Best Django hosting
6 providers on this site publish their own Django documentation. Every row below links to the guide it came from.
Python web framework with an ORM, admin interface and long-running server process.
Where you can deploy it.
Ordered by how the provider documents it — a dedicated guide first, a generic path second. There is no score: the ordering is the evidence.
| Provider | Category | Documented fit for Django | Entry paid plan | Free tier |
|---|---|---|---|---|
| Fly.io | App platforms | Dedicated guide | Not documented | No |
| Google Cloud Run | App platforms | Generic path | No plan fee and no minimum commitment. Cloud Run bills per vCPU-second, GiB-second and request once the monthly free allowance is used, so there is no entry price to compare, only a rate card. Recorded without a magnitude for that reason. | Yes |
| Heroku | App platforms | Dedicated guide | $5 per month (Eco dynos plan, 1000 dyno hours shared across the account) | Not documented |
| Railway | App platforms | Dedicated guide | $5 / month (Hobby) | Yes |
| Render | App platforms | Dedicated guide | Not documented | Yes |
| Vercel | Frontend & serverless | Dedicated guide | $20 per developer seat / month (Pro) | Yes |
What actually works.
Every platform here claims Django support, so the claim on its own says little. These are the capabilities each provider documents, and the constraints it documents alongside them.
| Fly.io | Heroku | Railway | Render | Vercel | Google Cloud Run | |
|---|---|---|---|---|---|---|
| background_workers | Supported | Supported | Supported | Supported | Limited | Supported |
| scheduled_jobs | Limited | Supported | Supported | Supported | Limited | Supported |
| persistent_disk | Supported | Not supported | Supported | Supported | Not supported | Limited |
| connection_pooling | Supported | Supported | Supported | Supported | Not documented | Supported |
| one_off_commands | Supported | Supported | Supported | Supported | Limited | Supported |
The detail.
Runs Docker images as lightweight VMs in chosen regions, billed on provisioned resources rather than plan tiers.
- background_workers: Supported
- scheduled_jobs: Limited
- persistent_disk: Supported
- connection_pooling: Supported
- one_off_commands: Supported
Sources (6) →Sources ↓
- Django on Fly.io · Fly Docs ↗
“Fly.io is a great place to run your Django application. fly launch can detect, configure and deploy Django apps.”
Read 2026-09-05 · official docs
- Run multiple process groups in an app · Fly Docs ↗
“Process groups are a way to configure a single Fly App to run multiple different programs. [...] Every defined process runs in its own Fly Machine(s) within the app, which means they don't compete for resources, and they can be scaled by process group.”
Read 2026-09-13 · official docs
- fly machine run · Fly Docs ↗
“--schedule string Schedule a Machine run at hourly, daily, weekly and monthly intervals”
Read 2026-09-13 · official docs
- Fly Volumes overview · Fly Docs ↗
“The root file systems of a Fly Machine are ephemeral and should only be used for temporary data, application code, and runtime files that can be rebuilt from scratch during deployments or restarts. [...] Use volumes to store your database files, to save your app's state, such as configuration and session or user data, or for any information that needs to persist after deploy or restart.”
Read 2026-09-13 · official docs
- Managed Postgres · Fly Docs ↗
“Your MPG plan determines the CPU and Memory configuration for your cluster. All plans include high availability, backups, and connection pooling.”
Read 2026-09-13 · official docs
- Existing Django Apps · Fly Docs ↗
“To SSH into your hosted Django application use the command fly ssh console. [...] The --pty flag tells the SSH server to run the command in a pseudo-terminal, which createsuperuser requires.”
Read 2026-09-13 · official docs
Managed application platform that runs apps in containers called dynos, deployed from Git or Docker images, with add-on services such as Heroku Postgres.
- background_workers: Supported
- scheduled_jobs: Supported
- persistent_disk: Not supported
- connection_pooling: Supported
- one_off_commands: Supported
Sources (6) →Sources ↓
- Configuring Django Apps for Heroku | Heroku Dev Center ↗
“This Procfile requires Gunicorn, the production web server that we recommend for Django applications.”
