Direct-to-consumer brands: inventory manager using Django

Summary

A inventory manager blueprint for direct-to-consumer brands built with Django on Ample. Domain schema: products (sku, name, collection, price, active): product catalog; stock_movements (sku, warehouse, delta, reason, moved_at): fulfillment stock; orders (order_no, channel, status, placed_at): orders with status transitions; order_lines (order_no, sku, qty, unit_price): order contents; events (seq, record_type, record_id, kind, at): application-owned history written in the same transaction. Public information: product catalog and collections, shipping and returns policy, brand story. Kept out of scope until handling is reviewed: customer contact and payment details, discount codes tied to individuals. Checkout and payment are handled by your commerce provider; only catalog, stock and order status are modeled here. Technical basis verified on Django: an optimistic version check and an event row written in the same transaction, a stale update rejected, and a history query over the application-owned events (history=2 conflict=rejected).

Infrastructure requirements

• Compute: verified (Apps run in isolated x86_64 Firecracker microVMs that auto-pause when idle and wake on request; sizes are the priced VM sizes.)
• Postgres: verified (Managed PostgreSQL 16 runs in its own microVM and is auto-provisioned when an app needs a database and no DATABASE_URL is supplied.)
• S3-compatible object storage: verified (Buckets are S3-compatible with issued credentials; PutObject and GetObject are verified by canary. Other S3 operations are not verified.)

Prerequisites

• A Django project (pip install into .ample/python from requirements.txt, then waitress serving project.wsgi from run.py reading PORT on the python-3.12 template)
• A PostgreSQL driver reading DATABASE_URL (auto-provisioned when omitted)
• Bucket credentials from ample bucket create passed as encrypted S3_* environment variables
• A review of which direct-to-consumer brands data classes may be handled at all; this blueprint models public information only

Exact tested configuration

• template: python-3.12
• runtime: python
• size: s-1vcpu-1gb
• install: python3 -m pip install --target .ample/python -r requirements.txt
• start: PYTHONPATH=.ample/python:${PYTHONPATH:-} python3 run.py

Steps

1. Model the direct-to-consumer brands domain. Create the tables products, stock_movements, orders, order_lines, events. Product catalog lives in products; keep the sensitive classes (customer contact and payment details, discount codes tied to individuals) out of this schema.

2. Workflow step 1. Model SKUs and stock movements; derive stock from movements

3. Workflow step 2. Apply movements transactionally with an optimistic version check on the SKU

4. Workflow step 3. Record an event per movement for history

5. Workflow step 4. Deploy and verify history and rejected stale updates

6. Deploy. Run the synchronous deploy once and read the result. Re-running with no change is a no-op.

   ample deploy . --name <app-name> --public --start "python3 run.py" --env S3_ENDPOINT=... --env S3_REGION=... --env S3_BUCKET=... --env S3_ACCESS_KEY_ID=... --env S3_SECRET_ACCESS_KEY=...
   

7. Verify. Run the pattern self-test(s) from the example (/p/transactional-workflows) and your own acceptance checks for the direct-to-consumer brands workflow.

   ample logs <deployment_id> --kind build
   

Tested examples

• Django pattern fixture (tests/deploy-canaries/django-patterns): Verified transactional workflows basis for this blueprint.

Success checks

• app responds on its public URL (/ on the live URL, expect ample canary django patterns)
• transactional-workflows self-test from the example (/p/transactional-workflows on the live URL, expect see the pattern fixture checks)

Limitations

• The technical basis (transactional workflows on Django) was verified with the pattern fixture; the direct-to-consumer brands schema and workflow are an original design for this blueprint and were not executed as a separate application.
• No health, financial, privacy or other compliance claim is made. Checkout and payment are handled by your commerce provider; only catalog, stock and order status are modeled here.
• Public content and synthetic examples only until actual data-handling requirements have been reviewed.
• Verified on the python-3.12 template at s-1vcpu-1gb; region, request-duration limits and other sizes are unknown or unverified.
• Managed PostgreSQL 16 only; extensions, connection limits and backup or restore procedures are not verified.
• PutObject and GetObject with path-style addressing are verified; other S3 operations and CDN cache rules are not.

Cost estimate

Estimated 10.00 USD per month (size prices from pricing.toml at build revision a1b8c38919e59cd035ebabaced73cf84ece24371).

• app server x1 s-1vcpu-1gb: 5.00 USD
• managed PostgreSQL database x1 s-1vcpu-1gb: 5.00 USD

Always-on monthly price of the tested sizes; apps and databases auto-pause when idle. Buckets are allocation-priced per quota and not included.

Verification evidence

• canary_run on 2026-09-20T03:31:44Z at revision 03b6402b1b3e19178985f08ea4033804521d85d4-dirty (CLI b75e104): Django pattern fixture deployed on Ample (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 transactional workflows checks passed: an optimistic version check and an event row written in the same transaction, a stale update rejected, and a history query over the application-owned events (history=2 conflict=rejected). The blueprint's Direct-to-consumer brands schema and workflow below build on that verified basis and were not separately executed. (expires 2027-03-19T03:31:44Z)