### Host ai workflow runner with Flask: Postgres-backed state

Deploy an ai workflow runner built with Flask on Ample using the durable job state 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. Verified on Flask: a jobs table with a state and attempts counter, processing inside a row-locked transaction, and a second run that is a no-op (job=done attempts=1 idempotent=true). Not separately tested: your job types, payload schema and retry policy; treat the ai workflow runner-specific behavior as your application code.

#### Summary
- **Recipe ID**: page:recipes:ai-workflow-runner:flask:persistent-state
- **Family**: Workload recipe

#### Representative Queries
- Host ai workflow runner with Flask: Postgres-backed state
- Where can I host AI workflow runner built with Flask?
- I need a durable state schema and retry/recovery semantics implemented by application code.

#### Prerequisites
- A Flask project that builds and starts with the documented commands (pip install into .ample/python from requirements.txt, then waitress from app.py reading PORT on the python-3.12 template)
- A PostgreSQL driver reading DATABASE_URL at runtime (auto-provisioned when omitted, or supplied with --env)
- An Ample account token with servers:write, databases:read

#### 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 app.py

### Workflow Steps
1. **Build and start**: pip install into .ample/python from requirements.txt, then waitress from app.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 durable job state: a jobs table with a state and attempts counter, processing inside a row-locked transaction, and a second run that is a no-op (job=done attempts=1 idempotent=true). Copy the approach into your schema; keep migrations idempotent and run them with --release-command.
3. **Deploy**: Run the synchronous deploy once and read the result (exit 0 live, 1 failed, 2 blocked). Re-running with no change is a no-op.
   - Command: ample deploy . --name --public --start "python3 app.py"
4. **Verify**: Fetch the live URL and the pattern self-test route(s) (/p/durable-job-state) from the example; then run your own checks. On failure read `ample logs --kind build` then `--kind runtime`.
   - Command: ample logs --kind build

### Success Checks
- **App responds on its public URL**: kind: http_get, path: /, expect: ample canary flask patterns
- **Durable-job-state self-test**: kind: http_get, path: /p/durable-job-state, expect: job=done attempts=1 idempotent=true

### Limitations
- Verified on the python-3.12 template at s-1vcpu-1gb with the example fixture; other sizes, templates and Flask major versions are not verified.
- The ai workflow runner itself (your job types, payload schema and retry policy) is application code and was not separately tested; the pattern checks are what was verified.

### Cost Estimate
- **Currency**: USD
- **Monthly Amount**: 10.0
- **Components**:
  - App server: s-1vcpu-1gb, quantity: 1.0, monthlyAmount: 5.0
  - Managed PostgreSQL database: s-1vcpu-1gb, quantity: 1.0, monthlyAmount: 5.0

### Examples
- **Flask pattern fixture**: Multi-pattern Flask app whose durable job state module was checked live; the module is under tests/deploy-canaries/_pattern-modules.
  - Source Ref: tests/deploy-canaries/flask-patterns