Read 2026-09-05 · official docs
- Using Celery on Heroku | Heroku Dev Center ↗
“Celery is fully supported on Heroku and just requires using one of our add-on providers to implement the message broker and result store [...] You deploy Celery by running one or more worker processes.”
Read 2026-09-13 · official docs
- Heroku Scheduler | Heroku Dev Center ↗
“Scheduler is a free add-on for running jobs on your app at scheduled time intervals, much like cron in a traditional server environment.”
Read 2026-09-13 · official docs
- Dynos (App Containers) | Heroku Dev Center ↗
“Each dyno gets its own ephemeral filesystem, with a fresh copy of the most recently deployed code. [...] Any files written get discarded the moment the dyno stops or restarts, including automatic restarts.”
Read 2026-09-13 · official docs
- Server-Side Connection Pooling for Heroku Postgres | Heroku Dev Center ↗
“Server-side connection pooling for Heroku Postgres helps applications use database connections efficiently to avoid reaching your database's plan connection limits. [...] Server-side connection pooling is available for Standard, Premium, Private, and Shield-tier databases.”
Read 2026-09-13 · official docs
- One-Off Dynos | Heroku Dev Center ↗
“One-off dynos allow you to perform administrative or maintenance tasks for your app and are helpful for debugging. [...] Running a console or a REPL shell to run arbitrary code or inspect the app's models against the live database”
Read 2026-09-13 · official docs
Application platform that builds services from a repository or Dockerfile and bills plan fees that include usage credits.
- background_workers: Supported
- scheduled_jobs: Supported
- persistent_disk: Supported
- connection_pooling: Supported
- one_off_commands: Supported
Sources (5) →Sources ↓
- Deploy a Django App | Railway Guides ↗
“Django is a powerful Python web framework that simplifies web development by providing ready-to-use tools for rapid development and clean design.”
Read 2026-09-05 · official docs
- Cron Jobs | Railway Docs ↗
“Cron Jobs allow you to start a service based on a crontab expression. [...] Railway will look for a defined cron schedule in your service settings, and execute the start command for that service on the given schedule.”
Read 2026-09-13 · official docs
- Volumes | Railway Docs ↗
“Volumes are a feature that enables persistent data for services on Railway. [...] When mounting a volume to a service, a volume is made available to the service on the specified mount path.”
Read 2026-09-13 · official docs
- PostgreSQL Connection Pooling | Railway Docs ↗
“Railway can add PgBouncer as a connection pooler in front of your PostgreSQL service. PgBouncer multiplexes many application connections into a smaller pool of server connections, reducing overhead and allowing your database to serve far more concurrent clients than max_connections alone would permit.”
Read 2026-09-13 · official docs
- railway ssh | Railway Docs ↗
“Open an interactive shell session inside a deployed Railway service container, or manage the SSH keys registered with your Railway account. [...] Opens an interactive shell in the service container.”
Read 2026-09-13 · official docs
Managed cloud platform that builds and runs web services, background workers, static sites and databases from Git repositories or Docker images.
- background_workers: Supported
- scheduled_jobs: Supported
- persistent_disk: Supported
- connection_pooling: Supported
- one_off_commands: Supported
Sources (5) →Sources ↓
- Deploy a Django App on Render – Render Docs ↗
“This guide walks through deploying a Django Python app on Render.”
Read 2026-09-05 · official docs
- Background Workers – Render Docs ↗
“Background workers are services that run continuously [...] Background workers help to keep your apps responsive by offloading long-running, asynchronous tasks from your services in the critical request path.”
Read 2026-09-13 · official docs
- Cron Jobs – Render Docs ↗
“You can create cron jobs on Render that run periodically on a schedule you define. You create cron jobs in the Render Dashboard, just like you create any other service type”
Read 2026-09-13 · official docs
- Persistent Disks – Render Docs ↗
“You can attach a persistent disk to a paid Render web service, private service, or background worker. This enables you to preserve local filesystem changes across deploys and restarts.”
