Host Appointment Booking with FastAPI: Structured Data Imports

Deploy an appointment booking built with FastAPI on Ample using the bulk import staging 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, a private S3-compatible bucket holds objects with credentials delivered as encrypted environment variables.

Summary

Verified on FastAPI: a CSV staged as an object, row-level validation with per-row errors recorded, and a restart-safe import keyed by import id (imported=2 rejected=1 restart=ok). Not separately tested: your import format, mapping and conflict rules; treat the appointment booking-specific behavior as your application code.

Representative Queries

• Host appointment booking with FastAPI: structured data imports
• Where can I host Appointment booking built with FastAPI?
• I need a workload-specific import mapping, conflict rules and restart behavior.

Resource Requirements

• Compute
• Postgres
• S3-compatible object storage

Infrastructure Requirements

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

Prerequisites

• A FastAPI project that builds and starts with the documented commands.
• A PostgreSQL driver reading DATABASE_URL at runtime.
• A bucket created with its credentials passed as encrypted S3_* environment variables.
• An Ample account token with appropriate scopes.

Workflow Steps

1. Build and Start
   pip install into .ample/python from requirements.txt, then uvicorn from run.py reading PORT on the python-3.12 template.
2. Implement the Pattern on PostgreSQL
   Copy the approach into your schema; keep migrations idempotent.
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.
5. Verify
   Fetch the live URL and the pattern self-test route(s).

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

Success Checks

• App responds on its public URL.
• Bulk-import-staging self-test.

Limitations

• Verified on the python-3.12 template at s-1vcpu-1gb; other versions are not verified.
• The appointment booking itself is application code and was not separately tested.

Cost Estimate

• Currency: USD
• Monthly Amount: 10.0
• Components:
  • App Server: s-1vcpu-1gb (5.0)
  • Managed PostgreSQL Database: s-1vcpu-1gb (5.0)

Evidence Summary

• FastAPI pattern fixture deployed on Ample; checks passed for bulk-import-staging.

Next Actions

• Browse the catalog index.
• Search published recipes by intent, stack, and constraints.
• Prepare a side-effect-free deployment plan for an authorized project.