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

## Summary
Deploy a ai workflow runner built with Starlette 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 Starlette: 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.

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

## Resource Requirements
- primitive:compute
- primitive:postgres

## Infrastructure Requirements
### 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.

### 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.

## 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. 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 run.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 starlette patterns

- Durable-job-state self-test
    - Kind: http_get
    - Path: /p/durable-job-state  
    - Expect: job=done attempts=1 idempotent=true

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

## Cost Estimate
- **Currency:** USD
- **Monthly Amount:** 10.0
- **Basis:** Size prices from pricing.toml (loaded by the API) at build revision 1ac5595375130d45290090e82ed0f554ccd45405-dirty
### Components
- **App server**  
  - Size: s-1vcpu-1gb  
  - Quantity: 1.0  
  - Monthly Amount: 5.0  
- **Managed PostgreSQL database**  
  - Size: 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. Buckets are allocation-priced per quota and not included.