Read 2026-09-13 · official docs
- Connection Pooling for Render Postgres – Render Docs ↗
“Render Postgres now supports integrated connection pooling with PgBouncer. [...] When enabled, Render runs PgBouncer in front of your database to manage its connection volume”
Read 2026-09-13 · official docs
Frontend cloud platform for modern web applications, with Git-driven deployments and a framework-aware CDN.
- background_workers: Limited
- scheduled_jobs: Limited
- persistent_disk: Not supported
- one_off_commands: Limited
Sources (4) →Sources ↓
- Deploy a Django app on Vercel ↗
“Deploy a Django app to Vercel with the Python runtime and Vercel Functions.”
Read 2026-09-05 · official docs
- Run background tasks with Celery on Vercel ↗
“Deploy Celery on Vercel with the Python runtime, Vercel Queues, and Vercel Functions. [...] Each invocation runs one task at a time. Your throughput comes from Vercel Queues invoking the subscriber function many times in parallel, not from worker processes or a pool size.”
Read 2026-09-13 · official docs
- Cron Jobs — Vercel Docs ↗
“Vercel supports cron jobs for Vercel Functions. Cron jobs can be added through vercel.json or the Build Output API. [...] An example endpoint Vercel would make a request to in order to trigger a cron job might be: https://*.vercel.app/api/cron.”
Read 2026-09-13 · official docs
- Vercel Functions Runtimes — Vercel Docs ↗
“Vercel functions have a read-only filesystem with writable /tmp scratch space up to 500 MB.”
Read 2026-09-13 · official docs
Google Cloud service that runs container images as request-driven services, scaling instances automatically and down to zero between requests.
- background_workers: Supported
- scheduled_jobs: Supported
- persistent_disk: Limited
- connection_pooling: Supported
- one_off_commands: Supported
Sources (6) →Sources ↓
- Deploy services from source code — Cloud Run — Google Cloud Documentation ↗
“In addition to sources with a Dockerfile, deploying from source supports the following languages using Google Cloud's buildpacks: Runtime Source deployment Configuration Go Deploy a Go service Configure Go buildpacks Node.js Deploy a Node.js service Configure Node.js buildpacks Python Deploy a Python service Configure Python buildpacks Java (includes Kotlin, Groovy, Scala) Deploy a Java service Configure Java buildpacks [...] PHP Deploy a PHP service Configure PHP buildpacks”
Read 2026-09-09 · official docs
- Deployment options and resource model — Cloud Run — Google Cloud Documentation ↗
“Worker pools are a Cloud Run resource specifically designed for non-request workloads, such as pull queues.”
Read 2026-09-13 · official docs
- Execute jobs on a schedule — Cloud Run — Google Cloud Documentation ↗
“This page describes how to execute Cloud Run jobs on a schedule using Cloud Scheduler.”
Read 2026-09-13 · official docs
- Container runtime contract — Cloud Run — Google Cloud Documentation ↗
“It is an in-memory file system, so writing to it uses the instance's memory. [...] Data written to the file system doesn't persist when the instance stops. [...] The supported path for mounting file systems in Cloud Run is through the fully managed volume mounts feature in your Cloud Run container configuration.”
Read 2026-09-13 · official docs
- Connect from Cloud Run — Cloud SQL for PostgreSQL — Google Cloud Documentation ↗
“You can use Managed Connection Pooling with your Cloud SQL instances, which lets you scale your workloads by optimizing resource utilization and connection latency for your Cloud SQL instances using pooling.”
Read 2026-09-13 · official docs
- Running Django on the Cloud Run environment — Python — Google Cloud Documentation ↗
“This tutorial opts to use buildpacks to build the container image, and custom entry points in the Procfile to handle management commands. These can then be referenced by Cloud Run jobs to perform the commands.”
Read 2026-09-13 · official docs
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