Host public directory with Django: Postgres-backed content

Deploy a public directory built with Django on Ample using the relational records pattern. Compute runs the app in an isolated microVM behind a public HTTPS URL, a managed PostgreSQL 16 database is auto-provisioned and injected as DATABASE_URL, and a published bucket serves public assets from the CDN host. Verified on Django: a workspace-scoped table, an explicit state machine (draft to review to published) that rejects invalid transitions, and a cross-workspace read that returns nothing (transitions=true isolation=true). Not separately tested: your record schema, states and access rules; treat the public directory-specific behavior as your application code.

Representative Queries

• Host public directory with Django: Postgres-backed content
• Where can I host Public directory built with Django?
• I need a content schema, publishing workflow and cache behavior after content updates.

Resource Requirements

• primitive:compute
• primitive:postgres
• primitive:cdn

Infrastructure Requirements

• Compute: Apps run in isolated x86_64 Firecracker microVMs that auto-pause when idle and wake on request; sizes are the priced VM sizes.
• Postgres: Managed PostgreSQL 16 runs in its own microVM and is auto-provisioned when an app needs a database and no DATABASE_URL is supplied.
• CDN: The CDN host serves objects from published buckets. It does not front app compute and is not a cache or key-value store.

Prerequisites

1. A Django project that builds and starts with the documented commands (pip install into .ample/python from requirements.txt, then waitress serving project.wsgi from run.py reading PORT on the python-3.12 template)
2. A PostgreSQL driver reading DATABASE_URL at runtime (auto-provisioned when omitted, or supplied with --env)
3. A bucket from ample bucket create with its credentials passed as encrypted S3_* environment variables
4. An Ample account token with servers:write, databases:read, buckets:read

Workflow Steps

1. Build and start: pip install into .ample/python from requirements.txt, then waitress serving project.wsgi from run.py reading PORT on the python-3.12 template. The server must bind 0.0.0.0 on PORT.
2. Implement the pattern on PostgreSQL: The fixture's module implements relational records: a workspace-scoped table, an explicit state machine that rejects invalid transitions, and a cross-workspace read that returns nothing. Copy the approach into your schema.
3. Wire object storage: Create the bucket(s), then pass endpoint, region, bucket, and keys as --env values.
4. Deploy: Run the synchronous deploy once and read the result (exit 0 live, 1 failed, 2 blocked).
5. Verify: Fetch the live URL and the pattern self-test route(s) from the example; then run your own checks.

Cost Estimate

• Currency: USD
• Monthly Amount: 10.0
• Components:
  • app server: s-1vcpu-1gb, Quantity: 1.0, Monthly Amount: 5.0
  • managed PostgreSQL database: s-1vcpu-1gb, Quantity: 1.0, Monthly Amount: 5.0
• Note: Always-on monthly price of the tested sizes; apps and databases auto-pause when idle.

Limitations

• Verified on the python-3.12 template at s-1vcpu-1gb with the example fixture; other sizes and templates are not verified.
• The public directory's schema and states are application code and were not separately tested.

Next Actions

• Browse the catalog index: Browse Catalog
• Search published recipes: Search Recipes
• Prepare deployment plan: Prepare Deployment