). Stateful mode copies the master container filesystem to new nodes, preserving installed packages and configuration.
How do load balancers work with Python apps?
A load balancer is added automatically when you scale out Python nodes. It distributes traffic across instances for both web and API workloads.
Which databases can I connect to?
MySQL, MariaDB, and PostgreSQL are all managed options. Python ORMs like SQLAlchemy and Django ORM connect with standard drivers. Database Clusters has replication and clustering details.
When should I choose Kubernetes instead of Python on App Hosting?
If your Python app needs custom orchestration, sidecar containers, or multi-service coordination, Kubernetes gives you that control. Kubernetes Hosting covers the options.
Can you help migrate an existing Python app?
We can help map your Python app to the platform: requirements, WSGI config, and database connections. Portability & Migration covers the steps.
Can I automate pre- and post-deploy checks for Python apps?
Deployment hooks let you run setup scripts, test suites, and verification steps as part of each release.
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